This site grew out of a question that I’ve been grappling with for at least the last five years:

How should the role of the Procurement practitioner transform over the next decade?

The impetus for the question came from the massive changes we’ve been seeing in the Procurement space over the last two decades - the explosion of both end-to-end as well as best of breed technologies that were changing the way work was being done, as well as the increasing sophistication of the function (from the caliber of its practitioners to the more sophisticated management approaches being deployed).

There was no question in my mind that, as a result of all of these changes and more, the function was going to have to move further up the value curve lest it be deemed irrelevant.

Then along came Gen AI.

Gen AI (and more broadly, AI in general) has, quite simply, accelerated this trend, making Procurement’s existential question even more, well, existential.

In my view, AI is coming for Procurement work faster than Procurement is preparing for it. Most are still figuring out how to best integrate this new tech into their workflows, and how to maximize the ROI from their investments. There’s discussion and experimentation with alternative operating models, hybrid staffing approaches, and more. Despite the massive hype and the perceived speed of change, it’s still early days, and we still have time to figure out the right path forward.

But even as all of this experimentation and value extraction has been occurring, one topic that almost no one has been talking about has been what all this change would mean for the practitioners themselves and, more specifically, what they need to do to not only survive but flourish in this new world. That is, what happens to the human?

Sure, there’s been plenty of lip service about the value of the human and how qualities such as judgement and empathy will be essential, but probe one level below and there’s little real insight.

Which is why I started Proquria - to answer these questions.

Thirty plus weeks later, I’ve run through all of the key questions I wanted to answer. This post provides a recap of the ground covered over the last eight months, followed by a discussion of what’s next.

The Argument In Brief

The premise is simple:

AI is absorbing the work that built Procurement careers and trained our very best people - work that not only fortified our domain knowledge but also gave us the procedural grounding that developed the intrinsic understanding we needed to have to serve our stakeholders.

With the machines now coming for this work very directly - not just in terms of the transactional but also tasks that are higher up the value chain such as cognitive and decision-support work - the nature of the function, in terms of size, caliber and focus, must be re-engineered very deliberately. And we’re going to have to do this work ourselves. No one’s going to do it for us.

If we don’t, Procurement faces an existential risk:

If the work we do is no better than the machines, then why not just let the machines do all of the work?

How The Argument Unfolded

I began the discussion by making the case for change. I argued that the ground under the function was shifting far more rapidly than any of us ever expected and that these shifting sands were introducing an existential risk. We needed to reshape the role of the function, which wasn’t about just the tech, but about operating models, incentives, mindset and, most importantly, the practitioners themselves, as my own (informal) research suggested that 60-80% of practitioners were not ready for this change. Along the way, I dove into topics such as the six forces shaping Procurement in a post-AI world, how we can become AI-confident as well as the cost of cognitive debt and what we need to do to maintain cognitive agency in this new environment.

I then dove into what work endures i.e. what work stays human as the machines take everything else. I presented the Human Edge Matrix, an eight factor framework that helps determine how much of our work will go the way of the machine and what will not. I discussed the human premium in detail - and the fact that the type of work that remains human may not even be Procurement’s decision to make, but that of its stakeholders.

I then laid out my model for future-proofing the Practitioner in detail, with its Enabling Layer (AI Literacy and Cognitive Discipline), Differentiating Layer (Orchestration, Business Acumen and Human Leverage) and Orientation Lens. I also provided a capstone playbook to diagnose, build and sustain this development, along with a deep dive into what we really mean when we talk about judgement as being the differentiator.

While the Future-proofing model is helpful for the senior practitioner, the whole idea of the machines taking away the work that has been foundational to our development begs another question: what do we do about our juniors, who rely on that type of work for their own development? I argued that AI was creating an apprenticeship crisis because it was breaking the developmental chain and creating the discernment trap. We were asking juniors to take on the supervisory work while removing the very mechanisms that built the judgement to perform that work in the first place. I put forward my perspective of the compounding spiral we need to focus on as well as my proposal for the rebuild of these skills in a post-AI world and a plan of action for the juniors themselves.

I capped the entire series off with a deep dive into what leadership needs to do. The CPO must be primed to capitalize on not only taking advantage of the promise of AI but also of making sure his or her team is able to deliver on that promise in a meaningful, value added way. I set out the CPO’s capability agenda in this new world, why and how we need to reshape the role of the manager to fully take advantage of it, and then how we need to rethink the way we hire, evaluate and promote those within our organizations - especially since everyone can now present as competent - to create the future-proofed Procurement team.

Where I Drew The Line

So in terms of the questions I originally set out to answer, which were focused squarely on the practitioners themselves, I’ve answered just about everything I’ve wanted to.

At the same time, though, I recognize that many of the function questions have not been addressed here i.e. what does the Procurement function itself become? What does the operating model look like, what is its precise architecture and its incentives in this new age?

I did touch on some of these issues - I dove into the six moats that underpin the future-proofed function as well as the issue of how we define and deal with more ambiguous metrics and incentives such that they can’t be gamed (though at the level of people rather than the function) - but they have not been my prime focus.

That said, there are plenty of good folks studying these issues, who are giving this the fuller treatment it deserves. I’ll highlight some of their work as we go forward but my own intent has been very much (and very deliberately) on the people inside the function.

What Comes Next

Two things specifically.

First, this series will become a book. The working title is “Future-Proofing The Procurement Practitioner” and it will be released in early 2027. The book will build on the body of content on this site (which will remain freely available) and provide more layers of depth to the topics discussed here, including my perspectives from my time building The Smart Cube and at Kearney, tools and templates, case studies as well as real-world narratives and sidebars. If you’d like to know the moment the book is available, just subscribe to Proquria at the link below.

Subscribe now

Second, Proquria as a blog will certainly continue. I’ll be posting regularly on a host of related topics and keeping an eye on the key developments and critical questions Procurement is facing as it pertains to the talent issue. I’d love to get your feedback on the body of work here and where it works, where it doesn’t and how I could improve it further.

Over To You

So, at least at this level, my perspective on the talent thesis is all here: the case for change, the practitioner’s playbook, the junior apprentice’s development plan, the manager’s role rebuild and the CPO’s leadership agenda.

The one last point I’d add is that, if you’re a practitioner, it’s incumbent on you to take charge, take the initiative and get yourself going. This is the opportunity ahead of us.

Nobody is coming to get us sorted. It’s down to us.

Let’s get going.

Work that not only fortified domain knowledge but also provided the procedural grounding that developed the intrinsic understanding needed to serve its stakeholders.

With the machines now coming for this work very directly - not just in terms of the transactional but also tasks higher up the value chain, such as cognitive and decision-support work - the nature of the function, in terms of size, caliber and focus, needs to be re-engineered very deliberately.

This is work the function need to do for itself - no one is going to do it for them.

Otherwise, Procurement faces an existential risk:

If the work it does is no better than the machines, then why not just let the machines do all of the work?

This book is about how we solve for that problem - focused squarely on the talent issue.

It is not a book about technology, and it’s not a book about redesigning the function.

It’s about the people inside it: what endures when the machines take everything else, how a practitioner builds toward this new world deliberately, how the next generation develops when the developmental chain is broken, and what leaders must do to build teams that will still matter in 2030.

What’s in it

Drawn from more than three decades of Procurement experience and more than a year of research at Proquria, the book builds on the thought leadership on this site to cover:

• The Future-Proofing Model in full - the Enabling Layer, the Differentiating Layer, and the Orientation Lens, with the playbook to diagnose, build and sustain it

• The Apprenticeship Crisis and the rebuild needed - including the plan of action for junior practitioners

• The Leadership Agenda - covering capability priorities, the manager’s role, and how to hire, assess and promote when everyone presents as competent and all of the old signals are gone

• Tools, templates and diagnostics not published on the site

• Case studies and real-world narratives from three decades at The Smart Cube, Kearney and Procurement functions across every industry worldwide

Get notified

To get notified when the book comes out and receive updates on the latest from Proquria, subscribe via the link below.

Subscribe now

Omer Abdullah is the founder of Proquria, a thought leadership platform helping Procurement practitioners future-proof their careers for a post-AI world. He brings 35 years of management consulting and global corporate experience across North America, Europe, and APAC, and more than three decades working alongside Procurement practitioners worldwide.

Omer co-founded The Smart Cube in the early 2000s, bootstrapping it into one of the world’s leading Procurement Intelligence and Analytics platforms before its sale to WNS in December 2022. Earlier in his career, he held roles at A.T. Kearney, Warner Lambert (U.S.), and The Perrier Group (Asia-Pacific).

Through Proquria, he writes on how AI is reshaping Procurement work — and what it takes for practitioners to stay relevant, and valuable, as the profession transforms.

Last week, I posted the following on LinkedIn:

In response, a reader asked:

How do you measure “judgement under ambiguity” without turning it into another dashboard people learn to game?

That’s a really great question because it hits on a number of veins - whether such metrics are relevant, whether we can even measure such traits effectively, and whether we can embed them while keeping the entire process honest.

It’s a topic worthy of deeper discussion and one I’ll dive into in this post.

We Keep Choosing Savings

The topic of metrics is one that Procurement and the broader business world has been tackling for years.

Ideas like the Balanced Scorecard have, for decades, pushed forward the idea that we need a diverse set of metrics to truly gauge the full spectrum and complexity of individual performance - and, therefore, that the measurement systems we utilize must encompass both hard and soft metrics.

But despite our best intentions, when push comes to shove, we don’t always practice what we preach. In actuality, we tend, more often than not, to revert back to the same old measures we’ve always relied on: savings delivered, cost avoidance achieved, cycle time reductions, compliance rates, spend managed, etc.

Why is that?

