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.

Procurement practitioners today are living in what seems like a strange split-screen reality.

On one side, every CPO agenda has “GenAI” stamped on it in bold ink:

• EY’s 2025 Global CPO survey found that 80% of CPOs plan to deploy GenAI in some capacity over the next three years (with more than a third having deployed GenAI in a meaningful way).

• Deloitte’s 2025 Global CPO Survey found that ‘Digital Masters’ (the top quartile performers) are allocating a quarter of their budgets to procurement technology (nearly double 2023 levels) and achieving a 3.2x ROI on their GenAI investments.

The promise (both real and hyped) is there for all to see (and desire) and the exhortations to chase after this ‘promised land’ are ubiquitous.

On the other side, the practitioner’s day job continues to have real teeth: contracts still need negotiating, stakeholders still want their answers yesterday, suppliers still need to be managed actively, while risks continue to show up uninvited. All of these need to be handled now. They require us to leverage all that we know to keep doing “good work”.

But hovering over both these realities is the idea that the baseline for what constitutes “good work” is shifting. The Hackett Group has put an even sharper edge on this:

64% of procurement leaders expect AI and GenAI to transform their roles within five years.

(Note that that’s research from early 2025, before the last year of model releases and product launches that have accelerated the conversation even further.)

And while we can get caught up in discussions about timing, it’s clear to me that the ground is moving. Change is coming.

So the real question isn’t whether AI will matter but, rather, what posture are you taking while AI is becoming the new ‘normal’?

In other words, how are you becoming “AI-ready”?

In this post, I’ll do three things:

1. Explain why ‘learning to code’ is the wrong bar for most practitioners

2. Provide you with a quick self-assessment, and

3. Describe the mindset shifts that move you up the curve.

Why I Stopped Believing “Learn to Code” Was The Answer

For a while, I believed that to be “AI-ready” you needed at least a working understanding of coding. I don’t believe that anymore.

It’s not that I think technical depth is worthless - it absolutely isn’t. It’s just that, for most procurement practitioners, “becoming technically proficient” becomes a treadmill with no finish line. The technology is constantly outpacing us - the models improve, the tools change, the interfaces shift, the jargon mutates, and so on and so on. You can spend months chasing proficiency and still feel behind.

In the meantime, you still have a day job to focus on - a day job that does not and will not pause for the transformation.

Add to this the fact that the “Tech” is also getting easier to use (”the best UI is a text box that you talk or type into”), and it all begs the question:

How do you prep for a new world in a way that advances your understanding and does so in a way that furthers your ability to do your real work more efficiently and effectively?

Or to put it more simply:

How do you become AI-Confident in a way that is meaningful and contextually relevant?

The answer, to me, isn’t technical but behavioral:

• the willingness to experiment (without expecting perfection)

• the ability to ask better questions

• the discipline to verify outputs and not take them for granted, and

• the ability to turn experimentation into repeatable ways of working

In other words: judgment, intention, practice.

The Three States I Keep Seeing in Procurement

This is why I keep coming back to a simple framing. I tend to see three broad types of practitioners: AI-ignorant, AI-curious, and AI-confident.

Let’s dig into each of these types more practically, via a quick self-assessment.

Self-Assessment: Where Are You Right Now?

1) AI-Ignorant (AKA “I’m Too Busy”)

You might be here if:

• You rarely use AI tools unless absolutely pushed to do so

• You think “AI confidence” equals coding or being a technical expert

• You assume the value will become obvious later…”I’ll figure it out then”

• You’re waiting for training, governance, or a corporate rollout to make it “official”

• You avoid experimenting because you don’t want to look foolish or wrong (”I’ve heard it hallucinates”)

• You’re just plain worried

The thing is, a lot of what we call “AI ignorance” is really just passive avoidance - and it’s not sustainable, because the world isn’t going to wait for you to ‘feel’ ready. Sure, the tools aren’t perfect but they are delivering value in different ways today, and they’re going to keep on improving.

So over time, avoidance doesn’t just keep you unchanged, it erodes your credibility because expectations around speed, clarity, and insight will keep rising even if your mindset and approach and workflow don’t. And that’s going to be far worse.

2) AI-Curious (AKA The Default Setting)

You might be here if:

• You follow the headlines and opinions (especially the loud ones)

• You’ve tried the big-name tools a few times

• You experiment for a day or two, then go quiet for weeks

• You don’t yet have a clear sense of where AI helps your work most

• Your exploration is driven by novelty, not outcomes

This is AI-curious, where most people are - and it’s a reasonable place to be, all things considered.

But there is a hidden problem: curiosity without structure doesn’t compound. It remains as a king of ‘sporadic entertainment’, something you “check out” rather than something that you use to change you.

3) AI-Confident (AKA It Isn’t What You Think)

You might be here if:

• You use it with intent, not just when you remember

• You translate experiments into repeatable workflows

• You can point to a few parts of your work where AI reliably helps

• You keep judgment in the loop: you sanity-check, triangulate, and challenge outputs

• You focus on outcomes: faster insight, better options, clearer stakeholder communication, sharper risk visibility

AI-confidence doesn’t mean you can build models, it means you’re focused on constant learning. It means you know where you’d try AI, how you’d evaluate what comes back, and how you would apply it responsibly.

