Monthly Archives: December 2025

Breaking Down The Barriers:Competing Priorities/Overcommitment/Lack of Buy-in

We’re continuing our foray into the top barriers to success that we outlined in our top barriers post that chronicles the barriers that keep coming up over and over again in every Procurement survey in our effort to ensure that you don’t have to read another state of procurement study for the next 5 years. Today it’s a matter of priorities.

A Brief History …

Once upon a time, in the Industrial Revolution and the Gilded Age that followed, there was only one priority for a business. Make profit. That was it. The rich controlled the businesses, the government, and the economy, so their only priority was their priority, and their priority was to stay rich and get richer. (Now, it could be argued that this is the situation today, and in many countries, it certainly is, again, but there was a period of time that it wasn’t.)

But then workers, tired of giving up 9.5 of every 10 cookies made to their rich bosses who did nothing but sit around all day in their sitting rooms and lodges, rose up and formed unions. Despite the best efforts of union busters, these unions became prominent and workers slowly got rights. About the same time, the masses, who were pursuing votes for all (and I mean all, in the early days in some countries, only the rich men could vote; and while we all, hopefully, remember women’s suffrage, before that the working class men had to go through the same thing in many of these countries and, honestly, really should have been more understanding when the women demanded equal voting rights, but this is neither a history site nor a feminist site so we will end this discussion here), slowly managed to elect officials that cemented the rights of unions and the working class.

Initially this led to fair compensation and worker’s rights that had to be respected, but when it became clear that companies were not only poisoning workers with unsafe working conditions (starting with the creation of asbestos and then hazardous chemicals and pesticides and PFES and so on), but the environment as well, then you had environmental laws to contend with. Then when mass marketing mania began in the 1960s, consumers began to realize how much power they had when there were alternative options to meet a household’s needs (as the increasing pace of innovation meant that it was only a few years before a competitor came out with a competing product), and the importance of brand management magnified. Then you had more laws, and sanctions, around import and export as global trade expanded and so on. Of course, this led to the rise in Human Resources departments, Risk Management departments, and even Brand Management departments in the larger corporations. Moreover, let’s not even discuss “Diversity Initiatives”, which fall under HR in the many countries they still exist in (because they have evolved from equal “opportunity” through equal “outcomes” to “outcome targets” and that is NOT equal opportunity)!

The Problem

Now, for every decision that needs to be made, you have a profit priority, an environmental/sustainability/carbon priority, a risk priority, a geographic priority (near/friend shoring, forced or corporate mandated sanctions, etc.), a workplace safety priority, and so on — and the “top” priority is different for every single department. HR: worker well-fare. Procurement: savings. Supply Chain: supply assurance. Logistics: carbon or cost, depending on the country. Manufacturing: quality. Brand: ESG. And so on.

The Necessary Realization

It’s a mouthful, but its existed for decades: multi-objective optimization subject to absolute and preferred minimums and maximums, and the estimated cost of breaking a preferred minimum or maximum relative to the dominant priority.

Basically, the C-suite agrees on an overall hierarchy of priorities as well as absolute and relative minimums/maximums and goals for each priority that have to be adhered to by each department, who will, of course, strive to put their priority first (but can only be allowed to do so to the extent that the other priorities aren’t compromised).

This means that, for supply chain, they can optimize for supply assurance and on-time availability provided that they meet the:

  • organizational carbon target
  • geographic priorities
  • cost targets (based on contracts, procurement models, etc.)
  • quality and safety targets

and that they can only

  • go above the carbon target,
  • choose higher risk countries,
  • increase the cost, or
  • decrease the overall quality

if the percentage increase in assurance is double the increase in carbon (or some other agreed upon multiple), prevents a significant stockout loss, etc.

Then, all of this can be fed into an appropriately defined optimization model that will present one or balanced scenarios that meets the absolutes and only misses a goal if it’s necessary to hit another goal or brings about more benefit on one dimension than detriment on another.

While not everyone will see the solution that Procurement, Supply Chain, Logistics, or (Brand) Marketing comes up with as optimal, at least their baseline requirements will be met and it will be easier to get agreement and encourage collaboration.

There’s no perfect answer here as there will always be multiple viewpoints, but if you can show that you took everyone’s priority and requirements into account, it will open opportunities for collaboration and get everyone started on the same page.

The Technological Requirements

The technological requirements are considerable and require supply chain aware sourcing and sourcing aware supply chain and expertise from source to sink and back again on both sides.