Not Fuzzy - Inconvenient

The usual explanation is that soft metrics are hard to measure. They’re fuzzy, hard to pin down and certainly harder to compare across people at similar levels than say, how much money did you save us last year?

This argument is only partly true - there is some truth to these concerns but the fact is also that a good manager can, given the chance, usually assess capabilities such as judgement fairly consistently and accurately when given the chance.

The real reasons, then, have to do with culture, design and reporting.

Many organizations speak early and often about the value of metric diversity, but when you look beneath the hood, you find that the culture doesn’t truly prize it. It’s not baked into the language of management, into the initiatives they fund, or the reward systems they put in place. At the end of the day, hard numbers matter because incentives (i.e. compensation and promotion) are aligned to them.

Second, the metrics themselves aren’t designed for success - which leaves them open to gaming by individuals who work to the letter of the law rather than the spirit of it. Alternatively, we also see situations where the manager assessing the employee’s performance on these measures simply doesn’t have the ability or experience to be able to make an accurate evaluation.

Last but not least, ‘soft’ metrics tend not to travel well up the chain. When managers have to report upwards on a team member’s performance, they find it harder to sum up such performance and compare them across individuals in a way that is reasonably objective and ‘backable’ - whereas hard metrics don’t have this problem.

The Harder Work

So what are we to do?

The point, in my mind, isn’t to abandon the idea of soft metrics. We absolutely need balanced metrics, especially in a post-AI world, where the machines are taking on much of the “easy to measure” work and where the ‘softer’ metrics matter even more.

We need to actually embrace them even more, and do the ‘harder’ work of doing it right - which means defining them better, teaching managers to evaluate better, and establishing real rewards and consequences.

Describe Reasoning, Not Results

First off, we need fewer metrics that are better defined; that is, each metric has clarity behind what it means and what we expect from someone exhibiting those ‘soft’ behaviors.

The key is to be careful not to turn this into a checklist. The more tightly we try and define it, the more specific we try and make the constituent parameters, the easier it becomes to game.

What matters, instead, is to define what ‘good’ looks like in terms of the metric itself - for example, how does the individual weigh different signals, what did they base their decisions on when there was little data available, what information did they consider more versus less relevant when making a decision.

In this sense, competency models, by level, do a great job. I used these all the time in both my consulting days as well as with The Smart Cube as I assessed my teams. Such models show the nature of the skill at each level as well as the expected trajectory. They allow both the manager and the team member to understand what “good” actually is and work towards it.

Assessing Judgement Requires Judgement

Irrespective of how well we define our metrics or how good our competency models are, the fact is that assessing judgement itself requires judgement.

What does that mean? Well, that means that we, as managers, have to be able to measure the decision-making process of our people rather than (just) the outcomes they’ve produced. I appreciate that this might sound somewhat counter-intuitive but it actually isn’t. Good decisions can sometimes go wrong while bad ones can sometimes get lucky. The point is to keep the focus more towards the reasoning and thought process behind each decision.

Which, of course, raises an obvious problem: an organization can only measure judgement to the extent that its managers actually have that judgement - and the ability to measure it - themselves. That’s not always the case.

Years of incenting hard metrics over soft ones means many managers either never developed this ability or, at the very least, their skills are flabby. This is the apprenticeship problem I’ve discussed before but one level up from that of the junior: many of our managers can’t make this evaluation because they themselves came up on the “savings as the only metric” regime. They don’t know how.

Solving for this needs work - and it isn’t going to be a simple fix.

It’s a multi-year build. It requires managers with the ability to make these judgements and the stomach to navigate this more ambiguous process rather than the formulaic one. It also requires managers calibrating their reasoning and how they evaluate the same behavior or trait; the divergence of viewpoints at such calibration sessions exposes how and where standards differ, allowing for shared understanding to be built.

Make It Count

Lastly, we won’t succeed unless we reward the behaviors we want and penalize the ones we don’t. At the end of the day, people will only do what they’re incented to do. Getting to this has a hard part and a practical part.

The hard part is that the process to make it count is not a simple one. The solution cannot be to establish a formula per se to determine who gets a reward and who doesn’t; we’re not trying to get to a “score”, because that can be gamed. We need, instead, the right manager to conduct an assessment that is thoughtful but still discretionary, one where he or she exercises their own judgement in the process. This is, as I’ve said before, hard work but the benefit is clear: without a score to be achieved, the team member must then understand the required behaviors to model and, then, actually be good.

In terms of practical levers, rewards and consequences matter. Compensation (in the form of a bonus) is certainly helpful in this regard, but promotion and advancement is more meaningful and long term. Advancement within the organization is the clearest signal of what the culture values. Promote the right behaviors even a couple of times, and suddenly everyone will get the message as to what’s important.

So, What’s the Answer?

Look, at the end of the day, I’m not sure you can build an ungameable metric, but you can build metrics that resist it.

Metrics that are finite but diverse. Metrics that are meaningful, encompassing the hard and the soft. Metrics that aren’t about “scores” but about competencies, decision patterns, and good reasoning, judged by strong managers consistently and regularly. Metrics that are rewarded appropriately.

As AI compresses our “visible” outputs and takes on much of our work that is measurable, the stuff that will remain relevant will be the work of the human, the ‘soft’ stuff. We need to learn to manage and reward these more consistently and effectively.

So my answer to the LinkedIn commenter on my post: you cannot measure judgement under ambiguity, you have to assess it, repeatedly, by someone qualified to do so, against a defined set of competencies and a trajectory (versus a score), with real implications (rewards or consequences) attached.

Over the last couple of posts, I’ve drilled into the CPO’s talent agenda, looking at where the development focus in a post-AI world should be, by level, within the organization, and then analyzing how we should rebuild the role of the manager, which happens to be the load-bearing wall when it comes to developing this talent.

That said, all of this only matters if you have the right talent to develop in the first place. Leaders can only build value-creating Procurement teams if they have people worth developing.

The challenge today, though, is that the measures and metrics we’ve relied on to hire, assess and promote talent are under serious question. Some are less effective than they used to be, while others are no longer fit-for-purpose.

In other words, the instruments we’ve relied on for decades to recruit, evaluate and promote our people have stopped working. And unless we get that right, we’re going to have a problem building Procurement teams that can fulfill their potential in this new world.

So how, then, do you know that you’re building the right talent pool for a post-AI world?

That is the question I’ll explore in today’s post.

The Signals Are Gone

Think about how we hire, evaluate and promote:

• We review cover letters and resumes en masse and cull them down to the select few worth interviewing, post which we conduct remote interviews to gauge candidate fit and quality, finally culminating in an in-person interview to make the final selection.

• Once hired, we look at the individual’s work outputs and impact (usually their savings delivered, efficiencies captured, etc.) to gauge their performance, using these measures (along with perhaps a couple of other metrics) to assess how they’re doing.

• We then aggregate these measures and performance history over time to determine their progression within the organization.

But there’s a problem: all of the old proxies that we’ve become used to are broken. Because, in a post-AI world, everyone can present as competent - and technology is the reason why.

Yes, of course, technology has been a boon in the talent management process. AI tools allow us to rapidly assess resumes at the press of a button; remote interviewing has materially reduced the costs of the interview process; goal-tracking software, feedback automation, workforce analytics allow us to infer ongoing performance and productivity; while skills and competency-inference tools help us to assess an individual’s development and readiness for promotion.

But, intentionally or otherwise, technology has also allowed the process to be (seriously) gamed.

To start with, AI has resulted in a near-total contamination of the hiring process. The majority of job applicants (up to 80% according to SHRM) are now using AI to generate cover letters, resumes, and to conduct interview preparation. Every single one of these outputs looks polished and reads great. On top of that, there are even emerging tools that provide real-time interview assistance, providing interviewees with live answer overlays to questions as they’re being asked, unbeknownst to the interviewer. The net of it all, as discussed in an HBR article that surveyed 6,380 screening sessions: companies are basically hiring for those who are best at navigating the recruitment process, not the best at the job itself.

Even when we look at ongoing employee performance, there are genuine question marks. People are using Gen AI to proliferate AI-slop, and it’s pervading the workplace: work product that looks polished but is actually low to moderate quality - and there’s a real question as to how much of this quality drop-off is being picked up (in time). We then have the potential for systems sitting on top of this work that generate and interpret aggregations and summaries and analytics that managers make decisions on. But if we have questions about the data at source i.e. the actual work product itself, then we have to ask whether these performance summaries will convey any real meaning, or whether we’re simply relying on interpretive signals based on work that has been hollowed out at the source. In other words, our traditional approach of assessing throughput and productivity may not measure quite what we think it should in a post-AI world.

This has natural follow-on implications as we assess who we should promote. We have to question whether we’re doing so on the right bases, whether these individuals are truly fit for the new Procurement world we’re in, and whether they will be capable of flourishing in an outcomes-driven world that demands the ability to deliver value beyond (simply) the traditional metrics of savings and efficiency.

TL;DR: what got us here almost certainly won’t get us there, because, again, in a post-AI world, everyone can now present as competent.

What To Focus On

So what is the CPO to do?

Their focus, as we’ve discussed over the last few months, should be squarely on assessing their people across the Future-Proofing model’s three constituent parts:

1. Enabling Layer: Is the individual AI-literate? How comfortable are they with tech and AI tools in general, and how well have they integrated these tools into their daily disciplines? Do they consume AI critically or passively?

2. Differentiating Layer: What is their inclination towards orchestration? What is their aptitude when it comes to business acumen? How strong are their human leverage capabilities?

3. Orientation Lens: Is the individual focused on driving towards outcomes? Are they focused on getting to results - defined broadly - versus ‘sticking to process’? Can they explain their work, their goals and their impact in terms of the organization’s overarching objectives?

