Category Archives: Talent

AI has NOT changed the fundamentals of Procurement. It HAS Strengthened Them.

Procurement, one of the last-areas of the back-office to be hit, is still drowning in the AI-Hype machine that is going full-force 24/7/365, as a result of the self-propagating A.S.S.H.O.L.E. that does nothing but excrete derivative nonsense on a continuous basis, piling it so high that it’s hard not be be Blinded By The Hype!

But, as we’ve seen, this new age of Agentic AI is not accelerating us into the Intelligence Age, but instead devolving us into the Neolithic Age (as it’s now been proven that these technologies are eroding [our] critical thinking skills, and only a few critical thinkers seem to realize that AI is dulling our minds).

Plus, it’s not effective. Studies by MIT and McKinsey last year demonstrated that only 5%/6% of early adopters saw a return. That’s a 94% failure rate, which is even worse than the general technology failure rate of 88% that is the highest it’s ever been in two and a half decades of project failure.

All AI has proven is that you can fail much faster than ever before, but still lost just as much money. That’s because the situation in Procurement is the same as in every other back-office function. Results come from the classic formula of:

  1. PEOPLE first
  2. PROCESS second
  3. TECHNOLOGY third

You need good people more than ever. Sure AI can “process” mounds of data at speeds we’ve never seen, but that doesn’t mean it can extract meaningful intelligence, and even if the intelligence is accurate, that it’s actually useful. Remember, these systems not only process data faster, they hallucinate faster than a field full of hippies at a Woodstock revival concert. But since their grammar and paragraph construction is now better than 90% of the population thanks to the social media revolution that has resulted in the average person having an attention span less than a goldfish and an IQ significantly less than our great-great-great Victorian grandparents, the majority of the population is willing to accept anything they pump out as accurate (even when it’s not).

Only top trained people can properly process complex situations, come up with the right solutions, and execute them. They should be using the most advanced tools available to them to process and make sense of the data using modern Augmented Intelligence technologies, but they should NOT be doing what a dumb system, guaranteed to hallucinate on a regular basis, tells them.

Once you have good people, they need to implement good processes that ensure best practice execution not only by them, but by everyone else who is involved in the process, inside and outside the organization (in partners, providers, and clients). Process allows emerging talent (with good education, great cognitive capacity, and an exceptional [dumb AI free] work ethic) to execute at the level of top talent with the guidance the top talent built into the process, and get the experience they need to become the next generation top talent in the organization.

Finally, once you have the right people, who know what to do, and the right processes, that help them get things done, then, and only then, do you identify the right technology to fit into, and accelerate, the processes. Maybe it’s AI, but chances are it’s traditional, domain-specific, (A)RPA that supports the process to automation levels of 95% to 99%. Dependable, fit-for-purpose, technology is always faster, better, and significantly cheaper than general purpose hallucinatory AI that may, or may not, work on any particular problem.

If you want to survive the current chaos, remember these fundamentals.

And if you can’t remember more than one fundamental, just remember PEOPLE first!

(While you can still find, and hire, people who know what they’re doing. Those of us who grew up before tech took over are getting older and greyer. Without us, not only will you not survive today, but you’ll have no one to train your staff for tomorrow. To think that, as a race, we survived The Great Extinction and, more recently, the The Great Decline during the Younger Dryas era only to risk global civilization collapse as a result of The Great Retardation.)

Dear Graduate, Don’t Skip the Internship … You Need a Gateway to an Apprenticeship!

A number of AI enthusiasts are advising soon-to-be and recent graduates to skip the internship and instead become proficient with AI because that’s how they are going to get a job. And, as you should know by now, it’s bullcr@p. Being able to write a prompt for a Gen-AI LLM that will return a convincing (but not necessarily sound) result is not going to get you a job. The only skill that’s going to get you a job is competence!

As with every over-hyped tech-du-jour that came before ([predictive] analytics, the fluffy magic cloud, SaaS, the WWW, etc), AI is not a silver bullet that’s going to solve all of an organization’s problems and grant magical status to those who have mastered it.

The only thing you’ll master with Gen-AI is the art of the con since whatever it spits out is so well written (compared to the average literary skill of an average high school, and even University, graduate these days) and so convincing that, without expert guidance, an average person is convinced that it must be right when they don’t know better. But that’s not a skill most organizations are going to hire you for (outside of sales and marketing), even if the organization is known for questionable ethics.