A continuing reminder that if you want guidance in the short term, hope that your favourite provider reaches out to Bob Ferrari of Supply Chain Matters or the doctor and enables us to focus on writing the series (or in-depth e-book) explaining what modern Procurement and Supply Chain Tech needs to look like (and how it needs to be implemented) to address the challenges, reduce the risks, and address the priorities versus just dripping out tidbits as free time permits.

Breaking Down The Barriers: The Talent Gap

We’re continuing our foray into the top barriers to success that we outlined in our top barriers post that chronicles the barriers that keep coming up over and over again in every Procurement survey in our effort to ensure that you don’t have to read another state of procurement study for the next 5 years. Today’s topic: the talent gap!

A Brief History …

As we have previously noted in our discussion of category and market complexity, with each successive innovation, business, and process improvement, processes and tasks became more complex and required more education and experience to perform. As a result, with each successive innovation, the available talent pool shrinks. When you consider that traditional post-secondary programs haven’t kept up with the pace of innovation in business in decades, there’s a lack of formal training programs, mentorship programs disappeared decades ago (and every time there’s a workforce reduction, the older generation is the first to be let go or be bought out), and most businesses haven’t invested properly in training in decades, you can see that, for many traditional complex tasks, the talent pool is shrinking quickly.

But the lack of talent in traditional areas is just one side of the coin. The other side is that technology is progressing faster than even technology professionals can keep up. Combine this with the recent SaaS explosion in most corporations, with some large corporations using over 1,000 different SaaS applications, and the increasing complexity of “AI”-X applications, and the talent gap is expanding in tech as well.

The Problem

The problem here, as we expounded upon in our brief history, is multi-fold. There’s not enough talent in many domains, and in domains where there is talent, the rapid pace of development and innovation is still leaving top talent behind.

The Necessary Realization

In theory, the multi-fold solution is easy, but in practice, it will take a lot of human effort to realize, just like execution support can’t be solved overnight.

1) Bring Back Training

Stop trying to hire someone with all the skills and knowledge they need to do the job that is at least somewhat customized to your operation and start training again. Formal programs. Ensure all the materials are accessible online all the time so the parts your employees tend to forget (because they don’t perform the tasks regularly) are quickly and easily accessible.

2) Mentorship and Shadowing

Don’t wait until someone leaves to start looking for a replacement. Start training a replacement for a key position within three months of someone new taking the role via shadowing and mentorship. Make sure there is no task that relies on one person (even if always done by one person as it’s not demanding enough to require more than one employee) and that there is always a backup person. Stop trying to replace teams with agentic software and start trying to empower small teams with augmented intelligence so they can do the work of teams three, five, and ten times their size but still ensure the knowledge remains in the organization.

3) KMS: Knowledge Management System

Despite being introduced in the late 2000s, these never caught on and this is, honestly, one of the biggest reasons we have a talent gap today. Not only do most organizations not do enough planning around succession for those that perform key functions (it’s not just the C-Suite you need a plan for), but they don’t capture the key knowledge built up by long time employees who know how to run certain functions efficiently and effectively. That’s why performance degrades over time as people leave and new people are hired because the days when you’d join a company and stay for five, ten, twenty years or life disappeared with the last millennium. When organizations failed to properly capture this knowledge when the first round of massive layoffs hit in the dot com crash of 2000, and then never learned from it (and we saw the same mass exodus of senior, knowledgeable, people in the financial crisis of ’08), we ended up with massive increases in the “talent gap” as the knowledge required to forge talent suitable for your organization was lost. (On top of the fact that knowledge was increasingly leaking to China with the global outsourcing system.)

The reality is that unless the knowledge needed to run your business is captured, you’ll never have the talent you need, real or virtual (as these new-age AI-based agentic systems that will, according to the marketing, solve all your problems won’t work at all unless properly trained, and they can’t be properly trained unless the right data, processes, and organizational knowledge are appropriately captured — that’s a big reason these efforts continually fail and will continue to fail).

4) Augmented Intelligence Systems

For well-defined tasks for which mature (pre Gen-AI) agentic workflows exist that can be appropriately defined, controlled, and tailored, implement such systems to ease the burden on new employees as they attempt to learn the role and be productive and strategic.

The Technological Requirements

The technological requirements, especially for the KMS and the Augmented Intelligence Systems, are considerable and require supply chain aware sourcing and sourcing aware supply chain and expertise from source to sink and back again on both sides.

A continuing reminder that if you want guidance in the short term, hope that your favourite provider reaches out to Bob Ferrari of Supply Chain Matters or the doctor and enables us to focus on writing the series (or in-depth e-book) explaining what modern Procurement and Supply Chain Tech needs to look like (and how it needs to be implemented) to address the challenges, reduce the risks, and address the priorities versus just dripping out tidbits as free time permits.