Moving Between The Stages: Mindsets

The really interesting thing about these types or stages is that the distance between them is not as large as it might seem. It’s not a leap from “non-technical” to “technical”; it is, instead a shift from avoidance to dabbling to disciplined use.

So what does moving between these stages actually mean in terms of behavior? How should we think when we make this shift?

From AI-Ignorant to AI-Curious

As I said, this shift - the first and most important one - isn’t about any sort of special intelligence.

It’s about dropping the “wait and see” posture and deciding that “clarity” isn’t something you can wait for. It’s something you can begin to create through low-risk experimentation.

This first step is, therefore, mostly psychological:

• “I don’t need permission to learn.”

• “I don’t need perfection to start.”

• “I can run safe experiments without creating risk.”

It is, therefore, about making the decision to actively learn, to understand what’s out there and begin to understand the potential use cases and value they can deliver.

It’s about just getting started.

From AI-Curious to AI-Confident

Here, you’re adding intent, focus and repetition.

AI-curious folks test tools while AI-confident practitioners build habits - and those habits have a specific flavor:

• They’re anchored to real work

• They’re measured (even informally)

• They improve over time

• They include verification, not blind acceptance

That’s the whole shift - from observation to intentional usage and from dabbling to conscious practice. The key is to move from “AI as novelty” to “AI as a work partner you manage”.

That last phrase - AI as a work partner you manage - matters the most, because in Procurement, as in any other function worth its salt - accountability doesn’t get outsourced to the tool. It has to stay with you.

The Ethos: Take Charge Yourself

If you take only one idea from this post, take this:

Don’t wait for someone to serve AI readiness to you on a corporate tray.

Yes, one day, the training will come, the policies will evolve and the tools will get embedded. There’s no doubt about that. The tech is moving to the point where there will be no option but to.

What you don’t want is for that to be the time you begin learning, because by that time, you’ll have a mountain to climb to simply get to first principles. Worse, by that time, you may be irrelevant.

The practitioner who builds an edge is the one who starts now, safely, and deliberately. And don’t do it because you want to become an “AI person”. Don’t do it with an end destination in mind - there is no end here, it’s a journey.

Do it because you want to stay “high-leverage” as the very definition of leverage changes. Do it because you want to stay relevant.

A Quick Sidebar for Leaders

If you lead a team, your job isn’t (just) to announce that “we are adopting AI”. Your job is to remove the fear and the friction so your people (who are watching you for guidance) can build competence without feeling exposed.

That means:

• create psychological safety for experimentation

• clarify the guardrails (even simple ones)

• reward good judgment, not shiny outputs

• normalize the idea that AI outputs need verification and context

• celebrate small wins that improve cycle time and stakeholder clarity

What’s next

But look, with or without a perfect “tech-forward, people-first” leader, the onus is still on the practitioner. The practitioner still has to choose this posture. It has to be a personal shift before it becomes an institutional one.

So, in the next post, I’m going to suggest ways to get practical. I’ll start with some common-sense boundaries (especially around confidentiality and policy), then lay out a simple playbook to move up the curve, including how to apply AI to procurement workflows where the impact is real:

• Supplier intelligence

• Supplier risk insights

• Stakeholder management

Remember, in a post-AI world, confidence won’t come from the guts of the technology, but from knowing how to work with it - repeatedly, safely and with judgment.

In my last post, I talked about six factors that are driving change across the Procurement function and why, as a result, most practitioners aren’t ready for the future that is unfolding.

For the most part, these factors are the result of developments or pressures from outside of Procurement that are subsequently having an impact inside the function.

In this post, I want to dive into three very specific considerations that Procurement will need to grapple with as it looks to reposition and reinvent itself for a Post-AI world:

1. New Psychology

2. Right Incentives

3. Relevant Investments

Rethinking these considerations isn’t a ‘Procurement only’ problem - it will require support from individuals and groups outside of the function as well. But the impetus for change must begin, as it always should, with us.

Let’s dive into each of these in turn:

1. Towards a New Psychology

When I was with The Smart Cube, there’s a statement I‘d make in discussions with Procurement leaders and practitioners that would always elicit an immediate, visceral reaction - almost always at one extreme or another:

“Procurement’s goal should be to become the biggest value creator in the enterprise.”

As soon as I’d say those words, half the audience would look up, eyebrows raised and energized by the idea, while the other half would, subtly (and sometimes not so subtly), shake their heads, put off by it.

To me, that divergence in reactions encapsulates one of the core issues that impedes the function even today, even after the years of progress the function has made (and it’s got nothing to do with whether you believe my statement - meant as a ‘North Star’ - to be achievable or even realistic):

Do we believe in ourselves and what our mission could be? And do those around us share in this belief?

In many organizations, Procurement is still constrained by this psychology: how the function sees itself as well as how others in the organization view the function.

