Over the last two posts, I laid out the rules for rebuilding junior development in a post-AI world, and then the nine building blocks themselves - the specific repairs for the threads AI is severing.

But knowing the rules and the parts is not the same as knowing how to assemble them - which is where the rubber meets the road. Drop a junior into owned, accountable work in month one and they’ll drown. Keep them in the shallow end for too long and they’ll stagnate - and, worse, the good ones will leave.

So this post is about the order - that is, what the first twelve months should actually look like, phase by phase.

A quick word on why twelve months and not longer. This development model can indeed stretch to eighteen or twenty-four months, and if you’re one of the handful of organizations with that kind of runway, have at it. But most Procurement teams don’t have that luxury - they need juniors to be contributing meaningfully, and fast. So I’m proposing twelve months as a workable, practical timeline.

That said, I appreciate even a twelve month timeline - in the context of junior development - is aggressive and, to that end, it’s worth being clear about what it does and doesn’t achieve. It will not give you a finished practitioner; as the compounding spiral showed a few posts back, real mastery takes years. What my program will give you is something more immediately useful: a junior you can trust with real, defined, accountable work - one who will compound their development from a properly built foundation rather than a hollow one.

Before we get into the phases themselves, we need to reiterate the two principles that run underneath the entire timeline.

Two Principles

A human stays in the loop on every AI tool. Last week’s rule was “AI as questioner, not answerer.” This is how you actually enforce it: every AI tool a junior touches is set up with guardrails and feedback loops, and a human - usually their mentor - is close enough to the interaction to see it and step in. The reason this has to be architected rather than left to good intentions is simple: the idea of “struggle first” will survive for about five minutes against a tight deadline if no one is watching - and unsupervised AI is how the discernment trap creeps back in.

Learning is continuous and embedded, not episodic. The phases below are not a ‘course’ the junior “completes” and “graduates from”. There’s no certificate, no point where the learning stops and the working starts. It’s one continuous, integrated, rising arc, embedded in real work throughout - which brings me to the single most important thing to understand about this whole model.

Contribution From Day One

The phased program defined below is not meant as a sequestered-trainee model (”first they learn, then they contribute”); that’s both unrealistic and wrong. No organization can afford to pull a junior out of day-to-day operations for three months, and no good junior would want you to either.

So I want to be precise about what’s actually being phased: the junior contributes real, operational work from week one; what’s graduated is not whether they do real work - it’s how much consequence rides on it, and how much support surrounds it. In the early months, the work is real but lower-stakes and heavily scaffolded (often the manual work run simply is a real deliverable - just done by hand first, then checked). As the months pass, the stakes rise and the scaffolding fades. In other words, consequence trending up and support going down, on a deliberate schedule.

One last thing before we start. The month markers below are a default for a typical junior, not a law. Leaders should flex the pace up or down against the individual’s starting capability and experience levels (exactly what the on-ramp assessment is for). But flex the pace, not the sequence. No phase should be skipped, even for your strongest hire, because the foundation is, well, foundational, and precisely what stops the junior falling back into the discernment trap.

Now, let’s get into the phases.

The On-Ramp (Weeks 0-2)

The starting point is a short assessment of what the junior actually arrives with, so that a mixed cohort doesn’t get ‘one-size-fits-all’ treatment. Weight your selection criteria towards a clear desire to learn (curiosity), strong drive (work ethic), bias for collaboration (teamwork) and a tolerance for ambiguity, over a polished CV. (This builds on the motivation constraint I wrote about a couple of posts ago.)

Then set up the scaffolding that will run all year: assign both the mentor and the sponsor, start the decision journal on day one, and record the baseline you’ll measure progress against. This is unglamorous plumbing, but it needs to be addressed, otherwise you have nothing to measure against.

Phase 1: Foundation (Months 1-3) - Stakes: Low

The emphasis here has to be on the load-bearing repair: procedural experience. Manual work runs - core tasks done by hand - and simulations are the essential tools, with independent learning (such as traditional coursework) building baseline domain knowledge underneath it all. The junior is already doing real work, but it’s lower-consequence and heavily supported, and wherever possible they do it manually first, before AI touches it. AI is present only as a Socratic questioner.

The temptation to resist: compressing or skipping this phase under the “we need them productive now” pressure. This is the single most common way the whole program fails - a junior who has never built the foundation can’t tell when the AI is confidently wrong, which means you’ve allowed for the exact trap this series exists to prevent. Three months is already a compressed timeline - don’t cut it further.

Phase 2: Contribution (Months 4-8) - Stakes: Rising

In this phase, the junior steps into live work in earnest - still supervised, but carrying real, shared accountability as the safety net loosens. Simulations, of course, continue, but with higher difficulty and their sponsors start putting them in the room for key events and initiatives. At the same time, the junior’s structured stakeholder plan also kicks in and they start actively building their network.

This is also where rotations begin. The junior spends short periods - a week or two - embedded both across different Procurement subteams, and also inside the internal functions Procurement serves. The point is to feel their pressures, their constraints, their trade-offs. This is not generalist tourism, it is to ensure they come back a more empathetic and more credible partner to Procurement’s stakeholders - and, one day, a far better category leader for that spend, precisely because they’ve spent time on the other side of the table.

The temptation to resist: getting the stakes wrong in either direction. Push too fast - load up real consequence before the foundation is ready - and you get false confidence, or a junior leaning on AI to cover the gaps. Move too slow - keeping them safe and simulated for too long without exposure to mounting stakes - and your most motivated people get bored and leave. Calibrating that balance is the core judgement of Phase 2.

Phase 3: Ownership (Months 9-12) - Stakes: Real

In this phase, the junior takes genuine ownership of a defined piece of work - building it, leading it, and being answerable for how it lands. This is where you deliberately manufacture the thing that AI removes (and that allows judgement to form): the felt cost of being wrong. The scaffolding is largely gone by this phase as the mentor shifts from catching mistakes in the moment to reviewing decisions after the fact.

Feedback and evaluation - while a part of all prior phases as well - takes on a deeper importance, via graded critiques, decision-journal reviews, and judgement assessed on the quality of the reasoning rather than the outcome, and across multiple assessors.

The temptation to resist: fake ownership. A project the junior nominally “owns” while a senior holds the real decisions is simply theatre, not ownership. It builds nothing because there is no genuine consequence. If you aren’t willing to let them own it for real, you aren’t in Phase 3 yet.

And underneath all of this, running the entire twelve months: the mentor relationship (weekly, throughout), the decision journal (from day one), and independent learning (front-loaded, but never fully switched off).

How You’ll Know It Worked

The entire argument of this series has been that the cost of getting junior development wrong is an iceberg - invisible for years, then catastrophic. The reverse is also true: the payoff from getting it right is invisible for years, too. You will not see the finished, seasoned practitioner at month twelve. You’ll see a junior you can trust with real work - and a curve bending in the right direction.

All of which is to say that, regardless of how well you implement the above program, you will not be able to fully measure its impact on the timescale your quarterly reviews run on.

So measure what you can see now: whether the reasoning is getting sharper (versus output speed); time-to-competence and error reduction (not volume of work). Make a thoughtful assessment as to whether discernment and judgement are forming - and then hold the nerve to keep investing while the lagging proof takes its time (years, often) to arrive. This nerve is the real test of leadership here - because anyone can fund a program, but far fewer can hold their conviction through the years before it visibly pays off.

Which leaves us with one last piece: everything across these last few posts has been aimed at leaders - at what the organization must build. But no program, however well-designed, works on a junior who is simply along for the ride. The spiral only turns if they push it.

So the final post in this series on junior development flips the lens entirely. Not what we must build for them - but what the early-career practitioner, sitting right at the start of all this, should be doing for themselves.

Last week, I laid out the rules - the principles any junior development program has to obey before you build a program that works. Make the invisible visible. Human and machine. AI as questioner, not answerer. Leadership measured in hours, not just dollars. All bounded by two honest constraints: co-location and motivation.

This week: the program you build on top of these rules.

First, a quick recap. The old junior development model wasn’t one thing - it was a developmental chain: three uneven inputs (domain knowledge, procedural experience, and network capital) that fused into discernment, matured into judgement, and culminated in a trusted, accountable practitioner. AI is severing those threads, and doing so unevenly - cutting deepest at the procedural work that had no substitutes and was, in many ways, carrying the other two. (See image below.)

An effective junior training program doesn’t need to start afresh, but it does need to rebuild each of those threads deliberately, putting special emphasis on the ones AI cuts most directly.

The building blocks of the program outlined below aren’t, therefore, a menu to pick from. Rather, each one is a repair aimed at a specific severed thread. To keep that visible - and to stop this becoming a generic list of L&D activities - I’ve divided the program into three clusters, with nine specific building blocks in total, each one tagged with what it actually rebuilds. (The four rules from last week are the test every one of them has to pass; assume they’re switched on underneath each block.)

One more note before we start. This post is the what. The order in which you assemble these - which is just as important as the parts themselves - is the next post.

For now, though, let’s get into the parts, grouped by cluster.

Cluster 1: Rebuilding the Procedural

This is the load-bearing repair - the procedural thread that AI cuts most directly and that has no real substitute.

1. Manual Work Runs

This is the deliberate reinstatement of doing the core work by hand, before AI is allowed anywhere near it.

Pick the defined core areas of the function - building an intelligent overview of the supplier landscape, running an RFP end to end, building a spend cube from raw data, drafting a contract summary, constructing a should-cost model - and have the junior do them manually. Synthesizing multiple reports, building spreadsheets, making phone calls to vendors, all of the actual grind. Then cycle them through these major areas over the course of their first year, so they build a manually-worked foundation across the breadth of the function.

This might, to some, feel like nostalgia, but it isn’t. It’s the direct answer to where the chain is maximally impacted by AI: procedural experience is the one input that can only be acquired by doing, so you have to reinstate it on purpose rather than let AI remove it. The manual run is, then, the floor everything else is built on.

Rebuilds: procedural experience (primary) - and, as a natural byproduct, domain knowledge and the first calibration of discernment alongside it.

2. Simulation

This is the safe, modeled practice of high-stakes work. The mechanism is deliberate practice - repetition with rigorous, specific feedback - in an environment where being wrong carries no real-world cost.

In Procurement, that looks like simulated sourcing events, mock negotiations against a deliberately difficult counterparty, or a library of past deals where the junior has to make the call before seeing what actually happened, and then debriefs against the actual outcome.

Two design notes matter here. First, the debrief is where the learning actually happens - the simulation is only the trigger. Second, you don’t need a purchased platform (although there are various low to high tech solutions available in the marketplace for specific areas): a well-built case and a good facilitator can capture most of the value, and AI, even the LLMs themselves, have now collapsed the cost of the rest. AI can role-play a stubborn sole-source supplier, or generate a flawed analysis for a junior to pull apart. But, as stated in last week’s rules, it should only ever do so as the counterparty or the questioner; the moment the AI hands over the “right” answer, we’ve rebuilt the discernment trap inside our own simulator. This is where “struggle first, AI second” does the most difficult work.

Rebuilds: procedural experience and judgement (primary) - as well as domain knowledge and discernment (via the debrief).

Cluster 2: Rebuilding Knowledge and Network

These repair the two inputs that do have substitutes so they lean on a series of familiar tools, but in an organized and more committed fashion.

3. Independent Learning

These are the tools we’ve already utilized to date: traditional coursework, classical training programs and certifications, as well as personal reading.

This is also the block organizations reach for first precisely because it’s the most familiar and the easiest to buy. It is also the least urgent block in the entire program (at least in terms of repairing) because domain knowledge is the input with the most substitutes. There are plenty of solid solutions in the market and AI actually adds a unique additional synthesis layer to it. As such, it still matters, and you should continue to provide it, but be careful how heavily you lean on it and, thereby, avoid the harder repairs that need to be made.

Rebuilds: domain knowledge (primary).

4. Rotation Program

Cycling the junior through different categories and targeted ‘client’ functions to build breadth.

Done well, this is designed backwards from a needs analysis - the areas of current and future demand, where the real skills gaps are, the highest-turnover roles, etc. - rather than from a generic “expose them to everything” instinct. The structure is well-established: assignments of a defined time period (weeks to months), cycling through the development timeline, each one deepening domain knowledge in context and - just as valuably - putting the junior in new rooms with new people.

Rebuilds: domain knowledge (primary); and network capital and procedural range alongside it.

5 & 6. Senior Relationships: Mentoring and Sponsorship

These two get conflated constantly, and the conflation is costly, because they do genuinely different jobs and repair different threads.

Mentoring develops the person. It is Rule 1 - making the invisible visible - turned into a relationship: a more experienced practitioner narrating their reasoning, coaching, correcting, modeling the judgement that otherwise stays locked in their head. The evidence for its impact is strong; the practical key is to formalize it - a mentorship charter with defined expectations and genuine senior accountability - rather than leaving it to whoever happens to be friendly and willing.

Sponsorship is a different thing entirely. A sponsor spends their own positional capital - their reputation, their relationships - to put the junior into the rooms and onto the work that builds a name. While a mentor develops the person, a sponsor moves the career. And this matters enormously here, because sponsorship is one of the very few real substitutes for the network capital the work used to generate for free. A sponsor trusts your performance, and stakes their own standing on it.

You need both, and for different reasons: mentoring rebuilds the discerning eye, sponsorship rebuilds access.

Mentoring rebuilds: discernment and tacit knowledge transfer (primary); domain knowledge alongside it. Sponsorship rebuilds: network capital (primary); and professional identity.

7. Structured Stakeholder Development

This is the junior’s own deliberate plan to build their network - a defined approach to meeting leaders across the function first, then counterparts across the business and, eventually, customers and suppliers, and learning from each group as they go along.

The logic is straight from the developmental chain. Network capital used to form by osmosis, simply because the work put juniors in the rooms where decisions were made. Remove the work - or scatter the team across home offices permanently - and the osmosis stops. So the network has to be built as an explicit, structured plan rather than left to chance. This is also where last week’s co-location constraint comes to the fore: some of this is far harder, and in some cases impossible, to replicate fully over video.

Rebuilds: network capital (primary).

Cluster 3: Building Judgement and the Discerning Eye

This cluster is the upper reaches of the developmental chain - the repairs aimed not at the inputs, but at what the inputs are supposed to produce.

8. Structured Project Ownership

This means giving juniors real accountability, early; not simply supporting a senior’s project from the wings - but owning one. Building it, testing it, leading it, and being answerable for how it turns out.

The sharpest version of this would be what some have called “structured entrepreneurial ownership” - internal programs where juniors can identify events or innovation opportunities, pitch and prototype them, run sprints, etc., handing them genuine ownership of a real business challenge. The underlying principle is the one that should govern this whole cluster: meaningful accountability builds judgement faster than passive exposure ever can (and yes, this entails taking some risk).

It’s also where you deliberately manufacture the thing the chain depends on and AI most cleanly removes - the felt cost of being wrong. Judgement doesn’t form without consequence, and consequence doesn’t exist when you’re only ever assisting.

Rebuilds: judgement and ownership/accountability (primary); procedural experience and network capital alongside.

9. Feedback and Evaluation

This is the block that tells you whether any of the other eight are actually working - and the one most programs get wrong, because they measure the convenient thing instead of the right one.

If discernment is the goal, you have to test discernment - not tool fluency, and not output speed. This means three things:

First, measure the process, not the outcome. A sound decision can produce a poor result through sheer bad luck, and a reckless one can get lucky - so judging people on outcomes alone teaches them nothing reliable. You have to assess the quality of the reasoning. The discipline here is to track whether someone is getting sharper, not just faster - time to competence and error reduction, not volume of output.

Second, use AI to provide graded critiques. Hand the junior a flawed AI-generated analysis and assess how well they find what’s wrong, and why. The point is to test their ability to discern and then whether they can judge - which is the exact faculty the whole program exists to build.

Third, utilize a decision journal - something regular readers will recognize from the practitioner playbook earlier in this series. The junior records the call and the reasoning at the time it’s made, and reviews it later against what actually happened. What was a personal habit for the individual practitioner becomes, in a program, the assessment instrument for judgement itself.

(It’s worth noting also that judgement-based assessments based on a single assessor or a single data point are inherently unreliable. Such assessments need multiple assessors and multiple instances, otherwise it’s just one senior’s gut feel wearing a rubric. Maintain that intent right from the start.)

Rebuilds: discernment (primary); judgement and the habit of reflection and articulation alongside.

These Are a System, Not a Menu

So those are the nine repairs and the basis for any strong junior development program.

Each block exists to rebuild a specific severed or impacted thread, reinforcing a particular aspect(s) of the developmental chain. As a result, the blocks work in concert with each other; if you choose only three because they’re cheap and the easiest to stand up, you’ll repair three threads and leave the rest cut. This is precisely how organizations end up with programs that look busy and develop no one.

But knowing the parts is not the same as knowing how to assemble them. Drop a junior into owned, accountable projects in month one and they’ll drown; never get them there at all and they stay a perpetual apprentice, lacking the confidence to take the next step up.

The parts have an order - and that order is governed by a principle we’ve already discussed: graduated stakes. What that actually looks like - the first twelve to eighteen months, phase by phase, with the stakes rising on a deliberate schedule - is the next post.

Last week’s post gave us the rules that tell us what good looks like. This one gave us what to build. The next post will give us the order to build it in.

In my last couple of posts, I made a case and then offered a diagnosis.

The case: Our junior Procurement talent is at risk, because AI is removing the foundational work that used to build the practitioner.

The diagnosis: That foundational work was never one thing - not a course of study nor a series of tasks. Rather, it was a developmental chain - three uneven inputs that allowed the development of discernment, which matured into judgement, and culminated in a trusted, accountable practitioner (see image below).

Our old apprenticeship models produced all of this (almost) for free. It was almost incidental - a by-product of juniors simply doing the work, day after day, next to people who were better and more experienced than them. They watched, modeled, learned, absorbed and became. But with AI changing that developmental model, we need to rethink how we get our juniors up and running.

We need, therefore, a new brief: to rebuild each element of the chain deliberately, defending hardest the ones AI severs most directly. Today’s and next week’s post will focus on how we can execute to this brief - with today’s post detailing the ground rules for this rebuilding i.e. what needs to come before we can implement any successful junior development program.

But before we do, it’s important to note a potential complication here.

When you try to produce something on purpose that used to happen by accident, you will either do it well, or you will do it badly. In most situations, “doing it badly” becomes self-evident, so you can see it and course-correct.

Not when it comes to AI. In this particular case, done badly doesn’t just mean “less effective”, it means building something that looks like development - busy, well-funded, full of activity - and yet not producing the discernment and judgement we need in our practitioners i.e. reproducing the very discernment trap we’re trying to escape. And we only figure it out years later.

So before we set about building a program, we need to understand the ground rules, as well as the constraints. Internalize them and you get a program that delivers. Get them wrong and no amount of programming will save you. (I’ll get to the actual building blocks - the simulations, the rotations, the mentoring and the rest - next week.)

Let’s start with the rules - and there are, specifically, four of them.

Rule 1: Make the Invisible Visible

When we think about traditional apprenticeship models, say, a blacksmith and his apprentice, the work is visible. The apprentice watches the master’s hands, sees the angle of the hammer, observes the results, copies it, makes mistakes, gets corrected, then learns to do it right. The entire curriculum is on display.

But in knowledge work, the important part - the thinking - is invisible. When a senior practitioner looks at a sourcing recommendation and says “no, this doesn’t work,” the analysis that produced that judgement happens inside their head. The apprentice only sees the conclusion and maybe an explanation. They don’t see the reasoning behind it and, no matter how good the explanation, they won’t intrinsically understand it.

This is the central problem when it comes to developing judgement, and the first rule of any rebuild follows directly from it: you have to deliberately make expert thinking visible.

This isn’t a new or fringe idea. There’s a whole body of work on “cognitive apprenticeship” built precisely around making the invisible thinking of experts visible to novices - and it’s serious enough that McKinsey built its own developmental model on it. It names six methods worth knowing: modeling (expert performs the work as the student observes), coaching (expert observes and facilitates as the student performs the work), scaffolding (expert provides support that’s gradually removed), articulation (student explains their knowledge and reasoning), reflection (student compares their performance with others), and exploration (student explores in diverse situations, solving their own problems).

In practice, this translates to the difference between a senior who hands a junior a finished category strategy, and one who narrates why as they build it: “I’m weighting this supplier’s capability over their price because our product roadmap is going to need those capabilities in two years, and switching later is going to be expensive.” That narration - the part most seniors skip because it’s obvious to them - is the actual curriculum.

The reason this is a principle and not just a nice technique is that every single building block I’ll detail in my next post is, underneath, just a delivery mechanism for those six methods. A high quality program has to make expert thinking visible, otherwise it’s just activity posing as development.

Rule 2: It’s Human and Machine - Not Either/Or

There are two lazy positions on AI that plague much of mainstream thinking, and both are wrong.

The first is nostalgic: keep AI away from junior work so they can learn the “real” way. But that’s neither possible nor desirable - the tools are genuinely valuable, and a junior who can’t use them is going to be unprepared for the actual job.

The second is the one my last post warned about: let AI do the work and have juniors supervise it. That’s the discernment trap - handing someone the job of judging output they have no calibrated basis to judge.

Rule 2, then, sits between them: the question is never whether juniors use AI, but in what sequence and in what role. AI is a phenomenal development tool when used one way and a discernment-destroyer when used another way. The difference is entirely in the design of how we use the tools, not in the tools themselves, which leads directly to the most important rule of the set.

Rule 3: AI Must Be a Questioner, Not an Answerer

If you take one thing from this post, take this:

AI that hands you the answer builds nothing that lasts. AI that makes you reason builds something that does.

Everything about how juniors use AI should be configured around that single distinction - which, specifically, means three things:

Struggle first, AI second. This sequence is non-negotiable. The junior attempts the work and commits to a position before the AI engages. Think about a junior building a should-cost model. The destructive version of this is “AI, build me a should-cost model for this part”. They might get a polished output in seconds, but they’ll learn nothing and have no way of knowing whether it’s right. The developmental version is the reverse: they build their own first, then bring AI in to pressure-test it - or, better yet, the AI asks them, “what did you assume about material costs, and why?” In the first version, the machine did the thinking. In the second, the junior’s own struggle did the thinking and the machine sharpened it. Always embed the ‘struggle’ first.

Use AI as a Socratic tutor. Configured well, AI can withhold the answer, ask guiding questions, and scaffold a junior toward their own conclusions - which is exactly what a good mentor does, but at infinite scale and availability. And there’s early evidence that this matters in precisely the way you’d hope: when AI is set up to reframe its explanations as questions, people get measurably better at spotting flawed reasoning. When it simply hands over the answer in conversation, the apparent gain evaporates when the conversation ends. Better for us to own the reasoning than to ‘borrow’ the answers.

Productive struggle is a feature, not a bug. The natural instinct of a well-meaning manager is to remove friction from a junior’s path. But when it comes to the learning process, this instinct is wrong. The struggle is not an obstacle to learning, it is the learning. It’s the thing that builds mental maps and intrinsic understanding. It’s important, therefore, for us to design for friction, and not against it.

I won’t pretend this is easy, because it does run against so many of the incentives that have become endemic in our daily work lives: the need for speed, efficiency, the “why are you doing it the slow way when the AI can do it instantly?”. All of those incentives push us in the other direction, which is exactly why this can’t be left to juniors to figure out on their own, or even to individual line managers. It has to be a deliberate, protected protocol, set and defended from the top. Which brings us to our next rule.

Rule 4: Leadership Shows Up in Hours - Not Just Dollars

The most common way training programs die is through “support” that’s no more than lip service. Leadership sponsors an initiative - approves a budget, sends a launch email, gives a nice speech and commissions a dashboard - and then goes back to their actual jobs. That isn’t sponsorship, not in the sense needed here.

If you go back to Rule 1, you’ll see that those six methods that make expert thinking visible all run on one thing: senior time. Modeling is a senior thinking aloud. Coaching is a senior watching a junior work and correcting them in the moment. There is no version of “make the invisible visible” that doesn’t cost the expert some real portion of their time. So when leadership funds a program but won’t spend the calendar time to back it up, they’ve built something superficial.

The rule, then, is: sponsorship is measured in senior hours on calendars, not just dollars in budgets. And it has to be visible, because juniors are watching - they calibrate what matters in an organization by what they see their leaders actually do, not by what leaders say they value. A CPO who blocks two hours a week to sit with juniors and think out loud teaches more about the function’s standards than any policy document ever will.

Two Constraints

The four rules above are choices. The two constraints below are not - they’re conditions that bound what’s even possible, and any blueprint that ignores them is simply not going to work.

Co-location. Part of what we’re trying to rebuild - network capital, and the tacit transfer that comes from sitting near someone better than you - was produced by proximity. Overhearing the difficult supplier call, pulling aside the senior as they walked out of the meeting, absorbing how the room handled a tense moment. And therein lies the problem: some of what we’ve been blaming on AI is, in part, a co-location problem. If the juniors are not in the office - at least on some regular basis - they will not benefit from the serendipity of in-person learning. (In fact, the recent decline in entry-level hiring (we can argue about the exact numbers though that doesn’t really matter) tracks at least as closely with how remote-able a role is as with how AI-exposed it is.)

I don’t want to relitigate remote work here - there are real and good reasons for it, and this isn’t the post for that debate. But any blueprint that pretends the relational layer can be fully rebuilt over Slack and video calls is just not going to hold over the long term. If your juniors are fully remote, you can rebuild the cognitive threads but you will genuinely struggle to rebuild the relational ones.

Motivation. None of this works on a junior who doesn’t want it. I’ve noted before that the spiral runs on the junior’s own drive, and I meant it as more than a flourish. A development program built for the unmotivated is just an expensive babysitting service - until the baby gets a bit older and leaves home.

Two things follow. You select for it - curiosity and a genuine willingness to work through ambiguity matter more than a polished CV. And you protect it - because the fastest way to extinguish a good junior’s drive is to make the work frictionless and therefore meaningless, which, ironically, is exactly what an over-helpful AI does when you let it. Keeping the fire lit is part of the program’s job.

What Comes Next

So those are the rules. Make the invisible visible. Human and machine, never either/or. AI as questioner, not answerer. Leadership measured in hours, not just dollars. All of it bounded by two honest constraints - co-location and motivation.

These are the physics that every program has to obey. They’re how you tell, before you’ve spent a dollar, whether what you’re about to build is real development or not.

In the next post, I’ll lay out the building blocks themselves, sequenced across a junior’s first year and a half, with steadily rising stakes holding it together.