(Once again, as I stated when I laid out the model, practical Procurement knowledge - the working understanding of the nuts and bolts of the Procurement function - is a hygiene factor rather than a differentiator, except in the case of junior hires, for whom these capabilities must, of course, be developed, which is why it isn’t referenced directly in the model.)

The key is to understand not only their proficiency and ‘current polish’ at each layer but also their trajectory i.e. whether they are trending in the right direction when it comes to these capabilities.

But these questions cannot be answered using the old assessment tools - paper records and ‘traditional’ work outputs - and then correlating accordingly. AI, as I’ve mentioned earlier, has severed that correlation.

Leaders must now assess these traits directly, in ways that many are not accustomed to doing. In other words, what the post-AI world also calls for is a rethink of how we assess hiring, performance and promotion.

Essentially, the governing test we need to apply at all stages is: Can this signal be simulated with AI assistance? If so, then that’s the broken bit that needs to be fixed.

Let’s dive into this from the three specific contexts: the people coming in, the people we already have and those we want to promote.

Context One: The People Coming In

The key during the recruitment process is to trust visibility over artifact quality. That is, evaluate what people are able to do and discuss versus (just) what they say they produced.

This translates into a host of tactics:

• Ask for structured, verifiable inputs versus open narratives: There is an argument to replace free-form resumes with structured surveys that ask about specific, checkable facts e.g. categories managed, spend under control, team sizes overseen, etc. These are facts that are easier to check and validate.

• Test adaptive reasoning: Give the candidate a scenario and ask them to take a position on it. See how they defend their argument. Pressure test during the discussion, taking alternative tacks and see how well they respond and, at the very least, reason their way through.

• Leverage AI to solve live problems: Instead of banning the use of tools, incorporate them into the interview process. Give them a realistic category problem and ask them to work through it live. The point is to gauge what questions they ask, how they break down the problem, structure their approach, and how they iterate their way to the answer (the exact nature of which doesn’t matter as much as their questions and their process).

• Evaluate seeded errors: Give the candidate an AI-generated analysis that contains subtle flaws - perhaps a supplier assessment based on overgeneralized or wrong assumptions, or a savings model with a buried error. Assess how they analyze the output, whether they can find what’s wrong and what they suggest should be done.

• Move key interviews to in-person: For all key decisions, move away from the remote interview, regardless of level. With the current and emerging state of AI tools, there is too much room for gaming the system and, given the importance of human leverage skills, in-person is the most effective way to assess personal capabilities.

The bottom line - stop weighting anything that can be AI-assisted unless it’s intentionally co-opted into the process. Otherwise, you’re just processing noise.

Context Two: The People You Already Have

The issue with external recruitment is that, in most cases, we are signal-poor i.e. we don’t have the evidentiary basis to assess an individual’s capabilities and so we need to engage and understand them via the tactics above.

Internally though, we are signal-rich: we have access to all the data we need. We just need to learn how to read it - by asking better questions:

• Observe how they operate in a hybrid environment: Watch how they actually work day to day - how well they integrate technology into their workflows, and how fluidly they navigate between humans and agents to get work done. Assign work that requires both and observe where they lean. Comfort with the hybrid approach must become a baseline signal of readiness, and it is only visible through observation, not self-reporting.

• Audit how they actually use AI - especially at the point of failure: Review their working process, not just their output. How and what do they verify before sharing an analysis? Where do they make decisions to override the AI output? Can they explain their reasoning, or why they rejected an alternative? Most revealing of all: when the AI output is confidently wrong, do they catch it? Ironically, the practitioners who take a little longer to produce might often be the ones adding the most value.

• Use reviews as assessment instruments, not simply status updates: Restructure review conversations so that defending the recommendation is the point. Question consistently and probe the reasoning behind every recommendation. Can they defend their position under sustained questioning, or do they repeatedly forward polished outputs that they can’t interrogate? This should be a standing discipline, not a periodic test.

• Track where they gravitate within the Future-Proofing model: Give people discretion in how they approach their work, and watch where they naturally move. Do they gravitate toward orchestration, stakeholder engagement and business context - or do they retreat to execution work? Can they think outside the ‘process’ to get to results? Can they hold their own in contextual and strategic discussions with principal stakeholders? Discretion reveals inclination, which reveals where their natural strengths lie.

Note the questions above reiterate the importance of the rebuilt manager role we discussed in the last post. They are the ones closest to the ‘signals’ and hence our primary assessment engine when evaluating talent within the organization.

Context Three: The Basis for Progression

One thing that should be clear based on the discussion above as well as prior posts is that promotion based on the metrics of old (savings delivered, efficiencies captured) will not suffice. Given how AI is taking over so much of the work that delivered ‘value’ historically, as well as how the nature of the practitioner role is evolving, we need to assess performance based on criteria that will ensure success in the future, not in the past.

These criteria encompass:

1. Judgement demonstrated, not output produced. Has the individual shown the ability to make decisions under ambiguity? Do they understand and rationalize the consequences of those decisions? Can they reconstruct their reasoning?

2. Orientation. Are they focused on outcomes versus activities? Can they speak to their work in terms of impact and changes delivered versus tasks accomplished? Do the stakeholders they serve credit them with the outcomes achieved?

3. Accountability and verification discipline. Do they take accountability for everything they produce? Are they able to determine when to deploy technology, when not to, and how to strike the right balance? Are they willing to slow down to get to the right results?

4. Development of others. Do they build judgement in the people around them, or do they simply dispense answers? How are they helping to build judgement versus simply ‘getting the work done’?

5. Trajectory of leverage. Are they trending in the right direction - towards the layers and capabilities within the Future-proofing model? Is the scope of what they can own expanding? Are they able to take on more ambiguity, more stakeholders, more orchestration?

Harder, But Fairer

The tactics detailed above are hard, in that they take work and investment to implement. But they’re essential.

They are the far fairer way to assess capabilities because they measure them directly, in action, versus on paper. They force validation via real time engagement and adaptive reasoning.

These tactics and metrics also provide an added benefit: those who are fully capable but present less well now have a valid shot at being seen and moving forward. These are the folks who have perhaps gone through career changes, who have track records that are less glamorous or “brand-forward”, who have always done the work but not been able to ‘showcase’ themselves effectively.

Real results over paper pedigrees. Probably the most valuable dividend from this approach.

The Compounding Advantage

All of this is learnable and quickly implementable. The tactics and measures can be embedded into existing hiring and evaluation cycles relatively quickly, without the need for new technologies. It just requires commitment and effort.

And, it is an approach that compounds over time. The CPO that implements this thoughtfully will build a durable, meaningful advantage - bringing in, retaining and promoting people who can deliver real human value in an era where the machines will play an ever larger (and more compelling) role.

The results should, and will, speak for themselves.

Over the last several months, I’ve laid out what it takes to develop Procurement talent in a post-AI world - from the Future-proofing model and the various learning layers that comprise it, to the junior apprenticeship crisis and the underlying developmental chain that needs to be consciously developed to resolve it.

I’ve also talked about the range of stakeholders involved in making sure this talent is developed optimally - from the CPO who sets the pace and the ground rules, to the individual practitioners themselves (senior and junior) who must do the heavy lifting of learning and growth.

Look closely enough, though, and you’ll see that there’s one stakeholder (other than the practitioner) upon whom most of the onus is placed: the manager.

Almost every program, every learning layer, and every mechanism I’ve proposed requires the manager’s time and attention. It is the manager who oversees the implementation of these talent programs, who models the thinking, enforces the cognitive discipline, coaches the discernment, and provides the space for the development of judgement.

Of course, it’s easy to just say that’s their job, that managers must step up and take on the responsibility of developing their people. I’ve said it myself, repeatedly. After all, it’s the reason they’re called managers: they manage.

And yes, of course, they should take on this responsibility, but for them to be successful in doing so, we need to also answer an important question: can the manager’s role, as it’s currently built, actually carry the weight we’re piling onto it?

My honest answer is “no”, at least not as the role currently stands, not in most organizations, and, therefore, not without a major role redesign.

That is what I’ll focus on in this post.

The Squeeze

The manager sits in a vice that’s been steadily tightening over the years - one that predates the emergence and growth of AI in our collective consciousness.

From above, managers have watched (and often helped) organizations continuously flatten. Their leaders have stripped out layers and widened spans of control. In many organizations, the number of direct reports has jumped to double digits over the last decade. And yet, even as they’re overseeing even more employees and receiving even less support than they might want or expect, many managers are doing larger chunks of the work themselves.

From below, those direct reports need more counsel and guidance, not less. With businesses facing ever more uncertain environments, those employees expect their managers to be beacons of certainty, or at the very least composure and direction. To add to this, the manager is now also being asked to develop judgement in their people deliberately - the very thing that, as I’ve discussed before, used to happen on its own, when the procedural grind did a lot of the teaching for us.

The results are exactly as you might expect: overload and burnout.

Managers tend to report burnout at higher rates than any other level in the organization, and there’s enough (at least anecdotal) evidence that says managers are simply overwhelmed. This, as you might expect, has knock-on effects in terms of team morale and engagement.

The net result: the single most important role that drives the development of your people is also the most stretched - a troubling idea for any CPO serious about talent development.

The Coaching Windfall That Hasn’t Arrived

Of course, with AI, we expected the opposite to happen.

The story we’re told is that AI will automate the administrative grind and hand our people - our managers especially - a windfall of time; time they can then pour into thinking strategically, engaging with critical stakeholders, and, yes, coaching and developing their people. The machines would take the drudgery and the humans would finally get to do human work.

But that isn’t how it’s playing out - for a number of reasons.