Of course, history has a part to play here. Procurement has come a long, long way from its ‘back office’ administrator era. We have better tools, more methodologies and a far better caliber of talent across the function.

But that progress is still unevenly distributed, both across and even within companies. Some Procurement teams have advanced, espousing ambitious goals, while others are still in ‘order taker’ mode. At the same time, many Procurement teams are still wrestling with others in the organization (from the C-suite to peers in other departments) who still hold on to the baggage of the “old” Procurement.

Beyond this baggage, there’s also the (rapidly growing) distance between where the Tech is taking the function versus what practitioners have been trained to do. Most Procurement professionals today are built for an era that is rapidly becoming “unfit-for-purpose”, one very much focused on:

• Knowing the process

• Working the data

• Being the ‘expert’

Procuretech - and specifically, AI-First/AI-Enabled Procuretech, is changing that paradigm. As we’ve discussed in prior posts, the model is evolving to one where the prime skills will revolve around:

• Optimal orchestration

• Sound judgement and,

• Delivering better outcomes.

In short, Procurement is moving from being valued for what it knows to being valued for the decisions it enables.

Thing is, we’ve not caught up to this shift, and even where we have in terms of mental acceptance of the idea, we’re certainly not there in terms of wholesale, practical, skills-based readiness.

So to me this is THE foundational issue. We’ve spent decades working to break out of the old psychology and, now, here’s this AI thing, further challenging our identity. That’s as much an emotional and cultural issue as it is a technical one.

2. Towards Better Incentives

If you want the right outcomes, incent the right behaviors.

That’s may be an age-old mantra, but it’s one we all agree on. So much so that we’ve seen significant progress putting it to work in Procurement over the years.

We’ve seen metrics galore: across the function as well as within every sub-function. We’ve seen them in cascading scorecards that encompass both hard and soft metrics. We’ve even seen two-way metrics that cross organization thresholds to include suppliers as well. (If you’re interested, I co-wrote an article for MIT’s Sloan Management review on this topic years ago.)

You would think that with all of this metric sophistication, we’d be in a far better place. Well, we are - and we aren’t. There are a couple of pretty major problems:

First off, most of our metrics today are focused on process and activity. Things like the number of RFQs run, or cycle time or pure savings booked. All useful metrics, for sure, but AI is making these metrics meaningless, because machines will administer a good chunk of the work that many of these metrics inform, to the point that they cease to become “human” metrics.

Instead, we need to focus less on activity efficiency and more on enterprise effectiveness i.e. outcomes (or as close to it as we can get). Outcomes like savings, for sure, but also our ability to:

• Improve time-to-market

• Avoid risk and increase resilience

• Create optionality to manage for unpredictable business outcomes (versus just maximizing realized savings in the moment)

In other words, less of just “we delivered 3% savings” and more of “we enabled a product launch six months earlier by rethinking the supplier ecosystem”.

This mean rethinking our metrics, including expanding our definitions to encompass what constitutes better outcomes and then better measure our ability to help internal customers achieve them.

The second, and to me far more important, issue is that despite the plethora of available metrics, only a limited few are perceived to matter - and, most of the time, just one: hard savings, and more specifically, hard savings that hits my P&L this Fiscal Year.

But this so often leads to conflicting incentives. Not just because of the short-term-ism that it manifests in behavior, but also because better outcomes don’t always manifest themselves in the form of dollars and cents (near-term or otherwise). We’re paying Procurement to be a steward of enterprise value but then we’re not measuring them (not truly) as such.

We need a better and more widely accepted definitions of value - and, yes, that will take more than just Procurement to get sorted. We need the C-suite to buy in as well. That means better ‘customer’ champions, defined quick wins to prove our case, stronger, more coherent communications about our impact, (real) leadership buy-in, and so much more. We cannot go on to ‘self-actualize’ the function unless we do.

Bottom line: Metrics matter. No question about that, but we cannot go on, especially in this Post-AI world that we’re moving into, optimizing for the obsolete.

3. Towards The Right Investments

Ask any leader and they’ll tell you that “people are our greatest asset”. How we operationalize that statement in a Post-AI world, is worth some deeper thought.

Historically, the vast majority of training and development expense has focused on “hard skills” - policy, process, tools, and compliance. And even the soft skills we trained for focused on narrow angles that made sense for a different time:

• Negotiations focused mainly on playing suppliers off against each other and extracting concessions

• Stakeholder management that was more political navigation than proactive multi-party orchestration under uncertainty

• Communications that was more focused on status and updates than narrative construction and management

• Relationship management viewed primarily from a supplier harmony lens than a more holistic portfolio-based perspective

All useful and necessary, no doubt, but sufficient? Optimal?

In an age where the machines will do a good chunk of the work, where so much of our involvement distills down to “dialog with the agent”, and where our engagement moves from engaging with our laptop most of the time to engaging with humans (for tasks beyond fire-fighting), where do we turn our developmental focus?