Organizations don’t need clueless idiots. They need experts who can assess situations, determine options, decide on the best option, and implement the decision. Someone who knows the analysis to run, the data to collect, the tools to use, the reports to create, the logs to keep, and the contracts to write.

And while you can’t graduate an expert, you can graduate with the skills to start you on the path to becoming one — the traditional skills of math, logic, critical reasoning, project planning, project management, and relevant domain knowledge — not creative crafting of perilous prompts for a flakey LLM that will eventually fail you no matter how much time and effort you put into that prompt.

And if you get get an internship and prove yourself, maybe that will lead to full time job where you can apprentice under a master in the real world and gain the experience you need to go from an adept (with the core knowledge and skills but not the wisdom needed to succeed in the real world) to practitioner (who has gained enough wisdom and experience to manage standard tasks and functions on their own, and who only needs guidance for new or complex situations not yet encountered) and, eventually, to expert where you become the new organizational mentor and the one that new hires turn to for help.

And organizations need (future) experts because only an expert knows when

  • it only has wrong/incomplete data (which will prevent an AI from ever working)
  • an analysis/outcome is wrong based on math fundamentals
    (and when an LLM-based AI multiplied by -1 because you told it to deliver savings vs. find the best opportunities based on price variability, lowest price, market trends, and differential analysis)
  • reasoning is correlative, not causative (which is a failure of not just LLMs, but many people as well)
  • an analysis is incomplete (because only they have specific insight that was not available to the machine or another analyst)
  • etc.

That’s why, if you want to become a true master of your craft, you need to forget the AI mastery and instead land an internship where you can apply the mastery of the real skills you learned in your degree program to stand out, get an apprenticeship, and learn how things work in the real world and acquire the real world mastery you need to get the job you want. Only then will you be able to work your way up to becoming the leader, and expert, you want to be.

There is no Artificial Intelligence (just Artificial Idiocy) and organizations will always need top talent. Automation, and well designed applications that solve real problems efficiently and effectively, will reduce the number of back-office employees that an organization needs and any employee who’s only skill is pushing bits will be eliminated. However, the need for talented employees will only increase to not only oversee the tools and handle the exceptions, but correctly analyze increasingly complex real-world situations and make the right decisions.

At the end of the day, AI tool mastery is meaningless if you can’t logically and holistically analyze the outputs with respect to math fundamentals and a real-world scenario!

How You Know Your Education System Is Broken!

Only 40% of employees say they’d be fine NEVER using AI again! (As per a recent Section AI survey in the Wall Street Journal of 5,000 white collar workers, as reported in a recent post by Stephen Klein who also noted that the majority of employees say it only saves them 2 hours or less per week. Furthermore, he also mentioned a Workday study that reported every 10 hours “saved” by AI resulted in 4 hours being lost due to required error corrections, flawed output revision, and necessary verifications, which means there aren’t much savings at all. [Specifically, for an average employee to actually save 10 hours, they’d have to save almost 16 hours, which would take them two months to achieve!])

Gen-AI is failing 94% of the time. It’s causing serious cognitive apathy and decreasing our IQs far beyond what Twitter achieved on its introduction (where it reduced our collective attention spans to that of a goldfish). It’s direct and indirect costs to run 8 hours a day are often more than to just hire another person (due to compute requirements that are 20X to 200X that of Google for a basic query, and the extreme amount of energy and water [for cooling] required on grids that are already stressed and ecosystems where fresh water is running out).

Chat-GPT. Claude. Grok. Rufus. Gemini. Meta. DeepSeek. Perplexity. Co-pilot. Poe. Le Chat. They’re all over applied due to over promises when they all have fundamental issues (like hallucinations) that cannot be trained out (as the issues are a result of their core design and programming), limited data sets (and now that AIs are being used to generate additional training data, performance is getting worse), limited guidance, and no guardrails.

There’s always been a time and a place for proper AI, but it’s not now, it’s not everywhere the investors losing Billions on Open AI and competitors are telling you, and it’s not the “AI” they are pushing.

Every time a new advancement in tech comes along, we always forget how long it takes to get from prototype to safe for unmonitored regular industrial and home use, be it hardware or software. With AI, it’s always been about two decades between a new algorithm being invented, and a production ready system with known performance, limits, and guardrails being ready for the mass market. In other words, this tech shouldn’t even be out of the research labs yet! We definitely shouldn’t have every major consultancy trying to push it as the cure-all for every problem throughout your entire enterprise. (Or new start-ups claiming they can offer you AI Employees!)