First, the manager’s role hasn’t simplified, its become more complex. In addition to the pressures cited above, they’re now expected to orchestrate a blended workforce of humans and AI agents and manage new workflows with different handoffs and quality parameters to consider - creating opportunities, yes, but also new failure modes that didn’t exist just a couple of years ago.

Second, the bar for what Procurement must deliver is rising at an ever increasing rate - for a host of reasons, ranging from the value the function is expected to realize for the organization (a result of its demonstrated successes over the years) to the need to justify its cost and impact beyond that of the machines. As a result, internal customers expect more, the C-suite wants more, and suppliers expect more. All of this is the manager’s responsibility to deliver.

Third, and this is a new one for our era, the manager now has to manage for AI workslop as and where it appears. As we’ve discussed before, AI has the ability to produce high quality work, but if we’re not careful, it can also produce output that looks polished but is actually hollow: plausible, confident, and wrong in ways that only reveal themselves once you’ve conducted considerable inspection. There is emerging evidence that many workers now receive this kind of output regularly, each instance of which takes time to identify and fix. The responsibility for that verification lands, again, on the manager.

(If we’re honest, we should also note that a meaningful share of AI workslop comes from the managers themselves. The very person we’re relying on to set the standard for cognitive discipline - to model what disciplined AI use looks like - is, in many cases, doing just the opposite. If a manager is just forwarding unchecked machine output, everyone below them learns this to be the acceptable norm.)

All of these pressures add up, and instead of the windfall of coaching time, the manager’s plate is now even more full.

“Coach More” Is The Wrong Prescription

So we see the problem and we accept that it needs to be fixed.

But telling the manager to simply “coach more” and “make the time” is not going to work. As well meaning our intent might be, it’s useless advice, because the problem is a structural one.

Faced with a hybrid human-agent workforce, far too many direct reports, increasing output volumes and heightened outcome expectations, doing “more of the same” isn’t the solution; it’s like telling someone who’s drowning that they need to just ‘swim better and faster’.

The manager’s job, as it stands, is no longer manageable, not for the new era we’re now in, and more training (while helpful) isn’t going to be the fix. We need to rethink the role.

So the core question to ask is: how do we rebuild the manager’s role so that coaching and developing people becomes the prime work - not the thing that’s squeezed in the margins after everything else is done?

Two Conditions

Before we get into the redesign, let’s be clear on two things.

First, the point of rebuilding the role - the underlying principle, if you will - is to make the manager less of a doer and answerer and more of a guide; someone who provides patient, questioning coaching that actually builds judgement. Keep that in mind as we dive into the redesign elements. I’ll come back to why it matters later.

Second, not everyone is built to be a people manager and this is important to take into account in manager selection. People management shouldn’t be the inevitable ladder of progress in every organization and some professionals don’t want to manage others at all. We need to identify who these people are and decide how they fit into our organization. There is, in my view, absolutely a role for senior individuals like these - experts in their fields who bring specific skillsets and value to the organization. They just shouldn’t be managers.

Redesigning the Role

There are seven levers for the CPO to pull - most of these are not things that a manager can pull for themselves, so the CPO has to take the lead:

Rebundle the role ruthlessly. Start by looking honestly at where your manager’s hours actually go, and automate or delegate everything that isn’t genuinely managerial - the status updates, report compilations, data consolidation. Hand as much of this work to the machines as you can. As important, be explicit about what the manager should no longer do. A “don’t-do list” is as important an element of the job description as the duties you actually want them to do. Without this latter list, any freed-up time simply refills with work they shouldn’t be doing - and nothing actually changes.

Push decisions down, with clear guardrails. A great deal of manager time is consumed in being the approval point for decisions that never should have reach them in the first place. Set clear parameters - financial thresholds, escalation triggers, pre-agreed playbooks - and delegate everything within those bounds to the people below. Decisions inside those guardrails shouldn’t come to the manager, only the genuine exceptions. (This might require, if you’re honest, a broader rethink of your decision-making culture.) This gives the manager time back, and it also forces judgement onto the very people they’re trying to develop.

Rethink meeting culture. This rule is easy to identify but difficult to implement: kill meetings that don’t need to be meetings. Review standing meetings and ruthlessly assess which ones are absolutely needed. Move status updates, basic reporting and routine reviews to asynchronous channels - a dashboard someone reads on their own time, not a meeting that requires a read out in-person or virtually. For those meetings that are necessary, establish the one required goal of that meeting and shorten it to the minimum length of time needed to get to resolution. Send pre-reads (where applicable) ahead of time, so that meeting time itself is spent on debate and decisions, versus recaps.

Change how work gets done. Make dashboards and analytics self-serve, so no one has to convene to review numbers they could have read over email or via an app. Protect a defined portion of every week - a day, or a half-day at least - as meeting-free time for the deep work of developing people. As the CPO, you need to model and mandate this. This will be a challenge (many organizations just don’t work this way), but it’s a commitment that will pay off.

Give them their own AI leverage. This one is obvious: the same tools eating the manager’s time can give some of it back, if used in the right way. AI can draft summaries, surface patterns, prep one-on-ones and flag anomalies - which frees the manager to do the human work on top of it. The goal is to automate around the manager, leaving them with the time to do the work they should be doing. (And don’t forget to factor cognitive discipline into the mix.)

Normalize micro-coaching. The image of coaching as organized, scheduled, hour-long sit-downs is part of the problem. Sometimes that is what’s needed but more often, it isn’t. The most effective coaching is quick and frequent - for example, a five-minute exchange in the flow of the actual work, tied to the live problem in front of the person; or a well-placed question just as a report goes out; or it could be as simple as a two-minute debrief after a supplier call. Frequent, tiny, in-context touchpoints build judgement far more effectively than monthly reviews, plus they fit inside real calendars in practical, workable ways.

Invest in the manager’s own development. We pour enormous energy into developing juniors and almost none into developing the people meant to develop them. We just expect our managers to develop on their own (or worse, think they’re past the need for development). That’s just wrong. Managers need explicit development plans of their own: how the role itself is changing, how to coach judgement rather than dispense answers, how to lead blended human-and-agent teams, and how to get genuine leverage from AI in their own work rather than drowning in it. The manager is the multiplier - and investing in them multiplies their realized value.

Coach as a Questioner - and Measure It

The levers above represent the key steps every CPO needs to take to rethink the manager’s role.

But even if we’ve done the hard work to restructure that role, even if we’ve automated what’s necessary and protected the manager’s time, the fact remains that the redesign can still fail, for two specific reasons.

First, how a manager coaches matters more than how much time they have to do it.

Consider the most natural thing in the world for a busy manager to do: a direct report comes to them with a problem and the manager, who’s seen this a hundred times, gives them the answer. Problem solved. It’s fast and it feels like good work, but it’s actually an incredibly corrosive habit.

Because every time the manager supplies the answer, they buying speed today but paying for it in dependency tomorrow. No judgement is built on the part of the employee, who learns over time that the fastest path is to just bring it to the boss. In time, the manager becomes the bottleneck as judgement across the team atrophies.

This issue has been around since the practice of management began- but it resonates even more strongly today in a post-AI world. If we accept the idea that handing people answers erodes their judgement, then we should trust that instinct just as much when AI does the same thing. The ‘manager-as-answer-machine’ is simply the human version of the discernment trap I described earlier in this series. AI just makes it more urgent.

Which is why the manager’s core developmental posture, in an age of AI, is the same one I’ve argued the tools themselves should take: be a questioner, not an answerer; a Socratic coach, not an oracle. When a direct report raises an issue, ask “what have you considered? What’s your strongest argument for an action? If this call is wrong, how reversible is it?” The manager that does this builds judgement in their people.

In addition, managers should also administer the explain-back test: “Walk me through how you got here - what did you assume, and where could this be wrong?” Direct reports who cannot defend their reasoning behind an AI-assisted piece of work are guilty of cognitive offloading, and explain-backs spot this in the course of the normal coaching process.

The second key reason a redesign can fail is to do with measures and incentives.

I’ve made the argument before that you get the capabilities you measure: talent development is stunted when performance reviews measure the wrong things. If a manager is measured purely on their team’s savings number and delivery throughput, coaching is always going to lose, because when the quarter gets tight - and it always does - the manager will, entirely rationally, drop the developmental work that isn’t part and parcel of their review to protect the numbers that do.

So the manager’s scorecard has to widen. Alongside the traditional delivery metrics, you have to find ways - however imperfect - to measure and reward the people development: whether the team’s judgement is visibly growing, whether their reports can defend their own reasoning, whether they’re building the bench.

Net-net: redesign the role to focus on coaching, insist on the Socratic method rather than answer-dispensing, and then measure it.

The Catch

None of this is easy. I recognize that. Everything I’ve described is genuinely hard.

The demands on the manager are real and rising, and the risk of burning them out is real. If you take this post simply as “here are more things for the manager to do,” you’ll make the problem worse.

But that is exactly the point of - and the need for - the redesign, and the reason the rebundling has to be disciplined rather than aspirational.

This is not “coach more, on top of everything else.” It is coach instead - a genuine swap, in which the execution and administrative work is removed, automated, delegated and pushed down, allowing the newly freed up space to be deliberately filled with the developmental work that is the manager’s actual job. To do otherwise, and to do it sloppily, means that coaching becomes additive, and we’re right back where we started.

Done properly, though, this is probably the single highest-leverage move a CPO can make. Because the manager is indeed the load-bearing wall of the talent development model we’ve spent months rebuilding. Every framework, every developmental model runs through the manager.

Strengthen the manager, and the whole structure holds. Neglect them, and it won’t matter how good the blueprint was. No one will be left with the time to build it.

