Category Archives: rants

Forty Years Ago It Was The Big Money …

However, not sure The Big Money quite captures it anymore. the doctor suggests The Dumb Money. Seems quite apt, eh?

Dumb money goes around the world
Dumb money underground
Dumb money got a mighty voice
Dumb money make no sound
Dumb money pull a million strings
Dumb money hold the prize
Dumb money weave a mighty web
Dumb money draw the flies

Sometimes pushing people around
Sometimes pulling out the rug
Sometimes pushing all the buttons
Sometimes pulling out the plug
It’s the power and the glory
It’s a war in paradise
A Cinderella story
On a tumble of the dice

Dumb money goes around the world
Dumb money take a cruise
Dumb money leave a mighty wake
Dumb money leave a bruise
Dumb money make a million dreams
Dumb money spin big deals
Dumb money make a mighty head
Dumb money spin big wheels

Sometimes building ivory towers
Sometimes knocking castles down
Sometimes building you a stairway
Lock you underground
It’s that old-time religion
It’s the kingdom they would rule
It’s the fool on television
Getting paid to play the fool

It’s the power and the glory
It’s a war in paradise
A Cinderella story
On a tumble of the dice

Dumb money goes around the world
Dumb money give and take
Dumb money done a power of good
Dumb money make mistakes
Dumb money got a heavy hand
Dumb money take control
Dumb money got a mean streak
Dumb money got no soul

Gen-AI Won’t Work For Procurement … And Neither Will Agentric AI if the foundation is Gen-AI!

Right now every vendor is pushing “AI”, and the vast majority of that “AI” they are pushing is a Gen-AI LLM, and often that is just a wrapper of a third party Gen-AI LLM, like Chat-GPT (which only the French know how to pronounce properly).

And they are pushing this as a cure-all for all your procurement ills. It’s the new magic elixir. The new panacea. But, in reality, it’s the ultimate silicon snake oil, because it almost works. And it makes you feel really good when you use it. In medical terms, it’s not a treatment, it’s a psychedelic that takes all your pain away (until it wears off that is). But, just like the spoonfuls of LSD that allowed Bender to become the Iron Chef, it will only last long enough for the vendor to win the contract from you, and then it will start to fade. Until it fades completely when you need it most and fails you utterly when you need to figure out how to deal with a border closing that just happened, a critical raw material shortage due to an unexpected natural disaster, or a trade war no one saw (but should have seen) coming.

This is because, as we keep telling you, Gen-AI, which was built as a predictor technology to predict what block of text, in natural language, should follow an existing block of text (using chain-of-compute), based on training across a very large corpus of existing documents. It’s no more, no less. That’s why it’s only good for tasks that can be reduced to large document search and summarization. (And natural language translation tasks, because it understands basic semantics and can easily be trained to translate to and from any machine language you train it to.)

However, this doesn’t help you with any task that requires actual computation! It’s not analytical data processing, it’s not optimization, and it’s definitely not advanced machine learning for advanced mathematical pattern detection. These are the majority of your tasks and the tasks you need to do to analyze a situation. Buys should be based on the lowest total cost of ownership at the maximum acceptable risk level. Sales predictions, and thus demand, should be based on tried and true mathematical trends, not hunches or market hype. Basic invoice processing should be against business rules for validation, approval, and payment, and that should be primarily based on rules-based automation.

Note that none of these core technologies you need to solve the majority of your problems are AI, as we pointed out in our recent article that said you don’t need Gen-AI to revolutionize procurement and supply chain management. Not to say that these technologies can’t be enhanced by the right application of AI — for example, AI could predict the optimization paths most likely to arrive at the optimal answer, the right curve fitting algorithms to match the trend lines, and the right outlier analysis to identify missing, off, or fraudulent information.

Real solutions come from real tried-and-true AI technology developed over years, or decades, that was designed to solve a specific type of problem, not generic text processing technology that was not designed for the problem, has no understanding of the problem, and will make stuff up in an attempt to solve the problem (which is referred to as a hallucination, but is not a bug, but a core feature of Gen-AI / LLM technology).

This is also why Agentric AI built on Gen-AI won’t work — you can’t automatically build an RPA sequence from a chain of compute that could be completely hallucinatory, and you certainly can’t rely on it to solve your problem.

This doesn’t mean there isn’t a use for Gen-AI, it can be trained to be a natural language interface to these other tools that will work reliably the vast majority of the time (say 95%+ if trained over time), but the use is definitely NOT what you are being promised.