How many more examples of (silicon) snake oil do we need before we accept there is no panacea for all your ailments — be they physical, mental, or industrial — abandon this current iteration of Gen-AI, and go back to the targeted, mature, solutions that were finally ready for prime time (as we finally had enough processing power, data, and research behind us to deploy them with confidence)?

And even though the technology might work as much as 12% of the time, as per a PwC study that found that 12% of 4,454 CEOs surveyed reported both revenue gains and cost reductions, that’s not much of a validation of the technology — especially since those gains and cost reductions could have nothing to do with AI at all (and the pilot success of 6% from a recent McKinsey is a much more reliable metric here).

If you want real success, find a (A)RPA solution that works, lie its AI and buy it while you wait another decade for this technology to mature to the point its reliable, guarded, and safe for mass market adoption and widespread application. (Or wait for an AI-enabled SaS provider to come along who will do the 24/7/365 human monitoring required for you and make its software is usable and safe through this monitoring. Because all the current generation of LLM[-powered Agentic AI] tech is doing is increasing the need for human monitoring, not decreasing it.)

You CAN Afford to Wait for AI. But you can’t afford to wait to

  • get your data under control
  • build an infrastructure to allow for greater connectivity between apps within your enterprise and its greater ecosystem
  • update your processes
  • acquire and train the right talent with the knowledge they need to compete in the modern world
  • get digital and implement modern, current, generation technology based on best practices, proven (A)RPA ([Adaptive] Robotic Process Automation), and last-gen “AI” tech like optimization, predictive analytics (based on clustering and curve fitting), and point based neural networks with proven reliability and mathematically understood confidence where those apps are needed (and not a Gormless AI)

The reality is that you have to operate as lean and mean as possible. And

  • without good data, you can’t make good decisions
  • without good connectivity, you’re manually re-entering data across systems or missing critical external data you need to make good decisions
  • without good processes, you are inefficient and if not already, about to be circling the drain
  • without good talent, you are running on fumes at best, your ability to compete is at risk, and you can never improve
  • without modern tech, you are at a continual disadvantage and will continually fall behind

So you can’t wait to

  • institute Master Data Management (MDM)
  • enforce Open APIs in your solutions and acquire integration and orchestration solutions
  • review and modernize your processes where necessary
  • focus on acquiring, train, and retaining top talent
  • modernizing your tech to CURRENT generation proven tech, not experimental HYPE tech

BUT YOU CAN WAIT ON “GEN-AI“. It’s about getting the job done as efficiently and effectively as possible … with a low error rate and no significant risk! 99 times out of 100, you don’t need experimental “AI” to do that. Only the investors who spent millions/billions/trillionsw on unproven tech and the consultancies who need massive projects to employe bodies do … but that’s not to help you. That’s to recoup their wasted dollars. And that’s NOT your problem.

There is No One Optimal Team Structure for Procurement …

… not even if you get industry and size specific! But first, let’s backup.

Today I’m going to pick on Tom Mills because he’s well followed, a great practitioner, and gets a lot of stuff right (and I mean a lot of stuff right) … including key functions your “optimal” procurement team needs to support. We’re tackling this now because, in addition to prophetic prediction posts which are full of fantasy, the new year also brings the annual posts that tell you what the Procurement function is, what it’s primary tasks are, and what team you need to address it. And even the most well intentioned ones by the smartest consultants and practitioners don’t always get it right — at least to the extent they think they do.

There’s a couple of reasons for this, and they all relate to their Procurement world view which:

  • boils down to their (limited) experience, which is usually with a few companies in a single industry or related industries
  • typically consisted of sourcing primarily one or two of the six major types of Procurement (which are indirect, direct, services, tail, software, and strategic consulting / commissioning projects — all of which need to be approached differently and often need completely different solutions from different providers to tackle)
  • and usually revolved around a small set of systems and software offerings

Now, I’m not saying I can give you a perfect team model for your company, because I can’t. (In fact, without a deep analysis and evaluation of your company, no one can!) Not even if I created a starting one by industry, size, and geography — because every company is different, and those differences will create minor variations in optimal structure — which sometimes comes down to the talent you can get your hands on.

For example, in most companies product management and product marketing is usually two different functions because it’s rare that one person can do both. But someone who could do both would shift the organizational structure because a person who can do both would bring unique value — being able to design product and communicate the unique value to the market not only ensures all communication is accurate but all design is influenced by market need and reiterated to the market in a meaningful manner.