Earlier this year, Greg invited me to be a guest on Executive Edge, a podcast/video series that he hosts for Tenzing Consulting. In that conversation, we touched on a host of topics relevant to the ideas I’ve been talking about here at Proquria, including:

• Why Procurement is at an inflection point because of AI

• Why most practitioners aren’t ready for this shift (which is an existential one)

• The individual path forward, encompassing both AI Literacy and Cognitive Discipline

• The four-part response leaders need to think through

• The durable (and very human) differentiators practitioners need to develop

It was a great discussion and you can check it out via the video link below.

Over the last several months, I’ve defined and built out the Future-Proofing model for Procurement practitioners, spelling out each layer and capability within this development model. I then turned my attention to junior practitioner development in a post-AI world, emphasizing the need to find ways to ensure our juniors develop a more intrinsic understanding of the work they do. All of these posts focused squarely on the individual practitioner.

While this certainly informs the work of the Procurement leader, the progressive CPO must also think more broadly, focused not just on juniors but also the seasoned individual contributors as well as their leaders. The core question for the CPO to answer is:

Across my entire team, which capabilities matter now, how do I gauge these capabilities and where do I allocate my investments?

In other words, the CPO needs to have a capability agenda: **a way to read each person’s development, what to build and in what order.

This post lays out that agenda in three parts:

• First, the capability architecture itself, which is the Future-Proofing model mapped across your entire team, by level

• Second, the incentives gap i.e. the gap between what you say you want and what you actually reward

• Third, the business case for all of this i.e. why, when budget crunches hit, talent development should be the last thing you cut, not the first.

Let’s start with the capability architecture.

Reading Your Team, Level by Level

The cleanest way to read your team is to look at it the way it’s actually structured - by level - and apply the Future-Proofing model to each one.

The interesting thing about the model is that, while it applies across the board and to all practitioners, the same capabilities will look different, and be assessed differently, depending on where the individual sits. For example, AI literacy in a junior is not the same thing as AI literacy in a leader. What you assess for - and therefore what you build - shifts as people rise.

To flesh this out for the CPO, I’ll use three tiers:

• Junior - your early-career people, who are still learning their craft.

• Senior - your experienced individual-contributor engine room, from mid-level analysts through senior category leads; folks who are proven past the junior level, carrying real work, but still doing it themselves rather than (primarily) leading others.

• Leader - your people-leaders, responsible for developing those who do the work.

Each tier has a centre of gravity when it comes to their development - the one primary capability that defines it, which is where your assessment and investment should concentrate. The other capabilities of the model still matter at every level, but the centre of gravity is the thing to get right. (At the end, I’ll provide the matrix that lays out the full grid.)

Note that one lens runs across all three - Orientation - which I’ll provide my read on, at each level as we go, and then return to at the end, as it’s the most important thing to assess.

Junior - Building the Foundation

A junior’s centre of gravity rests on two aspects: the Foundational Layer and the Enabling Layer.

The Foundational Layer is understandable for those just starting out: learn the core technical skills that underpin the Procurement function. Understand why and how a spend cube is built and how to assess it in-depth; analyze a category and understand its dynamics and implications for the internal customer; organize, develop and run an event to meet specific end goals and objectives; assess supplier performance and health in order to maximize the defined end goals for that category within the organization.

In prior posts, I’ve referred to this as table stakes for everyone, and it is, but, for the junior, there is a level of understanding - both domain level and procedural - that must be mastered before all else. The point is to ensure they have an intrinsic (versus surface level) understanding of the everyday work of Procurement, regardless of how effectively or efficiently AI tools take over this work in future.

The second aspect is the Enabling Layer - and, within it, cognitive discipline above all.

This might be surprising to some: surely, the most obvious thing to assess a junior for is AI literacy i.e. can they use the tools? Of course, that matters, but, just like the Foundational Layer, that, too, is table stakes; they either have it or they can acquire it fast. An ambitious junior will have no issues getting up to speed and becoming AI-proficient. (In fact, they’re more likely to teach you, the CPO, a thing or two when it comes to AI!)

The thing that actually determines whether a junior progresses optimally is the opposite discipline: the brake, not the accelerator. Can they resist offloading their thinking to the machine? I’ve written about why this matters so acutely for juniors before: they are the group most at risk of outsourcing the very judgement they most need to build.

So cognitive discipline is important to assess - and yet it isn’t easy to see, because a junior over-relying on AI produces perfectly acceptable output. You have to look past the output to the thinking behind it.

So the test I’d apply here is one I’ve spoken about many times before: the explain-back. Hand them their own analysis and ask them to defend it - what it recommends, why, what it assumes, where it breaks down. A junior who can’t do that didn’t produce the work; they simply regurgitated what they were told by a machine. That gap is the single most important signal at this level.

Beyond Procurement knowledge and cognitive discipline, the Differentiating capabilities from the future-proofing model are all present as well, but they’re nascent in the junior. You have to assess them at task level: can they orchestrate a single workflow end to end, including the handoffs, rather than just a task within it? Do they understand how their specific category connects to the P&L? Do they appreciate how the broader corporate climate is influencing how and why we budget and set category goals the way we do? Can they hold a straightforward stakeholder relationship and make themselves understood?

The point here is to view these as indicators of potential, not proof of capability. Capability will take time to develop, but they need to at least show inclination and aptitude towards these differentiating levers.

Finally, orientation, in a junior, is about line of sight: can they see how their piece of work ladders up to a given outcome? The junior who asks “what is this actually for?” - rather than just completing the task in front of them - is showing you the beginnings of the thing that matters most. The one who executes flawlessly but never asks is a flag, not a star.

Build priority: Embed Procurement knowledge and lock in cognitive discipline first. Everything else compounds off it, and it is far harder to instill later once habits have set.

Senior - Earning Differentiation

This is a pivotal tier, as it’s the one that’s probably the easiest to assess least rigorously - because practitioners at this level are, by definition, competent; they get their work done. The trap, though, is mistaking “the work gets done” for “this person is developing”.

The senior practitioner’s centre of gravity is the transition into the Differentiating Layer. This is the level where you find out whether someone is becoming a leader or plateauing as a strong executor. Both look identical in terms of throughput but completely different in terms of potential and options eighteen months later.

What you’re assessing for is whether the Differentiating capabilities are actually showing up, and deepening:

• Orchestration has widened from the single task to the whole project or category - designing the work, sequencing it across people and agents, and stepping in at the moments that matter. This is where project-management capability, genuine comfort working with agents, an outcomes orientation, and the ability to explain not just what got delivered but how it got there all come together.

• Business acumen has moved up from “my category” to the business unit - understanding what their internal customers actually optimize for, the commercial structures at play (and even possible), and where the value really sits and how to get at it.

• Human leverage has become genuine cross-functional influence - not just managing a relationship, but influencing people and shaping outcomes across boundaries.

The senior who’s differentiating is visibly operating at a wider scope than their job description strictly requires. The one who’s plateauing is executing their category flawlessly but showing no signs of reaching beyond it. Both are “good” but only one is a good bet for the future.

Of course, the Enabling capabilities don’t disappear here, but you’re no longer asking whether a senior has cognitive discipline - you’re asking whether they model it, whether they hold the struggle-first line even under deadline pressure when it would be faster to just let the machine do the work. And AI literacy has matured from using tools to selecting them - making the build-versus-buy calls and designing the workflows others will use.

Orientation at this level is ownership: do they orient their own work toward category and business value, adapting as conditions shift? The junior needs line of sight; the senior needs to self-direct, without having the outcome pointed out to them. A senior who still needs the outcome defined for them hasn’t made the transition, however good their work is.

Build priority: force the differentiating transition. Don’t let strong doers stall in the comfort of competent execution (unless that’s their defined career track, in which case, expectations should be clear on all sides). Give them the wider-scope work that either surfaces leadership capabilities or reveals its absence.

Leader - Multiplying Through Others

The line that separates this tier from the ‘Senior’ isn’t even more seniority. It’s that a leader’s value is now delivered through other people, not through their own output.

Which is why the leader level isn’t really about new capabilities at all. It’s about all five capabilities from the Future-Proofing model viewed through a central question: can they build these in others?

A leader might have superb personal orchestration abilities - but the thing you’re now assessing is whether they can develop it in their team. Can they coach judgement - not merely make good calls, but teach others to make them? Can they model and incent cognitive discipline in a way that sets the norm for everyone beneath them? Can they orchestrate not just work, but people-plus-agents across a whole team - which is a genuinely different skill from orchestrating their own projects?

This is the hardest transition in the entire architecture (for good reason), and the one organizations most often get wrong - because it is tempting to promote your best individual contributor into a leadership role and assume the multiplication simply follows. We should know by now that it doesn’t. Personal brilliance and the ability to build brilliance in others are different capabilities, and plenty of superb seniors make mediocre leaders precisely because no one assessed them for this multiplier skill before handing them the team.

(This is also why the manager’s role is the load-bearing wall of the whole rebuilt apprenticeship model - a point important enough that it’s the subject of an upcoming post.)

So at this level, read every capability twice: once for whether they still have it themselves - they should - and, more importantly, once for whether they can grow it in others. The second read is the one that really counts.

Orientation, in a leader, is framing and foresight. The junior sees how work connects to outcomes; the senior owns outcomes for their patch; the leader sets the outcome direction that others orient around - across categories - and senses where the function and its markets are heading well enough to reorient the team ahead of the change rather than after it. A leader whose team is perpetually responding to shifts rather than anticipating them has an Orientation gap, however strong the execution beneath them.

Build priority: assess and build for multiplier ability, not individual brilliance - and stop assuming the two are the same thing.

