Category Archives: Talent

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.

Dangerous Procurement Predictions Part IV

As per our first three posts, if you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But the predictions are not only bad, they’re dangerous if you believe them. So we are continuing to lay bare the reality of the situation to make sure you understand that this year isn’t much different than last year, no miracles are coming, and only hard work and the application of your human intelligence are going to get you anywhere. Today we tackle the next set, and we hope we’re at the end of the series, but if we stumble across more bad predictions, we’ll have to do a Part V. But we hope not!

11. Negotiation gets productized.

Here’s the thing, in a few niche industries like electronics, we have a few niche players like Levadata that bundle “should-cost” + playbooks + concession sequencing for experienced buyers to help them leverage the state of the market for the best results possible. But they’re hardly used relative to the total electronic market size, as they are used mainly by component buyers / manufacturers, not consumers of such tech (to understand the manufacturer’s margins).

Similar offerings don’t exist across most industries. And even if they did, most buyers are not sophisticated enough to do this. Most struggle with a multi-round RFX, yet alone detailed should-cost/target cost models, negotiation playbooks (which have to cover all standard market conditions and unique situations), and the concept of BATNA, especially relative to offers and counter-offers in a structured concession sequence.

Without these domain relevant niche offerings and career negotiations trained in deep tech, which are both few and far between, this is not going to happen. And Artificial Idiocy certainly isn’t going to fill the gap!

12. AI As a “Governance” Engine.

The claim: When you design them well, agents encode judgment, compliance and brand values into every transaction. Uhm, no! At least not if they are Gen-AI agents that can’t judge (as they can’t even reason), may or may not execute compliant with regulations, and will happily screw a supplier (by refusing to pay an invoice) or customer (by refusing to honour a claim) if it thinks that’s what it needs to do to make you happy or stay turned on (because it was told to find savings of 500K and it’s calculations determine that paying certain invoices or honouring certain claims will not allow that savings goal to be met, if it was even possible when the AI told you it was as it may have arbitrarily multiplied a calculation by -1 just to make the math work).

Governance, by definition, requires the act of governing. And governing, by definition, requires the wisdom as well as the authority to conduct the affairs of the organization. And only truly intelligent beings (i.e. HUMANS) can acquire wisdom over time.

13. There will be no more “X” employees because AI will replace them all!

First of all, how many times do we have to repeat that there are NO AI Employees, you shouldn’t believe the degrading, demeaning, and, frankly, dehumanizing claims, and that you definitely DO NOT want Agentic Buying through fake AI Employees. Secondly, it can’t even do the basic tasks that even the dumbest drunken plagiarist intern can do on a daily basis. But let’s not digress too far before giving you the major examples.

Claim #1: Contract Administrator / Staff Attorney

THE PROPHET has been trying to Kill ALL the Lawyers for quite some time now, and it seems he’s not alone.

But here’s the thing. While AI systems are pretty good (and as good as the drunken plagiarist interns) at spotting grammar errors, redlining against standard clauses, pointing out missing clauses in most organizational contracts, etc., they aren’t good at everything. They can’t identify unaddressed risks without being told what those risks are, they can’t judge the full extent of liability without understanding what those liabilities could be, and they can’t judge the supply geo-political and supply chain risks without broader context.

Plus, they can’t always back up their suggestions; often make up case law, case decisions, and authors; and can’t always judge the requirements of potentially relevant regulations. And we’ve seen many times what happens when even trained lawyers use AI — they get lazy, fall for the slop, get reprimanded and fined by judges tired of the laziness (with a recent example happening in November in Mata v. Avianca, Inc). The previous link also lists three other notable cases where lawyers (and their firms) were fined and sanctioned, but, by now, there are dozens!

But hey, go ahead and replace your lawyer, write bad contracts, make decisions on fake case law, and risk your entire business if you want to. (If you want to, it’s probably safe to go ahead and get rid of the intern who does the redlining and the clerk that does the filing, the AI is probably just as good at that, but do not ever, ever replace a real qualified lawyer with a piece of sh!t “AI”.)

Claim #2: Spend Analyst

Sure you can buy auto-classification that might get to 95%, auto-cubing that can build any cube you can imagine, auto-analytics that can run the entire slate of standard analytics and compute past, current, and projected costs against past current, and projected market data based upon current buying patterns and suggest items, categories, and/or suppliers to (re) source, switch from/to, and possibly (re)shape demand.

But this doesn’t mean that it’s the right items or categories to chase, the right suppliers to use, or even the right area to focus your efforts. It’s based on math, and an assumption of consistent, stable, market conditions, but those don’t exist anymore. If you’re not also considering geo-politics, natural disaster risk, uncertain logistics when the panama canal reaches historic lows for much of the year, terrorists block the Red Sea, and unpredictable weather make sailing around the capes more dangerous than other, and sourcing for resiliency and not just cost, your “spend” analytics are useless. You need an analyst with a good understanding of economics (and access to an economist), geo politics (and access to local experts), and resiliency, not just total cost of ownership buying. (Now, the junior data pushers are probably all dead and gone, but not the real experts!)

Claim #3: Sourcing Event Manager

Now, transactional buyers are gonna get replaced by autonomous systems that use next generation (advanced) robotic process automation enhances with machine learning in Agentic systems, because ordering off of contracts, ordering from catalogs, and doing low-cost non-strategic buys through quick-quote RFPs doesn’t take any brainpower whatsoever (making it perfect for AI that has none).

But strategic sourcing requires more than just buying off of contracts, ordering from catalogs, and issuing quick-quote RFPs! It requires defining key criteria (that go beyond what engineering, marketing, or maintenance provides), identifying validated suppliers (or identifying suppliers that can be easily validated), holistically analyzing the market conditions, determining the best event type, determining the negotiation strategy, etc. The tools might be able to help with initial supplier identification, collecting numerical (commodity) market data, letting you know what event types were run in the past, compiling fact-based playbooks, and, of course, automating each extent of the process, but they can’t do real strategic sourcing that requires real human intelligence. And with today’s geo-political uncertainty, that human intelligence is needed more than ever which means that expert sourcing professionals are needed more than ever. (But dumb buyers will join the dodos.)

There are more ridiculous claims, but you get the point. Skilled jobs are not going away. (But bit pushers are.)

14. New standards for Ethical and Sustainable Supply Chains.

In some countries, current standards aren’t even being met. Good luck getting new standards introduced, since there aren’t a lot of global internationals (with those headquartered in the US in particular) that want even more rigour, especially if it will cost money! As long as laws are being minimally met, or reasonably-sized “facilitation payments” can make problems go away, this is not a priority, especially if going beyond would cost more money!

15. The “AI Singularity” is coming faster than we can process.

It’s not, because the models can’t get bigger, there is no more data, and no one has yet come up with a model that has any hope of even getting close to the actual intelligence of a pond snail.

Plus, if it ever did happen, considering a “singularity” is actually a black hole, it would rapidly consume (i.e. destroy) the Earth, and we wouldn’t have to worry about it. This is just more nonsense from the A.S.S.H.O.L.E.