The story being sold to us right now is an appealing one.

AI takes away the drudgery and you’re freed to do higher-value work. As a result, your job shifts from doing the work to directing and checking the AI. You become the supervisor, the editor and the one applying the judgement - allowing you to get to better outcomes faster than any generation before you.

It sounds like the promotion we always wanted - free of the grunt work! - but, in practice, it can be a trap, and for no one more so than the junior practitioner.

Because to direct and check an AI’s output, you have to be able to tell the good from the bad. You have to be able to look at a sourcing recommendation, a contract summary or a supplier analysis and know, sometimes instantly, that something is off.

This ability has a name: discernment. Discernment isn’t something we’re born with, or pick up in any training course. It is the resultant artifact of having produced the work ourselves - often badly at first, then less badly, over and over again, until we’ve developed a calibrated sense of what “good” actually looks like.

And therein lies the trap:

AI hands the junior practitioner the job of judging its output, while removing the very work that built the judgement to do so.

Because supervisory roles assume a discerning eye which, in itself, was a by-product of the production work. Take away the production, and you take away the means by which a junior person can learn to do it well.

From Symptom to Cause

This is the mechanism underneath the apprenticeship crisis I wrote about in my last post. There, I argued that our junior Procurement talent is at risk - that AI is threatening to remove the foundational basis that used to build a practitioner’s judgement, and that the apprenticeship model (using that term broadly to encompass all training and development) we’ve relied on for generations is breaking as a result.

The discernment trap is why this matters so much. It isn’t simply that juniors will know less. It’s that the new role we’re handing them structurally depends on a faculty that the old work was building at a very fundamental level i.e. we are removing the cause yet keeping the expectation.

So if our goal is to produce junior practitioners who actually develop into trusted, accountable senior Procurement professionals, the kind who become our future leaders, then we have to get specific about the path that produces them. And simply focusing on broad notions of “apprenticeship” - a simple agglomeration of tactical actions and tools - is to treat it as one undifferentiated thing, making it too blunt to be useful.

We need to be clear about the different parts that need to be worked on.

The Destination: Judgement-in-Action

Let’s start with our destination.

In the conceptual framework I’ve been developing (see image below), the destination is what I call trusted, accountable judgement-in-action: a practitioner who can make the hard calls under situations of uncertainty, stand behind it, and be trusted by others to do so.

That, increasingly, is the senior practitioner’s actual job in a post-AI world. Not producing the analysis, because the machine can do that, but being the human who owns the call - not simply when the machine’s analysis is right, but especially when its recommendation is plausible and wrong.

The question, then, is how does a budding junior get there? What is the developmental flow that turns a new entrant into that caliber of practitioner? Once we understand this clearly, we can then see exactly where AI cuts - and only then can we think about what to deliberately rebuild.

Let’s walk through this framework.

Three Unequal Inputs

At the base sit three sources of a junior’s early development:

Domain knowledge - understanding the category, the supply market, the commercial and contractual fundamentals, etc.

Procedural experience - actually doing the work: running the RFP, building the spend cube, drafting the summary, sitting in on the negotiation, and more.

Network capital - the relationships built with colleagues, stakeholders and counterparties.

All three contribute to the learning journey, and it’s tempting to treat them as three equal pillars. But they aren’t - and that inequality is the whole point.

Domain knowledge has substitutes (or multiple ways of building the capability). You can acquire a great deal of it from a training course, a category report, or a good book. Network capital has partial substitutes - relationships can be built deliberately and actively but much of it can also be formed by simply being in the room (a partial case, by the way, against remote work, but that’s a discussion for another day). But procedural experience has no real substitute. It is the one input that can only be acquired by doing. And, not coincidentally, it is the one AI removes most completely.

There’s a deeper point here, and it’s the reason procedural work is load-bearing. Doing the work was never only about building procedural skill; it was the enabling and delivery mechanism for the other two. You absorbed domain knowledge in context - not as abstract facts, but as patterns you noticed because you were elbow-deep in the actual data. You built your network because the work put you in the rooms, on the email threads and in the negotiations. If you take away the procedural work, you don’t just lose one of three inputs. You weaken all three at once.

This is also where the richer learning research points. The reason apprenticeship worked - going back to the cognitive-apprenticeship literature - is that doing real work alongside someone more expert makes their normally-invisible thinking visible to you. And the reason struggling with a problem before you are handed the answer builds deeper understanding (another robust finding from decades of “productive failure” research) is that the struggle itself is what encodes the lesson. The productive struggle is the point, as that is what rewires your neurons, allows you to build a mental map of the topic or issue at hand, and develop an intrinsic understanding of the problem at hand - so that your ultimate solutions make more meaningful sense.

When AI supplies the answer first, it closes that window. You keep the output but you lose the learning, because all you have are surface representations of the problem at hand - a ‘stab’ at an answer that is at the same level.

Inputs Become Discernment

Of course, these inputs don’t stay separate. With enough practice - including feedback, consequences, the felt cost of being wrong - they fuse into quality discernment: the trained eye that recognizes the good from the bad.

Discernment is evaluative, and it is retrospective. It judges work that already exists. It is the capacity to look at a finished piece of analysis and know that it is missing the point that actually matters (or simply naive). And, to come back to where we started, it is precisely the faculty the AI-supervision role demands, and precisely the one that only forms through the procedural work discussed above.

Discernment Becomes Judgement

Discernment, in turn, is the substrate for judgement. These two are easy to confuse, so it’s worth being exact about the difference.

Discernment, as we’ve said, is retrospective: it recognizes quality in work that exists. Judgement is prospective; it is generative. It is the capacity to decide what to do under uncertainty, to commit to a call before complete data is available. We should invest in this supplier because its capabilities will better align us with our product future, even though the incumbent is materially cheaper.

Judgement isn’t about evaluating a finished output; It’s about making a decision, and then owning it.

You cannot develop judgement without discernment. Judgement is, in large part, running your discernment standard forward - applying your hard-won sense of “good” to options that don’t yet exist. A practitioner with no calibrated eye for quality has nothing to project into an uncertain decision. They are guessing.

Judgement Becomes the Practitioner

Judgement, exercised and tested over time, is what finally produces the apex: the trusted, accountable practitioner.

This has two faces that develop together.

• One is professional - others come to trust this person’s calls. They have earned a reputation and internalized the norms of the function. They have become a procurement person in the fullest sense.

• The other is internal - the earned self-confidence that comes only from having made real calls, genuinely owning those outcomes, and exercizing the resilience to sit with the ambiguity inherent within them.

Neither of these can be borrowed. Confidence that was never earned is either hollow or as good as having none. Ownership you never actually felt will never parallel real ownership. And this is where some of the most striking recent evidence is worth mentioning (though I would treat it as suggestive rather than settled, given it’s early work). When researchers at MIT had people write with heavy AI assistance, the quality of the output was perfectly fine, but the sense of ownership of it simply wasn’t there. Many of the “doers” could not even recall what they had just “written”.

To reiterate, the tool produces the artifact. It does not produce the practitioner who feels responsible for it.

Why This is a Spiral, Not a Ladder

One refinement: I have described this as a sequence, and the dependencies are definitely real - you cannot develop judgement without discernment, or discernment without repetitions (doing the work).

But this isn’t a one-way escalator. It’s a spiral that compounds (see image below). Once you have a little discernment, your next experience teaches you more, because you now notice things you couldn’t see before. Once judgement begins to form, it changes what you pay attention to while you work. The practitioner that pulls ahead isn’t simply doing more work, they’re doing that work under steadily rising standards.

This is why removing the early foundational work does so much more damage than it first appears. You’re not simply subtracting one rung from a ladder, you’re foregoing years of compounding - a curve that was meant to bend upward but instead goes flat. And because it is a curve, the gap is nearly invisible at first. It only shows up five or ten years later, when you reach for a bench of seasoned judgement and find it was never built.

One last thing: none of this happens to a junior who doesn’t want it. The practitioner has to bring some motivation of their own - ideally an understanding of precisely the perils of over-reliance we have been describing. The work then reinforces it. The stakes, and the struggle, are part of what makes a person care enough to climb.

What Comes Next

So that is the bundle. Not “apprenticeship” as if it were one undefined thing, but a developmental chain: three uneven inputs, fusing into discernment, maturing into judgement, culminating in a trusted and accountable practitioner - and then compounding, in a spiral, the whole way up.

Seeing it this way changes how we tackle the problem. “Replace the training ground” sounds hopelessly vague, but “Deliberately rebuild each of these components, defending hardest the ones AI severs most directly” is a brief we can actually work with.

Which is exactly what the next post will focus on: a blueprint for developing junior talent in a post-AI world. How to engineer each of these effects on purpose, now that the delivery mechanism that once produced them for free is being taken away.

When I started in consulting several decades ago, a sizable part of my work as a newly minted Associate was what one might affectionately call “foundational work”. This comprised of both the cognitive - e.g. reading secondary research and preparing industry analyses - and the tactical/executional - e.g. creating PowerPoint decks, etc.

While a lot of that work was interesting and educational from a content standpoint, quite a lot of it was not. It was what you might refer to as grunt work.

But it all served a purpose: it laid the foundations for my development into a strong consultant. It taught me how to understand what was important to pick out from a research study in the context of the objectives of the engagement. It taught me how to craft the right sequence of messages and then convey them in a way that resonated with my audience. It taught me the discipline and discernment needed to deliver value as a consultant.

And on that basis, doing that work day after day, month after month, year after year, I was able to build not only my engagement delivery skills, but also the nuanced understanding and the judgement needed to progress up the consulting ladder over the following decade.

If, back then, I had the kinds of AI tools we have access to today, I would no doubt have been able to save a tremendous amount of time by having them do so much of that work for me, radically compressing the time to delivery of every engagement I worked on. So there would, no doubt, have been significant benefits for me, my team and my clients.

But I also wonder what kind of a consultant I would have become.

Because if AI could do the very work that laid the foundations of the consultant I became, what would that have meant for me and, more broadly, for the development ladder that consulting firms - and investment banks and corporations all over the world - relied on to grow their next generation of talent and develop their future leaders?

What the Grind Was Really Teaching

To be fair, today’s AI tools are genuinely valuable. As productivity enablers and thought partners, they bring a myriad of benefits to the work we do every single day, regardless of our professions. I’ve talked about these benefits, as well as the skillset implications these tools have for the Procurement practitioner in a post-AI world.

But for the junior practitioner - the new entrant into Procurement - AI poses a particularly unique problem. It is taking away the very foundational work that new entrants actually do - and this encompasses the cognitive as well as the tactical work. Historically, this work served as the training ground for junior folks to not only learn the function itself (running RFPs, building spend cubes, drafting contract summaries, doing supplier research, sitting in on negotiations) but also then develop the higher order skills needed to function effectively as a senior practitioner - skills such as judgement, relationship management, the ability to navigate ambiguity, etc. But now, the very training that taught juniors their craft is being taken away by the machines.

Of course, there are those who argue that this is actually a boon for the junior practitioner, that they’re now freed from the drudgery of the tactical work they never even wanted to do (and what was really just low-value busywork that deserved to be automated anyway). There is some merit to this argument. Juniors are often given low value busy work, and AI does indeed free them up to do higher value work.

But that argument misses the point: it wasn’t the drudgery that mattered - it was what the drudgery taught. The grind was the delivery mechanism for judgement. Running a low-stakes RFP didn’t just teach a junior the mechanics of an RFP, it taught them which suppliers were sandbagging, how different vendors negotiated and where their leverage points lay. Building the spend cube didn’t just teach Excel, it taught them how to find the opportunities buried in the data. If you remove this labor, you also lose the lessons that come from it.

Certainly AI can augment the junior in these cases - I won’t argue with that. But when the junior practitioner’s job moves from “doing the work” to “directing the AI” without the requisite foundation, I worry that they lose a key developmental pathway. They lose the grounding experience of not just the nuts and bolts of doing the work, but also the lessons that come from making mistakes while doing so, then learning to spot and fix those errors, etc.

In other words, they lose the repetition, the pattern recognition, the experience, and the acquired judgement that comes from doing that very work.

The Problem Everyone Sees

There’s another problem that AI poses for junior practitioners, and at first glance it looks like the bigger one. Corporations are noting the depth and (general) quality of what AI is capable of and many are asking themselves: Do we even need junior practitioners at all?

What’s worse, the question isn’t just being posed, there’s evidence to suggest it’s becoming a practiced reality.

A lot of this evidence is, of course, anecdotal in nature. You hear in the media about how new entrant hiring has slowed and how organizations are rethinking hiring (or at the very least, hesitating to do so) as they grapple with the changes AI will bring and how they can use these tools to drive even greater profitability by replacing people with machines.

But it also seems to be borne out by the data. A Stanford study last year - fittingly titled “Canaries in the Coal Mine?” - found tangible evidence that AI is starting to have a significant and disproportionate impact on entry-level workers in the U.S.:

“The analysis revealed a 13% relative decline in employment for early-career workers in the most AI-exposed jobs since the widespread adoption of generative-AI tools, ‘even after controlling for firm-level shocks.’ In contrast, employment for older, more experienced workers in the same occupations has remained stable or grown.”

The largest declines, the study notes, are concentrated among young, entry-level workers - those whose skills are most easily replaced by AI systems automating routine, codified tasks. Experience and tacit knowledge, in other words, are becoming the buffers against displacement.

So this hiring decline is certainly troubling. It’s the part of the story that everyone is talking about, because it’s visible. But it’s only part of the story.

The Compounding Cost

When you combine the visible (the drop in hiring) with the invisible (what happens to the juniors we do bring in), you can see the extent of the problem. This combined erosion builds over time, showing up five or ten years later, when we reach for a bench of seasoned judgement only to find it was never built.

(By its nature, this second erosion can’t yet be measured - its costs will land years downstream - but that’s precisely what makes it dangerous: it won’t show up in any dashboard until the bench is already thin.)

This plays out in three ways:

1. The Loss of the Apprenticeship Dividend

When Generative AI takes over the foundational work that serves as training for new entrants and junior practitioners - tasks such as drafting, research, modelling, and more - and we don’t provide the right roles and appropriate learning opportunities for them, we lose the apprenticeship ladder as well as what Forbes calls “the Apprenticeship Dividend”: the compound return created when people learn by doing, grow into new responsibilities, and then pass their knowledge on to others.

We need to ensure we retain the long view and provide the guidance and structure needed to not just keep but fortify that development ladder.

2. The Digital Native Trap

New entrants and fresh graduates into the function are typically far more progressive when it comes to their understanding and use of new technology, particularly AI. They are effectively digital natives. And there’s a real organizational case for that, best summed up by a recent World Economic Forum report:

“Without an influx of digital natives, organizations would experience a range of detrimental impacts: slower AI adoption and application, weakened succession plans, stalled knowledge transfer and cultures that struggle to renew themselves.”

That case is genuine - but notice what it is and isn’t.

It is an argument about organizational AI adoption: digital natives help the enterprise absorb these tools faster.

It is not an argument that solves the developmental problem. In fact, it can mask it. The same WEF report goes on to suggest that newcomers can “enjoy instant access to expertise that used to take years to gain” and “use AI to acquire skills more quickly and rapidly ascend to higher value roles.” There’s truth to this - but it’s also a seduction. Expertise you can borrow from a tool on demand is not the same as judgement you’ve built and own for yourself. That difference only becomes visible when the stakes are high and the tool is wrong.

My point is, keep hiring digital natives, but do it for the right reason (because they accelerate the organization’s AI fluency), and don’t let that benefit lull you into believing the developmental problem has been solved.

3. The Loss of Collaboration

One of the temptations of AI tools is that they make us self-sufficient: If the tool is our partner, then we don’t need to work with humans quite as much. Taken to an extreme, this creates a host of unintended consequences, especially when it comes to collaboration.

As AI takes on the foundational work, employees have less need to interact with each other - either with peers or with their senior leaders. This weakens mentorship and lessens the mutual support that is part and parcel of working with colleagues. It also reduces or eliminates the informal, serendipitous learning that simply happens when you least expect it.

Why This Matters

So the apprenticeship crisis is real and its implications are significant.

If we remove these roles - or hollow them out while keeping the headcount - and capture the savings that come from it, we will do so at significant opportunity cost: by sacrificing future skills, our talent pipeline and, ultimately, long term growth.

We need to use AI to accelerate human learning and capability, not as a substitute for it. We need to use it to free us up to become more strategic and able to do higher value, more fulfilling work.

Which brings me back to the question I started with. I wondered what kind of consultant I would have become if AI had done my foundational work for me.

The honest answer is that I don’t know - and that’s exactly the point. The judgement I rely on today was built over time, doing work I didn’t always enjoy and couldn’t have known the value of at the time. Junior practitioners entering Procurement today deserve a path to build that same judgement, especially as the old path disappears beneath them.

So it’s in all of our interests to fix this. That is what the next few posts are designed to do.

Over the last few weeks, I’ve laid out my model for future-proofing the Procurement practitioner, which is made up of three layers:

1. The Enabling Layer — comprising AI Literacy and Cognitive Discipline

2. The Differentiating Layer — comprising Orchestration, Business Acumen and Human Leverage

3. The Orientation Lens — comprising the outcomes towards which we orient our efforts

I’ve detailed the key capabilities within each layer and also provided practical suggestions as to how to best build those capabilities.

(It’s worth reiterating that foundational Procurement skills are not on this list and that’s entirely intentional. My assumption is that you have already developed that Procurement knowledge - core sourcing skills, category expertise, etc. - that forms the technical and foundational basis of your work. These are table stakes, not differentiators.)

To wrap up our discussion of the Future-Proofing model, there’s one final question we need to address - and it’s a harder one than it might at first look:

How do you build and keep these capabilities over time?

AI Is Changing How We’re Learning

In the world before AI (certainly Gen AI), simply doing the work developed you automatically. You understood and managed spend, and then ran sourcing events, and got better at all the requisite skills required to do that work. In other words, the skills you needed to succeed as a practitioner were baked into the work itself. The more you did it, the better you got.

That’s no longer the case. AI is now absorbing exactly those reps that built the muscle. Today, you can pick any core procurement task or capability and there’s a piece of technology ready to take it over and do it 24/7.

So for the first time, the work will no longer develop you by default - and certainly not when it comes to the more progressive skills laid out in the future-proofing model above. You, as the practitioner, have to deliberately manufacture the development that the job used to hand you for free, especially in terms of these higher order skills.

There’s one more issue, and this is what makes the situation urgent rather than just interesting: your skills can decay even as you feel more productive than ever.

Why? Because, from an activity standpoint, nothing in your weekly calendar will tell you you’re not delivering. The deliverables will still ship and the events will still close - but that’s because AI keeps the output flowing even as the requisite capabilities and judgement underneath it erode. That might be tenable, for now. But at some point, it won’t be.

Which is why capability development - in the right areas - cannot be left to chance. It has to become a practice.

So how do we build that practice? Three actions: diagnose, build, review.

Manufacturing Your Development

1. Diagnose your trajectory - not just your level

Most self-assessments ask “Where am I?” The more useful question in an age of AI is “Which way am I moving?” The risk isn’t being an 8 out of 10 on cognitive discipline today; it’s sliding from an 8 to a 5 without noticing because AI took the work.

So map yourself across the seven capabilities of the model on two axes.

The first is Proficiency - but assess it by evidence, not feel, because feel is exactly what AI corrupts. Don’t ask how confident you are in your business acumen or your human leverage; ask whether you can point to a specific, recent moment where you visibly exercised it - and what came of it. When did your read of the business actually reframe a category decision? When did you move a resistant stakeholder? When did you catch an AI output that was plausible but wrong? If you can’t name an instance, that’s your answer, however strong you feel.

The second axis is Trajectory - which direction that proficiency is heading. And the cleanest way to gauge this is to assess whether your role still hands you real opportunities to exercise this capability, or has AI absorbed them? “I used to do this constantly, but lately the tool handles it” is not a neutral observation - it’s indicative of a downward slope.

Basically, that’s only two questions per capability: show me a recent instance, and are the reps still coming? - so assessing where you stand on all seven capabilities is a five-minute task, not a fourteen-point audit. If you do this honestly, it gives you four positions (see image below):

• High proficiency, Rising Trajectory: You’re strong here and still getting sharper; protect the work that’s keeping you there

• High proficiency, Falling Trajectory: The dangerous box: you’re good today but coasting on a melting asset

• Low proficiency, Rising Trajectory: No cause for alarm; you’re early but on the right trajectory, so keep feeding it

• Low proficiency, Falling Trajectory: The capability is weak and getting weaker; this is where you either intervene deliberately or consciously let it go.

The value of this map is that it tells you where to spend your development time, because, critically, you shouldn’t be spending it evenly across all seven.

2. Build the practice - defend hardest what AI erodes fastest

The capabilities in the model don’t all decay equally: AI erodes the muscles tied to the work it does for you. The thinking work - for example, cognitive discipline, and downstream of it, judgement - is under active erosion every single day you use the tools unconsciously. The capabilities AI doesn’t do for you - your relationships, your exposure to the business, etc. - erode the same way they always have, through your own neglect.

So we defend hardest what AI erodes fastest and focus on the most prominent skill gaps. In rough order of priority:

• Keep a decision journal. This is one of the single highest-leverage habits you can have, because it hits three capabilities at once - judgment, cognitive discipline, and, if you log how you read the room, human leverage. Record the consequential calls you make, your reasoning, and what you expected. Then go back and close the loop on whether you were right. This is how judgment is maintained when AI is making the easy calls for you.

• Embed deliberate friction. Be skeptical about what you’re getting back - from AI, from the process, from the way things have always been done. Before you accept an AI-generated answer on a call that matters, write the one-line counter-case: what would make this wrong? That single sentence is cognitive discipline in practice.

• Re-architect one workflow each quarter. Orchestration doesn’t erode so much as it ossifies - you settle into a default human-plus-AI process flow. Maintenance here means deliberately redesigning a workflow you’d otherwise run on autopilot: who does what, in what sequence, where you step in at the seams.

• State the outcome before the activity. We naturally drift, over time, back toward activity and process - it’s baked into many work environments. So on any given project, force yourself to name the outcome you’re orienting toward before you touch the activity. Are you focused on a meaningful outcome? Are you contributing to what matters? This ensures orientation stays alive instead of falling back into busywork.

• Maintain an exposure diet. Business acumen and AI literacy grow through exposure - to the business, to supply markets, to what’s actually being impacted by AI and related tools. This is an ongoing, deliberate intake over time.

The key with the above is not to create a parallel development calendar to the work you’re already doing. Instead, bolt these onto the rhythm your Procurement work already has - the sourcing cycle, supplier reviews, QBRs, budget season. The work is already happening, so add in the necessary reflection attached to them. In other words: same work but with added intentionality.

3. Review - and course correct

A practice you never check is a resolution, not a regimen. So once a quarter, return to the diagnosis, not to just repeat it, but to understand if your efforts have been worth it.

The first diagnosis you run tells you where to spend your development time. The subsequent reviews every quarter ask a direct question: did it work? That capability you’ve spent the past quarter building or defending, is it actually growing and holding, or did it slip back? If it slipped despite your attention, then rethink your practice.

Another reason this regular review cadence matters is that the map moves under you. AI improves and evolves every quarter, which means capabilities that once sat safely in the “rising” column can slide toward erosion without you doing anything wrong. The fact is that the technology evolved and the ground simply shifted; something the tool couldn’t touch ninety days ago, it may absorb now. So re-scan: what does AI now do that it didn’t last quarter, and which of my capabilities did that just put at risk? Then you adjust where you spend your time, and reset for the next quarter.

That moving target is exactly why this can’t be a one-time audit, and why staying sharp is an ongoing commitment, not a box you tick once.

The Real Shift

For most of your career, staying good was a byproduct of showing up - the work did the developing for you. That era is over.

From here on, staying sharp has to be a deliberate act: something you actively focus on and develop, or else it becomes something you lose.

The practitioners who pull ahead in a post-AI world won’t be the ones who simply use AI best. They’ll be the ones who keep developing the right complement of skills while they use it - the ones who refuse to let fluency with the tools hollow out their judgement.

Of course, this entire playbook presumes a foundation. You can only maintain judgment you’ve already built, you can only journal decisions you’re already trusted to make. That works for the experienced practitioner.

But our juniors have none of that: they have no foundation to maintain or build on, while, at the same time, AI is busy removing the execution work that used to build it.

Which raises a genuinely difficult question: how does anyone come up the curve now?

That’s going to be our next set of topics, starting next week: the apprenticeship crisis.

Two practitioners can have the same skills but still face opposite futures.

One spends their time executing - running sourcing events, refreshing contracts, turning around spend analyses - and then watches as AI absorbs each of these tasks in turn. The other points the same skills at what the enterprise actually needs - and becomes harder to replace every year.

The difference isn’t about capability - it’s about orientation.

Over the last several weeks, I’ve laid out my model for future-proofing the Procurement practitioner - the Foundations you build and the Capabilities you develop.

This final piece is about where you aim them.

Orientation, Defined

The Orientation lens, in this context, is the art and science of applying the skills and capabilities we’ve learned towards Procurement-focused goals that move the enterprise. It is about ensuring that the work we do is relevant and meaningful - not just for the function itself, but the organization as a whole.

Orientation is, therefore, the lens through which we apply what we’ve learned - a lens that ensures Procurement remains positively positioned within the organization. In the context of our future-proofing model, think of it this way:

Foundations are what you know, Capabilities are what you can do, Orientation is what you point them at.

And what do we point them at? In my view, our orientation lens should be focused on outcomes. When we understand and focus our work around outcomes, we ensure that our total focus is on delivering value, not just for Procurement and the stakeholder in question, but for the enterprise as a whole.

I wrote about the seven most critical Procurement outcomes in detail here but to recap, they are:

1. Speed and Responsiveness of the Procurement Process: When a business unit needs something purchased, how quickly and painlessly can they get it?

2. Achieving Optimal Total Cost of Ownership: Are we optimizing the full economic cost of a buying decision, including implementation, maintenance, switching costs, quality, etc.?

3. Maintaining Supply Resilience and Mitigating Risk: Can the business count on having what it needs, when it needs it, without disruption?

4. Ensuring Compliance and Ethical Assurance: Do stakeholders know and believe that what they’re buying, and who they’re buying it from, won’t expose the organization to legal, regulatory, or reputational harm?

5. Ensuring Optimal Supplier Relationships: Are we managing supplier relationships such that the organization earns unique and preferential access?

6. Driving Supplier-Enabled Innovation: Is Procurement unlocking real value through suppliers and ensuring those suppliers see the organization as a customer worth innovating for?