We need training & development that is focused on two explicit categories:

• Business Judgement Capabilities such as value chain economics, P&L literacy and the ability to make strategic trade-offs. In other words, we need to be able to better assess the business situation at hand and then configure and cultivate the best path to the optimal decision

• Human Leverage Capabilities that go beyond simple concepts such as AI literacy and technical proficiency (both necessary but just part of the overall equation) to also consider relationship management, narrative development, creative problem-solving, the ability to be comfortable and operate with ambiguity, etc.

The underlying point is that we need to cultivate skills that reposition the practitioner as orchestrators and architects. And that’s about more than getting them ready to just execute.

The one other - perhaps even more important - point I’ll make here is that, even with the best tools and intentions, one fundamental issue still remains: when times are tough, training is always the first budget to get cut.

I understand why (in practical terms anyway). Training is flexible and modular, more often than not, seen as a nice-to-have. There’s no immediate system failure when training is paused, no visible outage when human development slows. So it becomes an easy head to serve up when budgets tighten.

But in a Post-AI world, this logic is deeply flawed. Cutting investment in capability building is, in truth, an unstated risk decision, increasing the likelihood of poorer judgment, weaker orchestration, and slower adaptation - exactly when the role of the human matters most.

We need to change this mindset. We keep saying that people are our most important asset and yet we don’t act like it.

If Procurement wants to remain relevant in a world where machines handle execution (and more), it cannot treat the development of judgment, business acumen, and human leverage, as optional.

Cutting investments in capability building in a Post-AI world is nothing less than strategic negligence.

In Summary

The transition to a Post-AI world doesn’t begin with technology. It begins with belief - belief about what Procurement’s mission really is, belief about how it creates value, and belief about whether developing human judgment is a cost or a strategic necessity.

AI will continue to do its thing - compressing execution, automating processes, and surfacing insights, whether we’re ready or not.

What will remain, though, is the distinctly human ability to frame the right problems, make trade-offs under uncertainty, and orchestrate outcomes across complex enterprises.

Procurement’s ability to flourish in this post-AI world means it must choose to see itself differently, align its incentives to that ambition, and, finally, invest accordingly.

Otherwise, instead of leading with value in a Post-AI world, we’ll still be answering questions about the function’s relevance.

In my conversations with CPOs, practitioners and founders over the last couple of years, there’s been a broad consensus around one idea: many of today’s Procurement professionals aren’t prepared for what’s coming.

That is, a sizable portion of today’s Procurement professionals do not have the skills needed to flourish in a Post-AI world. (I fleshed out my perspective on what a “post-AI” world is in my last post.) This is borne out not just in the informal chatter online but also by research:

• Deloitte’s 2025 Global CPO Survey calls this out directly, noting that while a ‘digital‑adept and digital‑capable workforce’ is now a critical differentiator and that next generation skills are needed, more than a third of CPOs cite a ‘talent gap’ as a top barrier to value delivery.

• In a McKinsey discussion on AI in procurement, 43% of CPOs identified strategic thinking as the most critical future competency for category managers, which underscores the clear move (or at least the desire to move) away from transactional skills toward higher‑order capabilities.

How prevalent is this issue? My own (admittedly non-scientific) estimate, yielded from hundreds of discussions over the last couple of years, suggests that the issue is far more serious than even the numbers above might suggest. The figures I hear are as high as 80%, and never below 60%.

In other words, somewhere between 60-80% of practitioners are not equipped with the skills needed to flourish in a Post-AI world.

Why is this the case?

The fact is that most Procurement roles were designed for a pre-AI world. These roles valued compliance, control and standardization as the means to drive efficiency and impact. Organizations and structures were built to reflect this ethos and investments were made in tools and technologies that enabled this agenda.

Then, partly as a result of the function’s success (and you could say its failures), expectations began to change. Spurred on by ever more competitive markets and internal pressures, internal customers and the C-suite began raising their demands of the function. Procurement responded with growing sophistication, led by progressive leaders and a higher caliber talent pool. Combined with the last decade and a half of Procuretech development, we saw the promise, and delivered reality (albeit unevenly) of a different, more value-added Procurement.

AI has now accelerated and magnified all of this.

The net impact is that the expectations of Procurement are shifting faster than the skills, incentives, and operating models can cope. We now are beginning to see a structural mismatch between what the role has trained people to do and what it is being expected to deliver.

Six factors underpin this shift of expectations:

1. Tech

2. Workflows

3. Operating Model

4. CFO

5. C-Suite

6. Internal Customers

Let’s look at each of these in turn.

1. The Tech is Absorbing More Than We Expected

Over the years, with the rise of Procuretech (and most tech in general), most of us assumed these tools would “take the transactional work” but not the “interesting stuff” i.e. all the decision support and cognitive work that humans do.

But, in reality, AI is moving up the value chain much faster than expected.