Successful Vendor Selection – The Series

A Review of The October Diaries (in 4 Parts)

Part I

The October Diaries is a supernatural drama centred on the interaction of the protagonist, Jon W. Hansen, a distinguished analyst with a 40 year career in tech and, in computing years, an AI RAM Model 5 based on centuries of development. His work becomes increasingly complicated as other models continually challenge his and self-proclaimed AI Experts continually threaten our space from the shadows. The book chronicles the complex relationships as Jon tries to find new ways to preserve the truth and protect …

Oh wait, that’s the plot archetype for the Vampire Diaries. Did I read the right book?

Yes I did. But I just made you think, and that’s one of the primary goals of Jon’s book and one of the key points I have to make.

Every Influencer, Consultant, and Analyst needs to read this book, but 99% won’t learn anything if they don’t think and question everything they read. (And that’s one of the unwritten reasons Jon says you’ll have to read the book two and even three times.) If they don’t come to suspect the truths on their own before Jon exposes most of them in later chapters. If they don’t understand that this is not a guide or manual for success or the answer to all their problems (as there is none) …

It’s a book designed to make you do what we don’t do enough of in the age of AI: think, and, most importantly think in a way that will, in time (may not today, tomorrow, or even next year) allow you to actually use modern AI tools productively and extract value in real time.

Gen-AI efforts are failing across all the board, from large scale corporate projects down to small scale individual efforts to extract useful content for reasons that include:

  • lack of focus
  • lack of verified data & reinforcement training
  • lack of knowledge
  • lack of skill

You see, for success, you need to have

  • focussed domain models
  • deep context
  • deep domain knowledge to know when the output is good, ok, and bad
  • appropriate skills to utilize the models effectively

Jon gets at this with his six skills of conversational fluency, which is his name for the methodology he uses to train the models to do what computers do best (identify patterns, surface them, and draw correlations) while he does the strategic thinking humans do best.

As well as his five common mistakes that are one of the reasons the vast amount of human prompted content generated is AI slop.

But he also goes deeper into what is truly required for long-term success. Which may shock many of you who aren’t from the old-school we are, but, like Billy Idol, you have to deal with the shock to the system it will give you and push forward.

Discuss Part I on LinkedIn

Part II

Every Influencer, Consultant, and Analyst needs to read this book, but not for the reasons they think. It’s because they need to think deeply about AI, and that’s what this book forces them to do. It may be framed as a step by step guide to take you from zero hero, but that’s just to psychologically convince you that this is the guide for you — and if you want to understand AI, it is!

Most people are using AI wrong. More specifically, they are using the A.S.S.H.O.L.E. to sh!t out plagiarized slop that is turning the internet into massive sewer that is likely making Jon Oliver rethink his Facebook is a Toilet rant (from 2018) (because now the entire Internet is a sewer).

While that is one of the few things that LLMs can actually do, that’s NOT what they should do. They might be lying, hallucinating, soulless algorithms that will happily tell you to commit suicide, suppress life saving alarms while you’re locked in a server room on fire, or even ignore your shadow and have the self-driving car run you over, but they have their uses.

While they can’t do 94%/95% of what the firms selling them advertise (or we wouldn’t have 94%/95% failure rates, as per McKinsey and MIT), they can do four things very well, with reasonably high reliability when appropriately trained and deployed, that we can’t. The first two, as I keep promoting, are:

1) large corpus search & summarization
2) natural language processing

The third, as Jon makes clear in this book is

3) deep pattern detection and surfacing

But only if you know how to get the algorithm to do it!

You see, all these systems are trained to deliver direct responses to direct requests. As a result, when you give them a typical direct request in your “carefully calibrated prompt“, they give you what they think you are asking for, and that’s it. But that doesn’t help you, or me, or anyone, especially if they weren’t trained on the right data or it’s not available and the only way they can give you what you want is to make sh!t up.

Sure it might spit out 2997 characters for your Linkedin post 10 times faster while addressing the seven points you wanted, but is that really helping you when you have to read it, edit it, and copy and paste and verify it? That takes time — and even worse, it’s not productive time. If you’re not thinking about the 5 Ws, not only are you not sharing anything valuable, but you’re not advancing your thinking. (Right now, the only edge we have over machines is our ability to think critically and strategically — so what happens if we lose that?)

But if you can learn how to work with the technology, instead of getting bland plagiaristic derivations, you can get it to surface patterns across related bodies of work, document progressions over time, and use that to more quickly validate your instincts and formalize your ideas, allowing you to advance your own abilities while ensuring you can serve your customers faster and better by speeding up research and delivery efforts by multiplicative factors.