7. Crisis Management: Is Procurement involved front and center in ensuring the organization survives crises with its financials, operations, relationships, and reputation as intact as possible?

Of course, these outcomes are not new; they have always mattered. What’s changed is that orienting around them is not only just good practice - it’s existential.

Why Activity Stopped Being Enough

The idea of orientation is particularly relevant in a post-AI world.

For years, the proxy for value (and the practitioner’s personal moat) was activity. That is, you were valued precisely because you were the one who could run the RFP, model the should-cost, and manage the process. But as we’ve discussed before, AI will absorb most of this activity-level work - and not just the transactional stuff (the “grunt” work), but the cognitive/decision support work (the analysis) as well.

What remains, then, cannot simply be a re-assembling of residual activities and tasks, but a fundamental rethink of who the Procurement practitioner is and what he or she does. What survives will be the progressive value driver who is focused on organizational goals, and ensuring Procurement leverages its core remit to achieve those goals.

The practitioners who will survive, then, will be the ones who define their value through their relentless focus on achieving relevant outcomes - versus their prowess at accomplishing specific tasks. The activity-defined practitioners will become automatable, but outcome-oriented ones will become indispensable.

So how do we become outcome-oriented?

Six Principles for Outcome Orientation

Orientation isn’t a step-by-step process that can be neatly applied. It is, at its core, a mental mindset that points everything at ‘results’ not ‘process’.

Installing this mental mindset requires internalizing a series of principles that ensure that the outcome focus is front and center:

1. Anchor every multi-year goal to at least one of the seven outcomes.

The simplest test of whether you’re oriented around outcomes is to look at your own goals.

If they describe activity - “run twelve sourcing events,” “complete the contract refresh,” “onboard the new spend-analytics tool” - then you’re measuring motion, not value. Rewrite them so each one ladders up to a named outcome: not “run sourcing events” but “improve total cost of ownership in the key categories where it’s most leveraged”.

If you don’t control how your goals are framed, push to renegotiate them. What your scorecard measures is what you and your team will actually orient around, so get outcomes onto it.

2. Actively prioritize your time around the critical outcomes.

Outcome orientation set once a year and forgotten is just a planning artifact.

The discipline is in the cadence. Each month and each week, look at where your hours actually go and ask whether they tracked to the outcomes you said mattered. Usually, the honest answer is no - calendars often fill up with the urgent and the procedural, and the outcomes that need sustained attention get whatever time is left.

Treating outcome focus as a recurring allocation decision, rather than an annual aspiration, is what keeps orientation real between planning cycles.

3. Make your tradeoffs consciously.

The seven outcomes are in constant tension. Speed pulls against resilience; cost pulls against innovation; compliance pulls against responsiveness. A practitioner who claims to be advancing all seven equally is usually advancing none of them deliberately.

The genuinely ‘oriented’ practitioner doesn’t just track the seven. They make conscious calls about which to optimize for, when, and at whose expense, and they can defend those calls when challenged.

This is also where AI changes the work. AI is going to make these tensions visible in ways they never were before - simulating cost-versus-resilience scenarios, flagging compliance-versus-speed conflicts in real time. As the work itself gets automated, the scarce and durable human act becomes owning the call.

4. As AI absorbs activity, redirect the freed capacity toward outcomes.

When AI accelerates sourcing, contract review and the rest, the path of least resistance is to simply do more of the same, faster. But that’s exactly the trap. Doing twice as many sourcing events isn’t future-proofing - it’s automating your way deeper into ‘activity’.

The disciplined move is to take the capacity AI gives back and reinvest it into the outcomes that chronically get starved: supply resilience, supplier-enabled innovation, the quality of key relationships. These are the outcomes that are hardest to automate and carry the highest human premium, precisely because they resist being reduced to a process.

To be clear, again, AI doesn’t change what matters - the seven outcomes are the same as they always were. It changes how much of your capacity you can point at them.

Consciously make the decision about where that reclaimed capacity goes.

5. Communicate your impact.

Work that moves outcomes but is never seen to have moved them tends, over time, to be valued as activity.

Make a habit of communicating impact in the language of the seven outcomes rather than the language of tasks completed i.e. not “we closed forty contracts this quarter” but “we cut supply risk in two critical categories and unlocked a supplier innovation that the business unit is now building on”.

I know many practitioners bristle at this idea, considering it to be self-promotion, but it isn’t. It’s how the organization learns to associate Procurement with value rather than throughput, and it’s a key part of how you keep a seat at the tables where consequential decisions get made.

6. Anchor your read on outcome impact in feedback - not self-perception.

The difference between the impact you think you delivered and the impact your stakeholders said you delivered is often where a lot of well-intentioned orientation fails. It’s easy to convince yourself you’re delivering on resilience or relationship quality; it’s harder, and far more useful, to hear it from the people on the other side of those outcomes.

So anchor your assessment in the voice of the customer and the voice of the supplier. If your stakeholders don’t experience you as fast and enabling, you aren’t - regardless of your internal cycle-time dashboard. If your strategic suppliers don’t treat you as a customer worth innovating for, you haven’t earned that outcome yet.

External feedback is the only honest scoreboard for work this qualitative.

The six principles above are not a pick-and-choose menu. They compound, building on each other and ensuring that Procurement not only focuses on value delivery but is seen to be doing so.

And while they describe the destination we’re after, we still need a way to tell us how far we are from it. Enter the diagnostic checklist.

A Diagnostic Checklist

If the principles are the mental mindset, or where we want to get to, we need to also understand where we stand today.

To do so, it’s worth working through a structured set of questions:

The terrain you’re operating in

• What is the “cultural” incentive of the organization? What tone does the C-Suite - through to the CPO - set about where impact is most valued?

• Which outcomes is my function structurally incapable of moving on today, and what would it take to change that?

What I’m planning, and where my time goes

• How many of my goals and objectives are genuinely oriented toward moving key outcomes, rather than describing activity?

• How many are multi-year? Am I working a portfolio of short-, medium-, and long-term changes, or only chasing short-term movement?

• How much of my time is actually allocated to each outcome - and does that match what I claim matters?

The calls I’m making

• What is the balance across the seven outcomes? Am I inordinately focused on Cost?

• Where am I making tradeoffs across outcomes implicitly that should be made explicitly?

• Which outcomes does AI augmentation most expand my reach on, and am I shifting effort to capitalize, or just doing the old activity faster?

How I know any of it is real

• Am I communicating impact in the language of outcomes, or the language of tasks completed?

• Am I proactively collecting outcome-impact feedback from stakeholders, both internal and external?

• If those stakeholders were asked which of the seven outcomes I’m known for, which would it be - and is that the balance I want?

It’s worth going through this checklist periodically. My suggestion would be three times a year - at the start of the planning cycle, mid-year as a performance check and finally at year end evaluations. This ensures we are not only calibrating correctly as we begin the year but then course-correcting and extracting the right lessons and learnings as we go through each annual cycle.

The Future-Proofed Practitioner

The future-proofing model comes down to three questions.

Foundations ask what you know. Capabilities ask what you can do. Orientation asks what you point them at.

And in a post-AI function, that third question is the one that decides, in a practical, delivered sense, who stays essential.

The fact is that AI will keep getting better at the what and the how: it will run the analysis, surface the options, even recommend the call. But what it won’t do is decide which of the seven outcomes the enterprise needs most right now, and then aim the function’s scarce human attention there.

That decision - that orientation - is the real work and it’s not a skill that AI can take from the practitioner. It’s what makes the practitioner all the more valuable.

The future-proofed practitioner, then, isn’t the one who does the most. It’s the one who focuses - and delivers - on what matters.

If you’ve been following along as I’ve laid out my model for future-proofing the Procurement practitioner, you will have noticed a glaring omission from the various layers that comprise the model itself.

Judgement.

This has, as I’ve stated before, been entirely intentional. Judgement isn’t a stand-alone skill that you can learn in the same way that you can study and develop financial literacy or even fundamental negotiations skills. You can’t go to school to learn judgement nor is it an “emergent” or “downstream” output that just flows automatically once you’ve developed the other skills in my model. It’s not a passive byproduct.

It is, in fact, a meta-capability that sits above all of the layers of the future-proofing model I’ve proposed. It’s a function of - the deliberate, practiced result of - all of the specific capabilities that make up each layer.

It is the act of integration: it consumes Business Acumen, Human Leverage and Cognitive Discipline (previously discussed here and here), and is refined through reps.

Why Judgement Matters More, Not Less

Back in 2018, the economists Ajay Agrawal, Joshua Gans and Avi Goldfarb researched and wrote about the value of judgement. Their core thesis: AI is, essentially, a prediction machine, and as the cost of prediction drops (which it has and will continue to do), its complements become more valuable. The three complements they identify are data, judgement, and action - with judgement defined in economists’ parlance as “the skill used to determine a payoff, utility, reward or profit”. It is the mental capacity to weigh facts, reason logically, and make sound decisions.

In the words of the researchers, human judgement becomes a complement (and gains value) as human prediction becomes a substitute (and loses value).

We need judgement to weigh the pros and cons and to value the payoffs and risks of specific decisions, with the focus on identifying the hidden costs of riskier actions.

In the Procurement context, what that means is that the practitioner who can make better calls wins: deciding when to override a sourcing algorithm’s lowest-bid recommendation because of relationship risk; choosing whether to push back on a CFO’s cost target or accept it and engineer around it; or making the call as to whether a supplier’s quality slip is a one-time blip or a leading indicator. In each of these situations, good judgement matters and makes a difference.

The Limits of Process (and Machines)

Historically, we’ve tried to grapple with this by using process as a means of replacing the very need for judgement. By codifying the work to be done and the path to the decision, we’ve tried to minimize, even eliminate the human in the decision-making process.

But this, of course, doesn’t work for complex, non-routine, non-standard decisions. Process cannot replace judgement (with all of it complexities) in these situations. Nor, for that matter, can the machines, even as we try to use AI to do the exact same thing we’ve tried to do with process. Short of an idealized AGI (which I don’t believe is realistic), AI will not be able to balance the contextual, the ethical, the political, the moral dimensions of difficult decisions.

It becomes important, then, to become deliberate in our quest to develop judgement. This is exacty where most practitioners get stuck.

Building Judgement Deliberately

Judgement is often viewed as this mysterious ability that is either inherent in an individual or results magically through experience. This is, to some extent, understandable as there are certainly non-codifiable, often indescribable, factors that judgement relies on.

Experience, of course, matters. Senior practitioners often have good judgement, even as they are unable to articulate why. There is research that finds that experts tend to pattern-match against thousands of prior situations without conscious deliberation. That is, judgement is partly tacit and developed through exposure, not through frameworks. So experience helps.

That said, building judgement should be viewed as a deliberate act - one that requires discipline and deliberate cognitive application.

There are four key steps to building judgement:

1. Expand your inputs

2. Run a better process

3. Stress-test your thinking

4. Close the loop

Let’s dig into each one of these in turn.

(Note: none of what follows substitutes for reps - but the reps only build judgment if you bring discipline to them.)

1. Expand Your Inputs

A. Seek multiple diverse inputs.

Good judgement starts with good inputs, and good inputs are rarely found in one place. Read across sources, functions, and perspectives.

If your information diet is limited to Procurement publications, your judgement will be limited to procurement-shaped thinking. Talk to suppliers, finance partners, operators, and customers - not just Procurement peers.

The goal isn’t to consume more information. It’s to consume different information, so that when you face a decision, you have a wider set of mental models to draw on.

B. Make a habit of studying decisions - by yourself and others.

Most practitioners experience decisions but don’t study them. Build the habit of pulling them apart.

When a senior leader makes a call, ask yourself: what did they weigh? What did they ignore? What would I have done differently? When you make a call yourself, look back at it the same way.

Study the public decisions of CEOs, investors, and historical figures - books, podcasts, and case studies are full of decision post-mortems if you go looking.

Treat every decision, yours or someone else’s, as a teaching artifact.

C. Read outside Procurement.

Functional reading sharpens functional skills, but judgement is sharpened by breadth. History, biography, decision science, behavioral economics, and even fiction give you mental models that procurement reading never will.

Reading about how military commanders made calls under the fog of war, or how investors think about asymmetric bets, or how scientists update beliefs in the face of new evidence - all of this builds the lateral thinking that good judgement depends on.

The best Procurement leaders I’ve worked with are almost always voracious readers, and rarely just of business books.

D. Take notes actively and synthesize, not just consume.

Reading isn’t learning - synthesis is. Pulling together different ideas and writing about them forces clarity in a way that reading alone never does.

Keep a notebook, digital or otherwise, where you write down the ideas that struck you, the frameworks you want to remember, and the connections you’re drawing across what you’re consuming.

The act of putting something in your own words is what turns it from information you saw into knowledge you own. Without this step, most of what you read evaporates.

2. Run A Better Process

A. Classify the decision before you process it.

Not every decision deserves the same level of process.

Jeff Bezos famously distinguishes between reversible and irreversible decisions: irreversible calls (one-way doors) deserve deliberation, while reversible ones (two-way doors) deserve speed. Oftentimes, we get this backwards, overthinking the reversible calls and underthinking the irreversible ones.

Before you spend cognitive energy on a decision, ask: is this a one-way door or a two-way door? Calibrate your process accordingly.

B. Define what success looks like before you decide.

You can’t evaluate a decision later if you didn’t say upfront what you were optimizing for. Get clear on the goal before you start weighing options.

Are you optimizing for cost, risk reduction, supplier relationship, internal stakeholder alignment, speed-to-market? In Procurement, multiple objectives are usually at play, and the trade-offs between them matter. Make those trade-offs explicit at the front of the process.

This single habit, more than any other, separates practitioners who learn from their decisions from those who just rationalize them.

C. Don’t make snap judgements - unless merited by the situation.

Most consequential decisions don’t need to be made in the moment, and most snap judgments are pattern-matching dressed up as decisiveness.

Give yourself the space to think. Sleep on it. Walk away and come back.

The instinct to decide fast often comes from discomfort with ambiguity, not from confidence in the answer.

(That said, some situations genuinely require speed. Know which is which - see 2A above - and don’t conflate decisiveness with thoughtfulness.)

D. Avoid the tendency to rely excessively on personal history and experiences.

Experience is valuable, but it’s also the source of your blind spots. The decision you’re facing today is not the decision you faced five years ago, even if it looks the same.

Markets shift, suppliers evolve, internal stakeholders change, and the lessons of past experience are only as good as their relevance to the present context.

Use experience as one input among many - not as the answer.

E. Separate facts from feelings.

Emotion isn’t the enemy of good judgement. Emotional signals often carry real information about risk, trust, and stakeholder dynamics that pure analysis misses.

But emotion shouldn’t be your conclusion. Notice what you’re feeling about a decision and ask what it’s telling you. Then put it alongside the facts, not in place of them, as another input for evaluation.

The practitioners with the best judgment are the ones who can hold both at once - alert to their gut, but disciplined enough not to let it drive.

3. Stress-Test Your Thinking

A. Have a values framework - and let it guide your judgement.

Decisions made without an anchor drift - know what you stand for, and know what the organization stands for; the principles you won’t compromise on and the trade-offs you refuse to make.

When the situation is ambiguous and the analysis is inconclusive, your values are what keep you steady.

This isn’t just about ethics in the narrow sense, but about having a stable frame of reference that lets you navigate complexity without getting lost in it.

B. Get opposing views.

Surround yourself with people who will tell you you’re wrong. Seek diversity of perspective, not just diversity of identity - people who think differently, work in different functions, come from different backgrounds, and have different incentives.

Understand the biases and motivations embedded in the views you’re receiving: who benefits if you decide one way versus the other? Who can you trust to give you a clean read?

The goal isn’t consensus - it’s pressure-testing. If everyone around you agrees with you, you’re not getting useful input.

C. Understand your own cognitive biases.

Every practitioner has characteristic ways their thinking goes wrong. Anchoring, confirmation bias, sunk-cost reasoning, recency effects, availability bias - the list is long, and they all apply to you.

Know your tendencies. The point isn’t to eliminate bias (you can’t), but to recognize when you’re most likely to be falling into it.

Self-awareness here is the difference between a practitioner who improves over time and one who repeats the same mistakes with more seniority.

D. Develop a pre-mortem.

Before you commit to a decision, run a pre-mortem: imagine it’s 12 months from now, and this decision has failed badly. Why? What went wrong? What did you miss?

This exercise surfaces risks that forward-looking analysis won’t always catch, because it forces you to imagine the failure rather than defend the choice.

If you can’t generate a plausible failure scenario, you probably haven’t thought hard enough.

E. Make the opposing case.

Beyond seeking opposing views from others, make yourself argue the opposing case. State the contrary position more strongly than its proponents would.

If you can’t make the other side’s argument well, you don’t understand your own argument well enough to commit to it.

This is often hard to do and most people resist it, which is exactly why it’s a high-leverage habit.

F. Practice probabilistic thinking.

Force yourself to put numbers on uncertainty. “I think this will work” is not the same statement as “I’m 70% confident this will work”.

Probabilistic thinking changes how you communicate, how you plan for downside scenarios, and how you learn from outcomes over time.

You don’t need to be precise; you need to be honest. A 70% bet that fails tells you something different than a 95% bet that fails - but only if you wrote the number down beforehand.

4. Close the Loop

A. Own your decisions.

Judgement doesn’t develop in people who deflect responsibility: own the calls you make - the good ones and the bad ones - openly with your team and with yourself.

The practitioners who hide behind committees, process, or “the data made me do it” never build real judgment because they never sit with the consequences of their own choices.

Accountability is the price of judgement.

B. Separate decision quality from outcome quality.

A good decision can have a bad outcome, and a bad decision can have a good outcome - this is one of the most important and least intuitive ideas in decision-making.

If you only learn from outcomes, you’ll learn the wrong lessons: you’ll punish good decisions that got unlucky and reward bad decisions that got lucky.

The discipline is to evaluate the decision based on what you knew and how you reasoned at the time, separate from how it happened to turn out.

C. Keep a decision journal.

Write down the rationale and your confidence level at the time of the decision. Revisit it regularly.

This is one of the most high-value habits you can build for developing calibrated judgment over time.

Without a record, memory rewrites your past calls to make you look smarter than you were. With a record, you see exactly where your thinking was sharp and where it wasn’t - and you get better.

D. Look for patterns across decisions.

Once you have a body of decisions to look back on, study the patterns. Where do you consistently overweight? Where do you underweight? What kinds of decisions do you handle well, and what kinds throw you?

Maybe you’re great at supplier selection but weak at internal political reads. Maybe you over-rely on relationships and under-rely on data, or vice versa.

Your characteristic failure modes are the most valuable thing you can learn about yourself as a decision-maker.

E. Update your beliefs explicitly.

After each reflection, ask: what do I now believe that I didn’t before? Then write it down. Most practitioners absorb lessons implicitly, which means they don’t really absorb them at all.

Explicit belief updates (”I used to think X, now I think Y, because of Z”) are how you compound judgment over time. Without this step, the reps don’t build anything durable.

What This Means For You

If there is a single takeaway from this post, it is that judgement is a deliberate act. It’s a process that you go through when making a decision - one that requires cognitive discipline, business acumen and human leverage skills.

At the same time, judgement is also partly tacit and non-codifiable, almost always built through reps and experience. Doing the work is an essential part of building judgement that makes a difference.

To this end, I’ll close with a few principles to remember when thinking about judgement and its development:

1. Look for opportunities to exercise judgement as much as possible - including the small decisions at work and in life, and not just the major, material ones at work

2. Trust yourself to make judgement calls - do the work (as described above) but don’t second guess yourself once the decision has been made

3. Stay flexible - be willing to update based on new and updated information. Conviction without flexibility is just stubbornness

4. Accept failure as part of the process - judge the decision, not the outcome; failure is just another data point. Accept it, learn from it, and move on.

One final thought:

Procurement spent the last twenty years trying to codify judgement into process. The next twenty will be about reclaiming it as a distinctly human capability. Process and machines can handle the routine (that’s what they’re for), but the hard calls - the ones that actually move the business - will always belong to the practitioner who’s done the work to earn them.

Be that practitioner. Build that capability - deliberately, repeatedly, with discipline and humility - until it becomes the most valuable thing you bring to the table.

Specifically, I’m sharing a discussion I had earlier this year with Rich Ham of Fine Tune and Philip Ideson of Art of Procurement on the need to rethink the Procurement scorecard, including that most traditional of metrics: savings.

In our chat, we get into:

• The problem of short-termism in Procurement’s incentive systems and how we keep making long-term sacrifices for short-term wins

• What a healthier incentive system could look like - including a scorecard that includes a multi-faceted set of metrics that truly capture long term value

• How this scorecard needs to encompass in-year performance expectations, multi-year outcomes that reflect longer term aspirations, as well as discretionary goals that, while somewhat subjective in nature, capture the contributions that matter

• The need for the CFO and CEO to buy into procurement’s expanded definition of value - but also the need for Procurement to advocate for itself and a “new normal” when it comes to metrics

You can check out our discussion in full via the video link below.

In a world where AI takes on a growing share of Procurement’s analytical and transactional work, what remains is work that’s undeniably human.

This type of work requires a completely different approach and perspective than what we’ve been traditionally used to. Practitioners who develop strong human leverage skills think and act like diplomats, not bureaucrats. A bureaucrat moves paper through a process while the diplomat reads rooms, builds trust, shapes narratives, and achieves outcomes through people.

In a post-AI world, the bureaucrat’s job is the one that gets automated; the diplomat’s job compounds.

Human leverage is, then, the third core skill of the Differentiating Layer - and it’s not just a skill. It is the survival capability for the post-AI Procurement function.

The skills that comprise it fall into three clusters:

• The Relational Cluster: how we build trust and navigate networks

• The Persuasive Cluster: how we move people toward outcomes

• The Cognitive Cluster: how we think, adapt, and exercise judgment (where machines cannot)

Let’s dig into each cluster in turn.

Cluster 1: The Relational Cluster

Relational skills sit at the foundation of human leverage. Procurement, more than most functions, depends on outcomes achieved through other people - internal customers, cross-functional stakeholders, as well as the supplier ecosystem. AI can and will help analyze every aspect of every one of these relationships, but it cannot keep and develop them for you.

Three skills make up the Relational cluster:

1. Emotional Intelligence

Emotional intelligence is the foundation upon which all of the other relational skills sit. It is the ability to recognize and manage your own emotional responses, accurately read the emotions of others, and use that awareness to navigate situations productively. It encompasses self-awareness, self-regulation, empathy, and social awareness.

What This Means For Procurement: Procurement operates in friction-rich environments - failed deliveries, missed budgets, contentious negotiations, internal political pressures, supplier disputes and more. Practitioners who can stay regulated under stress, read what’s actually happening in a room, and adjust their behavior in real time have a clear advantage. AI can model sentiment from a transcript (after the fact), but it cannot sense, in the moment, that the CFO’s body language has shifted, or that a supplier’s silence is masking a deeper concern. The ability to do that is unquestionably human.

How to Cultivate:

• Build a reflection practice - even ten minutes at the end of the day or week reviewing key interactions (”what did I feel, what did they feel, what did I miss?”)

• Stress-test yourself deliberately - high-pressure presentations, difficult conversations, public speaking - and debrief honestly afterward

• Invest in a 360-degree feedback exercise - most professionals significantly overestimate their self-awareness, and the gap between how you experience yourself and how others experience you is a development opportunity

• Work with a mentor (or, if possible, an executive coach) for a structured period; this is one of the highest-leverage development investments at the senior level

• Read widely outside business to build empathic range (including literary fiction, memoirs, and biographies)

2. Relationship Management

This is the skill of developing one-on-one relationships with different stakeholders across departments. It involves understanding individual interests, motivations, and hot buttons to build genuine trust that creates long-term optionality - the kind that serves you when you need it. Note the emphasis here: the focus is on depth and authenticity in relationships, not about being transactional and ‘banking favors’.

What This Means For Procurement: This is an especially essential skill for Procurement practitioners, where strong relationships are essential to achieving successful outcomes. This is particularly true for instances where Procurement has indirect influence rather than direct control and authority. The better the relationships, the higher the probability of successful outcome achievement. Relationship management must operate across three groups:

• Internal customers: those the practitioner directly serves and for whom he or she is the category leader

• Key stakeholders: those across the organization who impact and influence key outcomes and through whom the path to results can be smoothed out

• Supplier ecosystem: the entire supply base, both current and potential

How to Cultivate:

• Practice “deposit-first” mode - give value, share intelligence, make introductions long before you need anything in return

• Establish a non-transactional cadence - regular 1:1s with key stakeholders that are not tied to a deal, an RFP, or an escalation

• Maintain a private stakeholder log (what matters to each person, recent wins, ongoing challenges, business priorities, etc.) used carefully and with discretion

• Volunteer for cross-functional initiatives that sit outside the procurement remit - the goal being to build relationships outside transactional contexts

• On the supplier side: visit operations, attend supplier events, and run quarterly business reviews that go beyond performance scorecards into shared strategy

3. Stakeholder Management

This builds on strong relationship management skills to encompass managing across multiple stakeholders with competing interests. Where relationship management is about individual depth, stakeholder management is about network breadth. It involves identifying all key influencers and gatekeepers across your ecosystem who can impact your ability to realize your goals - and then leveraging the relationship equity needed to navigate this network.

It’s worth noting that this is where coalition building and organizational politics live. Knowing how to package wins for different sponsors, when to escalate, and how to build the political momentum required for cross-functional change is a distinct sub-skill within stakeholder management. It should be viewed as a craft that can be used for positive purpose and positive effect.

What This Means For Procurement: Whether or not Procurement “owns” the spend for a category, the practitioner must navigate a host of stakeholders to achieve outcomes. This type of stakeholder management becomes even more critical where the function has indirect influence rather than direct control. Knowing how to map out the full network of stakeholders, distinguish the formal org chart from the actual decision flow, and manage that network is, and always will be, an essential skill.

How to Cultivate:

• Build a stakeholder map for major initiatives and update it as the work evolves

• Make a habit of distinguishing the formal org chart from the actual decision flow; ask yourself: “Who gets the call before the decision is made?”

• Run pre-meetings before key decisions - never let a major recommendation be heard for the first time in the room where it will be approved

• Identify the “translators” in your organization i.e. the people who bridge functions and can carry your message in a language each constituency hears

• Build coalitions before you need them, not when the fire starts; the time to recruit allies is when there is no battle to fight

Cluster 2: The Persuasive Cluster

If relational skills are about being known and trusted, persuasive skills are about moving people to outcomes. Procurement is, at its core, a function that achieves its best results through influence rather than authority.