With the advent of today’s crop of tools (powered by AI), we’re seeing the tech take away work we’ve come to expect as our exclusive domain; not just the transactional stuff but so much more than that. A few examples:

• Supplier discovery becoming an ‘auto-generated’ feature

• Market benchmarks created in minutes

• Cost breakdowns inferred from sparse, public data

• Contract risks flagged before Legal even gets its turn

• Scenario modeling (“what if we dual source?”) done instantly

All of this is decision support work - traditionally a “high value” activity that was part of the developmental trajectory of Junior Analyst to Manager to Category Lead. But with AI absorbing this work, many practitioners are losing not only their learning path but also their value narrative and their differentiation.

Think about it: if you traditionally optimized for producing analysis, you now need to move beyond. Producing the analysis isn’t good enough - machines will do that and you will need to optimize for interpreting, challenging, and owning decisions.

2. Workflows Are Changing Faster Than Skills

If your job is to administer a process, you’re at risk, because AI isn’t just automating tasks, it’s reordering - changing - the way work is done.

Traditionally, we saw workflows that were reasonably sequential:

Intake → Requirements → RFx → Evaluation → Negotiation → Contract

But AI-enabled workflows are different:

Problem framing → Constraint definition → Outcome modeling → Exception handling → Stakeholder alignment

They force us to focus on the bigger picture: What are we trying to achieve? What are alternate paths there? How can I get creative and get to the solution quickly and efficiently and optimally?

In other words, there’s less demand for process administration and a greater demand for judgement, especially on critical projects and at critical moments. The days of “we have a process so let us follow it” are numbered.

So the value premium will be knowing which outcomes matter, where human intervention is necessary, and then being able to figure out when not to follow the process and get to the right outcome.

In the past, we rewarded for procedural rigor, not discretionary judgment. That’s going to change.

If your job is simply administration, coordination, and communication within a process, your job will go.

3. The Operating Model Is Flattening

AI is compressing layers and removing hierarchies.

We’re going to see fewer “Analyst to Manager to Director” handoffs and more direct ownership of outcomes by individuals - because a sizable chunk of the work done in “administering the process” and “analyzing and synthesizing the data” is going the way of the machine.

This means flatter models where visibility increases and output matters more than activity. There will be fewer opportunities (and far lesser tolerance) for excuses. Procurement professionals will be asked:

• “Why did you choose this path?”

• “What trade-offs did you identify and accept?”

• “Which risks did you consciously take or avoid?”

These are questions that go deeper than they might at first sound and the more critical the category (the ones where we want our cliched ‘seat at the table’, the more pronounced they’ll be.

In the past, it was easier to hide behind structure, hierarchy and process, which shielded individuals from accountability and deeper explanation. AI is going to remove this.

4. CFO Pressure will Increase

Given that most CPO’s tend to report to the CFO (and even where they don’t, are very directly influenced by them), the priorities of the CFO are paramount to the existence, focus and future of the function.

If your CFO (truly) believes in Procurement, the world is your oyster. But if he or she doesn’t, then Procurement’s role will be relegated, for the most part, to the transactional. The advent of AI tools has only exacerbated this.

Because CFOs don’t see AI as just a “nice to have”, they see it as a capacity unlock - though perhaps not entirely in the way that the progressive practitioner might want or expect.

CFOs are, rightly, asking questions such as “If intake is automated, why do we need as many buyers?” or “If sourcing cycles are faster, why isn’t throughput higher?” or just simply “Why do we need as many people in Procurement?”

And to be fair, the answers given the current pace of technology are “We don’t”, “It should”, and “we don’t”.

AI is creating an expectation, initially implicit but becoming much more explicit: Headcount should not scale linearly with workload anymore. In fact, we are likely past the point of peak employment in the function.

In other words, if we have any hope of staying relevant, AI demands that practitioners clearly articulate business impact every step of the way.

So we have two options: Either we accept this and the functions turns into a distributed, self-serve, transactional driver of spend, or we upskill and focus on becoming value creators.

If the hope is to stay relevant, there’s only one answer.

5. C-Suite Expectations Have Shifted

In 2024, the main question from the C-suite was “Are we using AI?”. Moving into 2025 and now 2026, the question has shifting to asking “Where is AI changing outcomes?”

In Procurement’s case, this translates into tangible outcomes:

• Cost reduction (of course)

• Faster time-to-market

• Measurable working capital impacts

• Supply risk anticipation and timely mitigation

• Margin protection under inflation

• Innovation through suppliers

The point is that we’re moving beyond AI theater, beyond the simple reporting to the Street about all the cool initiatives we’ve implemented, to ‘here’s what the new tech has actually delivered and changed’. Procurement professionals, for their part, are increasingly being expected to:

• Identify where AI should be applied

• Define clear success metrics

• Explain why its use case matters commercially

This means a shift from executing initiatives to shaping them - a skill that will be the domain of not just a few senior folks but that of the entire (smaller but more capable) function.

6. Internal Customers No Longer Tolerate “Generic Procurement”

Some practitioners bristle at the idea that they have “customers”. They say, instead, that “we have partners, we are equals”. This is an argument around semantics and misses the point. Procurement exists to serve the business, which has asked it to manage a specific set of spend to better meet the goals of the overall business.