Above It All: Trajectory Over Position

Step back from the three levels, and take note of the lens that runs through all of them: Orientation. What you should see is that, at its core, orientation is a read on one’s trajectory rather than position. Every other capability tells you where someone is today, but orientation tells you where they’re going. And in an environment moving this fast, direction of travel matters more than any current snapshot. A strong-but-static senior is a worse long-term bet than a rougher one who is visibly climbing. Because the world is changing so fast, static practitioners are already falling behind - which has existential implications for not only the practitioners themselves but the function as well.

So the overarching message is simple: assess trajectory over position.

Weigh where each person is heading at least as heavily as where they sit today. This is the single most useful discipline a CPO can bring to reading their team - and, as we’ll see when we get to hiring and promotion, it’s what separates the talent decisions that hold up from the ones that don’t.

The Full Grid

Below is the full capability matrix in one, clean view - what good looks like by capability and by level:

You Get the Capabilities You Measure

Of course, even our best-laid plans can go awry and, in the context of the CPO’s capability agenda, there’s almost always one specific cause that undoes the entire architecture.

The agenda can tell your team what you’d like them to build, but how you incent, measure and promote them in accordance with this agenda will either ensure success or guarantee failure.

What you measure and reward will win every single time.

And this is exactly the problem. Most Procurement functions still measure and reward the same things: savings delivered, spend under management, cycle time, etc. That is, we’re rewarding execution - better, faster, cheaper. But you can’t ask a junior practitioner to slow down and focus on cognitive development yet still keep rewarding speed to output every single time. Neither can you expect leaders to expand their team’s potential and build their bench while incenting (mainly) their savings numbers. Those traditional metrics are fine as the base layer, but we need to also expand our focus to include traits such as judgement and the ability to develop others.

Point being, you can stand up the perfect development program, publish the perfect capability matrix, and then watch your people, understandably, ignore it all in favor of those metrics that the organization really values.

You get the capabilities you measure. Everything else is just slideware.

Fortunately, as the CPO, all of this is entirely within your control, but it does mean there’s more work to be done when it comes to your evaluation and reward systems, which need to be pointed in the same direction.

That means finding (and fighting to embed) ways that, however imperfect, measure and recognize the things that matter: a senior taking on the ambiguous, wider-scope brief; the leader whose people visibly levelled up; the junior who can defend their reasoning rather than just deliver fast output.

(I’ll come back to this point in a future post when we look at hiring and promotion.)

Why This Is the Last Thing to Cut

One last point, and I’ve discussed this in prior posts as well.

When budgets tighten - and rest assured, they will - training and development is always the first line item to get cut. It’s a discretionary line, the benefits are much further out and operations aren’t immediately impacted when it’s gone. Basically, it’s the easiest thing to do and, well, isn’t prioritizing hitting this year’s numbers the most important thing?

The problem, of course, is that you’re borrowing against a future bill.

Much like the iceberg I talked about a few weeks ago, many of the issues simmer below the surface. The cost of not developing your people is invisible this year but catastrophic in five - when you reach for a bench of seasoned judgement, only to find it not there. By then, it’s too late to reverse course without considerable cost.

AI is making this worse by automating away much of the work that built our development, and if we’re not then intentionally replacing that development due to budget cuts, we’re not just reducing our bench, we’re eliminating it entirely.

The business case, then, is simple: Talent Development cannot be the first thing to cut when times are hard. It should be close to last, because it is the main investment standing between you and a senior team that, five years from now, doesn’t exist.

Over the last several weeks, I’ve focused on why leaders need to develop their junior talent, and I’ve laid out a blueprint and a timeline for doing so. That’s all good from the perspective of the CPO intent on building a junior development program - one focused not only on getting their juniors up and running in an age of AI, but also building a strong talent bench over time.

The fact remains, though, that many Procurement organizations don’t have formal, structured junior development programs at all. Too many are simply brief rotation programs, while others are a few hours of in-class training. Still others are informal in nature, usually assigned to the supervisor in charge to manage in whatever way they see fit. In such organizations, juniors are effectively left to fend for themselves.

If you’re a junior in just such an organization, then today’s post is for you. I’ll focus on how you can take charge and build your own development program, so that you’re not dependent on an organization that isn’t doing enough to aid in your development.

(And if you are in a well-designed program, all of this still applies - it just means you’re pushing on an open door.)

AI as a Substitute for Thought

I’ve talked at length about the need to develop discernment - the ability to assess whether an output is sound or not - as the basis for building judgement, and how AI tools take away the very foundation we need to be able to do so.

But recent research shows another problem that is just as significant: the people who most need to build judgement are also the ones most likely to offload it. Studies have found a significant negative correlation between frequent AI use and critical thinking, as a result of cognitive offloading - and younger people (aged 17-25) show both the highest dependence on AI tools and the lowest critical-thinking scores.

In other words, AI is most seductive exactly where it’s the most dangerous - at the start of a career, before judgement has had a chance to form. Juniors end up bypassing the necessary cognitive struggle that is part and parcel of developing discernment and judgement: forming a view on how best to attack a problem, analyzing the results, and drawing conclusions for themselves.

There are a host of reasons for this: the demands placed on juniors by their organizations to get to results quickly, the desire to ‘show up well’ as compared to their peers, and also the ease with which training budgets are cut, leaving juniors with no choice but to rely on AI tools to help them think and do ‘more’. Whatever the reasons, excessive reliance on AI for complex reasoning, in the pursuit of instant answers, ultimately weakens a junior’s independence.

So you, as a junior, need to think and do things differently for yourself - which brings me to the core precept every ambitious junior needs to internalize.

Take Ownership

When you join an organization as a junior practitioner, it’s natural to assume the organization will put in the time and effort needed to facilitate your development - but most won’t.

So you cannot wait to be developed. You will have to architect your development path for yourself. And the fact is that this is something you absolutely can do: you have far more control over your situation than you think. The challenge lies not in the availability of tools and resources - those have never been more accessible - but in crafting a clear plan of action and cultivating the habits to stick to it.

(Ideally, you’ll have informal help from a supportive supervisor as well. But it isn’t a prerequisite for taking positive steps forward.)

Taking ownership of your own development is the essential foundational stance that it is built on.

The Three-Part Plan

Once you’ve made the decision to take charge, real growth is possible. To realize this growth, you’ll need a practical, structured plan. Here is that plan, in three parts:

1. Diagnose - evaluate your role

2. Build - develop the capabilities

3. Sustain - establish compounding habits

Let’s look at each step in turn.

1. Diagnose: Evaluate Your Role

Back in March, I introduced The Human Edge Matrix - my framework for assessing whether, and how much, AI will impact a given role. It assesses eight factors: codifiability, ambiguity, judgement complexity, creativity, stakeholder complexity, political and organizational sensitivity, ethical and values-based reasoning, and decision risk/reversibility/ownership.

The Matrix is a good starting point for assessing the exposure of your own role today.

The point is not to alarm you. It’s to do something far more useful: to objectively assess which capabilities your current role is not developing in you.

That’s the diagnostic you actually need. Because if your day job consists largely of highly codifiable, low-ambiguity, low-stakeholder work - which is exactly what most junior roles have historically consisted of - then your role is not building the capabilities that will make you valuable in five years. The Matrix makes that visible.

To apply The Human Edge Matrix to your own role, click here. You’ll need to enter your name and email (you’ll be subscribed to my site), and then you can complete the 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 - is retained in any way. This is purely for your own assessment.

Here’s what to do with the result: The gaps it surfaces are not an interesting observation to file away. They are your development priorities. Whatever your role isn’t building, you now have to build deliberately - which is exactly what the next step in the plan is for.

2. Build: Develop the Capabilities

In this step, you’re applying the components of the Future-Proofing model (detailed here) to structure and guide your own personal development. Look at the results of your assessment from the Human Edge Matrix and see where the largest gaps are and, hence, where you need to put in more work relative to other areas.

Of course, not all of the factors in the Matrix will map one-to-one with the model’s layer elements below - though a number of them will. The key is to identify if there are specific gaps that may need a bit more attention than others. All that said, the model is meant as a holistic development methodology - so both layers and all of their sub-elements must be worked on, particularly as a junior just coming up the ranks.

A. The Enabling Layer

This involves developing two skills that work in tension: AI Literacy (the accelerator that pushes you forward and helps you capture the value AI tools provide) and Cognitive Discipline (the brake that ensures you don’t go so fast that you lose your ability to think, comprehend and retain).

One point worth being clear on: AI literacy is merely the price of entry. It is not the differentiator. Every junior entering this profession will either have it or will be able to develop it very quickly. Being good with the tools will not make you valuable in the long term. What separates you is everything in the Differentiating Layer - and, ironically, the discipline to not over-rely on the very tools you’re expected to master.

That said, let’s get into it:

1. AI Literacy: The Accelerator

I wrote about how to build AI literacy in the corporate context in this article. In your context, the key is to make sure you understand the available tools and their impact:

1. Understand what tools are available (general and Procurement focused), and their current and future potential

2. Learn to think through the issue at hand, structure it appropriately and ask the right questions, with the goal of getting to quality output reliably and quickly

3. Learn to tell good output from plausible-looking garbage - which means never taking AI output for granted

4. Understand the build vs. buy trade-offs; for many simple applications you don’t need to buy, you can build quick and efficient solutions yourself

5. Understand how different tools work together and connect into workflows

6. Build the discipline to stay current and learn about new developments consistently

2. Cognitive Discipline: The Brake

I’ve discussed this at length as well in prior posts, and the same ground rules apply to juniors as to every other practitioner: use AI to supplement, not to substitute. The academic literature points consistently to the same conclusion - metacognition and self-regulated learning are the antidote to AI dependence.