Discuss Part II on LinkedIn

Part III

Today we continue with our review of the supernatural drama that chronicles the interaction of the protagonist, Jon W. Hansen, and the RAM Model 5 that we’re sure you’ll find more thrilling than the pages of the Vampire Diaries we thought we were reviewing (due to the similarities in plot archetypes). You might not have the love triangle, but I’m sure the dollar signs will be more than enough to get your attention. (What dollar signs? Well, you’ll have to read it.)

In part one we said that you need to read the book because it will make you think (if you’re reading it right).

In part two we said you need to read the book because it helps you understand the power of LLMs is not its ability to create watered down plagiarized slop 10 times faster than the drunken plagiarist intern ever could but uncover patterns that you might never uncover on your own due to lack of time.

Today we’re giving you a third reason — and that reason is that it helps you understand why you are invaluable in the age of AI. While it has been true since the introduction of computers that monkeys could do all back office jobs if they knew what buttons to push, the reality is that AI, which should be called Artificial Idiocy, still doesn’t know what buttons to push, it’s just able, in many situations, to compute what button to push with high probability. But it DOES NOT know. Only YOU know! (You see, what AI really stands for is Algorithmic Improvement, as it is the label that is consistently applied to any algorithm that is an advancement over a previous algorithm, and that has nothing to do with intelligence.)

Now, it does mean that if your job is simply tactical data processing then you’re out of work, and it does mean some of your peers who aren’t as good and efficient as you are also out of work since the tech will make those who know how to use it up to 10 times as efficient at some tasks, but if you’re a skilled expert, then you are more desperately needed than ever because, as per our last post, only you will be able to detect the very convincing inaccuracies, lies, and hallucinations it returns.

But understanding is not enough, you need to be able to explain it, and when pressed, demonstrate it. That is what the book, after a few reads, will help you do. Use AI in a way that demonstrates you are what’s needed to make AI effective and make sure the organization isn’t part of the 95% failure statistic.

Part IV

In Part I of our review of Jon W. Hansen’s October Diaries, his take on the modern thriller, I told every Analyst, Consultant, and Influencer (ACI) that they need to read it because it will force them to finally think — deep — about AI.

In Part II of our review I told the ACI they need to read it because it will help them use LLMs properly and surface patterns they might not ever find on their own due to time constraints.

In Part III of our review I told the ACI that it will help them defend their positions in the “Age of AI” purge that is coming. (Since it’s a new excuse to fire people so the organizational shareholders can [temporarily] get richer!)

Now, in Part IV, I tell most of the ACI that I’m sorry. You shouldn’t read it. You want a quick fix and an easy solution to your relevance problem and this isn’t it. In fact, for some of you, it won’t even be worth the cost of the minuscule amount of storage it takes up on your hard drive.

Because it makes a few assumptions.

1) You have, or are willing to build (with your own hands), a deep archive of unique, human authored content to augment the models with.

2) You are willing to take the time to not only ensure the models are trained on this, and only this, archive but to learn how to both use the models appropriately and get them to retain and access relevant context across multiple sessions over days, weeks, and months, which is a skill that goes beyond creating executable ChatGPT prompts.

3) You have, or are willing to develop, the expertise necessary to know when the model is 100%, 95%, 90%, 50%, and 0% right, no matter how convincing the words are that it returns, and how to correct it and guide it to 95% every time (so you can make the corrections faster than doing the work from scratch), which could take minutes, hours, or days for any particular request you throw at it.

But let’s face it.

1) Most of you don’t have the archive, unless you work for a consultancy that has been delivering projects for at least five years, and preferably 10. Jon and I remember the early days with hundreds of blogs, and the 3/3/3/3 rule. Up to 90% of wanna-bees would quit after 3 posts/3 days, then the next batch by 9 posts/3 weeks, then the next batch by 27 posts/3 months, and the majority by 3 years would say “hey bloggie, I’m packing you in“. The hundreds of blogs I chronicled on the now-defunct SI resource site were down to a few dozen by the 2010s.

2) You won’t put in the months necessary to get the model and your skills to the point you are getting close to what you want every time. And it will be months!

3) Not only do you have to keep learning tech, you have to be constantly seeking out experts to learn your trade. That’s also a lot of work. When you’re Bowling for Soup, you know that High School Never Ends!

In an age where founders want to vibe code and flip companies within 3 years, you want instant gratification, but you’re not going to get that!

All it will give those of you starting out is a way to build a skill that is sustainable for life. But the vast majority of you will have to wait for the good things to come. And I don’t think you will. Sorry.

But if you want to prove me wrong, get the book!