In other words, Procurement has customers. And those customers are demanding a much higher caliber of performance - especially as so much of a corporation’s work is “AI-ified”. Their key question is:

“If you cannot deliver clear and specific value to my BU or department, then I don’t need you.”

Think about it. If AI can run an RFQ using simple chat prompts, conduct the analytics, benchmark pricing and even flag Contract-to-RFQ risk, then it’s right for the business to question the value of a Procurement model that cannot meet it’s specific requirements.

This means a shift from historical emphases on ‘standardization and scale’ to ‘contextual, custom relevance’.

The real value drivers, then, are category intimacy and business understanding as well as the ability proactively spot opportunities to create custom value.

Ask yourself, how many of your team are ready for this future?

In Summary

The above, in my view, are the prime reasons why Procurement is changing and why the function, as a whole, is not quite ready for the shift.

(In my next post, I’ll speak to some of the internal structural and historical factors within Procurement worth considering as we work to engineer the shift for ourselves.)

The net of all this is that, if we’re not prepped and ready, then all of the concerns we have about “the machines coming to take our jobs” and “Procurement becoming irrelevant and being phased out of existence” will come to fruition.

And this won’t be because of a lack of intelligence or work ethic on the part of the individual practitioner. It’s got nothing to do with that. It will be because AI is eliminating the version of the function that we have historically optimized for.

And, in my mind, if we let that happen, then it will be a problem not only for the function and its Practitioners, but for the corporation as a whole.

We will lose the human element. We will lose the ability to truly manage through ambiguity. We will lose accountability.

In the end, we will lose as a corporation.

The “Post-AI” world.

I’ve used this term a lot and often get asked what I really mean - not just what it is, but when it will arrive, and what the world will look like when it does.

In this post, I’ll parse through exactly what I mean - and also why we need to move beyond the conventional interpretations of the term.

AI is Already at Work

There’s plenty of debate about what AI is, it’s “true value” and what it means for the work we do. There are even conversations about whether AI is “real” and if, in time, it’ll end up being just another simple tool, like so many other tech solutions out there.

I’ll start by saying that the bus has left the station. AI is real and it’s already delivering value.

Individuals are using it at work to do their research, think through strategies, and approach new business interactions in thoughtful ways. (And that’s not even touching personal use cases outside of the workplace.)

Companies are using AI as well to automate transactional processes, implement agents to take on specific tasks (reasonably) autonomously, and accelerate time (and path) to outcome. (Obviously, there’s much more work to be done here, but the trend is positive.)

So, sure, we can debate the nuances of AI all we like, but the fact is that its value is here, at the very least, in its early stages.

AGI is a Red Herring

Another discussion that you hear a lot in conventional media as well as the social media sphere is about Artificial General Intelligence or AGI - an all knowing technology that will do everything we humans can do but better.

Personally, I think all this talk of AGI is a red herring. No one (really) knows if it’s even possible or, if it is, when it might become a reality. So, for all practical purposes, until we know substantially more, it doesn’t matter for what we do.

(Personally, I don’t believe we’ll get to a perfect replication of the brain in machine form, at least without severe deficiencies and/or material adverse consequences. Our brains are just that complex. You can fight me on this later.)

In other words, AGI as ‘full autonomy’, ‘zero humans’, etc., is just a talk track. Let’s not waste cycles on remote eventualities. It just delays preparation for what already matters.

Instead, let’s focus on tech we can actually work with. The AI of the here and now.

Post-AI is Not a Future State

When I say “Post-AI”, I don’t mean when “AI is finished and perfect”.

I mean when “AI stops being optional”. It ceases to become a differentiator, it becomes an assumption. It’s when AI is embedded, imperfect, unevenly deployed, constantly improving and already reshaping expectations. It is NOT a future state waiting for the perfect tech, sometime in the future.

It is, in actual fact, now. It’s our current reality in which AI is already embedded, one way or another. Imperfect, yes, but still immensely valuable.

In this reality, we’re already seeing the signs in Procurement - a trendline moving away from a process and task focus to one where Procurement roles are being reshaped around outcomes, orchestration, and human judgment.

In this world, advantage accrues from better roles, better decisions, and better interactions - not (just) from adopting more AI.

The Four Pillars of Post-AI Procurement

So my point is that the post-AI world is now.

And to make sense of it requires us to understand it’s multiple facets. Not just “AI as Tech” but rather, its four foundational pillars: Technology, Focus, Operating Model and Mindset/Human Capability. These pillars are the lenses that help us frame this world we’re now dealing with.

Let’s look at each one in turn.

Pillar 1: Technology

Yes, the tech is an essential ingredient. But in a post-AI Procurement world, the tech doesn’t need to be perfect, nor does it need to be everywhere. It just needs to be intentionally and precisely deployed.