Now let’s review Tom’s proposal. As per our opening, it’s quite good. In fact, the elements are really good. You need business and category leads. You need sourcing and supplier value. You need operations and governance and someone definitely has to do that. And you need data and digital.
(And if it’s so close, why are we picking on Tom? Because to pick on someone who’s model is bad would require us to write a long multi-part essay or book chapter, and that’s just too much to make a single point.)

So, you need all of the people that are named (or at least the skillsets), but are they leads? Maybe. Maybe not. And is the model appropriate. Somewhat, but not really — not for a lot a of organizations (not being run by Tom or those with his Procurement world view).

But let’s start with the business and category lead and sourcing and supplier value lead. Maybe these are separate, maybe they are not. It all comes down to your philosophy on how you run Procurement. Are you event-based or category-based? If you are truly category-based, sourcing is part of category management, it’s not a separate function or activity — and your category leads know how to source. They will use analysts to help them understand the current market conditions; break down the cost structures; create should and target costs; identify the most likely suppliers; etc. But they will choose the strategy and own the sourcing event. There will be no “sourcing leads”, just “analyst leads” and “supplier development” leads.

Now let’s tackle the “data and digital lead” category. You’ll have a senior analyst lead who runs the team, which will consist of one or more spend and performance analysts and risk and resilience analysts, but the most critical member will be the Procurement Master Data Manager who will work with IT to ensure the necessary data is captured, maintained, enriched, and applied appropriately. Especially since any AI tool you use will blow up in your face without good data. (And if you’re using an LLM there’s no guarantee that it won’t blow up even with good data, but it’s much less likely to blow up with good data than with bad data.)

As for “digital and enablement specialist”, let’s start by clearly stating that any professional that isn’t digital 31 years after Nicholas Negroponte published Being Digital isn’t going to survive much longer in a world where everyone is chasing the AI Dream and trying to automate everything, even that which can’t be automated. Especially since those departments that lie and say it’s AI and adopt tech that works will be three, five, and even ten times more efficient than those that don’t. Every member will be responsible for digital enablement, not just a lead. The team may use expert consultants to help them pick the right tech and evaluate AI (to identify the hybrid or, better yet, old-school AI that actually works), but it shouldn’t be a separate lead in a modern organization.

Working back through the structure, let’s review the ops. An ops manager is critical — and a lot of departments miss this trying to be lean and mean. Someone has to ensure that all of the operations are aligned to support all of the category manager’s requirements from analysis through sourcing support though supplier development through compliance and risk management. And you probably will need a policy and compliance specialist, but should buying channel leads be separate from category management? And if so, is it a channel manager or a technology manager you need? You’re either buying off of contract, usually through an auto-reorder or catalog; from a marketplace; or through a sourcing event. Are those channels? (We’re not talking sales.) But you probably need an internal catalog manager and a marketplace expert.

Finally, the commercial advisory specialist and the contract and commercial manager should probably be on the same team in many organizations (i.e. the commercial advisory team).

In other words, the presented team structure is a great start for identifying key roles, but might not be the perfect org structure for you … or it might be. As noted above, it depends on whether or not you are category driven or not, tech centric or tech supported, and how much support the different roles need.

But most importantly, it depends on what industry you are in and what you are primarily purchasing. If you are in manufacturing, and are primarily purchasing direct, you will need a category manager for each major category as well as a liaison in the appropriate R&D and Manufacturing production teams for each major category. And since, in some categories, the supply will be limited it will be more about negotiation and target costs than open strategic sourcing, you will need engineering experts for target costs; risk experts to identify potential regional, natural, and economic risks related to a supplier; negotiation experts who understand BATNA who can balance supply assurance, quality, and cost; etc.

But if you are a retailer and just need finished goods, you barely even need a category manager. And you certainly don’t need to have a category expert embedded in another department. You just need to source, source, source. And there’s not a lot of risk analysis that needs to be done. It’s finished goods. If one supplier doesn’t supply, you go to another. Unless the retailer is a luxury retailer, it doesn’t care too much what the brands are as long as it can supply products that will satisfy its customers’ needs. And it will be the one organization that latches onto the digital and AI specialist as it will need tech constantly scouring for new suppliers, distributors, and marketplaces that can enhance supply certainty, quality, and/or cost effectiveness — because achievine any two of its three desires ain’t bad!

In other words, the optimal team depends on what the organization actually needs to succeed based on its industry, size, and maturity. It can start with a great template, but it will need to customize based upon its specific circumstances, processes, and maturity. And it might need help to define what that is.