The three skills in this cluster are how that influence is exercised - strategically over time, situationally in the moment, and across the table in formal negotiations.

4. Influence and Persuasion

This is the ability to move people in a specific moment and situation - being able to convince individuals to move toward specific outcomes in a way that aligns with overall objectives. It is a blend of art and science: it requires understanding individual motivations in context and the ability to map out how best to move that individual, in alignment with their motivations and the goal at hand.

What This Means For Procurement: Stakeholder influence is a key part of any role but critically so for the Procurement professional. In the age of AI (in particular), there is a need to move people toward decisions on the basis of not just the facts but competing priorities and agendas. While this skill is applicable regardless of category, it is often argued to be especially important for indirect category leaders, where demand management is a relatively more important driver than in direct categories - though direct category leaders face their own influence challenges with engineering, plant operations, and R&D.

How to Cultivate:

• Practice tailoring the same message to four different audiences (e.g. the analytical CFO, the visionary CEO, the operational COO, the skeptical functional VP); the message stays constant, but the framing changes

• Get explicit feedback on your executive presence - most practitioners overestimate theirs significantly

• Run a “rehearsal of objections” before any high-stakes pitch: anticipate the three hardest pushbacks and know your response cold

• Watch senior leaders work a room and decode what they’re actually doing; copy what works and discard what doesn’t

5. Narrative Development and Communications

Narrative is the art of hearing what stakeholders actually mean versus what they say, crafting a story that shapes the requisite outcomes, and managing communications to achieve those goals. These communications must be both written and verbal, and across multiple levels of the organization.

What This Means For Procurement: Strong communications are an essential tool for all management professionals, and equally so for Procurement practitioners. From influencing an individual one-on-one to making the case to the CFO for a material investment choice, the ability to craft a narrative and communicate those ideas separates effective practitioners from ineffective ones. The function’s perception inside an enterprise is shaped by the cumulative narrative its leaders tell over years - about its role, its value, and its trajectory.

How to Cultivate:

• Invest in your writing - this is one of the most underrated leadership skills. Take a course, work with an editor, study the house styles of prominent publications (e.g. HBR, FT, and The Economist), etc.

• Build the “30-second, 3-minute, and 30-minute” version of every major idea you carry; if you cannot tell it in 30 seconds, you do not yet understand it

• Get coaching on the difference between informing and persuading in business writing; most procurement communication defaults to the former when it should be the latter

• Read fiction seriously - people who only read business books tend to write far too formally

6. Negotiation

Negotiation is the live, real-time skill of reaching agreements that work for both sides - across the table, on the phone, or in the back-and-forth of a deal. AI will model scenarios, draft talking points, run BATNA simulations, and prep your data better than any analyst ever could. But the live negotiation itself remains stubbornly human: reading micro-signals, knowing when to push and when to walk, building rapport across the table, and adjusting in real time when a counterpart shifts.

What This Means For Procurement: Procurement is the negotiation function in most enterprises. As AI raises the analytical floor for everyone, what differentiates great negotiators from average ones will increasingly be the human craft of the negotiation itself. The practitioner who can use AI to prepare exhaustively and show up to the table with elite live skills will be the one who delivers disproportionate value.

How to Cultivate:

• Treat every real negotiation as a learning opportunity - debrief honestly, alone or with a trusted colleague, on what worked and what did not

• Seek out negotiations training that includes live practice with feedback, not just frameworks; the gap between knowing and doing is where most practitioners get stuck

• Observe master negotiators at work whenever you can - sit in, watch the choreography, notice what they say and what they deliberately do not

• Practice in low-stakes settings: salary discussions, vendor negotiations on personal purchases, etc.

Cluster 3: The Cognitive Cluster

The first two clusters cover how we engage with people. This cluster covers how we think, decide, and adapt, particularly under conditions that resist machine optimization. AI excels at solving well-defined problems within known solution spaces. The three cognitive skills below are about working in spaces that are not yet defined.

7. Creative Problem-Solving

This involves ideating and developing unique solutions that solve problems and achieve key outcomes - especially in environments faced with resource constraints and competing agendas. The goal is to find innovative ways to get to the right outcomes, balancing process alignment with organizational realities and constraints.

What This Means For Procurement: Given shrinking budgets, tight timelines, and competitive pressures, the practitioner’s ability to achieve results creatively (rather than simply following the process) will be critical to the function’s long-term relevance. In a post-AI world, internal customers and stakeholders alike will demand more of Procurement than just process execution and risk mitigation. While AI will optimize within known solution spaces, it will not reframe a problem from “how do we cut $X out of this category” to “what if we eliminated this category entirely?”. That kind of reframing is the human edge.

How to Cultivate:

• Practice constraint-flipping: “we have no budget” becomes “what is the zero-budget version of this?” The reframe often unlocks the real answer

• Design-thinking sprints work surprisingly well in Procurement contexts; structured ideation outperforms unstructured brainstorming

• Build cross-industry exposure: how do operations, finance, and R&D solve analogous problems? Steal liberally

• Build a peer or mastermind group outside your company - your in-house environment will only generate in-house ideas

8. Comfort with Ambiguity

This is the ability to function and flourish in contexts of incomplete information, changing landscapes, and economic uncertainty. When coupled with the need for speed in competitive environments, being able to make decisions with confidence despite a lack of information becomes a critical capability.

What This Means For Procurement: In times of crisis or in steady-state situations, there will never be the full complement of insight and analysis that would ideally be needed to make decisions. AI will reduce some of the information gap, but it cannot close it (and it introduces new ambiguities of its own e.g. model uncertainty, data quality, hallucination risk). The practitioner who is comfortable acting on 70% information will consistently outperform the one waiting for 95%.

How to Cultivate:

• Deliberately practice deciding with incomplete information rather than waiting for “the 95%”; calibrate your confidence and review outcomes honestly

• Take stretch assignments (new categories, new geographies, new functions) that force you to operate without the comfort of expertise

• Keep a decision journal: record what you decided, what you knew at the time, and what you assumed. Review it months later to learn how your judgment actually performs

• Practice scenario planning: hold multiple plausible futures in mind simultaneously rather than committing prematurely to one prediction

• Spend time around founders and entrepreneurs; they live in ambiguity professionally and develop intuitions worth absorbing

9. Curiosity and Adaptive Intelligence

Curiosity drives practitioners to continuously learn, moving beyond superficial data to understand the underlying drivers of value and risk for the function and the enterprise; it is particularly important given today’s fast-moving technological trends, ensuring practitioners can adopt new tools effectively. Adaptive intelligence is the natural follow-on: the ability to adapt to changing market conditions, technologies, and corporate strategies. It is an essential component of resilience.

What This Means For Procurement: Curiosity and adaptive intelligence are essential competencies for the modern Procurement practitioner, especially given the transition to strategic value creation. Supply markets are extraordinarily dynamic right now: new technologies, new geopolitical risks, new supplier categories (especially AI services), and new commercial models. The practitioner who is not actively curious about adjacent fields will be obsolete in five years. These skills are essential to not just coexisting with AI as it evolves but thriving in genuinely dynamic environments.

How to Cultivate:

• Read widely outside Procurement and outside business - geopolitics, behavioral economics, technology, history, etc.

• Track adjacent fields hard right now: AI capabilities, sustainability regulation, geopolitical realignment, key spending shifts, and the changing supplier landscape they create

• Teach or write - both force clarity and surface gaps in what you actually understand

• Build a deliberate learning rhythm with explicit time blocks for reading, listening, and thinking

• Practice the beginner’s mind in areas where you are an expert; the moment you stop questioning your own assumptions is the moment you become replaceable

Why These Skills Compound

A useful way to think about the post-AI Procurement function is that AI flattens analytical capability across the board. Every practitioner, every supplier, every counterparty will soon have access to roughly the same level of modeling, benchmarking, and data preparation.

But what will not converge is human leverage.

The practitioner who can build genuine trust with a CFO, read what a supplier is actually signaling across a table, craft a narrative that lands with the board, navigate a coalition through a politically charged transformation, and make a confident call with incomplete information is doing work that AI cannot do.

And this is often work that other practitioners cannot do either, as many are still focused on execution. The bureaucrat’s edge (knowing the process and executing the steps) will diminish while the diplomat’s edge (moving people, reading rooms, exercising judgment) will increase.

As such, human leverage is not the soft side of Procurement. In a post-AI world, it is the hard core. It is what separates practitioners who are made obsolete by their tools from practitioners who leverage these tools to make them indispensable.

Two weeks ago, I laid out my model for future-proofing the Procurement practitioner, in which I outlined its three key parts:

1. The Enabling Layer: AI literacy and cognitive discipline

2. The Differentiating Layer: Orchestration, business acumen and human leverage

3. The Orientation Lens: The lens through which the first two are pointed

Having covered The Enabling Layer in prior posts (here and here), last week I dove into the first key skill of the Differentiating Layer (Orchestration) in detail.

This week, I’ll discuss the second key skill: Business Acumen.

Business Acumen, Defined

Business acumen is the fundamental understanding of the entire environment that the company operates within, from macro (its market and customers) to micro (the company, stakeholders and suppliers). It is about more than simply understanding the nuts and bolts of each element; it’s about building on this understanding to be able to connect the various pieces together so that you’re able to understand the business in context.

To use the chess analogy, it’s being able to think in terms of positions on the board, not just the pieces on it. A novice thinks about each piece individually but an experienced player understands how the pieces relate, where pressure is building, and why each move is being executed.

In this way, business acumen is about more than functional competence, which, on its own, will not protect Procurement talent in the future. The new floor is enterprise-level understanding of the business and how it creates value - a floor that too many (current) Procurement careers fail to clear.

Business acumen matters more in a post-AI world for two reasons:

• First, AI eats the codifiable, so what’s left for the human is judgement that requires understanding and context. Business acumen is the development of that context.

• Second, AI gives every Procurement professional access to analysis they never had before. We’re no longer focused on “getting the right data” but on “asking the right questions” and “drawing the right conclusions”. This is acumen-dependent.

Where Acumen Begins: The Seven Literacies

Developing Business Acumen is not a simple one step process. There is no single path you can take to develop it to the level needed for a post-AI world.

Rather, it’s a journey, one that begins with literacy, specifically, seven layers of literacy:

1. External Market

2. Strategic

3. External Customer

4. Business

5. Financial

6. Internal Customer and Stakeholder

7. Supplier and Ecosystem

Before we dive into each one, it’s worth pointing out that these layers are not discrete. While they are each broadly distinct, there is a natural overlap, much like the natural interconnected, interdependent nature of markets and businesses in general.

For example, where does business literacy end and financial literacy begin? The corporation’s economics sit inside both. Similarly, External Market, Business and Supplier/Ecosystem literacy all touch the value chain, but from different angles.

So, as you think about each of the layers, understand that the overlap you see is natural and to be expected; it is representative of the interconnectedness of all commercial environments.

With that clarified, let’s dive into each layer in turn.

1. External Market Literacy

This is the most ‘macro’ layer and represents the system within which everything else operates. It involves understanding the industry your company competes in - its structure, dynamics, economics, and the forces shaping its future.

External Market literacy provides the context for every other literacy layer we’ll discuss.

What’s included:

• Industry structure: concentration, fragmentation, key players, market shares

• Industry economics: profit pools, capital intensity, scale dynamics, margin patterns

• Competitive forces: rivalry, entry barriers, substitutes, buyer/supplier power

• Growth dynamics and where value is migrating within the industry

• Regulatory environment and direction of government influence

• Technology disruption vectors and likely timing

• Geopolitical exposure and macro/cycle sensitivity

• Industry-specific norms (standards, distribution models, contracting conventions)

What this means for procurement. Procurement decisions don’t happen on a blank canvas - they happen inside an industry with its own particular dynamics. Knowing your industry means you can better understand why your CEO worries about what they worry about, anticipate where competitors will move next, and recognise which categories in your sector carry strategic weight in this industry versus those that are more generic. (A category that’s a back-office cost in one industry can be a competitive battleground in another.)

How to cultivate:

• Build your own one-page “state of the industry” view and refresh it every six months.

• Attend at least one industry (not procurement) conference each year.

• Subscribe to one credible industry analyst (subsector-specific).

• Read your top three competitors’ annual reports and earnings transcripts each quarter.

• Most importantly, learn to read your industry through your CEO’s eyes, not your function’s i.e. how leadership thinks about industry evolution and how that influences your company’s go-to-market.

2. Strategic Literacy

This is the next key layer - understanding why your company has chosen to compete the way it has. This layer focuses on the deliberate choices your leadership has made about where to play, how to win, and what to bet on. External market literacy is the playing field, while strategic literacy tells you the game your company has chosen to play on it.

What’s included:

• The “where to play” choices: which segments, geographies, channels, customer types

• The “how to win” choices: cost leadership, differentiation, scale, ecosystem, etc.

• Strategic priorities and the time horizon attached to each, including:

  • The bets the leadership team is placing

  • The strategic risks the company is consciously accepting

• Capital allocation logic: organic growth vs M&A vs returns to shareholders

• Innovation and R&D strategy

• ESG, sustainability, and reputational positioning as strategic choices

What this means for procurement. Procurement that doesn’t understand strategy ends up optimizing against it - for example, achieving cost savings that erode a differentiation play, or supplier consolidation that undermines an innovation bet. Strategic literacy ensures that Procurement weights decisions correctly, knowing when to push hardest on cost, when to prioritise speed, when to protect optionality, when to pay for capability. Strategic literacy ensures that Procurement recognises when its own function-level strategy diverges from enterprise strategy and needs to be redrawn.

How to cultivate:

• Read every investor day deck and CEO letter from your company over the last five years.

• Find someone in the strategy team and engage with them periodically to better understand strategic and competitive choices.

• Build a mental model of why the company chose its current path versus its other credible alternatives.

• Pressure-test your own category strategies against enterprise strategy explicitly; if you can’t draw a clear line of sight (e.g. how it directly impacts your company’s core USP), there isn’t one.

3. External Customer Literacy

This involves understanding the company’s actual end customers - who they are, what they value, how they buy, what they’re trying to do. Most procurement people are two or three steps removed from the end customer so the progressive practioner knows that closing that distance (in any way possible) is what ensures Procurement truly supports the business and remains relevant.

What’s included:

• Customer segments and their respective economics

• What outcomes customers are trying to actually achieve

• Buying behaviors, decision criteria, and switching costs

• Channel and journey dynamics: how customers actually find, buy, and use

• Brand perception, loyalty drivers, and emotional triggers

• Customer lifetime value dynamics and what drives them

• Emerging customer expectations

What this means for procurement. A material share of procurement spend touches the customer experience directly - packaging, retail design, marketing, digital platforms, product components, last-mile logistics, etc. Procurement that doesn’t understand the end customer in-depth will optimise for the wrong variables e.g. cheaper packaging that impacts brand equity, the consolidated supplier base that slows time-to-market, the standard component that erases the feature customers actually pay for.

How to cultivate:

• Sit in on customer research sessions.

• Read customer feedback reports and NPS verbatims rather than overall dashboard metrics.

• Do ride-alongs with sales or store visits with retail periodically.

• Talk to the people who answer customer service calls.

• For B2B businesses, attend customer events and ask/listen to their feedback.

• Treat any report that reveals what customers actually think as required reading.

4. Business Literacy

This involves understanding how the business itself creates value, captures it, and actually delivers it. This has two faces: the economic (how the business model works) and the operational (how the work gets done). Both are required; most procurement people have a partial view of one and little of the other.

What’s included:

Economic dimension:

• The value chain: where value is created and where it leaks

• The business model: how value is captured (pricing power, lock-in, defensibility)

• The handful of variables that actually drive enterprise value

• Revenue model and growth model

• Cost structure: fixed/variable, direct/indirect, scale dynamics

• Unit economics and what makes them work or break

Operational dimension:

• The production/service delivery model

• Supply chain architecture and footprint logic

• Operating model: organizational design, decision rights, governance

• Make-vs-buy choices at the enterprise level

• Quality, safety, and compliance considerations

• The operational KPIs the business actually runs by

What this means for procurement. Procurement is the literal interface between the external supply base and the company’s value chain (both structurally and operationally), so navigating that interface effectively is essential. Procurement that knows the economics but not the operations writes contracts that look good on paper but break in execution e.g. choosing suppliers that fit the company’s cost model but not the extent and depth of supply chain footprint required. Procurement that knows the operations but not the economics optimizes throughput while missing margin e.g. choosing suppliers that meet the current delivery model but lack the ability to adapt to alternative/emerging business and revenue models.

How to cultivate:

• Learn to ‘walk the process’ before you try to source the inputs to it.

• Map your company’s value chain end-to-end on one page and stress-test it with operators.

• Build relationships with the heads of operations and supply chain.

• Conduct plant tours, distribution center visits, store walks to better understand the operational nuts and bolts.

• Read operational reviews whenever possible.

5. Financial Literacy

This involves gaining fluency in the numerical language the enterprise actually runs on - not just reading financial statements, but understanding the financial logic by which decisions are evaluated, capital is allocated, and performance is judged. This is the language of the C-suite and the board; procurement that can’t speak it gets translated for rather than spoken with.

What’s included:

• P&L mechanics and what moves which line

• Balance sheet basics, especially working capital and asset intensity

• Cash flow: operating, investing, financing, and free cash flow

• Strategic financial metrics: ROIC, EVA, EPS, EBITDA, FCF - and which ones your CFO actually cares about

• Capital structure and the cost of capital

• Capital allocation logic and capex vs opex treatment

• The cash conversion cycle and Procurement’s instruments within it (payment terms, inventory, supplier financing)

• Forecast, budget, and variance discipline

• Accounting treatments that affect procurement decisions (lease accounting, hedging, revenue recognition)

• Reading supplier risk and financials to assess viability and leverage

What this means for procurement. Procurement that only speaks “savings” is seen as a cost function. Procurement that translates its work into ROIC, free cash flow, working capital, and earnings impact gets seen as a value function. Financial literacy is also what lets procurement read a supplier’s accounts and form an independent view of risk, rather than relying only on third-party scores. It’s the difference between negotiating from inside the CFO’s worldview and negotiating from outside it.

How to cultivate:

• Take a serious finance-for-non-finance-leaders course - the versions with real modelling, not the executive-summary version.

• Partner with your FP&A counterpart on a category review and let them push back on your framing.

• Reframe every initiative tracker you maintain in Finance language before Procurement’s language.

• Read your top suppliers’ financials quarterly and form a view before any rating agency does.

6. Internal Customer and Stakeholder Literacy

This layer focuses on understanding the people you actually work with and serve inside the firm - what they’re trying to achieve, what they’re measured on, what threatens them, and how they make decisions. Every stakeholder is a decision-maker with its own agenda, constraints, and politics. Knowing them as individuals and as a system is the key literacy outcome here.

What’s included:

• Their priorities, time horizons, and risk tolerance

• Their language and conceptual frames

• What each key stakeholder is:

  • Formally measured and compensated on

  • Informally judged on (the unwritten scorecard)

• Decision rights, governance, and how decisions actually get made (formal versus informal)

• The political realities they navigate: their standing, who they need to keep happy

• Influence networks: who shapes whom, who’s rising, who’s exposed

• Relationship history and accumulated trust or grievance with Procurement

What this means for procurement. Procurement that understands each key stakeholder as a co-decision-maker - with the intent of making each one successful in their terms while delivering enterprise value and pushing back when those diverge - becomes strategic. The skill is in having empathy for their individual realities while maintaning focus on enterprise priorities.

How to cultivate:

• Conduct ‘listening tours’ with no agenda.

• Shadow your internal customers for a full day or week, not just a meeting.

• Ask each major stakeholder what they’re measured on.

• Read what they read - their trade press, their conference outputs, their internal comms

• Learn to speak their language, so you don’t need translation.

• Map influence, not just reporting lines.

• Build your relationships before you need them.

7. Supplier and Ecosystem Literacy

Last but absolutely not least is this final layer, which involves understanding the external supply base and the wider ecosystem that it sits inside - including supplier economics, market structures, power flows, technology trajectories, capital movements, and regulatory tides. This is, of course, Procurement’s home patch, but the true literacy bar is higher than most teams operate at: the shift is from “who supplies us and at what price” to “what is actually happening in this ecosystem, and what it means for the enterprise.”

What’s included:

• Market structures: concentration, capacity, switching costs, substitution risk

• Capital flows in the supply base: PE roll-ups, IPOs, M&A, distressed positions

• Power dynamics: where leverage actually sits in each supply market

• Technology disruption vectors in the supply base

• Geographic and political concentration and the fragility it creates

• Regulatory direction in your supply markets (trade, ESG, data, labour)

• Adjacent ecosystems, substitutes, and emerging entrants

• Supplier economics: how each supplier makes money, where their margins come from, where they’re squeezed

• Supplier financial health and viability beyond surface ratings

• N-tier visibility: your suppliers’ suppliers, and their exposure

• Customer-of-choice positioning: how the market actually sees you

What this means for procurement. This is Procurement in its element - no other function has continuous, structured, daily contact with this slice of the external world. Many Procurement teams treat that contact transactionally and waste the intelligence that comes from it. Procurement that views its supply base as its value engine - and feeds its captured insights back into the enterprise (as foresight on technology, geopolitics, regulation, and capital) - becomes indispensable in a way no other function can replicate.

How to cultivate:

• Insist on supplier strategy days where suppliers present their world to you, not pitch just their products.

• Read your top suppliers’ financials and earnings calls quarterly.

• Attend supply-side conferences, not just Procurement conferences i.e. your suppliers’ industry events.

• Subscribe to analyst coverage of your major supply markets.

• Build a quarterly external intelligence note for your executives even if no one’s asked for it - the simple act of producing it forces this literacy.

Literacy Is Not Enough

The seven layers above provide the foundation for developing business acumen - but that’s all they are: the foundation.

The fact is that you could spend a career on these seven and still not have business acumen. Because achieving literacy means exactly what it sounds like: it means you can read. That is, you understand the nuts and bolts, the vocabulary and the mechanics. You’re not going to be lost when it comes to understanding the language.

But it doesn’t mean you can reason: that’s acumen.

Acumen means you can think critically, spot what matters versus what doesn’t, see second-order effects, recognise patterns across situations, etc.

Literacy is something you can acquire by study, but acumen requires reps: exposure to enough situations that pattern recognition kicks in.

Once you’ve developed acumen, you can then exercise judgement - meaning you can make thoughtful decisions. You can apply your understanding under uncertainty, with stakes, and own the outcome. Judgment requires experience and skin in the game. You have to have gone through cycles, been wrong, owned it, and recalibrated.

This is particularly critical in a post-AI world because AI compresses the time to literacy dramatically, modestly compresses time to acumen, but barely touches judgment, because judgment requires accountability, which is very human.

But what does that mean in practice? Let’s take a look at an applied example - in the literacy layer that is the most relevant to Procurement (as well as the one it can most directly impact).

In Practice: When PE Buys Your Supplier

One of your top-five suppliers in a strategic category e.g. a specialist contract manufacturer gets acquired by a private equity firm. The announcement is brief with a reassuring message that the existing leadership “remains in place”. As the category leader, you need to decide what this means for your company.

Literacy. A literate procurement professional understands what just happened. PE ownership typically means a multi-year value-creation thesis built around margin expansion, cost optimization, and an eventual exit at a higher multiple. The new owners will likely take on debt to fund the acquisition, layer on financial discipline, hunt for cost takeout, and prepare the asset for sale or IPO in four to seven years. The literate professional recognises the standard PE playbook, digs into the post-acquisition debt structure in any available filings, and articulates what’s likely to happen (in broad terms) with the supplier over the medium term.

That’s the nuts-and-bolts read - what usually happens.

Acumen. Acumen is what lets you see the variation beyond the average. Not all PE plays are the same so you note that this particular PE firm has a track record of operational improvement rather than financial engineering. The leadership “remaining in place” comes with an earn-out structure that will change their incentives sharply over the next 24 months. The supplier’s customer concentration means margin expansion will land disproportionately on a small set of customers - and you’re one of them. In addition, the supplier’s last product roadmap requires capex the new owners are less likely to fund. You recognize from three previous PE acquisitions you’ve watched in adjacent markets that the first 18 months are typically stable, but the next 18 will likely see service degradation, while year four is when the asset gets dressed up for sale and customer relationships get monetized hard.

The value of acumen is in helping you understand that this isn’t a generic PE situation, it’s a this-specific-PE-firm-acquiring-this-specific-supplier-in-this-specific-market situation.

Judgment. Then comes the actual implications for you: how do you decide to react? Do you lock in current pricing on a multi-year deal before the new owners reset? Do you accelerate dual-sourcing now while the supplier still has bandwidth to support a clean transition? Do you exit entirely and absorb the switching cost? Do you lean in - sign a deeper relationship to position yourself as a customer-of-choice through the value-creation period? Each option has costs, risks, and second-order effects on the rest of your supply base. You have incomplete information but you make a call, defending it to your CFO and your operating peers, and owning what happens over the next four years.

Judgment is all of those things that AI can’t do, because it can’t take accountability for the call. The accountability is what makes the decision a decision rather than an analysis.

The Floor Has Moved

For most of procurement’s history, the function was paid for literacy: knowing the supply market, reading and managing the contract, understanding the spend. That knowledge was scarce, and scarcity created value. Most procurement careers were built on that foundational literacy.

But that era is ending: AI now delivers literacy in minutes at the push of a button so, unfortunately, most procurement careers will stall as that type of literacy commoditizes. That’s a tough thing to hear.

The new floor is enterprise-level business acumen, in the form of the seven layers above, escalating through practiced reps into judgement that can be trusted by the enterprise. And while that bar is higher than where most Procurement professionals operate today, it is very much reachable, deliberately, over time, by anyone willing to do the work.

But, of course, business acumen alone isn’t enough.

The next skill in the Differentiating Layer - human leverage - is about how procurement actually moves its organization through people and people-centric capabilities.

That’s where we’ll go next.

Last week, I laid out my model for future proofing the Procurement practitioner, in which I outlined its three key parts:

1. The Enabling Layer: AI literacy and cognitive discipline

2. The Differentiating Layer: Orchestration, business acumen and human leverage

3. The Orientation Lens: The lens through which the first two are pointed

I’ve already covered The Enabling Layer (the skills that get you in the game) in prior posts:

• I wrote about how to build AI literacy in the corporate context in this article.

• In terms of Cognitive Discipline, I wrote about the problem of cognitive debt here and how not to lose your cognitive agency when using AI here.