Don’t worry about how advanced it is, just focus on using it thoughtfully:

• Consider narrow sourcing use cases that accelerate outcomes

• Recognize its value as a tool for decision support (vs decision replacement)

• Treat the need for the “Human-in-the-loop” as a feature, not a bug

Pillar 2: Focus

Shift Procurement’s focus from its pre-AI emphasis (compliance, consistency and process fidelity) to business outcomes and internal customer satisfaction. This is, of course, harder than it sounds, because it’s weighted down by myriad factors, which we’ll get into in future posts.

The point is to focus on delivering the right results for the business. Stakeholders don’t care how Procurement gets there, they just want to know:

• Did you help me hit my goals?

• Did you reduce friction i.e. did you make the process as easy as it could be?

• Did you improve the final decision aka ‘did having you in the room matter’?

Pillar 3: Operating Model

We need to rethink our operating models (how we deliver) from “empires” to “orchestration”.

AI breaks the logic of large centralized teams, hierarchies and the hoarding of capabilities. Instead it demands that we move towards a modular mix of small cores of accountabilities, optimal expertise (both internal and external), carefully considered Make vs Buy decisions, and the liberal (but thoughtful) use of Agents.

Practically, this means:

• More rapid movement from requirement to insight to decision

• Less ownership and, maybe more accurately, administration of the tools

• More ownership of outcomes (which requires much clearer accountabilities) and,

• Fewer fiefdoms (because Procurement’s scale comes from orchestration not from headcount).

Pillar 4: Mindset and Human Capability

In the post-AI world, outcomes are all that matter.

Which means judgment, trust, and business fluency will matter more than ‘working through the system’. Which means technical differentiation will matter far less than human differentiation.

Post-AI Procurement professionals will value judgment under uncertainty, stakeholder influence, commercial intuition (aka business sense), narrative and storytelling as well as sound reasoning.

Soft skills, more than anything else, will become differentiators as Practitioners thrive by generating options, helping stakeholders choose better paths, manage for the right trade-offs and own the outcomes (and their consequences).

Post AI is Already Here

So, to me, we’re entering the Post-AI world, if not already very much in it.

The tools and technologies to help us are already out there. They may carry different labels - AI-First, AI-Native, AI enabled - and they may be imperfect, but they’re already changing expectations. What matters now is how we thoughtfully adopt them.

And, as I’ve outlined here, this will be about so much more than the tech itself.

It will be about our ability to rethink the very pillars that define the work of the Procurement Practitioner - delivering better judgment, clearer accountability, and a redefinition of what it truly means to add value.

AI will keep improving either way. Whether Procurement adapts its roles, models and mindsets fast enough to stay relevant is the real question.

One theme that keeps resurfacing in conversations about AI in Procurement is that the technology is advancing far faster than the practitioner. And that gap matters far more than most tool-level debates.

I explored this tension and why the human must remain at the center of any credible AI roadmap in a recent conversation with Shaun Syvertsen of ConvergentIS. We went beyond surface-level hype to examine what this shift really means for how Procurement works and how practitioners lead.

We covered a host of topics, including:

• Why today’s practitioner needs to adapt to the Procurement of the future

• What we can learn from software development in terms of AI adoption

• What Procurement leaders need to remember as the model shifts

You can watch the full discussion below:

There’s a quote doing the rounds that I keep coming back to:

“I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.”

In today’s procurement world, that sentiment feels salient.

AI is here and, all around us, we’re hearing about how it’s going to change everything we do: next-gen our category management; source more and better - mostly with and for us (but sometimes without us!); help us analyze our contracts and identify massive potential; drive far better supplier management; and on and on and on.

But many practitioners are having an ‘existential’ moment.

Instead of seeing AI as freeing us up to do the creative, strategic work we’ve always wanted to do, it’s making us question what we even can do anymore - or worse, whether we’ll even have a job to do in a few years?

(To be fair, a good number of folks in Procurement have been having such conversations for some time - with a few very progressive thinkers even questioning whether (and how) the function can stay relevant in the (not too distant) future.)

I think the answer to this question lies in the fact that AI is not just another tech wave. This isn’t like e-sourcing in the 2000s or cloud migration in the 2010s - all fantastic developments that, to be sure, changed the game for practitioners and providers alike. AI is deeper than that.

The current wave of Procuretech being developed and delivered promises to change the way we think about and do our work, to an extent that I’m not sure many have come to grips with.

And so they react in the ways humans always react in these sorts of situations: they blank it out and keep plugging away, or they get worried and have an existential crisis.

Or they decide to do something about it.

This post (and my new site, Proquria) is my opening salvo to get to the bottom of what we can do about it.

The Buzz (and the B.S.)

Look, I get it. We’re in a hype cycle. I can feel it. You can feel it.

New product releases just about every week. Sky-high valuations. LinkedIn flooded with “AI-first” everything. SaaS platforms claiming to be “AI-enabled”, “AI-native”, “AI-augmented”. VC investments breaking records. One-person companies building agents that do everything under the sun. Talk of AGI either coming for all of us or coming to save all of us (I can’t quite tell which).

It’s all overwhelming.

Conversations have moved from automation to “agentic workflows.” Traditional service providers are suddenly, now, AI platforms. Boutique firms are calling themselves AI consultancies. Everyone’s rebranding.