The actionable implications for you:

1. Always develop your point of view first.

Think before you prompt. Before using AI, write down:

• The problem statement

• The desired outcome

• The likely risks

• Your own first-pass recommendation

Always start with the human frame - even a brief set of notes, a hypothesis, or a few framing questions will do.

2. Use AI as a sparring partner.

Once you’ve built out your human frame, even at a high level, use AI as a challenger, expander and editor.

The 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 recommendation

• Offer 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 actually agree with. Ask it questions, pressure-test key statements, push back where push-back is needed. Take nothing for granted.

This preserves your ownership of the core judgement task while still harvesting the benefits of the tool’s speed and breadth.

3. Build explain-back discipline.

This is a simple but powerful safeguard. For every key analysis where you’ve leveraged AI, be able to explain:

• What the recommendation is

• Why it makes sense

• What assumptions it depends on

• What the risks are, and where it could fail

• What you changed from the AI output

The rule to live by:

“I will not submit an AI-assisted recommendation until I can fully defend it in my own words.”

4. Practice hard mode.

Deliberately do parts of the work without AI, purely to develop and maintain the muscle.

This means going “old school” - reading through multiple reports in detail, working with Excel spreadsheets, hand crafting RFPs, emailing suppliers directly, calling them to discuss specific topics, etc. Some of this is interesting work, while much of it is tedious and time-consuming but all of it will be valuable to help you grasp the nature of the work at an intrinsic level.

5. Engage in regular self-critique.

Reflect on your thought processes, your biases, and how you learn. Consider how you can continue to push your thinking and your ownership of your work.

Make a note of the tools you use and their relative strengths and drawbacks. Establish personal “red-flag” triggers for when to use AI and when not to. And adopt a two-source rule for anything high-stakes - verifying critical claims against primary documents or an independent reference.

B. The Differentiating Layer

The Differentiating Layer comprises three core skills. These are where your actual value gets built - and they are the ones your day job may well not be developing for you, which is precisely why you have to go after them deliberately.

1. Orchestration

Orchestration is the core skill of any good Project or Engagement Manager - the ability to design the work, allocate it intelligently across humans, agents and systems, sequence it correctly, and step in when it matters. It requires understanding the organization, the function, the tools, the people and the processes, and then tailoring the work to drive towards desired outcomes.

The core abilities: systems thinking; task decomposition; resource matching; sequencing and handoff; quality verification; exception handling.

What you can actually do to build it:

• Own something end to end. Volunteer to run a small, complete piece of work - a low-value RFP, a supplier onboarding, a piece of market analysis - where you own the whole sequence, not just a task within it. Owning the steps and the required handoffs is where orchestration is learned.

• Map before you work. For any process you’re part of, map it out first: who does what, where the handoffs are, where it typically breaks. You’ll be surprised how few people around you have actually done this - and how valuable it is to get a sense of the big picture.

• Design a human-plus-AI workflow, explicitly. Take a piece of work and consciously decide which parts go to you, which to a tool, and which to a colleague - and be able to say why. Pilot-test and run the workflow. This is the single most future-relevant practice available to you today (and also a path that many organizations have barely embarked on to date - allowing you to show progressive value).

• Volunteer for the messy stuff. Cross-functional work with unclear ownership is where sequencing and exception-handling get built. It’s uncomfortable work and that is the whole point. Take this work on and learn to structure, sequence and troubleshoot. Figure out, through trial and error and discussion, how the pieces are put together.

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, their goals and the outcomes they’re seeking, so you can mediate towards the optimal solution.

The core abilities: business and financial literacy; commercial acumen; stakeholder/internal customer literacy; market and ecosystem literacy.

What you can actually do to build it:

• Read the annual report. Actually read it - your own company’s, and then your key suppliers’. Then be able to explain, quickly and precisely, how your category connects to the P&L.

• Ask your stakeholders what they’re measured on. Not what they want from Procurement - what they are judged on, and what would make their year. Then write down how their answer changes what you focus on. Most juniors never ask this question and yet the answer entirely changes how you work with them.

• Learn the supplier’s economics. For any category you touch: what’s their margin structure, their cost drivers, where does their power come from? You can’t negotiate well against a business you don’t understand.

• Translate everything into their terms. Whenever you make a recommendation, state the business consequence in the stakeholder’s language, not Procurement’s. “This saves 8%” is a Procurement sentence - but “This accelerates your launch date” or “This allows you to reduce administrative waste and focus more on higher value work” is a business one.

3. Human Leverage

This is the third core skill, focused on developing the human capabilities that drive valuable outcomes.

The core abilities: relationship and stakeholder management; influence and persuasion; narrative development and communications; creative problem-solving.

What you can actually do to build it:

• Present to rooms more senior than you, regularly. Volunteer for it. Then ask for feedback on the delivery, not just the content. This is a muscle, and it only grows under load.

• Apply the “so what” test. For every analysis you produce, be able to say why it matters to leadership in a single sentence.

• Sit in on negotiations - and take notes on the humans. Not just the terms, but on what actually shifted the outcome: which argument landed, what was said at key moments, how stances were made to shift.

• Take the uncomfortable conversation. When there’s a difficult message to deliver, do it in person rather than by email. Every single time you do this, you build a capability that no tool can ever give you or replace.

The Rooms You’re No Longer In

There’s one more thing that the model above alludes to but doesn’t cover explicitly, and it’s worth calling out.

When AI takes away the foundational work, it doesn’t just remove much needed practice, it also removes you from the rooms (and networks) that matter. Historically, a junior built their network because the grunt work put them in the meetings, on the email threads, and in the negotiations.

Remove the work, and that network no longer builds itself - which means you now have to build it deliberately. Here are three things to do:

Build a personal board of directors. Not one mentor - several. One person you go to for technical questions, another for navigating the organization, another for career decisions, another who will simply tell you the truth. Identify different people for different things - but make sure you are giving something back to each of them (see below).

Understand that sponsorship must be earned - and go earn it. A mentor will advise you while a sponsor will advocate for you in rooms you’re not in. Mentors can be assigned but sponsors cannot. When one senior leader was asked how she built relationships with three great sponsors, her answer was: “I’ve delivered.” Performance is the entry fee, but it isn’t sufficient - you also need visibility. So go and get work that is visible to the people whose sponsorship you’d want, and deliver on it. Then, when a mentor knows your work well, ask them directly (and tactfully) to advocate for you - and be specific about what for.

Use reverse mentoring as your way in. On the point of giving back value, here’s an advantage you have that you may not have considered: you are probably more fluent with AI tools than many of the senior people around you. That is genuinely valuable up the chain. Offering to help a senior leader get more out of these tools (and showing them how e.g. via the workflow example cited earlier under Orchestration) is a way to create a two-way exchange - and one of the easiest ways to open doors into a relationship that wouldn’t otherwise be possible.

3. Sustain: Establish Compounding Habits

Every successful plan requires not just diligent execution but also thoughtful reflection. The following steps are designed to help you make the learnings stick:

Keep a decision journal. For any decision of consequence, write down - at the time you make it - what you decided, why, what you assumed, and how confident you are. Then go back later and compare it to what actually happened. This is the single most effective tool I know for building judgement, because it’s the only way to separate a good decision from a lucky outcome. Take the ten minutes needed to do it.

Enforce the explain-back line. I’ve mentioned this above, but it’s worth reiterating: never submit an AI-assisted recommendation you cannot fully defend in your own words. If you can’t explain it, you don’t own it.

Do one thing a week entirely by hand. Put away the AI and pick something real to do - a piece of analysis, a model, a supplier assessment - and do it the slow way. It will feel inefficient. That’s the whole point: the struggle is what builds the mental map that lets you spot when the machine is confidently wrong.

Review with a mentor, regularly. Bring an agenda to discuss. Bring your decision journal. Bring those things you got wrong. Don’t just do it when you’re stuck. There’s value in the regular debrief.

And stay honest with yourself. At the end of the day, you will be able to tell when you’re abdicating your thinking to the tool. You’ll sense it - because you can’t recall what you produced, or you can’t explain it back easily. When you notice it, be honest about it and correct it.

The Compounding Arithmetic

None of the above tasks or habits will pay off this quarter.

They’re slow and many of them are less efficient. In fact, the colleague sitting next to you who skips all of the above will probably produce more and faster than you for a while - and that’s precisely why most of your peers won’t do any of it.

But remember the development spiral I wrote about a few posts ago: each action you take sharpens the next, such that capability doesn’t just add up, it compounds. (It works the same way in reverse: skip the reps, and you forgo the entire learning curve.)

So the fact is that true gains will only be noticeable over time. In year one, the gap between you and the colleague taking the shortcut will be invisible. In year three, it will be noticeable. In year five, it will be enormous - and by then, effectively uncloseable.

That’s the whole point - don’t let AI do the work for you, decide to use it in a way that builds you.

No one is going to make you do it - which is exactly why you need to do it for yourself.

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

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

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

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

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

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

Two Principles

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

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

Contribution From Day One

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

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

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

Now, let’s get into the phases.

The On-Ramp (Weeks 0-2)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How You’ll Know It Worked

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

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

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

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

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

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

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

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

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

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

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

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

Cluster 1: Rebuilding the Procedural

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

1. Manual Work Runs

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

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

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

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

2. Simulation

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

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

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

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

Cluster 2: Rebuilding Knowledge and Network

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

3. Independent Learning

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

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

Rebuilds: domain knowledge (primary).

4. Rotation Program

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

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

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

5 & 6. Senior Relationships: Mentoring and Sponsorship

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

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

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

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

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

7. Structured Stakeholder Development

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

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

Rebuilds: network capital (primary).