In the next three posts, I’ll dive into the three core skills that comprise the Differentiating Layer, starting with Orchestration today.

Orchestration ≠ Project Management

Orchestration is the first core skill within the differentiating layer because it is the skill that brings coherence across a fragmented set of capabilities, to achieve the outcomes we seek. It is the ability to organize, provide direction and ensure execution in terms of the work to be done.

Think of the film director. He or she doesn’t (necessarily) act, doesn’t operate the cameras or any of the other technical equipment, and doesn’t score the music. The director’s job is to know what each specialist can do, sequence their contributions, manage the execution, and ensure the final result delivers on its objectives.

Orchestration in the Procurement context, and applied at the practitioner level, is that same discipline, but applied to executing targeted outcomes.

You might think that this sounds like Project Management, but there’s a difference.

Orchestration in the AI era is fundamentally different because the nature of the resources being directed has changed. You’re now coordinating across humans, AI agents, and systems simultaneously. Whereas a traditional project manager sequences human work, an orchestrator sequences a mixed ensemble where some contributors are deterministic (systems), some are probabilistic (AI agents), and some are judgment-driven (humans).

To illustrate, let’s say you’re the category leader for Professional Services (PS) spend (consulting, legal, contingent staffing, IT advisory), which is fragmented across four business units, each with its own preferred suppliers, contracting practices, and stakeholder relationships. You’ve been tasked with consolidating this into a managed framework, one with fewer suppliers, standardized terms, better visibility and a 15% cost reduction target.

This initiative cannot be executed as a straightforward sourcing event. A single RFP won’t do the job and significant coordination is required across Business Unit stakeholders, legal, finance, incumbent suppliers, potential new suppliers, AI-driven tools (encompassing spend analytics, market intelligence as well as sourcing tools). All of these aspects will move at different speeds and each stakeholder will have different incentives.

Orchestration, in this context, becomes an essential skill; it is the art of juggling all of this intelligently, not simply “executing the project”.

Before You Can Orchestrate

To be an effective orchestrator, though, requires foundational knowledge that is rooted in the technical as well as the organizational. For the Procurement practitioner, this translates into the following:

• Understand the organization:

  • This means not just understanding what your company does but more specifically the nature of the organization. How is authority and responsibility distributed? How and where do major decisions take place? What incents specific decisions to be made e.g. cost versus innovation versus speed? Where are the “organizational brakes” and blockers e.g. organizational friction, approval bottlenecks, risk aversion patterns?

  • This involves not simply understanding the formal organization and key players but also the informal power networks and decelerators within the organization.

  • In our example above, you might discover that two of the four BU heads have P&L authority over their own services spend and see consolidation as a loss of control. You might also find that the CFO supports your initiative but won’t override the BUs publicly. None of this is on the org chart but it’s a practical reality you need to grapple with.

• Know Your Internal Customer:

  • This is the full understanding of the internal function or department you serve as a practitioner (i.e. your internal customer).

  • This encompasses not just the structural (how they are organized, who are the key players, etc.), but also their business dynamics (what drives value, how is work done and delivered, what is their economic model, what are the key metrics, etc.) and the political (where does the function stand in terms of corporate dynamics, who really makes the decisions, how do they do it, etc.)?

  • In our PS example, let’s say you find that the Engineering BU uses specialist technical consultants whose work directly affects product development timelines, whereas Corporate uses general management consultants with a range of different objectives. Combining these two would be a design error, so you need to understand each stakeholder’s differing requirements and incorporate these nuances, knowing where and what to standardize, how value will be impacted, etc.

• Comprehend the Processes and Technologies:

  • This means developing a full understanding of the relevant processes and ‘paths to outcomes” as well as the related technologies in question.

  • This covers not only the formal means to get work done (in terms of the procurement process) but the informal as well (that is, the informal avenues through which the process can be accelerated, obstacles bypassed, etc.).

  • This also covers the technology landscape that impacts, alters and changes these procurement processes, including which AI tools, platforms, and data sources are available as well as what they can and can’t do.

  • Applying this to our PS example, you might understand that you have a formal procurement process that requires a business case, strategy sign-off, and competitive bids, but you also understand that key leaders will slow-walk the formal process if they feel railroaded. This could require an informal path (pre-reads, socialization, one on ones, etc.) to get each BU head to co-own the category strategy design so the formal approval becomes a formality. Separately, you will need to get smart about alternative AI tools that drive your spend analytics more quickly, leverage and organize external intelligence more deeply and then drive the sourcing process more flexibly and intuitively.

• Understand yourself:

  • This is the art of developing a level of self-awareness so you know how to best orchestrate.

  • This means knowing your own strengths, biases, and blind spots. It means understanding where your time is most valuable and how and where to focus on the work yourself versus work with others to execute. The best directors know what they’re good at and what they need to trust their specialists on.

  • In our PS example, this means taking stock of your network of relationships, your personal biases and ideas about the “right” path forward, and then understanding the pitfalls and traps you yourself need to watch out for as you orchestrate - as well as who you might need to call in to help as you navigate. Perhaps you have strong relationships with two of your BU heads but a terrible one with the biggest BU head, who just happens to be the biggest driver of spend in this category. You may need to leverage key influencers to help with organizing your messaging and socialization with this individual, so that you can smooth out the path to outcomes.

These prerequisites form the foundational basis with which you can effectively orchestrate. They provide the map. How you navigate this terrain, though, is where a specific set of abilities come in.

We’ll focus on that next.

The Orchestrator’s Toolkit

The core abilities of the Orchestrator encompass:

• Systems Thinking

• Task Decomposition

• Resource Matching

• Sequencing and Handoff

• Quality Verification

• Exception Handling

Let’s dive into each specific ability.

• Systems Thinking

  • This is the ability to understand the problem end-to-end and then the path to the solution. It encompasses the ability to:

    • Map interdependencies (understanding how changing one variable affects others)

    • Identify feedback loops (where outputs become inputs)

    • Distinguish root causes from symptoms, and

    • Hold multiple time horizons simultaneously (what needs to happen now vs. what this sets up for later).

  • In the procurement context, this means seeing a sourcing event not as an isolated transaction but as a node within the broader architecture of supplier relationships, business unit strategies, risk exposure, and market dynamics.

  • Applying this to our PS example, this involves mapping the full picture: spend data from multiple ERPs, stakeholder dynamics across BUs, supplier interdependencies, contract expiry timelines, and the CFO’s budget cycle. You would organize the data and analysis in line with stakeholder communications and buy in requirements, including even sequencing the entire initiative to ensure specific ‘easier’ BUs go first, allowing you to build momentum and political cover for the more difficult ones later.

• Task Decomposition

  • This is the ability to break down the end goal and requisite outcomes sought into its sub-tasks and activities, which calls for:

    • Defining the end-state clearly (decomposition without a clear target just creates busy work)

    • Understanding granularity (how small is small enough? Too coarse and you can’t allocate effectively; too fine and you create coordination overhead)

    • Identifying dependencies between sub-tasks (what’s sequential vs. parallel), and

    • Determining which tasks require integrated judgment and can’t be parceled out - especially critically in the AI context.

  • Back in our PS world, this would mean breaking the initiative into workstreams: spend baselining and cleansing (AI-heavy), market analysis (AI-assisted with human synthesis), stakeholder alignment (entirely human), supplier evaluation design, negotiation, and transition planning. Each will have different timelines, owners, and dependencies.

• Resource Matching

  • This is the ability to determine which tool/human/agent is best for which sub-task. Key considerations here include:

    • Matching based on capability (what can each resource actually do well?)

    • Cost-effectiveness (what’s the most efficient allocation?), and

    • Risk tolerance (where do errors matter most, and does that argue for human oversight?)

  • The added dimension here is the human/AI/system triaging i.e. understanding what AI can do reliably, what it can do with human oversight, and what still requires entirely human execution. (This is, of course, an evolving assessment as the technology continues to improve.)

  • Looking at the PS initiative, this could parse out as spend cleansing done by the AI analytics platform with a junior analyst validating the output, and market intelligence developed in conjunction with AI but interpreted by you as the senior category leader. Stakeholder conversations would be done by you alone as the leader.

• Sequencing and Handoff

  • This involves ensuring each aspect of the process is seamlessly executed as needed and by the right individuals/agents. The prime considerations here include:

    • Defining clear input/output specifications at each stage

    • Managing the interfaces between contributors (handoffs where quality could degrade)

    • Building in checkpoints rather than only verifying at the end, and

    • Managing the tempo i.e. which sequences need speed, which need deliberation.

  • (It’s worth noting that the handoffs between AI and human work are particularly error-prone because the human may over-trust the AI output and not apply adequate scrutiny. This is worth keeping a conscious eye on - especially the idea of retaining cognitive agency of the work being done.)

  • In the PS context, this would mean the spend baseline must be completed and external intelligence sorted before you can have credible conversations with BU heads. It also means translating the data outputs into a narrative that speaks to each BU head’s specific concerns (versus just presenting the spend cube with key overall, corporate level insights).

• Quality Verification

  • This means ensuring the work is done to the requisite standards and the key elements here include:

    • Defining “done” before work begins (what are the metrics/acceptance criteria)

    • Understanding where to sample versus conduct a comprehensive review (you can’t check everything so knowing where to look and which aspects to trust is a skill in itself - especially true for complex projects)

    • Distinguishing between quality of output and quality of process (a good result from a bad process isn’t repeatable) and

    • Calibrating standards to context (i.e. not everything needs to be perfect; knowing where “good enough” applies is, itself, a judgment call)

  • In the PS example, you might find that the AI spend classification has a known error issues when it comes to miscategorized tail spend, so you might build in a human audit of the top 20% of spend by value and a random sample of the tail to ensure quality.

• Exception Handling

  • This involves stepping in to manage issues as and when they arise (and, to be clear, not just stepping in to do the work yourself when problems arise). The key aspects to look out for here include:

    • Understanding early warning signals (delays, misaligned outputs, stakeholder discomfort)

    • Distinguishing between exceptions that need intervention and normal variance that can self-correct

    • Having pre-defined escalation thresholds rather than reacting ‘in the moment’, and

    • Knowing when to intervene personally versus when to redirect the work to a different resource

  • In the PS initiative, you might find that the Engineering BU head escalates matters to the CEO, and argues that consolidation will compromise a critical product launch. You might then choose to work with key internal influencers to carve out specific launch-critical engagements from the consolidation scope for six months, preserving the overall initiative while defusing the objection.

These six abilities form the orchestrator’s toolkit - and many of its component aspects are similar to that of the Project Manager’s. But there are nuances here - and that relates to the integration of technology, agents and the discipline and care with which we integrate and deploy them across our work. These nuances are worth paying specific attention to.

That said, having the toolkit isn’t enough. We need to remain vigilant to a trap that even skilled orchestrators fall into.

Where Orchestrators Go Wrong

Great orchestration is as much an art as it is a science.

It requires marshalling your available resources to achieve your desired outcomes in a manner that is most efficient and ‘least friction’. But in trying to achieve this, we must also remain diligent to not fall into an age old trap: orchestration can become micromanagement if you don’t trust your resources, or it can become abdication if you over-delegate without verification.

In our PS scenario, this could show up as the moment when the Engineering BU pushback happens and you’re tempted to personally take over the supplier negotiations to keep timelines on track. Or it could be when, for example, you delegate the spend analytics entirely to the AI tool and a junior analyst without defining validation criteria, only to discover (two months later) that the baseline data is unreliable.

The great orchestrator lives in the productive middle, walking that fine line between abdication and micro-management. This puts even more emphasis on the prerequisites discussed above - the deeper your understanding of the terrain, the more effective you will be as an orchestrator.

Why This Matters

Orchestration is the key skill of the Differentiating Layer for a reason.

In the PS example we’ve discussed so far, the practitioner who orchestrates Professional Services consolidation per the CEO’s directive, didn’t just save 15%. They demonstrated something no AI could replicate - the ability to read an organization, sequence a complex initiative across human and machine contributors, and navigate political terrain that would have stalled a less capable practitioner.

This is the type of skill that gets noticed by leadership because it makes you irreplaceable, even as AI handles more execution. The practitioner who can orchestrate effectively across a mixed human-AI ensemble is the one who remains relevant.

So, great orchestration keeps you in the room. Business acumen, though, is what gives you a voice in it.

We’ll tackle that next week.

Waiting for your organization to future-proof you is, in a post-AI world, a career-defining mistake. Future-proofing the Procurement practitioner is an initiative that is on the individual, not the organization.

Most organizations will, of course, support your efforts in one form or another, but the pace of change, the sheer number of tools available and the nascent stage we’re at on the AI journey means change is happening faster than anyone can fully fathom - and certainly faster than many large, traditional, bureaucratic organizations (not to mention IT teams) can cope with. (And that’s for those organizations willing to do so - many are still grappling with the fear that AI will upend everything.)

So how, then, does an individual go about ‘future proofing’ themselves?

The first step is, of course, self-diagnosis: that is, how much of your role is at risk due to AI? In this post, I identified the eight factors that define whether a role or set of tasks will be automated, augmented or remain human. That’s the right starting point to understand where you personally are today. (Use the interactive tool linked within the article to conduct this assessment for your own role.)

The next step is to understand how to begin the future proofing journey, and there are three parts to this discussion:

1. The Enabling Layer

2. The Differentiating Layer

3. The Orientation Lens

I’ll cover parts 1 and 2 in this post. Part 3 is the lens through which the first two are pointed, and I’ll cover that in a future post.

Before we proceed, please note that I am making an important, underlying assumption: I am presuming that you have already developed the Procurement knowledge (core sourcing skills, category expertise, etc.) that forms the technical basis of your work. These skills are important but they’re foundational. They’re simply table stakes, not differentiators. As such, I will not be covering any of this in this post.

With that out of the way, let’s dive in.

A. The Enabling Layer

This is the first layer of capability and it’s comprised of two important skills - AI Literacy and Cognitive Discipline.

These are related ideas in that the former pushes you forward and capture the value that AI tools can provide, while the other ensures you don’t go too fast and lose your ability to think, comprehend and retain. In this way, AI literacy is the accelerator and Cognitive Discipline is the brake. You want to be able to deploy them both.

1. AI Literacy: The Accelerator

This is the foundational work of becoming AI-literate, that is, understanding the available tools and their impact. There are a wide range of tools already covering a wide range of applications, and it’s important to get smart, not about everything, but about what’s available and its potential.

The core skills to be developed here are:

1. Tool-mapping - look to understand the landscape. You don’t need to know every tool (and that isn’t even going to be possible)

2. Prompt design - learn to ask the right questions in order to be able to get quality output reliably and quickly

3. Output evaluation - learn to tell good output from plausible-looking garbage, which means not taking AI output for granted

4. Build vs. buy literacy - understand (practically) that, for many simple applications, you don’t need to buy, you can also build quick and efficient solutions yourself

5. Integration fluency - understand how different tools work together and/or connect into workflows.

6. A currency system - learn and embed the discipline to stay current and learn about new developments without drowning

It’s important to NOT limit your learning to work tools, but also bake AI into your daily personal use. Apply these tools to different personal use cases (trip planning, schedule development, vacation research, etc.) - which can provide for a safe space to learn about AI’s value and impact.

(I wrote about how to build AI literacy in the corporate context in this article.)

2. Cognitive Discipline: The Brake

A driver who follows the GPS without ever learning the city is going to have a problem navigating when the signal drops or the algorithm hiccups. Cognitive discipline is what keeps you from becoming that driver.

I wrote at length about the problem of cognitive debt (here) as well as the importance of not losing our cognitive agency when using AI (here).

Building Cognitive Discipline requires:

1. Thinking before prompting - always form your own view before asking AI to do anything; never start with AI

2. Challenge the output - never accept first drafts uncritically; consider each argument, click through to the sources and do your own reading as well; verify before you trust

3. Practice hard mode - deliberately do aspects of work without AI to maintain the muscle

4. Separate divergent from convergent thinking - use AI for the first point (to push you to think differently, identify gaps, etc.) but be careful with the latter (especially as AI has a tendency to always tell you your ideas are great!)

5. Practice metacognition - be cognizant of any tendency to offload your thinking; offloading execution is perfectly fine and safe to do

B. The Differentiating Layer

The Enabling Layer gets you in the game, but the Differentiating layer is what truly future proofs you. It comprises of three core skills:

1. Orchestration:

I talked about this idea at the organization level in my post last week on the six moats of the Procurement organization of the future. This is its application at the individual level.

Orchestration - in the context in which I am using it - is the core skill of any good Project or Engagement Manager. Think of it as the ability to design the work, allocate it intelligently across humans, agents, and systems, sequencing it correctly, and stepping in when it matters. This requires understanding the organization, the function(s), tools, people and processes and then tailoring the requisite work to drive towards desired outcomes.

The core abilities here include:

1. Systems thinking - understanding the problem end-to-end and then the path to the solution

2. Task decomposition - breaking the requisite outcomes sought into its sub-tasks and activities

3. Resource matching - determining which tool/human/agent is best for which sub-task

4. Sequencing and handoff - ensuring each aspect of the process is seamlessly executed as needed and by whom

5. Quality verification - ensuring the work is done to the requisite standards

6. Exception handling - stepping in to manage issues as and when they arise

2. Business Acumen:

This is the ability to think beyond your role and function to solve problems for the business. At its essence, it’s understanding your (internal) customer and their outcomes and goals sought to mediate towards the right and optimal solution.

There are host of subskills here, including:

1. Business & Financial literacy: including the corporation’s value chain and economics, how procurement’s and the category’s economics play into the P&L and create shareholder value, etc.

2. Commercial acumen - understanding deal structures, pricing models, contract economics, incentive design, etc.

3. Stakeholder/internal customer literacy - understanding what your internal customers actually optimize for, how their incentives work, what success looks like in their terms

4. Market and ecosystem literacy - understanding supply markets, supplier economics, where power sits in the value chain

3. Human Leverage:

This is the third and final core skill and is focused on developing the requisite human skills to drive towards valuable outcomes.

This builds on the concepts discussed above to encompass:

1. Relationship & Stakeholder Management - developing one-on-one relationships with different stakeholders and departments, managing competing interests, etc.

2. Influence and persuasion - moving people in a specific moment and situation, that is, being able to convince individuals to move towards specific outcomes in a way that aligns with the overall goal

3. Narrative development & communications - hearing what stakeholders actually mean versus what they say, crafting a narrative or story to shape the requisite outcomes and managing communications and understanding to achieve these goals

4. Creative problem-solving - ideating and developing unique solutions that solve problems and achieve key outcomes in the midst of resource constraints and competing agendas

Why Judgement Isn’t On The List

You’ll notice that I didn’t reference Judgment anywhere on the capabilities and skills above. This omission is entirely intentional.

That’s not because I don’t think judgment is an essential human skill. If you’ve read anything that speaks to Humans and AI, you’ve heard the argument that judgement is the differentiator, the one skill that will always remain human. I fully agree with that.

For me, though, judgement is a different kind of capability. It’s a meta-capability that sits above all of the other skills I’ve discussed - it’s the result of strong business acumen and human leverage skills.

As such, it’s important enough to merit its own post, and I’ll write about it soon.

Fluency - Not Sequence

The Enabling and Differentiating Layers discussed above need to be learned and absorbed such that we are intuitive and fluent in how we deploy them. They are not to be sequentially applied but in tandem, and fluidly, across a given situation.

For example, let’s say you’re leading a sourcing event under time pressure:

• AI literacy ensures you ask the right questions and seek the right intelligence (internal and external) to dissect the issue at hand. Cognitive discipline allows you to question the AI-generated market analysis, which you can then tailor based on your own thinking and understanding of your stakeholders, your relationships and the corporate environment

• Orchestration ensures you then take that strategy and sequence it appropriately: translating the insights gained into key tasks, incorporating stakeholder conversations and inputs, developing, for example, an RFP with agentic support, analyzing RFP submissions with the help of agents, and even questioning an AI-generated selection shortlist that looks suspicious, allowing you to then personally review and refine the analysis

• You then weigh your options with all of the intelligence and agendas understood to date, conduct the (human) negotiations (possibly with AI support) and then finalize the go-forward recommendation, which is then communicated effectively to all concerned stakeholders across the enterprise (as well as outside of it).

My point is, the capabilities discussed are not individual items on a checklist that need to be run through, but instead should be understood and absorbed such that you develop fluency. In situations that matter, you won’t have time to consult checklists; you’ll either have what’s instinctive, or you won’t.

Your Operating Stance

I’ll get into the Differentiating Layer elements further in subsequent posts but in the interim, I’ll leave you with a few thoughts that underscore all of the above.

First off, it’s worth remembering that this is a journey, not a destination. Learning is never one and done. It’s ongoing, particularly in this space, because you can never be fully, perpetually future proofed. As the tools and capabilities and technologies evolve, so must you.

Second, understand and accept that failure is part of the journey. Believe it or not, AI is still in its early days and the technology still has issues, so mistakes and ‘failure to achieve outcomes’ are to be expected. That said, the technology will continue to get better. So, understand also that there will be issues and learn not to write off a specific tool just because it didn’t deliver as expected today. Experiment, learn, adopt and adapt. Keep moving.

Third, as you experiment, define your ‘safe’ spaces. This means identify how and where you can experiment without impacting ongoing or critical operations. Start with small impacts and initiatives and build on them from there. (Ideally, this will be done in conjunction with your employer - it is incumbent on them to create these safe spaces as well.)

Finally, this shouldn’t be a solo exercise. Sure, you can do it on your own but why take that path? We learn more from each other’s different takes and approaches. Co-opt your colleagues, peers and/or friends. Learn from each other. ‘Co-understand’ what good standards are and share learnings and best practices.

The Work Starts Now

The future-proofed practitioner won’t be someone waiting for permission or a corporate roadmap. They’re treating their own capability development as the most important project they’re running this year. Because in a post-AI world, it is.

Start with the self-diagnosis, then build the Enabling Layer so you can deploy AI without losing yourself. Then build the Differentiating Layer so you can do the work that AI can’t.

You have the model now. What’s left is the doing.

Over to you.

Over the last few weeks, I’ve talked about what remains human in Procurement at the individual role and subtask level, introducing the Human Edge Matrix, which allows practitioners to assess how much of what they do remains human, what is augmented, and what can be fully automated.

I also put forth my argument that what actually remains human - independent of capability - really isn’t even procurement’s decision to make. It’s shaped by what its internal customers and suppliers value and trust. As part of this argument, I laid out the seven outcomes that stakeholders actually care about and how to assess what remains human in the context of those outcomes.

All of this gives us a view of what stays human.

But it also raises the next obvious question: if we know what stays human, what do we actually build around it?

In other words, once we’ve identified the work that remains ours, we can’t just cobble together the remnants into our prior architectures. We need to rethink what the enterprise genuinely values - ideally, into something that cannot be eroded by the next wave of AI tools or absorbed by an adjacent function because ‘Procurement isn’t delivering commensurate value’.

To me, this is where the conversation needs to go next, especially for CPOs. Because if you’re sitting at your desk today, trying to build a Procurement function that will still matter in five years, you need a clear answer to the question: what am I building toward?

This post is my attempt at that answer.

A Different Way To Think About The Function

So what does the architecture of the Procurement organization look like in a Post-AI world?

While it’s tempting to think of this in the form of boxes on org charts or specific skills that need to be retained or developed, I think it’s more appropriate to visualize this architecture in the form of organizational moats. That is, capabilities that Procurement needs to build deliberately if it wants to remain relevant, valued, and credible in a post-AI world.

Note that I’m using the term ‘moats’ in the loose sense - borrowing from the competitive strategy world, where it refers to a structural source of defensibility (something that’s hard to replicate and that compounds over time). Michael Porter alluded to this through his Five Forces work, Warren Buffett popularized the idea in his shareholder letters, and Hamilton Helmer formalized it through his seven “powers” framework.

I’m not trying to shoehorn Procurement into any of those perspectives but, instead, I’ve taken inspiration from their models, especially the idea that defensibility should constitute both a benefit and a barrier, and applied it to what I believe a post-AI procurement function needs.

A quick note before I get into the moats: this is not a list of skills. Skills live inside individuals and I’ll tackle those in upcoming posts. For example, judgement is a differentiating skill but it’s embedded in all six of the moats I’ll describe. It’s not a moat in and of itself.

Similarly, the ability to develop the next generation of talent is also not a standalone moat, in my view. It’s the maintenance layer that keeps it all intact over time. Important but not distinct items on this list.

The moats I’ll discuss are the organizational capabilities that CPOs need to be deliberately building: defensible terrain that comprises the architecture of the function, not the people inside it.

The Six Moats That Matter

There are six moats I’ve identified:

• Two customer-facing moats (commercial partnership and execution orchestration)

• One internal-facing moat (hybrid operating model design)

• One supplier-facing moat (supplier intelligence broker)

• One structural moat (commercial accountability and governance authority) and

• One about perception (enterprise positioning).

These cover, in my mind, the major surfaces where procurement creates and defends value.

Some of these moats exist in mature form in some functions today, though most don’t. All of them, I’d argue, are the things a CPO should be investing in now if they want their function to matter in a decade.

Let’s dive into each one.

1. Commercial Partnership

The first moat is procurement’s ability to act as a genuine commercial partner to the businesses it serves - not a process gatekeeper, not an order taker and not the function that shows up late to tell someone their preferred supplier isn’t on the approved list.

I mean a genuine commercial partner - someone who understands the business well enough to help shape what it’s trying to achieve, knows the category well enough to translate between commercial reality and operational need, and has earned the right to be in the room when real decisions get made.

There are three components to this that have to be built together:

• Business intimacy - the ability to hold a substantive conversation with the ‘consumer’ of the category about strategy, market dynamics, competitive pressures, and operating model choices, without needing a translator

• Category depth - genuine expertise in the supply markets, cost drivers, and supplier landscape relevant to that internal customer’s work, so procurement can contribute ideas rather than just react to requests

• Stakeholder relationships - the accumulated trust and rapport that makes internal customers want to involve procurement early rather than late (or worse, not at all)

Why this is a moat: The combination of these three things is rare, takes years to build, and is nearly impossible for AI to replicate - because it depends on context, relationships, and tacit knowledge that only comes from being in the conversations. An AI tool can surface market data but it can’t sit in a room and read the organizational or political dynamics of a leadership team debating whether to restructure a category. Once Procurement has earned partnership status, though, that status is sticky - once accumulated, it cannot be rapidly built by internal ‘competitors’.