But, beneath the buzz (and, yes, plenty of BS), something real is happening:

• Autonomous sourcing agents are generating RFQs, inviting approved suppliers, analyzing responses and recommending optimal award scenarios

• AI-powered tools are reviewing SOW’s against RFPs, highlighting negotiation areas, redlining contracts and prepping all outgoing comms

• Supplier Risk monitors are leveraging real-time data to monitor risk factors, scoring suppliers and suggesting mitigation actions

This new breed of Technology (which, by the way, isn’t just about Gen AI or agentic systems, but encompasses rules-based automation, workflow orchestration, traditional RPA, and decision support tools) promises to restructure the way Procurement work is done.

We can’t ignore it. In fact, we have to expect it.

If we look at how Procurement work is structured, in a simple sense, there are three layers:

1. Strategic activity (strategy development, supplier partnerships, stakeholder alignment)

2. Cognitive or Decision support work (running RFPs, implementing strategies, data synthesis and analysis)

3. Transactional work (PO processing, invoice matching, triage, etc.)

The Procuretech revolution of the last few decades has shown us that the transactional work is going the way of the machine and AI is simply accelerating this trend.

But what’s new is that AI is now promising to take on a ton of the work in bucket 2 (and some of bucket 1) as well. Decision-making is being expedited and better informed by Generative AI. AI can simulate/guide negotiation outcomes more fruitfully. It can even provide guidance around alternative category strategies.

ROI Lags (For Now)

That said, we’re still early.

For all the excitement, many organizations are still struggling to get past the pilot phase. Leaders talk about AI strategy, but most deployments are siloed, underfunded, or misaligned with how the function actually operates. Here’s what I’m seeing:

• Top-down mandates: Executives want AI efficiencies, but often don’t think beyond implementing tools

• Vision issues: Teams trying to create or stitch together solutions without clear maps, ownership or training

• Overhyped expectations: Trying to go big too fast leading to underwhelming results

• Security concerns: Data governance and confidentiality fears that delay adoption

• Buy or Build: The perennial debate about DIY versus buying expertise. all he more prominent given the rise of vibe coding

• Lack of skills: Practitioners who are AI-curious, not AI-confident, and then limited by some or all of the above

For all of the above reasons, the short-term payoff isn’t clear yet.

Long-Term: Still Wildly Underhyped

BUT, for all the noise, the long-term potential of AI in Procurement is still underhyped, because the nature of Procurement work makes it incredibly well-suited for intelligent systems. From supplier discovery to risk modeling to contract intelligence, the opportunities are enormous - and the value is massive.

Done right, AI can help the function:

• Shift us from ‘Spend Policers’ to ‘Value Orchestrators’

• Free up time to focus on human-centered challenges such as stakeholder influence and SEI

• Serve as true advisors to the business

But, of course, we won’t get there just like that, and it won’t be because of the tech.

It’s Not About the Tech

The biggest barrier is us.

It isn’t about the tech stack, it’s about the practitioner - or to temper the subtitle to this post just a little:

We need to reshape the practitioner, not just the Tech stack.

As AI becomes more powerful, expectations of Procurement will only increase.

• The C-suite will demand more: more insights, more speed, more “AI-ification”

• The CFO will expect sharper business cases and fewer headcount asks

• Stakeholders will want smarter, more embedded support

Meanwhile, the function itself will (be expected to) shrink - at least in terms of headcount. (I’m not the first person to say this but we are likely past peak employment in traditional Procurement roles.) Tools will become easier to use. Platforms will be abundant. Plug-and-play options will proliferate.

So what’s going to matter will be the human at the center of this operating system. And that human will be a results-focused architect and orchestrator.

Are Practitioners Ready?

In my informal conversations with leaders and practitioners, there’s a shared concern: many of today’s Procurement professionals aren’t prepared for what’s coming. Estimates (non-scientific, admittedly) for how many, range as high as 80%, but never fall below 60%, at least in my conversations.

And this goes beyond simple concepts such as AI literacy and technical proficiency, which is just one part of the equation. It’s also about:

• Relationship management

• Narrative building

• Creative problem-solving

• Comfort with ambiguity

• Enterprise thinking

• New expansive definitions of value

• And a host of other factors that influence our ability to become the orchestrators and architects of the future - including alternative definitions of the Procurement “org chart” and operating model (including the end of “fiefdoms”)

In other words, what is the practitioner going to do when there’s no more admin work and far less fire-fighting? Are you really ready to optimally do all of the above?

So What Now?

This isn’t a call to panic. The good news is that we’re still early. The transformation is still unfolding and, honestly, no one is an expert. We’re all learning at the same time - the pace of innovation ensures that will be the case.

But it is coming and we need to be ready if we want to stay relevant, as individuals and as a function.

That’s what Proquria is here to explore. It’s a call to prepare, and also understand how.

So, let’s start asking better questions. Let’s figure out how we prepare the Procurement practitioner, and not just the tools, for the brave new world ahead.