Cluster 3: Building Judgement and the Discerning Eye

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

8. Structured Project Ownership

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

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

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

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

9. Feedback and Evaluation

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

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

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

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

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

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

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

These Are a System, Not a Menu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Rule 1: Make the Invisible Visible

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Two Constraints

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

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

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

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

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

What Comes Next

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

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

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

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

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

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

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

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

And therein lies the trap:

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

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

From Symptom to Cause

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

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

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

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

The Destination: Judgement-in-Action

Let’s start with our destination.

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

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

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

Let’s walk through this framework.

Three Unequal Inputs

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

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

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

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

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

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

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

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

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

Inputs Become Discernment

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

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

Discernment Becomes Judgement

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

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

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

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

Judgement Becomes the Practitioner

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

This has two faces that develop together.

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

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

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

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

Why This is a Spiral, Not a Ladder

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

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

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

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

What Comes Next

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

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

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

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

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

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

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

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

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

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

What the Grind Was Really Teaching

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

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

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

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

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

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

The Problem Everyone Sees

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

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

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

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

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

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

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

The Compounding Cost

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

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

This plays out in three ways:

1. The Loss of the Apprenticeship Dividend

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

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

2. The Digital Native Trap

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

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

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

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

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

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

3. The Loss of Collaboration

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

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

Why This Matters

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

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

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

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

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

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

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

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

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

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

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

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

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

How do you build and keep these capabilities over time?

AI Is Changing How We’re Learning

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

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

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

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

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

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

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

Manufacturing Your Development

1. Diagnose your trajectory - not just your level

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3. Review - and course correct

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

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

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

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

The Real Shift

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

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

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

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

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

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

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

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

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

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

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

This final piece is about where you aim them.

Orientation, Defined

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

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

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

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

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

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

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

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

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

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

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

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

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

Why Activity Stopped Being Enough

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

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

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

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

So how do we become outcome-oriented?

Six Principles for Outcome Orientation

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

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

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

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

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

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

2. Actively prioritize your time around the critical outcomes.

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

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

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

3. Make your tradeoffs consciously.

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

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

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

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

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

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

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

Consciously make the decision about where that reclaimed capacity goes.

5. Communicate your impact.

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

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

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

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

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

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

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

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

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

A Diagnostic Checklist

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

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

The terrain you’re operating in

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

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

What I’m planning, and where my time goes

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

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

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

The calls I’m making

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

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

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

How I know any of it is real

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

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

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

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

The Future-Proofed Practitioner

The future-proofing model comes down to three questions.

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

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

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

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

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

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

Judgement.

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

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

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

Why Judgement Matters More, Not Less

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

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

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

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

The Limits of Process (and Machines)

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

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

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

Building Judgement Deliberately

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

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

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

There are four key steps to building judgement:

1. Expand your inputs

2. Run a better process

3. Stress-test your thinking

4. Close the loop

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

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

1. Expand Your Inputs

A. Seek multiple diverse inputs.

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

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

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

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

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

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

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

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

C. Read outside Procurement.

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

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

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

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

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

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

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

2. Run A Better Process

A. Classify the decision before you process it.

Not every decision deserves the same level of process.

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

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

B. Define what success looks like before you decide.

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

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

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

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

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

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

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

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

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

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

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

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

E. Separate facts from feelings.

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

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

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

3. Stress-Test Your Thinking

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

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

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

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

B. Get opposing views.

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

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

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

C. Understand your own cognitive biases.

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

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

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

D. Develop a pre-mortem.

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

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

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

E. Make the opposing case.

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

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

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

F. Practice probabilistic thinking.

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

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

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

4. Close the Loop

A. Own your decisions.

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

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

Accountability is the price of judgement.

B. Separate decision quality from outcome quality.

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

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

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

C. Keep a decision journal.

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

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

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

D. Look for patterns across decisions.

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

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

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

E. Update your beliefs explicitly.

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

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

What This Means For You

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

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

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

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

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

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

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

One final thought:

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

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

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

In our chat, we get into:

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

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

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

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

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

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

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

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

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

The skills that comprise it fall into three clusters:

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

• The Persuasive Cluster: how we move people toward outcomes

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

Let’s dig into each cluster in turn.

Cluster 1: The Relational Cluster

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

Three skills make up the Relational cluster:

1. Emotional Intelligence

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

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

How to Cultivate:

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

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

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

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

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

2. Relationship Management

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

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

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

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

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

How to Cultivate:

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

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

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

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

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

3. Stakeholder Management

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

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

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

How to Cultivate:

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

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

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

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

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

Cluster 2: The Persuasive Cluster

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

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

4. Influence and Persuasion

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

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

How to Cultivate:

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

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

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

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

5. Narrative Development and Communications

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

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

How to Cultivate:

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

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

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

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

6. Negotiation

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

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

How to Cultivate:

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

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

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

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

Cluster 3: The Cognitive Cluster

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

7. Creative Problem-Solving

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

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

How to Cultivate:

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

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

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

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

8. Comfort with Ambiguity

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

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

How to Cultivate:

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

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

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

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

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

9. Curiosity and Adaptive Intelligence

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

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

How to Cultivate:

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

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

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

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

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

Why These Skills Compound

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

But what will not converge is human leverage.

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

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

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

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

1. The Enabling Layer: AI literacy and cognitive discipline

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

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

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

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

Business Acumen, Defined

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

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

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

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

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

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

Where Acumen Begins: The Seven Literacies

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

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

1. External Market

2. Strategic

3. External Customer

4. Business

5. Financial

6. Internal Customer and Stakeholder

7. Supplier and Ecosystem

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

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

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

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

1. External Market Literacy

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

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

What’s included:

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

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

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

• Growth dynamics and where value is migrating within the industry

• Regulatory environment and direction of government influence

• Technology disruption vectors and likely timing

• Geopolitical exposure and macro/cycle sensitivity

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

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

How to cultivate:

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

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

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

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

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

2. Strategic Literacy

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

What’s included:

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

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

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

  • The bets the leadership team is placing

  • The strategic risks the company is consciously accepting

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

• Innovation and R&D strategy

• ESG, sustainability, and reputational positioning as strategic choices

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

How to cultivate:

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

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

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

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

3. External Customer Literacy

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

What’s included:

• Customer segments and their respective economics

• What outcomes customers are trying to actually achieve

• Buying behaviors, decision criteria, and switching costs

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

• Brand perception, loyalty drivers, and emotional triggers

• Customer lifetime value dynamics and what drives them

• Emerging customer expectations

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

How to cultivate:

• Sit in on customer research sessions.

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

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

• Talk to the people who answer customer service calls.

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

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

4. Business Literacy

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

What’s included:

Economic dimension:

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

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

• The handful of variables that actually drive enterprise value

• Revenue model and growth model

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

• Unit economics and what makes them work or break

Operational dimension:

• The production/service delivery model

• Supply chain architecture and footprint logic

• Operating model: organizational design, decision rights, governance

• Make-vs-buy choices at the enterprise level

• Quality, safety, and compliance considerations

• The operational KPIs the business actually runs by

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

How to cultivate:

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

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

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

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

• Read operational reviews whenever possible.

5. Financial Literacy

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

What’s included:

• P&L mechanics and what moves which line

• Balance sheet basics, especially working capital and asset intensity

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

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

• Capital structure and the cost of capital

• Capital allocation logic and capex vs opex treatment

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

• Forecast, budget, and variance discipline

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

• Reading supplier risk and financials to assess viability and leverage

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

How to cultivate:

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

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

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

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

6. Internal Customer and Stakeholder Literacy

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

What’s included:

• Their priorities, time horizons, and risk tolerance

• Their language and conceptual frames

• What each key stakeholder is:

  • Formally measured and compensated on

  • Informally judged on (the unwritten scorecard)

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

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

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

• Relationship history and accumulated trust or grievance with Procurement

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

How to cultivate:

• Conduct ‘listening tours’ with no agenda.

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

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

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

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

• Map influence, not just reporting lines.

• Build your relationships before you need them.

7. Supplier and Ecosystem Literacy

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

What’s included:

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

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

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

• Technology disruption vectors in the supply base

• Geographic and political concentration and the fragility it creates

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

• Adjacent ecosystems, substitutes, and emerging entrants

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

• Supplier financial health and viability beyond surface ratings

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

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

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

How to cultivate:

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

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

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

• Subscribe to analyst coverage of your major supply markets.

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

Literacy Is Not Enough

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

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

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

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

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

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

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

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

In Practice: When PE Buys Your Supplier

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

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

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

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

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

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

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

The Floor Has Moved

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

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

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

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

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

That’s where we’ll go next.

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

1. The Enabling Layer: AI literacy and cognitive discipline

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

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

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

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

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

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

Orchestration ≠ Project Management

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

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

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

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

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

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

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

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

Before You Can Orchestrate

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

• Understand the organization:

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

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

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

• Know Your Internal Customer:

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

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

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

• Comprehend the Processes and Technologies:

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

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

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

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

• Understand yourself:

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

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

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

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

We’ll focus on that next.

The Orchestrator’s Toolkit

The core abilities of the Orchestrator encompass:

• Systems Thinking

• Task Decomposition

• Resource Matching

• Sequencing and Handoff

• Quality Verification

• Exception Handling

Let’s dive into each specific ability.

• Systems Thinking

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

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

    • Identify feedback loops (where outputs become inputs)

    • Distinguish root causes from symptoms, and

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

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

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

• Task Decomposition

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

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

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

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

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

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

• Resource Matching

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

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

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

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

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

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

• Sequencing and Handoff

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

    • Defining clear input/output specifications at each stage

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

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

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

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

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

• Quality Verification

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

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

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

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

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

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

• Exception Handling

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

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

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

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

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

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

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

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

Where Orchestrators Go Wrong

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

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

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

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

Why This Matters

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

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

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

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

We’ll tackle that next week.