What CPOs should be building: A deliberate model for developing commercial partners, not just category managers. That means rethinking hiring (possibly business generalists with curiosity, not just sourcing specialists), broadening training paths (focused on business acumen and stakeholder management), redesigning development opportunities (rotations into the business, not just within procurement), and creating explicit time and permission for the relationship work (including embedding these outcomes into individual metrics).

2. Execution Orchestration

The second moat is procurement’s ability to actually make things happen - to take a commercial problem and mobilize the people, process, suppliers, and systems needed to deliver the outcome.

And yes, I know the procuretech crowd uses “orchestration” in a narrower sense these days, usually around orchestrating AI agents across a workflow. That’s not what I mean. I mean orchestrating the enterprise: turning strategy into delivered value across stakeholders, suppliers, internal teams, and whatever AI-assisted workstreams sit in the middle. (A more sophisticated form of project management, if you will.)

This capability spans two ends of the spectrum:

• Bespoke work - taking on a complex, ambiguous commercial problem that no playbook covers and figuring out how to deliver

• Routine work - ensuring that standard buys happen without friction so the business never has to think about them

Both matter, and they reinforce each other. The trust earned from frictionless routine work is what buys the credibility to take on the complex, strategic problems (though not on its own and not without Moat #1 above).

Why this is a moat: Orchestration at this level depends on influence without authority, which is one of the hardest organizational skills to build and one AI has virtually no ability to replicate. It also compounds. A function known for making things happen gets invited into more things, which creates more opportunities to demonstrate the capability, which deepens the reputation further. Procurement’s very own flywheel.

What CPOs should be building: Explicit development of this type of orchestration muscle - which means giving people increasingly complex delivery challenges with real accountability, resisting the temptation to reduce orchestration to a process manual, and coaching them through the political and influence dimensions. Orchestration in this sense relates to judgment, intelligence, flexibility and an outcomes orientation - not a process orientation. You need a particular caliber of individual to be able to do this.

3. Hybrid Operating Model Design

The third moat is the internal architecture of how procurement work actually gets done (and the closest I’ll get to the idea of boxes on org charts in this conversation).

This involves the deliberate design of the Procurement operating model so that it’s clear which decisions route to humans, which route to AI, where the handoffs sit, how exceptions get escalated, how the AI tools themselves get governed, and how the function’s own workflow is structured so that humans stay sharp rather than getting progressively deskilled (or distracted by low value activities).

I appreciate that this can sound like an overlap with the first two moats but it’s actually quite different in nature. Commercial partnership is about how procurement engages the enterprise. Orchestration is about how it delivers outcomes. The operating model is about how procurement organizes itself to sustain both over time in a hybrid human-AI environment.

It’s effectively the plumbing, which most functions haven’t deliberately built as yet. The default approach to AI in procurement today is additive: bolt an AI tool onto an existing process, hope for productivity gains, and move on. That approach creates hidden liabilities:

• It optimizes locally without asking whether the overall flow still makes sense

• It embeds AI decisions in places where accountability may still be unclear

• It deskills the humans who used to do the work

A well-designed operating model does the opposite. It asks explicitly: where does human judgment add value here, and how do we protect the conditions that let it develop? Where does AI genuinely help, and how do we govern it? These are design questions, not technology questions - and answering them well is a capability in its own right.

Why this is a moat: Most procurement functions aren’t even framing these questions yet, let alone answering them deliberately. The ones that do will have a structural advantage in sustaining all the other moats because their humans will stay sharp and their work will stay coherent. As such, this is a meta-moat: one that holds the system together and allows the other moats to flourish.

(It’s also worth emphasizing that operating model design in a hybrid human-AI environment is an emerging discipline with very few practitioners. Building the internal capability to do it well is itself a cornered resource, because the people who can actually design these systems are few and far between, and will likely remain rare for years.)

What CPOs should be building: An explicit operating model design function - probably a small team or a named role - responsible for thinking about the human-AI-process architecture as a living system, not just implementing whatever the latest vendor sold them. This is among the least developed of the six moats in most procurement functions today, and the one where deliberate investment pays off fastest.

4. Supplier Intelligence Broker

The fourth moat is building on Procurement’s privileged position in the supplier ecosystem - specifically, the ability to extract genuine strategic intelligence from suppliers and translate it into value for the enterprise. This is one of the most underused sources of defensibility in the function today and, in my view, one of the most durable.

Here’s the core idea:

Suppliers - especially in strategic categories - know things the enterprise doesn’t. They see the market differently. They have perspectives on competitors, on technology trends, on regulatory shifts, on what other customers are doing. A procurement function with deep, trust-based supplier relationships can become an intelligence broker between that external knowledge and the internal decision-makers who need it.

But - and this is the catch - it only works if the relationships are genuinely trust-based. Which means procurement has to earn the right to that intelligence through years of reciprocity, discretion, and actually treating suppliers as partners rather than counterparties to be squeezed.

But most procurement functions don’t operate this way. The ones that do have built something extraordinarily hard to replicate, because the trust that enables the intelligence flow was accumulated over time and cannot be bought, copied, or AI-generated.

Why this is a moat: It’s a cornered resource in the strictest sense. The relationships are unique, the trust is non-transferable, and the intelligence flow depends on a position that only Procurement is structurally positioned to hold. AI tools can aggregate public supplier data; they can’t replicate a twenty-year relationship with a supplier’s CEO who is willing to share something they haven’t told anyone else. And the value to the enterprise - genuinely differentiated insight into the supply markets that matter most - is exactly the kind of thing internal customers will pay a premium for in attention, engagement, and budget.

What CPOs should be building: A deliberate architecture for supplier intelligence. That means identifying the categories where intelligence matters most, investing in the relationships that unlock it, creating internal mechanisms to translate supplier insight into decision-ready intelligence for business unit leaders, and - most critically - protecting the trust that makes the whole thing work by resisting the temptation to exploit suppliers for short-term cost gains. This moat is the easiest to destroy and the hardest to rebuild.

5. Commercial Accountability And Governance Authority

The fifth moat is different in character from the others. It’s not a capability procurement builds alone but a structural position procurement claims, formalized at the executive level, that establishes the function as the organization’s accountable owner for commercial and supplier decisions.

The question this moat answers is as follows: In a world where AI is increasingly recommending suppliers, flagging risks, scoring bids, and triggering contract actions, who is accountable when those calls turn out to be wrong?

The answer shapes where authority, budget, and relevance concentrate in the post-AI enterprise. And there are really only three possible homes for this accountability:

1. The business units that use the suppliers - which creates a clear conflict of interest, since the people making operational decisions shouldn’t be the same people overseeing them

2. A new enterprise function - like governance or technology risk, which gradually absorbs procurement’s commercial oversight role

3. Procurement itself - as the function with the cross-enterprise view, the supplier relationships, and the commercial context to bear it credibly

The third option is the one that preserves Procurement’s structural relevance. But it doesn’t happen by default; it has to be claimed, negotiated, and formalized - typically in partnership with the CFO and General Counsel, and ratified at the executive level. Once claimed, it becomes one of the most defensible moats on this list, because no other function is structurally positioned to take it over.

Why this is a moat: It’s a claim on organizational territory that, once established, is genuinely hard to dislodge. It also has an interesting property the other moats don’t: the threat isn’t primarily from AI itself, but from other functions - risk, legal, technology - that might otherwise absorb this accountability by default. Procurement that doesn’t claim this role loses it, and losing it erodes the rationale for procurement as a central function at all (which as I explained above, comes at a cost).

What CPOs should be building: The arguments, the relationships, and the internal capability to bear this accountability credibly.

• Step one is a conversation with the CEO and CFO about where commercial accountability for decisions relating to the supply base - human and AI-enabled alike - should sit (and why procurement is the right home).

• Step two is the internal capability - governance frameworks, decision audit trails, escalation protocols - that lets procurement actually discharge the responsibility once it’s been claimed

Without step two, claiming step one is reckless. Without step one, building step two is pointless.

This is the moat most dependent on executive alignment, and the hardest to build through sheer functional competence alone. Unlike the other five, you can’t start building this one tomorrow morning unless you already have the executive relationships to do so.

6. Enterprise Positioning

The sixth moat is the one Procurement has historically been worst at - and the one that most urgently needs to change.

It’s the deliberate work of shaping how Procurement is seen, engaged, and valued by the rest of the enterprise, including the service model that delivers on the positioning and the ongoing communication that sustains it.

Call it “branding” if you like, though I appreciate that that word triggers all the wrong associations in a Procurement audience. This isn’t about marketing fluff, logos, or internal newsletters. It’s about the gap between what procurement actually does and how the enterprise perceives what procurement does - a gap that, in most organizations today, is enormous and damaging.

If the enterprise sees procurement as a process gatekeeper, a cost cop, or a source of friction, then in a world where AI makes much of the process and friction disappear, the enterprise will stop routing work through procurement at all. The function gets disintermediated - for reasons not at all related to capability. Perception and reality have to move together, and right now they’re badly misaligned in most organizations.

Closing that gap is itself a capability, and it has to be built deliberately because it won’t happen on its own.

What does good positioning actually look like?

• A clear, honest articulation of what procurement offers that no one else in the enterprise can

• A service model designed around outcomes rather than process - for example, an account executive approach, where named procurement leads own the relationship with specific business units and are measured on commercial outcomes rather than activity metrics

• Sustained, proactive communication that keeps the function’s value visible rather than assuming it’ll speak for itself

Most procurement functions do none of these well. The ones that do, stand out immediately.

Why this is a moat: Positioning, once established, compounds the same way brand does in consumer markets. A Procurement function that’s understood as a commercial partner gets invited into more strategic conversations, which produces more opportunities to demonstrate value, which reinforces the positioning. A function that’s understood as process overhead gets routed around, which reduces its visibility, which accelerates its decline. The flywheel runs in both directions, and the direction you end up in depends on whether you invested in the positioning work when it mattered.

What CPOs should be building: A deliberate positioning strategy, treated with the seriousness a marketing function would treat its brand. That means defining what procurement stands for, designing the service model that delivers on that promise, and creating the ongoing rhythm of communication and engagement that keeps it alive in the enterprise’s mind.

(Of course, it also means honestly assessing the gap, as uncomfortable as it may be - between current perception and desired perception, and building a plan to close it.)

One brief acknowledgment on this moat: it’s possible that in ten years, positioning won’t need to be a distinct capability, because Procurement will have rebuilt itself enough that the function’s value is self-evident. I don’t actually think we’ll get there that quickly - but even if we did, it would be a good outcome. For now, though, the gap is real, and pretending it isn’t would let CPOs off the hook for work that desperately needs to happen.

Where This Leaves Us

Six moats, then. Two customer-facing, one internal-facing, one supplier-facing, one structural, and one about perception and reputation. Each of them hard to build. Each of them defensible once built. Each of them compounding over time.

Two final points:

First, it’s worth noting that these six moats are not independent of each other:

• While Commercial partnership and Execution Orchestration can exist without the Hybrid Operating Model Design moat, they won’t sustain in a post-AI environment unless the redesign happens

• Commercial partnership and Supplier Intelligence Broker reinforce each other, as the insights from suppliers are often what make procurement valuable to internal customers in the first place.

• Commercial Accountability & Governance Authority becomes credible only when the Commercial Partnership, Execution Orchestration and Hybrid Operating Model Design moats are in place to credibly justify it.

• Enterprise Positioning will be hollow without the substance of all of the other moats before it.

A CPO thinking about where to start cannot, therefore, treat these as six separate investment decisions; they form a system, and the sequencing matters.

Second, if you’re a CPO reading this, ask yourself this: how many of these six do I have in any meaningful form today?

My own answer, based on the functions I’ve worked with across my years at A.T. Kearney and The Smart Cube, is that most have fragments of one or two, almost none have all six, and the most neglected moats aren’t always the ones you’d expect.

I want to come back to this topic in a future post, specifically on the topic of where a CPO serious about post-AI relevance should actually start.

For now, though, the framework itself is the starting point.

The CPOs who invest in these six moats now will run the procurement functions of 2030. The ones who don’t will be running something smaller, more marginal or possibly nothing at all.

If you read last week’s post, you’re left with an uncomfortable question:

“If what stays human in procurement is determined by stakeholders rather than by the function itself, how does a practitioner actually figure out where the line falls?”

It’s one thing to accept that your relevance is shaped by the people you serve. It’s another to know what to do about it.

This week, I want to give you a tool for answering that question: For each key outcome, how do we determine what stays human and what doesn’t?

Why The Matrix Isn’t Enough

Of course, one approach could be to apply the Human Edge Matrix to each outcome, which I discussed in this post a couple of weeks ago (along with an interactive tool that you can use to assess how vulnerable your own role is to AI).

However, the Matrix was designed to classify work at the role and task level; it tells you whether a specific activity should be automated, augmented, or kept human.

For example, I’d posit that outcome #1 (Speed and Responsiveness of the Procurement Process) can be almost entirely machine in its execution (perhaps with some/limited human involvement where needed) while outcome #8 (Crisis Management) is one that will almost certainly remain fully human (even if those humans are somewhat augmented with AI.

But, in between these extremes - depending on the individual company situation (influenced by everything from its market, competition, financials, category focus, etc.) - all eight factors in the matrix could run the gamut from human to nonhuman.

What we need now is a complementary lens that operates at the outcome level and answers the question: “For this outcome, does human involvement change what the stakeholder receives?”

Two Questions That Draw The Line

So what is the right way to understand what stays human at the outcome level?

At least at the Internal Customer level, I’d suggest two core questions need to be answered:

1. Does human involvement produce a value premium that justifies the cost AKA “Am I making this better”?

   1. This question is purely economic: is the outcome measurably better, or perceived as meaningfully more legitimate, when a human is involved? And is that difference worth what the human costs?

   2. This covers a host of considerations including:

      1. Relative quality differentials i.e. is there a material quality differential between the ‘human only’ versus ‘human plus machine’ versus ‘machine only’

      2. Business partner requirement - does the work require Procurement to partner with the stakeholder to arrive at an optimal solution? Does he/she bring advisory value to the table?

      3. Experience levels - Does the practitioner bring a depth of experience and insight that makes a difference?

2. Does accountability require a human AKA “Does someone need to own this”?

   1. This question isn’t about whether AI can “do the analysis” but “can the organization accept a decision where no human bore the responsibility?”

   2. This covers a host of issues including:

      1. Regulatory sign-off

      2. Ethical and/or value-based oversight

      3. Complexity that demands multiple human eyes (for validation or risk mitigation) and,

      4. Situations where someone needs to be personally answerable for the outcome.

This allows us to create a simple 2x2 that connects naturally to the Human Edge Matrix as a complementary lens rather than a competing one: Accountability required / not required on one axis, value premium present / not present on the other - giving us four quadrants, each with a clear implication for the internal customer:

• Accountability Required:

  • Human Value > Machine Value: Procurement should handle

  • Human Value < Machine Value: Procurement needed ONLY if it can show material, incremental value: technical knowledge, regulatory understanding, etc. otherwise stakeholders stop calling (risk of rogue duplication)

• Accountability Not Required:

  • Human Value > Machine Value: Procurement should handle

  • Human Value < Machine Value: Automate and retain with Procurement IF there is consistency across customers, ELSE co locate with customer

The framework should be applied at two levels and at different cadences:

1. The Category Strategy level:

   1. When a CPO or category leader is designing or redesigning how a category operates, they apply the 2x2 to the outcomes that matter for that category.

   2. This is a periodic, strategic exercise. You do it when you’re setting up the category strategy, and you revisit it when something material changes e.g. new regulation, new technology capability, a shift in what the business expects from that category.

   3. The point is not to be recalibrating constantly, but factoring in this analysis at deliberate review points.

2. The Exception/Escalation level:

   1. In day-to-day operations, the default mode is whatever the category strategy determined.

   2. But specific situations will arise that challenge the default: a supplier relationship that was fine on autopilot suddenly needs human attention because of a quality failure or an internal stakeholder who was happy with automated reporting now needs human counsel because they’re facing a board question about supply risk.

   3. These are the moments where a practitioner applies judgment about whether the current situation has shifted the accountability or value-premium calculus. They use the two questions as a gut-check: has something changed about who needs to be accountable here, or about whether my involvement changes the outcome?

The Framework in Practice

Let’s apply this framework to three different scenarios:

Scenario 1:

Your VP of Manufacturing needs to consolidate your packaging supply base from five suppliers to two

This impacts the Total Cost of Ownership and the Supply Resilience outcomes.

The AI can do a lot here - spend analysis, supplier performance scoring, TCO modeling across the five suppliers, scenario modeling for different consolidation options and more. And it can do all of this faster and more comprehensively than any human analyst.

But let’s apply the two questions:

• Does accountability require a human?

  • Yes - consolidating from five to two suppliers is a decision that increases concentration risk. If one or both of the remaining two suppliers fail, production is materially impacted.

  • Someone needs to own that call, explain the rationale to the plant director, and be answerable when the board asks why the company is now dependent on two packaging providers instead of five.

  • No organization is going to accept “the algorithm recommended it” as an answer when a production line goes down.

• Does human involvement produce a value premium?

  • Yes - but not where we might expect. The analytical work (spend modeling, TCO calculations, performance benchmarking) is exactly the kind of structured cognitive work that AI does well and arguably better than humans.

  • The value premium shows up in the judgment calls the data can’t make: which two suppliers have the management quality and financial stability to handle twice the volume? Which ones will invest in our relationship if we double their share? How will the three suppliers we’re exiting react — will they become hostile in other categories where you still depend on them or will there be political/social implications?

  • Those are questions that require contextual insights (market, relationship and commercial) that no model currently possesses.

This lands in the top-left quadrant: accountability required, human value premium present. Procurement should own this. But notice that the analytical work within this outcome can and should be machine-augmented. What stays human is the judgment, the stakeholder conversation, and the accountability for the decision.

Scenario 2:

Your Chief Compliance Officer needs assurance that a new raw materials supplier in SE Asia meets the company’s labor standards and environmental commitments before the first PO is issued.

This impacts the Compliance and Ethical Assurance outcome.

AI can do a significant amount of the legwork - screening the supplier against sanctions lists, pulling public records, analyzing ESG ratings from third-party databases, even scanning news sources for red flags. In fact, the machine will almost certainly be more thorough and faster at this screening than a human would be.

But again, let’s run the two questions:

• Does accountability require a human?

  • Yes - this is one of the clearest cases. Regulatory frameworks increasingly require demonstrable human oversight of supply chain due diligence decisions.

  • Beyond the legal requirement, there’s a governance reality: if this supplier ends up on the front page for labor violations, someone in the organization needs to have signed off on the decision to onboard them.

  • “We ran the algorithm and it came back green” is not a defense that any general counsel will accept.

• Does human involvement produce a value premium?

  • Only partially - this is where it gets interesting. For the screening and data-gathering work, the machine is arguably better than the human. It’s more comprehensive, less prone to oversight, and doesn’t get fatigued reviewing supplier questionnaire number forty-seven.

  • But the human value premium is there - even if it is narrow but critical. It shows up in the interpretation of ambiguous signals (the supplier’s ESG score is acceptable but their audit history shows a pattern of just-in-time remediation before inspections), in the ethical judgment calls (the data is technically compliant but something doesn’t feel right), and in the conversation with the CCO where someone needs to say “I’ve looked at this and here’s my assessment”.

This lands on the top-right quadrant BUT somewhere towards the left: accountability is required, but the machine does most of the heavy lifting. Procurement is needed, but primarily for the sign-off, the judgment on edge cases, and the ability to stand behind the decision. If the procurement person is simply rubber-stamping what the AI screening tool produces without adding interpretive value, the function is at risk of being reduced to a compliance checkbox.

Scenario 3:

Your Head of R&D wants to explore whether any of your existing chemical suppliers could reformulate a key input to reduce costs and improve sustainability - but she doesn’t know which suppliers have the capability or the willingness.

This is the Supplier-Enabled Innovation outcome.

And it’s worth noting what AI can and can’t do here. AI can scan supplier capability databases, analyze patent filings, identify which suppliers have R&D facilities working on relevant chemistry, and even draft an initial outreach brief. All of which is useful groundwork.

So let’s, then, run the two questions:

• Does accountability require a human?

  • Not really - at least not in the regulatory or compliance sense.

  • Nobody is going to get fired or face legal consequences for how the innovation exploration was conducted. There’s no structural mandate for human sign-off on “let’s have a conversation with Supplier X about reformulation possibilities.”

  • This isn’t a risk or governance question.

• Does human involvement produce a value premium?

  • Overwhelmingly yes - this is perhaps the clearest case of human value premium across all seven outcomes. Innovation from suppliers doesn’t happen because you send them a brief.

  • It happens because a procurement professional who has built trust with the supplier’s technical team over years picks up the phone and says, “I think there might be something here. Can we get your head of applications science in a room with our R&D director?” It happens because the procurement person understands both sides well enough to see the connection that neither party would see on their own. It happens because the supplier’s commercial director is willing to invest internal resources in the exploration because she trusts the procurement person’s judgment that this company will actually follow through (and not just run a free innovation workshop and then give the business to a cheaper competitor).

No AI can replicate the relational capital, the cross-organizational pattern recognition, or the credibility that makes a supplier say “yes, we’ll invest our best people in this.” The machine can identify the opportunity. The human creates the willingness.

This lands in the bottom-left quadrant: no accountability requirement, but strong human value premium. Procurement should own this - but there is a nuance: it has to earn it. There’s no structural mandate keeping this work in procurement. If the R&D director doesn’t believe the procurement person adds value to her supplier innovation conversations, she’ll go directly to the suppliers herself. Procurement’s ownership of this outcome is justified entirely by demonstrated value, not by rules or policies. And that makes it both the most rewarding and the most fragile kind of human work.

(It’s worth dwelling a little on this last point: The work that has the highest human value premium but the lowest accountability requirement is the work practitioners most need to protect. Unless they are genuinely, demonstrably good at it, there will be no requirement to keep it with Procurement and/or work with the function.)

The Supplier’s Test Is Simpler - But No Less Important

You’ll notice I mentioned earlier that the 2x2 is primarily a tool for thinking about the internal customer relationship. So what about Procurement’s other prime stakeholder: the supplier?

Well, the supplier/market frame operates on a different logic.

When a supplier evaluates whether they need a human counterpart in Procurement, they’re not running an accountability-versus-value-premium calculation. They’re asking something more fundamental:

• Does this person have the authority to commit?

• Do they understand our business well enough that I don’t have to re-explain our constraints every quarter?

• Will they still be here next year, or will I be rebuilding this relationship from scratch with their replacement?

In other words, the supplier’s test for what stays human is about continuity, authority, and contextual depth. For the supplier, there is real signaling value in human engagement.

Because the fact is that a supplier will accept automated purchase orders, automated invoice processing, and even automated performance measurement.

But what they won’t accept - at least for relationships that are important to them - is a rotating cast of humans with no institutional memory, or worse, no human at all when they need to have a difficult conversation about pricing, capacity, or priorities.

This is, therefore, a simpler assessment than the internal customer 2x2, but it carries its own implication: the human work that matters most on the supplier side is relational infrastructure.

And relational infrastructure takes time to build, is easy to destroy, and impossible to automate.

Four Things The 2x2 Doesn’t Show You

First, decision making cannot be a cold process.

We are humans after all, and hence have to make reasonably human decisions. And so there is a rational case to be made that, in many instances, human involvement has economic value precisely because people aren’t rational about it. The empathy research discussed in the last post shows us this: the “human empathy premium” is an economic fact, not a sentimental plea.

Second, none of the above should be seen as an argument to reduce the practitioner to becoming a passive recipient of stakeholder judgment.

“What stays human is what your stakeholders demand to be human” can be seen as disempowering but that is not at all the point. The practitioner has the opportunity to shape stakeholder expectations rather than merely respond to them, because the best procurement professionals don’t just answer what stakeholders ask for, they influence what stakeholders think they need.

This means orchestration and judgment and creativity and outcome orientation beyond the traditional cost savings rubrics.

Third, what happens when the two stakeholder groups - internal customers and suppliers - disagree?

A business unit might be perfectly happy receiving an AI-generated market analysis and never speaking to a procurement person. But the supplier on the other end of that same category might need human engagement for the relationship to function e.g. contract renegotiations, performance conversations, innovation discussions, etc.

The reverse is also possible: a supplier might be fine dealing with automated PO systems while the internal stakeholder insists on a human procurement partner for strategic advice.

These situations will occur, and it’s worth acknowledging that the two frames can produce conflicting signals and that navigating that conflict is itself irreducibly human work.

Finally, we have to note the temporal factor. That is, things do and will change.

Whatever our analysis shows today (especially about AI’s current capability thresholds), its worth noting that that is the current view. It’s incumbent on us to keep reading the signals as the window moves.

The Real Point

The point of this framework is not to identify what’s ‘irreducibly’ human, because that line will keep moving as AI improves. The point is to identify what’s economically and organizationally irrational to hand to a machine, even if you technically could.

So it’s worth asking two questions: Does someone need to own this? Am I making this better?

If the answer to either is “yes”, the work stays human. If the answers to both are “no”, it doesn’t, regardless of tradition, comfort, or sentiment.

And if you’re sitting in a quadrant where accountability isn’t required and your value premium is the only thing keeping you relevant, you need to see that as a signal, not a safety net.

Because your premium has a shelf life. The question is what you’re doing to extend it.

In my last post, I introduced The Human Edge matrix, a framework for classifying procurement work in the AI age - what should be automated, what should be augmented, and what should remain human.

(I also included an interactive tool to apply the framework to your own role to assess how much of it should be done by AI as well as which aspects should remain human.)

The response was encouraging, but one question keeps surfacing in conversations with practitioners and leaders:

“Sure, but will any of this still be human in five years?”

It’s a fair question. And the honest answer is: probably not all of it.

The boundary between human and machine work is not a fixed line; it’s moving - but only in one direction. That doesn’t mean everything eventually will go to the machines. Some work will remain human for a long time, perhaps permanently. The question is: what work is that and who makes that call?

Here’s what I’ve come to believe: it’s not procurement’s decision to make.

In this and next week’s post, I’ll get into why this is as well as how to assess what Procurement work stays human and what goes the way of the machine.

The False Binary

Is there any work that is irreducibly human?

I’ve been thinking about this question a lot lately, as have many others. If you read the popular press, you can’t help but be pulled in two completely different directions.

At one end of the spectrum are the Dismissers, those who believe that AI is overblown and there is, and always will be, plenty of work that is and should remain human.

Some of this is based on beliefs that are grounded in sentimentality and emotion, but many dismissers have valid reasons to feel this way. AI hallucinates, it makes mistakes, it can’t do the simplest things (for humans) like read the room. It also lacks real world context, doesn’t understand people in the full human sense, or make thoughtful trade-offs based on variables not codified in the data.

But it’s also worth remembering that AI is still in its infancy, so we should expect the technology to continue to improve. It has already improved considerably in the last few years, and as this improvement continues, we should expect its capabilities viz-a-viz work traditionally done by humans to expand. So it’s entirely plausible that what is human today may not remain human in future.

That’s not to say all of it will go the way of the machine.

At the other end of the spectrum we have the Utopians, the believers who argue that AGI will be upon us soon enough, and that we will have amongst us a superintelligence that will be able to do everything a human can do and more. There will be little or nothing that won’t be done by machines, allowing us all more time to do all we ever aspired to do.

I get where they’re coming from as well - capability gains are arriving faster than ever, the machines are becoming multi-modal and agentic, and there are a ton of incentives, suggesting that human-like general intelligence may not be far off.

But today’s systems still confuse fluent performance with genuine understanding, and I believe that we don’t even know what we don’t know in terms of our brain’s architectures. As such, I personally don’t believe AGI is realistic or achievable, at least not in its full utopian form, any time soon.

What AI Can Fake…And What It Can’t

That said, there is some research that suggests AI has qualities that border on the “human” - and even improve on them in some ways.

For example, there is some evidence that AI can model ‘empathy’, though not without caveats. Specifically, research over the last couple of years finds that:

• AI is clearly getting good at empathy-shaped language - that is, for many text-based tasks, LLMs can produce responses that people rate as highly supportive, compassionate, and emotionally intelligent, even outperforming humans in benchmarked test.

• But this is mostly evidence of simulation, not sentience - they show patterns of empathy (cognitive empathy and emotionally appropriate language), with no evidence of real empathy (concern, emotion, consciousness, etc.)

• In addition, human authenticity still matters - even where AI responses are rated as excellent, people value empathy more when they believe it comes from a human.

• Labeling effects matter a lot - there is a ‘human-label’ premium; AI could outperform in building closeness when labeled as human but explicit AI-labeling reduced closeness.

(For the specific sources, look here, here, here, here, here, here, here and here.)

It’s Not About Procurement…

We can draw some interesting insights from this research.

Broadly speaking, people accept AI for analysis and understanding, but resist it for emotional sharing and genuine care. As good as AI might be, the value of human involvement in certain work is not about capability but about perceived legitimacy and trust (that is, perceived intention, shared vulnerability, and the belief that another mind is genuinely with you).

In other words, what people value isn’t capability but perceived human investment, which means that what stays human is going to be determined by what stakeholders value, not by what machines can or can’t do.

This leads to an uncomfortable conclusion for procurement professionals: the question of what stays human isn’t entirely for Procurement to answer. It belongs to the people it serves and the markets it manages; their willingness to trust, to accept, to engage, is what draws the line between human and machine work. Which means if we want to understand what remains human, we need to stop looking inward and start looking outward.

…It’s About The Stakeholders

Of course, there are multiple stakeholders, including the CFO, the board, regulators, internal customers, and suppliers. While the CFO and the board shape what Procurement is measured on, theones that shape how the work gets done are the two constituencies “external to the function”:

• The “Internal Customer” (Who Procurement Serves):

  • What do the internal customers that procurement serves (the business units, support functions such as marketing or HR or legal etc.) think about the work that procurement does?

  • What are the things that they absolutely want a human to do?

  • In which situations do they absolutely want a human to interact with?

  • What will they NOT trust from a machine output?

  • What do stakeholders actually pay for (in attention, trust, political capital) when they engage procurement?

• The “Supply Market” (Who Procurement Manages):

  • Given that Procurement represents the organization to and manages suppliers (and supply markets at large), how do they perceive the work that the function does?

  • In which instances do they require a human presence for reasons that go beyond capability?

Outcomes, Not Tasks

So what kind of Procurement work do these stakeholders value?

There are two ways to look at this - at the role/task/subtask level (the actual work Procurement does) or at the outcome level (the key outcomes expected by each stakeholder group)

The task/subtask levels is where most practitioners normally look to answer the question of what remains human. But there’s a problem with this approach.

Procurement’s stakeholders don’t care about the practitioner’s task list. A marketing director who needs a creative agency contracted doesn’t think about whether the procurement person ran a three-bid process or used an AI-assisted evaluation tool. They care only about whether they got a capable agency, on reasonable terms, without it taking months to complete. Therefore, the practitioner’s role/tasks is too insular a view to take. It doesn’t take into account the perspectives of those the function serves.

These outcomes, on the other hand, represent what Procurement’s prime stakeholders - those it serves and those it manages - actually care about. Not policies, not processes, not tools, but outcomes.

As such, outcomes are, then, the most appropriate basis upon which to conduct this analysis.

Seven Outcomes That Define Procurement’s Value

So, what are those outcomes?

Broadly speaking, there are seven broad outcomes worth considering:

1. Speed and Responsiveness of the Procurement Process

   1. When a business unit needs something purchased, how quickly and painlessly can they get it? The point here isn’t just speed but overall efficiency and effectiveness i.e. is the function seen to be an enabler or a bottleneck? Think elapsed time from request to delivery, in how many times they had to chase someone, and whether the process felt proportionate to what they were buying.

   2. A three-week sourcing exercise for a $5,000 software license is a failure of responsiveness regardless of how well the process was executed.

2. Achieving Optimal Total Cost of Ownership

   1. This goes beyond the purchase price to encompass the full economic cost of a buying decision, including implementation, maintenance, switching costs, quality failures, etc. From a stakeholder standpoint, this manifests itself when the cheapest option ends up costing more in rework, downtime, or internal frustration.

   2. The valued outcome here is not a 12% reduction of the unit price, but that the money spent yielded the highest utilization and produced the best possible return over the life of the relationship.

3. Maintaining Supply Resilience and Mitigating Risk

   1. This is a ‘sleeper’ outcome i.e. you only notice it when there is a disruption. Can the business count on having what it needs, when it needs it, without disruption?

   2. Is the category’s distinct risk profile (supplier dependence, geopolitical exposure, commodity volatility, technology obsolescence, regulatory change, cyber threats, etc.) understood and being well managed on their behalf?

   3. The valued outcome here is supply continuity that encompasses not just reliable delivery but supply base resilience itself. The goal is also not to eliminate risk but to ensure risks are identified, quantified if possible, and either mitigated or consciously accepted with the right people informed.

4. Ensuring Compliance and Ethical Assurance

   1. Stakeholders need to know that what they’re buying, and who they’re buying it from, won’t expose the organization to legal, regulatory, or reputational harm. This covers a host of factors, including sanctions screening, labor and environmental standards, anti-bribery obligations, data privacy requirements, and industry-specific regulations.

   2. The outcome isn’t just that the organization passed an audit, but that the business can operate confidently knowing procurement has built guardrails that protect them from risks they may not even be aware of.

5. Ensuring Optimal Supplier Relationships

   1. Suppliers are not interchangeable inputs; they each have their own capabilities, knowledge bases and priorities, and the quality of the relationship directly affects what the business gets from them.

   2. This outcome is about not just performance to agreed standards and remediating problems when they arise, but also whether the relationship is managed in a way that earns the organization preferential treatment (better allocation, priority access to new tech, faster response times, access to senior attention, etc.).

6. Driving Supplier-Enabled Innovation

   1. Some of the organization’s most valuable innovation doesn’t come from internal R&D but from suppliers who bring new materials, processes, technologies, or ideas that accelerate product development and time to market, improve quality, or open new market possibilities.

   2. This outcome is about whether procurement is positioned to unlock that value: identifying innovation opportunities, creating the commercial structures that incentivize suppliers to share their best thinking, connecting the right suppliers with the right internal teams, and ensuring the organization is seen by the supply market as a customer worth innovating for.

7. Crisis Management

   1. When something breaks e.g. a key supplier goes bankrupt, a pandemic disrupts global logistics, a geopolitical event closes a trade corridor, a quality failure triggers a recall, the organization needs procurement to respond with speed, judgment, and authority. This is episodic work that demands real-time decision-making in challenging circumstances.

   2. The outcome is that the organization survives the crisis with its financials, operations, relationships, and reputation as intact as possible - which calls for coordination across functions, direct engagement with suppliers and stakeholders, and the willingness to make consequential calls without the luxury of full analysis.

The Harder Question

The question that follows, then, is the harder one: for each of these outcomes, what must remain human, what can be augmented, and what should be fully automated?

That’s not a question you can answer in the abstract. It depends on who’s asking, what they’re willing to trust, and whether Procurement’s involvement makes the outcome measurably better.

In Part 2, I’ll introduce a framework for making that determination - one that’s grounded not in what AI can or can’t do today, but in what Procurement’s stakeholders will and won’t accept, and why that distinction matters more than capability ever will.

By now, you’ve probably seen the spider chart from Anthropic (below) that plots AI’s theoretical capability against observed AI coverage by occupational category. The blue area represents the share of job tasks that LLMs could theoretically perform; the red area shows the share actually being performed by AI, based on real world usage data from Claude.

The thing about this chart that had LinkedIn and other social platforms in a tizzy was not so much the red areas but the fact that the greatest theoretical exposure was in those occupations that perhaps even 5-10 years ago, we wouldn’t have thought susceptible to “automation” (I’m using that term in its broadest sense).

But that’s one of the prime implications of AI today.

Where AI Lands First

AI usually lands first on work that is repeatable, rules-driven, text/data-heavy, and separable from messy real-world context - work that just happens to be a lot of what the average LinkedIn user does: management, finance, IT, office administration, etc. (Yes, I appreciate there’s more to it than that, but that sort of work does form the basis of these functions.)

And as AI tools continue to improve (which they will), we can expect the gap between the blue and red dots to close.

None of this should come as any real surprise. There’s plenty of research and anecdotal evidence that this will be the case:

• The ILO’s 2025 exposure index assessed task-level exposure across nearly 30,000 occupational tasks and found that one in four jobs globally is exposed to GenAI to some degree, with clerical support roles still the most exposed. They highlighted that “some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles”.

• KPMG cites that its own analysis indicates “50-80% of current procurement work can be automated, eliminated or shifted to self-service models”.

• More conservatively, McKinsey says that its analysis suggests that “technology will reshape the procurement function into an organization that is 25 to 40 percent more efficient, more agile, and increasingly agentic”.

Whatever numbers you choose to believe, it’s indisputable that AI, in one form or another, is going to change the way work is done.

For Procurement, the implications are obvious.

The earliest procurement impact is showing up in transactional and process-heavy cognitive work such as intake, data cleanup, PO support, contract reviews, supplier communications, first-pass analyses, and workflow orchestration.

But that’s just the start of it. AI is already coming for more Procurement work - including the analytical and decision support work that we previously thought would remain the domain of humans. We’re already seeing AI assist heavily with market intelligence synthesis, option generation, scenario modeling, contractual ‘red-flag’ detection, draft strategies, and negotiation preparation (though humans still own prioritization, trade-off selection, timing, and commitment).

The point is that machines are only going to get better - so the list of what AI can do will only keep expanding.

The Limits of a Task-Based View

The most fundamental takeaway for the practitioner, then, is that your role is going to change. There are (many) aspects of your role that AI will be able to do faster, cheaper and, yes, better (and not only that but it’s going to be able to do it 24/7).

But how exactly will your role be impacted?

There are plenty of institutions that have looked at specific Procurement roles and assessed the impact of AI on those jobs. Typically, they’ve taken a specific role, broken it down into its constituent tasks, and then assessed how susceptible each task is to AI.

In my view, this is useful but not enough.

Most procurement roles don’t fall into clean, well-defined sets of tasks. Practically, there are real-life complexities that force each role to morph in one way or another. These complexities can be external to the role (budget pressures, organizational or managerial demands, etc.) or specific to the individual (personal goals, expectations and desires).

As such, while these task-based analyses are helpful, the better question to ask is: how can we think differently about roles and really get to the root of what makes them human? This will allow individuals to determine for themselves why and how your particular role will be impacted by AI.

In this post, I’ll present one way to think about this: The Human Edge Matrix©.

A Better Way to Assess What Remains Human

The Human Edge Matrix provides us with a diagnostic structure, one that speaks to the nature of a given task or role and whether or not it will remain ‘human’ in the long term.

Specifically, there are two categories of analysis to consider with this matrix - the tiers of impact as well as the determining factors.

1. The Three Layers of Procurement Work

The first thing to understand is that this isn’t an “either/or” discussion. Every role won’t be either automated away or remain fully human. Work will split into three layers: Machine-Executable, Augmented and Human.

The three tier approach gives us a more realistic way to think about Procurement work.

It’s also worth noting that the assessment of what work falls within which tier is always going to be a point-in-time assessment. That is, while the 3 tiers hold, the work that falls under each tier is not static. As AI capabilities evolve, work currently in Tier 3 may migrate to Tier 2, and Tier 2 work may become Tier 1. It makes sense, therefore, to revisit any classifications periodically.

2. The Factors That Make Work More or Less Human

Within any role or set of tasks, a host of factors will determine where any given procurement activity falls in terms of the three tiers - eight to be precise.

Each of the factors operate as a spectrum, and it is the combination of factors, not just any single one, that determines classification.

These eight factors are as follows:

Factor 1: Codifiability

Can the decision logic, workflow, and success criteria be explicitly defined and systematized?

This encompasses both the structural clarity of the process (are there defined steps?) and the degree of precedent (has this been done many times before in similar ways?).

Highly codifiable work has clear inputs, known decision rules, and measurable outputs.

Example: Tail-spend PO processing against pre-approved catalogs is highly codifiable. Developing a category strategy for a new market with no prior supplier relationships is not.

Factor 2: Ambiguity

How much of the relevant context is tacit, situational, or absent from the available data? How rapidly is the relevant context shifting?

Ambiguity can be high for structural and dynamic reasons.

Structural ambiguity is high when the “right answer” depends on information that exists in people’s heads, in organizational culture, or in the dynamics of a specific moment. Hence, the the relevant context is tacit, relational, or simply not captured in available data.

Dynamic ambiguity is where the environment is changing so rapidly that the context for the decision is shifting faster than models or processes can incorporate it.

Example: A supplier’s public financials look strong, but the category manager has heard through industry contacts that the founder is planning to exit - tacit knowledge that could fundamentally change the sourcing decision. Allocation during supply crises, pricing shifts during geopolitical disruption or sudden regulatory changes create dynamic ambiguity (not because information is absent but because it’s changing in real time).

Factor 3: Judgment Complexity

Does the decision require weighing incommensurable trade-offs, interpreting incomplete signals, or making calls where reasonable people would disagree?

Note that this is distinct from ambiguity: a situation can be perfectly clear and still require sophisticated judgment. The question is whether the decision involves genuine dilemmas rather than optimization problems.

Example: Choosing between a lower-cost supplier with a questionable sustainability record and a more expensive supplier aligned with corporate ESG commitments. Both options are well-understood, but the judgment lies in how to weigh competing priorities.

Factor 4: Creativity

Is the work about optimizing within known parameters, or does it require imagining genuinely new approaches?

Optimization is AI’s strength. Genuine invention - new commercial models, unconventional partnerships, category strategies that redefine the problem - remains a human edge. The distinction is between finding the best answer within a known solution space versus redefining the solution space itself.

Example: Optimizing payment terms across a supplier portfolio is an optimization problem. Reimagining the procurement operating model to shift from transactional buying to outcome-based partnerships requires a creative rethink.

Factor 5: Stakeholder Complexity

How many stakeholders are involved, how conflicting are their interests, and how much does success depend on navigating those dynamics?

This encompasses both internal stakeholder management (business units, leadership, legal, finance) and external relationship management (suppliers, intermediaries, regulators). The underlying skills required - reading interests, building alignment, managing conflict - are the same.

Example: A routine MRO renewal involves one budget holder and one supplier. A strategic outsourcing decision involves C-suite sponsors, multiple business unit leaders with competing priorities, legal, HR, affected employees, incumbent suppliers, and potential new partners.

Factor 6: Political and Organizational Sensitivity

Is the work visible to senior leadership, does it touch on organizational power dynamics, or could it create reputational exposure?

Political sensitivity isn’t about the technical difficulty of the work but rather the organizational consequences of how the work/decision will be perceived. Identical analytical tasks carry different political weight depending on who is watching and what is at stake.

Example: Running a competitive tender for the CEO’s preferred consulting firm requires navigating political dynamics that have nothing to do with the mechanics of the RFP process itself.

Factor 7: Ethical and Values-Based Reasoning

Does the decision involve genuine ethical dimensions that require moral reasoning and alignment with organizational values?

This is less about compliance (which can be codified) and more about whether the organization’s identity and reputation are at stake. AI can flag such ethical risks, but the weighing of ethical trade-offs is fundamentally human.

Example: Deciding whether to continue sourcing from a region where labour practices are legal under local law but violate the company’s stated human rights commitments. No algorithm can resolve this as it requires a values-based judgment that the organization must own.

Factor 8: Decision Risk, Reversibility and Ownership

What is the magnitude of downside if the decision is wrong and can it be undone, and does the organization (or external stakeholder) require a human owner to stand behind it?

AI can handle high-volume decisions even if some are wrong, provided the errors are low-cost and correctable. Irreversible, high-stakes decisions demand human ownership. In addition, some decisions are auditable, require relationship legitimacy, and/or require an accountable human sponsor (even if AI did 80 percent of the work).

Example: Automatically reordering office supplies based on consumption patterns is low-risk and easily reversed. Signing a five-year sole-source contract for a critical component is high-risk and essentially irreversible. In other instances, a human will still be required to defend a decision to leadership, legal, the business, and/or a regulator.

How to Apply the Framework

Taking the three tiers and the eight determining factors together, the following matrix can be used as a diagnostic. For any procurement activity, assess where it falls on each factor. The majority of evidence will indicate its specific tier.

To apply The Human Edge Matrix to your own role, click the button below to access the interactive tool:

Interactive Tool

You’ll need to enter your name and email (you’ll be subscribed to my site) and then you can complete this assessment for your role at an overall level or by sub-task. Note that none of the information you input (other than your name and email) will be retained in any way. This is simply for your personal assessment.

(I’d love to get your feedback on the tool itself and whether you agree with its findings.)

What This Looks Like in Practice

The following examples show how specific procurement activities map against the framework. The tier assignment reflects the overall weight of evidence across all eight factors.

Tier 1 Examples: Machine-Executable

• Catalogue-based PO creation and approval routing for pre-negotiated items

• Invoice matching and exception flagging against contract terms

• Supplier onboarding document collection and compliance verification

• Automated spot-buy execution within pre-set parameters

Tier 2 Examples: Augmented

• Spend analytics and category spend classification

• Market intelligence synthesis for category strategy input

• RFP development and supplier response evaluation (AI drafts, human refines and decides)

• Contract redlining and risk identification (AI flags, human negotiates)

• Negotiation preparation: BATNA, scenario modeling, and playbook generation

• Supplier performance monitoring and scorecard generation with recommended actions

Tier 3 Examples: Human

• Category strategy development for volatile or strategically critical categories

• Cross-functional alignment on make-vs-buy, insource-vs-outsource decisions

• High-stakes, complex negotiations (multi-year, multi-party, novel deal structures)

• Strategic supplier relationship management and joint value creation

• Ethical sourcing decisions involving values trade-offs and reputational risk

Caveat: It’s worth noting that some procurement work will stop being human-executed before it stops being human-owned. That is, leaders may decide that there may well be work that remains human-supervised (even if AI can do it) because the task shapes learning and judgement.

From Task Taxonomy to Role Redesign

The goal of this framework is to provide a deeper way to think about AI’s impact on current roles, both overall as well as at the task level. It serves multiple audiences:

• CPOs and Procurement leaders: Use the matrix to audit your function’s activity portfolio. Identify which Tier 1 activities are still being done manually (automation opportunity), which Tier 2 activities lack AI tooling (augmentation opportunity), and which Tier 3 activities are being underinvested in because the team is trapped in lower-tier work.

• Procurement practitioners: Use the Tier-Factor matrix to assess your own role’s exposure to AI. The goal is to deliberately build capabilities in those areas that keep humans essential - judgment, stakeholder navigation, creative strategy, ethical reasoning, etc.

• For Procuretech leaders: Use the tier definitions to set realistic expectations for AI deployment. Tier 1 is ripe for full automation today. Tier 2 requires thoughtful human-machine workflow design. Tier 3 requires AI to serve as decision support, not decision maker.

One last point: What should emerge from this analysis is not just whether a role is at risk or to what extent - very few roles, if any, are going to survive intact in a Post-AI world.

What should emerge is a clearer indication of how to future-proof the practitioner for a post-AI world.

In addition, when you subtract tasks that will be automated - and even accounting for augmented tasks - what is left will almost certainly need to be rethought. The very nature of roles will need to be changed and, likely, rebundled across the function.

As such, Procurement roles will need to be redesigned around orchestration, exception management, business judgment, stakeholder alignment, supplier strategy, risk governance, and decision accountability, among other considerations. This will force us to move from a task taxonomy to a role redesign model. I’ll cover this topic more deeply in future posts.

In my last post, I flagged the idea of cognitive debt, which is the hidden cost we incur when a tool helps us complete a task without making us do enough of the thinking to truly understand, evaluate, or reproduce the outcome ourselves.

I also discussed how, if this debt goes unpaid, it leads to a loss of cognitive agency, which is the ability to understand the reasoning behind an outcome, assess whether it is sound, adapt it when needed, and take real ownership of the judgment (and output) involved.

This loss is a real problem, particularly in a world where we must grapple with information (and cognitive) overload, an attention economy, as well as performance metrics (explicit and implicit) that reward volume and activity over quality and individual development. The proliferation of AI tools is only exacerbating this problem, taking it to another level entirely. As I mentioned in my prior post, it’s the difference between creating sounds and developing musicianship.

And yet these tools aren’t going anywhere. Stand alone or embedded within more traditional SaaS tools, AI is becoming ubiquitous and will soon reach the point where it is no longer a feature but an expectation.

Used thoughtfully, they will dramatically enhance both our efficiency as well as our effectiveness - but only if we take the initiative. The onus remains on us to use them thoughtfully, not only for the betterment of our workplaces but for ourselves as well.

In this post, then, I’ll outline a practical framework for using AI in ways that preserve cognitive agency rather than erode it.

The Cognitive Agency Framework

At the heart of this framework is a simple principle:

Use AI to reduce mechanical effort, not to replace formative judgment.

But while this idea is simple in concept, it’s much more involved in execution. There is no point solution when it comes to the problem of minimizing cognitive debt and retaining our cognitive agency.

Our approach has to be multi-faceted - from how we lead to the parameters we set for it to what we individually must do. This is not a tool problem as much as it is an operating model problem.

And this operating model - The Cognitive Agency Framework, as I call it - has three layers:

1. Leadership (conditions)

2. Guardrails (workflow rules)

3. Individual Practices (specific habits)

Let’s look at each of these in turn.

Layer 1: Leadership Sets the Conditions

Everything starts with leadership, which must set the stage for their teams at the outset. The central question they need to address is:

“How do we use AI to capture efficiency gains without removing the cognitive reps that build and preserve judgment?”

There are four specific actions they should take:

1. Establish the Vision:

   Clarify the kind of practitioner the organization will value going forward. That is, enabled by AI tools, practitioners must:

   • Move from task completion to outcome ownership, where judgement and defensibility will become the differentiators

   • Remain focused on driving effectiveness as much as efficiency, which prioritizes achieving results for (internal) customers and moving the needle on their metrics.

2. Align incentives:

   If leaders only rewards efficiency metrics such as faster turnaround, more output or shorter cycle times, then they will end up (accidentally) training people to maximize AI throughput, not human discernment.

   Instead, they should balance these metrics (which will still be important) with more ‘human’ metrics that measure and reward on the basis of the progressive vision laid out in point 1. Specifically:

   • Measure for impact, not simply process compliance

   • Recognize innovation and progressive thinking in annual evaluation cycles

   • Reward reasoning quality and assumption strength, including risk awareness, contextual judgment, defensibility and trade-off understanding

   • Reward practical, sustainable AI deployment e.g. embedding AI into workflows

3. Build the Training Loop:

   Early, heavy AI reliance can weaken independent reasoning, ownership, and recall so it’s imperative to ensure that team members put in the mental reps needed to build their cognitive scaffolding.

   In other words, the key is to ensure that “judgment reps” are baked into the work itself.

   So set the expectation that each team member (in particular, junior team members) must have practical experience in “doing the work” (for specific types of work - see the next section on Guardrails) and not let AI remove ‘first-principle’ practices.

   This means, therefore, deliberately creating “unassisted reps” - for example, when it comes to issue framing or contract risk spotting, and even recommendation writing.

   For junior team members, this could be having them alternate between a solo first pass, an AI-assisted revision and then a (senior) human review.

4. Broadcast Learning:

   Every team will have individuals who are leading the charge when it comes to innovation and AI deployment. It’s important to identify and encourage these early ‘champions’.

   But long-term value for the function will only come from scale, and that starts with strong communications and information sharing. The more people find out about the potential, the early wins/realized value and the personal upside, the higher the chances of success.

   It’s important, then, to communicate broadly, which means:

   • Hold regular town halls to share AI wins, relevant use cases and key lessons learned

   • Invite demos by Builders to show the potential and value of current and emerging tools

   • Host hackathons on critical functional issues

   • Provide peer recognition, including symbolic as well as cash prizes

Layer 2: Guardrails Shape the Workflow

With the leadership posture defined, it’s important to set appropriate guardrails that help ensure intent turns into behavior.

This requires defining the specific rules that need to be put in place to ensure team members work with AI tools in the best, most optimal way. Five key guardrails should be stipulated:

1. Classify Tasks:

   In terms of allowing AI to run autonomously, what kind of work is OK versus what is not OK?

   Not all tasks should be treated the same; some need little to no attention while others need a human to keep driving it. As such, separating autonomous versus assistive versus formative work is an essential first step.

   Some tasks - such as formatting, information organization, summarizing, first-pass drafts, etc., are good candidates for heavier AI use.

   Others are formative in nature because they build or preserve judgment. Think:

   • Framing the problem

   • Identifying what’s missing in an analysis

   • Making trade-offs in difficult situations

   • Deciding what matters in moments of ambiguity

   • Defending recommendations

   These are tasks where humans need to stay in the loop. (No one is going to ask the algorithm for the justification of the decision. They’ll ask the human who put it forward.)

   As a practical rule, then, create a team-level list of:

   • Tasks where AI can lead

   • Tasks where humans must lead

   • Tasks where AI can support but not substitute

   Document this in a one-pager by workflow, potentially in an “AI RACI” format. This document should be reviewed at regular intervals (quarterly or semi-annual at minimum) as models and tools evolve.

2. Ensure No AI-First for Judgement Tasks:

   For specific tasks that require human judgement and involvement, stipulate that a human must always do the first pass. AI cannot be allowed to initiate the ‘thinking’.

   Embed the idea that people must think before they prompt, fleshing out their ideas, the core problems, constraints, etc. This brief doesn’t need to be a thesis, it can be relatively brief. But the discipline must be demanded; there needs to be a reasonable level of ideation and thought provided by the human as the first step.

   Post that first step, AI can then be utilized as the next step of assessment and input.

3. Use AI as Challenger:

   Never use the AI as a substitute or as the sole author of the output. Instead, treat is as a sparring partner. Reiterate that AI should be used to:

   • Expand options

   • Stress-test thinking

   • Surface blind spots

   • Improve articulation

4. Embed “Explain-Back”:

   Let team members know that they will be expected to explain the bases and implications of their analyses. Let them know that they will be asked whether they are comfortable owning the outcomes of their assessment and why.

   • What is the decision?

   • What trade-off is being made?

   • What would make this wrong?

   • What information is missing?

   • What is their level of confidence and why?

   If someone cannot defend their AI-assisted output, they cannot own it.

5. Insert Friction Gates:

   Well-placed friction preserves thinking quality.

   For high stakes categories and key process points that matter, clarify that team members will be asked to present key findings and rationalize their thinking.

   These key insertion points should be defined, and could encompass:

   • Award decisions over $X

   • Contract deviations that shift liability/indemnity/termination

   • Negotiation postures and walk-away thresholds with strategic suppliers

   • Risk acceptance (cyber, continuity, regulatory)

   • External communications that could create reputational exposure

   For particularly critical decisions, require red-teams be involved to challenge the analysis (e.g. “make the case against this” and ask the team to defend their point of view).

   The key here is simple: to raise the “cost of cognitive offloading” - when this cost rises, people offload less, retain and own more.

Layer 3: Individuals Build the Habits

Of course, the rubber meets the road with the individual. While the guardrails define the rules, individual practices determine whether a team member actually builds judgement.

As such, each individual’s goal must be to master ‘Attention Sovereignty’, that is, to actively direct attention rather than surrendering it to the algorithm. Attention sovereignty is the precondition for everything below.

Four key habits are essential here:

1. Develop Your Point of View First:

   One of the biggest protections against cognitive debt is requiring human pre-processing before AI enters the picture. So ask people to think before they prompt.

   For example, before using AI, require the user to first write:

   • the problem statement

   • the desired outcome

   • the likely risks

   • their own first-pass recommendation

   Even a brief set of notes or hypothesis or key framing questions and ideas helps. Start with a human frame first.

2. Use AI as Sparring Partner:

   With the human frame fleshed out (even at a high level), use AI as a challenger, expander, and/or editor.

   The safest pattern to deploy is not “do it for me” but to iterate with you. Ask it to work with you as a consultant or analyst. Ask it to:

   • Challenge your assumptions

   • Identify three risks you may be missing

   • Critique your recommendations

   • Provide you with alternative considerations

   • Test your logic for holes

   • Help you compare different scenarios

   At the same time, thoughtfully evaluate what it gives you back, and test its thinking to ensure that what it’s telling you is something you agree with. Ask it questions, pressure-test key statements and ideas, push back where you feel push-back is needed. Take nothing for granted.

   This preserves human ownership of the core judgment while still harvesting the benefits of the tool’s speed and breadth.

3. Build explain-back discipline by using a checklist

   This is a simple safeguard but also a powerful one. For every key analysis that leverages AI heavily, be able to explain:

   • What the recommendation is

   • Why it makes sense

   • What assumptions it depends on

   • What the risks are and where could it fail

   • What you changed from the AI output

   The practical rule to follow is:

   “I will not submit an AI-assisted recommendation until I can fully defend it my own words.”

4. Engage in Regular Self-Critiques:

   Regularly reflect on your thought processes, biases, and how you learn. Consider how you can continue to push your thinking and ownership of your work and analyses.

   Make a note of the various tools you utilize and their relative strengths and drawbacks. Establish personal “red-flag” triggers about when to use AI and when not to, based on individual usage.

   Adopt a two-source rule for high-stakes facts. Treat AI as a draft, not a source. Be selective about facts provided by AI tools and verify critical claims against primary documents or an independent reference.

   Reconstruct from memory. After using AI, restate the reasoning without looking. If you can’t explain it cleanly, you’ve borrowed output without building understanding.

The Procurement Cheat Sheet For Leaders

The three layer Cognitive Agency Framework is a useful way to think about retaining cognitive agency, both as a leader and an individual practitioner. But it does require a good measure of work to ensure it’s implemented effectively in any organization.

While in my view, the realized value is worth that work, I appreciate that ‘speed to action’ is more important than ‘perfect implementation’. To that end, if you do nothing else then, at minimum, implement the following four rules of thumb:

Rule 1: Define “When AI Should Be Avoided”

Some contexts are fragile and the downside of a subtle error can be asymmetric, in that small mistakes can create outsized legal, financial and/or reputational consequences.

As such, heavy AI reliance is best avoided in specific defined contexts. These could include:

• Sensitive negotiations

• Legal commitments without counsel review

• Reputational risk communications

• Compliance/regulatory issues

• Situations requiring confidential data handling policies

Rule 2: Preserve Human-First Reps in Judgment-Heavy Workflows

Make clear that Humans must think first and foremost when it comes to Judgement-heavy workflows, which you should spend a little bit of time thinking through. These workflows could include:

• Supplier selection/award situations

• Risk acceptance decisions

• Negotiation strategy and walk-away scenarios

• Contract deviations and redlines that shift risk

• Stakeholder trade-offs and prioritizations

• Financial/business case assumptions

Rule 3: Let AI Challenge and Draft, Not Decide

Have your team develop the first draft of any analysis or output. AI can then assist by:

• Summarizing bids

• Drafting supplier emails

• Structuring comparison tables

• Synthesizing documents

That said, the human should still own:

• Supplier selection logic

• Trade-off decisions

• Stakeholder balancing

• Risk acceptance

• Negotiation posture

Rule 4: Require Defense, Not Just Delivery

A recommendation must not be considered complete until the owner can explain the:

• Logic

• Risks

• Alternatives rejected

• Contextual factors

This ensures the organization is developing professionals, not simply ‘output assemblers’.

Closing Thought

AI is going to keep getting better. The real question is whether we (as leaders and as individual practitioners) will get better with it.

If leaders reward throughput, teams will optimize for throughput. If workflows don’t require defendable reasoning, people will stop building it.

Cognitive agency won’t survive on its own or by some accident. It will survive by design: when our systems make thinking both mandatory and unavoidable.

It’s incumbent then on leaders to set the conditions and establish the guardrails that shape the workflow, and for individuals to practice those habits that keep their judgment sharp.

This is the aim of the Cognitive Agency Framework. Use AI to remove mechanical effort, but protect the cognitive reps that build discernment. Otherwise, we’ll ship polished outputs but, over time, lose the capability underneath them.

The ultimate goal is simple: capture speed without surrendering musicianship.

AI is an incredible technology - one that’s taken the promise of classic SaaS and amplified it exponentially.

We now have tools that can help us produce better-looking work, faster, and with less friction than ever before. That’s a real and massive gain, but it’s one that comes with a hidden risk: these very same tools make it easier to skip the mental work we need to be doing to truly own our work. Or, to be more precise:

What happens when the tools that improve our outputs also reduce the amount of formative and evaluative thinking we do to truly understand, evaluate, and own them?

Creating Sounds vs. Developing Musicianship

Let me explain this with an analogy from the music world.

A couple of decades ago, if you wanted to record an album, you had to learn to play an instrument, put together a band, hire out a studio, bring in a producer and an engineer, record multiple takes and then piece together and master the final output.

Today, you can bypass much of that. Modern technology has made it easier than ever to create something that sounds polished. Synths, virtual instruments, multitrack recording, editing software, and now a host of AI tools that do all of the above with just a prompt, help generate impressive results in a fraction of the time.

And yet, there remains a difference between creating sounds and developing musicianship.

Musicianship means developing a sense of taste, timing and feel. It means having an ear for what works, understanding when to elevate tension and when to release. It means developing an understanding of which elements work together and why.

Developing musicianship requires doing the work. But creating sounds? Today, anyone can produce something that sounds good without developing the underlying fluency needed to create, diagnose, adapt and finalize with intention.

This same distinction is playing out in other spheres as well, including Procurement knowledge work. And that presents us with a challenge.

Cognitive Debt and the Erosion of Agency

In Procurement, we also have access to a host of incredibly powerful AI tools, but their deployment is all across the map.

I don’t just mean their usage but the intentionality of that usage. Some are using them well, others less so (often because these tools sound so authoritative and confident that users mistake their polished language for sound judgement).

This undisciplined and unintentional deployment in our knowledge work comes with a cost. It creates a cumulative risk: repeated cognitive outsourcing creates Cognitive Debt which, over time, erodes our Cognitive Agency.

Let me explain both of these terms briefly.

Cognitive debt is the hidden cost we incur when a tool helps us complete a task without making us do enough of the thinking to truly understand, evaluate, or reproduce the outcome ourselves. It’s a real problem because the convenience of today’s tools can, if we’re not careful, result in a complacency of understanding. That is, convenience borrows against comprehension, with very real, very material trade-offs.

The result, if this debt continues to go unpaid, is a lack of cognitive agency, which is the ability to understand the reasoning behind an outcome, assess whether it is sound, adapt it when necessary, and take real ownership of the judgment involved and, hence, the output generated.

This ‘cognitive offloading’ has real and practical implications: these tools might improve immediate task performance, but they do so while also reducing retention and internal encoding (i.e. weakening the depth of neural processing required for learning and recall), especially when our goal is to ‘grow’ as much as it is to just ‘get the work done’.

Why This Matters in Procurement

Now, this might sound like a problem for just the new entrants into the function, but it’s really a problem for all practitioners, junior and senior alike.

For juniors, the risk is failing to build the foundation of what makes for true, high quality performance in the long run. No ‘cognitive scaffolding’ being built in the first place, no formation of those foundational thinking skills that are so essential to ‘good judgement’.

For experienced practitioners, the risk is a loss of sharpness, because over-reliance and over-delegation can lead to complacency and declining vigilance. Passive reliance is never a good thing.

(And for organizations as a whole, the risk is that we normalize all of the above, with long term detrimental impacts.)

This matters especially in Procurement because our ability to apply good judgement and make strong decisions that serve the corporate good, even as we grapple with a multitude of competing agendas and demands, is what defines our success. Our work isn’t just about producing outputs but generating better outcomes.

In that regard, AI can help us draft, summarize, analyze, and recommend but it cannot, by itself, deliver real procurement judgment. We still need discernment. We need the ability to read a stakeholder or interpret the nature and magnitude of a particular risk. We need to be able to make real-time, thoughtful, conscientious trade-offs. For example:

• Summarizing a contract is not the same as understanding its comparative risks in context of organizational realities

• Generating a sourcing recommendation is not the same as exercising true commercial discernment that incorporates the nuances of the situation

• Producing a negotiation script is not the same as reading leverage, timing, and context

The fact is that Procurement decisions are often made under ambiguity, across competing stakeholder incentives, with incomplete information and real commercial consequences. So our judgement matters.

And even putting aside the idea of ‘low quality outputs that play at being correct’ (due to hallucinations (still a concern) and errors that will surely diminish as the tech continues to improve), if we don’t own the work, then what is our value?

It’s also worth asking ourselves: If we’re no better than the machines, then why does the organization need us at all?

The Real Question

Look, none of this means cognitive offloading is inherently bad. In many contexts, it’s rational and valuable. The problem begins when we offload the very parts of the work that are building, testing, or preserving judgment.

The real question, then, is not whether we should use AI; that cat is out of the bag. It’s whether we will use it in ways that preserve the foundations and habits that judgment depends on.

Because the danger is not that these tools do our work for us, but that, used carelessly, they can leave us producing the appearance of strong work without building or sustaining the capability to truly own that work.

The challenge for the practitioner, therefore, is to learn how to create music without surrendering musicianship.

The Practical Challenge

That leaves us with a practical challenge: how do we use AI to capture the gains in speed and quality without outsourcing the very cognitive reps that build judgment in the first place?

The answer to that question is multi-faceted - from how we lead on this issue, to how we manage it, to how we train for it.

In a follow-up piece, I’ll outline a practical framework for using AI in ways that preserve cognitive agency rather than erode it

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In Post 1, I argued that “AI-ready” isn’t a technical identity, more of a behavioral one. This post is the companion piece: how do we put these ideas into practice.

The point of this post is to be practical - not to present shiny tools or some grand transformation program. We have enough of those elsewhere.

My goal is to present a practical way to begin the move from AI-curious (sporadic experimentation) to AI-confident (repeatable, outcome-driven use), while keeping the two things that Procurement can’t outsource: judgment and accountability.

The discussion will be divided into four parts:

1. Common-sense guardrails

2. Practical playbook

3. Useful workflows

4. Getting started this week

First: Common-Sense Boundaries (AKA Don’t Do Something Dumb at Speed)

Let’s start with a reality check.

AI can accelerate your work but it can also accelerate your mistakes. It’s not tuned to do the ‘right things’ all the time, so the onus is on you.

So before we get going, let’s lay down some common-sense boundaries for ourselves. (I know, I know, this is like one of those disclaimers at the beginning of every self help book: consult your doctor/financial advisor/legal professional/etc.)

Here are the guardrails I’d apply at this stage, whether you’re a category manager, an analyst, or a CPO:

1) Treat public tools like glass conference rooms

If you wouldn’t say it on speakerphone in a crowded airport, don’t paste it into a public AI tool. Don’t put any of the following into the public AI tools you use:

• No regulated data (PII, export-controlled, etc.)

• No confidential supplier data

• No non-public pricing, rate cards, rebate terms

• No contract language covered by NDAs

• No customer-sensitive info

• No proprietary strategies or negotiation positions

I know plenty of AI tools have options to protect your data but, for now, I would still err on the side of caution. If in doubt, treat the data as confidential and don’t input it into the tool.

2) Follow Company Policy

Again, this goes without saying, but follow your company policy.

And if your company doesn’t have one, or has a very loose set of guidelines, then assume the strictest stance until your company actually does develop one.

A lack of (or even a loose) AI policy is not permission to do whatever you want, certainly not with company information.

Until you have clear rules, behave like you’re operating in a regulated environment:

• Stick to approved tools only or, where this is no guidance, choose your tools carefully

• Redact all inputs thoroughly and appropriately

• Experiments only with “no risk” and “low stakes” work

• Document everything you do

3) Don’t Treat AI Outputs as “Answers”

AI can be a very fast, very capable intern that displays very high confidence, even as it displays uneven judgment. It will reinforce what you want to hear and will sometimes even tell you things that just aren’t true or valid or right.

So, don’t take it for granted. Don’t outsource your thinking and judgement:

• Verify facts

• Sanity-check logic

• Ask for sources, assumptions, and alternatives

• Pressure-test the output the way you would a supplier claim

Converse with the tool, push back, question its ‘thinking’. At the end of the day, it’s your output and you will be on the hook for it.

4) Keep the Human in the Loop

This is related to point 3, but keep yourself in the loop, especially where it matters - on decision making, judgement issues, etc. If the output affects money, risk, reputation, or legal exposure, then the bar should be even higher.

AI can help you think, draft, and explore but you must still own:

• Decisions

• Communication

• Accountability

Ask yourself: am I comfortable defending this output in front of my boss?

5) Build Muscle Safely

If you’re new to this, don’t start with the crown jewels. Start with non-sensitive use cases that show you the power and capability of the tools.

Start with:

• Meeting prep

• Stakeholder emails

• First-pass research frameworks

• Neutral summaries

• Checklists and question banks

All of these should be more than enough to build confidence without creating risk.

How to Move Up the Curve (And Still Have a Life)

The point of this whole exercise is to get beyond ‘dabbling’ (AI-curious) to ‘standardize’ and ‘incorporate’ into your workflows (AI-confident).

The simplest way I can think of to get there is to make progress without getting overwhelmed:

Step 1: Pick One “Lane” for 30 Days

Choose one part of your job where you want leverage. For example:

• Contracting support

• Supplier intelligence

• Supplier risk insights

• Stakeholder management

Start small. Get results. Embed into your daily work. Expand later.

Step 2: Run Two Reps Per Week

Each rep can be no more than 15–30 minutes:

• Try a prompt (not a one liner, imagine a conversation)

• Produce an output you can actually use

• Improve the prompt next time (”What could I have said/asked that would have given the tool more context/information to have been able to provision a better output?”)

That’s it. I know there are plenty of folks who will tell you to do more and immerse yourself even more deeply - and you can do that. But at least start here. Small reps compound.

Step 3: Keep an “AI Wins Log”

As the saying goes, “If you don’t track it, it never becomes a practice”.

Make a point of tracking what you’ve worked on, what the issues were, what value you saw, etc. You can do this as thoroughly as you like, for example:

• Date / workflow lane

• What I was trying to do

• What I fed the tool (redacted)

• Output I got

• What I changed / validated

• Time saved (or quality improved)

• What I’ll reuse next time

OR just keep it simple: keep a note of what you did, what you learned, what value you recieved and how you could have done better. Make this a personal operating system of sorts.

The point is to capture insights and learn; from “I tried AI once” to “I work differently now”.

Step 4: Define “Better” in Procurement Terms

Stay focused on the practical, tangible, applicable value. Not just “this is really cool output”, but what it means for your work and how you could (and why you should) deploy this on an ongoing basis.

In other words, “Better” means:

• Faster cycle time

• Clearer stakeholder alignment

• Sharper negotiation options

• Fewer risk blind spots

• Better supplier conversations

Anchor your practice to the stuff that you (Procurement) cares about. The more it ‘enables’ you, the better you will be.

Four Procurement workflows where AI can create real leverage

OK - let’s get started.

What follows are practical tasks and patterns you can use immediately, without pretending that AI is some magical, mythical tool.

For each workflow, I’ll suggest:

• What AI is good for

• What you must verify

• A prompt you can reuse

NOTE: I have drafted the prompts to provide guidance for junior as well as senior folks. It goes without saying that if you already have some experience, then tailor this as appropriate to your experience level.

In addition, if any of detail in the prompts below run afoul of the common sense boundaries laid out above, then adjust/edit those, as appropriate.

1) Contracting:

The focus here is on faster comprehension, better questions and cleaner negotiation preparation. The core value is that AI can accelerate your first pass. What it can’t do is replace your counsel or your own scrutiny.

Where AI helps

• Summarize long clauses quickly

• Create a “risk heatmap” of key provisions

• Draft redline questions and negotiation talking points

• Generate fallback language options (as ideas, not legal advice)

What you must verify

• Legal interpretations

• Company- and Jurisdiction-specific implications

• Defined terms and cross-references

• Anything that affects liability, indemnity, termination, IP, data, compliance

Again, AI can accelerate your first pass. It cannot and should not replace counsel or your own scrutiny.

Reusable prompt:

You are a procurement contracts analyst.

My company is a [mid-size buyer] with [moderate] leverage. This is a 3-year agreement valued at approximately $X. We have [one/multiple] alternative suppliers.”

*I’m reviewing a contract for [category/service type] with a supplier. Here are the [redacted] clauses for your review. *****

1. Summarize each clause in plain English.

2. Identify the top risks for the buyer.

3. For each risk, propose questions to ask the supplier

4. Help me identify acceptable fallback positions for risks identified in 3 above.

5. Flag any ambiguous language and suggest how to clarify it.

6. Identify any standard clauses that are missing and explain why they matter

7. Note where any terms deviate significantly from market standard for this category. (If you don’t have market data, label as hypothesis)

8. Flag any clauses that should be reviewed by legal counsel rather than handled by procurement alone

Provide the output in plain-English summary.

Key Note: The goal, as I’ve said above, isn’t “AI reviewed the contract” but that you are able to walk into a legal/stakeholder review with a deeper comprehension and sharper questions.

2) Supplier Intelligence:

The goal here is to use AI to better prepare you for supplier conversations and moving your sourcing strategy forward.

Where AI helps

• Structure and develop a supplier profile quickly

• Turn scattered information into a coherent narrative

• Draft supplier interview questions

• Generate hypotheses about strengths/weaknesses and differentiators

• Build an initial supplier landscape by segment

What you must verify

• Factual claims (revenue, ownership, capabilities, certifications)

• Marketing fluff vs actual valid insights

• Anything that becomes part of a sourcing decision record

Reusable prompt:

You are supporting a sourcing initiative in [category].

Create a supplier intelligence brief for [Name of Supplier (ideally)] or [Supplier Type (less ideal but still workable)]. Do not assume facts. Provide citations (and if you can’t, say so). Label all assumptions.

Include:

• What the supplier likely does well (hypotheses)

• How differentiated these strengths are relative to its competition

• The typical cost structure and where pricing leverage exists for the buyer.

• Common risks in this supplier type

• What creates dependency or switching costs with this supplier type, and how can we structure the engagement to minimize lock-in?

• 12 due diligence questions (commercial + operational + ESG + cyber/data)

• What would make us not choose them

• What should we look for and ask about when requesting customer references?

• What to listen for in discovery calls:

  • Green flags (signals of a good partner)

  • Yellow flags (things that need follow-up)

  • Red flags (signals to walk away)

Keep it concise, bullet-based, and designed for a stakeholder readout. Include a one-paragraph executive summary at the top with a preliminary recommendation or stance.

Key Note: The point here is to use AI to generate structured thinking that you can then validate with real data and supplier calls.

3) Supplier Risk Insights:

AI tools can be great for helping identify early warning signals and develop sharper mitigation plans. The key, as always, is to use them thoughtfully and with your own judgement as central to the analysis.

Where AI helps

• Create a risk taxonomy for your category

• Develop “what could go wrong” scenarios

• Draft monitoring questions and risk dashboard elements

• Generate mitigation options you might not have considered

What you must verify

• Company-specific qualifiers/disqualifiers

• Real-world risk signals

• Financial exposure

• Operational dependencies

• Any recommendation that affects supply continuity

Reusable prompt:

You are a procurement risk advisor.

For [category] with suppliers in [region(s)], create a risk assessment framework.

1. List major risk types (financial, operational, geopolitical, compliance, cyber, ESG, logistics).

2. Rank risk types by severity and likelihood for this specific category-region combination, and explain your reasoning. Not all risk types are equally relevant — deprioritize where appropriate.

3. For each risk type, define leading indicators we can monitor. Suggest specific free or low-cost data sources a procurement team could use to monitor each indicator

4. For each leading indicator, recommend a monitoring frequency (daily/weekly/monthly/quarterly).

5. Create a simple scoring model (1–5) with definitions for each score. For each score level, provide a concrete example relevant to this category so the user can calibrate their assessments.

6. For each risk type, define a threshold score that should trigger an escalation or action, and describe what that action looks like.

7. Provide mitigation strategies (dual source, inventory buffers, contractual protections, audit cadence, etc.). Note those strategies that are proportionate for a contract of [approximate value], and flag where the cost of mitigation may exceed the expected cost of the risk event.

In addition to any descriptive output for the points above, also provide a summary dashboard table (risk type, severity ranking, top indicator, primary mitigation).

Key Point: The point here is not to be exhaustive but to help you identify the breadth of the major risks. You will still need to provide judgment about what’s plausible, material, and actionable.

4) Stakeholder Management:

This is not a flashy use case, but it provides real value in the form of stronger communication, clearer alignment, fewer rework loops, and much faster (and more credible) decisions.

Where AI helps

• Draft crisp stakeholder updates

• Tailor messages to different stakeholder types

• Prepare for tough conversations

• Turn messy meetings into clean decision memos

• Generate options and trade-offs summaries

What you must verify

• Tone and political nuance

• Commitments, timelines, and approvals

• Anything that could be interpreted as binding

Reusable prompt:

You are helping me manage a stakeholder in [function].

Context: [short description].

Goal: [what I need from them].

Constraints: [timeline/budget/risk].

Considerations: [Any history with the stakeholder; key ideas and preferences]

Stakeholder’s influence level: [decision-maker/influencer/gatekeeper/end-user]

Stakeholder’s likely priority: [cost/speed/quality/risk/control].

Suggested tone of communication: [assertive/collaborative/deferential/urgent/relationship-building]

Draft:

1. A 6-sentence email that is clear, calm, and action-oriented.

2. A one-paragraph “decision memo” summary with options and recommended next step.

3. 5 objections they might raise and how I should respond.

4. For each objection, provide the underlying concern driving it, your recommended response, and any phrases to avoid.

5. If I need to compromise, identify the one thing I should protect and the one thing I can concede.

6. Recommend whether this conversation is better handled via email, a brief call, or an in-person meeting, and explain why.

Key Point: The point here is that you’re using AI to remove friction and enhance credibility, so you can spend your energy on judgment and relationship development.

The Difference Between “Using AI” and “Becoming AI-Confident”

At this point, you might notice an underlying theme: none of this requires you to become technical. What it does require is:

• Comfort in experimentation

• Thoughtfulness (and appropriateness) in the prompt structure

• Discipline to verify

• A bias toward turning experiments into habits

• The humility to treat outputs as drafts, not truth

A Short Note for Leaders

If you lead a team, your most impactful move is to make “responsible practice” the norm. You can do this by taking four simple actions:

1. Publish guardrails people can actually follow - what’s off-limits, what requires review, what’s fair game

2. Create a safe space for experimentation - off-limits categories (if any), anonymized data, etc.

3. Reward small, verified wins tied to outcomes - e.g. a better supplier question, a faster risk assessment, a key nugget of insight that moved a conversation or deal forward, etc. - and not “I used the AI tool”

4. Make sharing the norm - via regular forums where people show what worked, what didn’t, and what they learned

The point here is to give your people clarity, safety, and permission to practice.

What To Do This Week

If you’re reading this and thinking, “OK - where do I start?”, here’s one suggestion:

1. Pick one lane

2. Run two reps this week

3. Start your AI Wins Log

4. Share one safe win with someone on your team

That’s literally it - just take simple steps to start becoming the kind of practitioner who can work with these tools, and incorporate them into your workflow.

In a post-AI world, confidence isn’t a result of the tech, but rather your ability to work with it - safely, consistently and with (your) judgment.