Why Does Everyone Believe the AI Hype?

the doctor used to love AI. He spent a decade and half actively promoting it (and wrote two extensive series on The Complete AI in Procurement, Sourcing, and Supplier Management), until Gen-AI and all the false promises bundled with it came along. (Neither it, nor its successor, will be your saviour. It’s not intelligent, not general purpose, and unless your problem ultimately reduces to large document summarization and query, will not solve your problem. Any claims to the contrary are, and, for the foreseeable future will continue to be, false.)

Recently, THE REVELATOR, who is also becoming a little jaded, decided to ask Why does everyone believe the AI hype? (Source)

Of course, the doctor needed to answer.

Why did the American public believe the administration would be any different this time?
(For that matter, why does any first world nation believe their newly elected administration will be any different this time?)

Why does the public at large still believe in the lies that have been fed to them since they were born?
(Primarily American, but Canadians are doing their best to learn from their neighbours!)

Because there is no better producer, packager, and purveyor of Bullsh!t than American Media!
(Although we try, we Canadians can only dream of producing BS that good!)

That’s what the Big AI players use to their advantage
(with their hundreds of millions to billions of dollars and their huge marketing budgets)!

The Procurement Dynamo put it best in a recent comment when he said that we are wired as humans to be lazy and it’s easier to just believe what is being pumped out to us on all the digital channels we consume everyday than do our research, understand the half truths being fed to us, and draw our own conclusions (especially when Math, where the US is now 35th in the OECD PISA rankings, is concerned).

But it doesn’t stop there, not only are we plagued with:

Laziness: Overworked workers being tasked with the nigh-impossible on a daily basis with limited TQ don’t want to design systems, especially when that’s what the vendor is being paid for.

We also have to deal with greed and stupidity making matters worse.

Greed: Investors and rich big company CEOs don’t want workers who want to be paid fair wages, as then they have to deal with worker’s rights (for now at least, but maybe not for much longer in the USA at the rate the government is being dismantled), maternity and sick leave, paid overtime, etc. when they are being promised a software robot that will work 24/7/365 without complaint for a “small” annual fee.

Stupidity: The zealots at many vendors have adopted tech as their religion and messiah and refuse to learn the domain and how to solve a problem with a human centric point of view, believing that, with just a little more development, the tech will magically get there.

And this is why we have so many people blinded by the hype and so many people buying into it.

This isn’t to say that there aren’t real vendors with real AI-backed technology that actually works (because there are, such as ForeStreet that we just covered), it just means that unless you find one of these vendors (which are now in the minority, but SI WILL cover these vendors as it identifies them), and hire intelligent, hard working people who WANT to solve problems and give them the necessary resources to identify these vendors and properly implement ad configure these solutions, you’re not going to get results. Just false promises.

Now that you have the unfiltered answer, do you need to keep asking the question? 😉

Blind AI “Agents” Will Only Worsen Any Situation!

THE PROPHET recently posted that The AI Overton Window is Open in Government Procurement and that makes the doctor scared for you. The damage they can do in private situations is bad. The damage they can do in public situations is much, much worse.

The following obvious outcomes that the doctor already noted in his rebuttal are just the tip of the iceberg:

  • biased awards
  • overpriced awards to holdings of the billionaires that provide the tech
  • non-compliant awards because submitting a form is NOT verifying quality
  • billions lost to fraud as foreign bad actors use their AI to game our AI and direct Billions to accounts that will quickly be emptied to offshore accounts and then untraceable crypto!

For those of you that haven’t figure it out yet, all AI is biased as it is trained to repeat the patterns found in the training data provided, and all of that data is biased to existing providers and decision patterns of biased award judges who find sneaky ways to direct contracts to the recipients they want to give the business too (whether or not they are the best value for the taxpayer’s money). If your President and his DOGE are telling you the truth, fraud (and thus bias) is rampant, and “AI” will just perpetuate that.

Since there are only a few players who are big enough to handle the data volumes and computational workload that would be required to support the US Federal Government, they have an effective monopoly. As a result, they can charge pretty much whatever they want and get it. (And we have already seen how overpriced this technology is. Total Open AI funding to date: 17.9B [TrackXn] compared to total DeepSeek funding to date: 1B [Pitchbook]. The model is more or less as good as the OpenAI model at less than 1/18th the cost [although there is the issue of the controlling company and country]. The next iteration will probably be built for under 100M. Just don’t expect any improvements in performance. There are inherent limitations in the underlying model/technology they keep building on, we don’t have anything better, and given that it usually decades between real breakthroughs in research, we likely won’t until the late 2030s.]) The end result is that the government will probably end up paying twenty (20) to one hundred (100) times what the technology itself is worth because of the lock on the market the big players have in the US.

Applications can only process the data given to them, they cannot confirm it’s validity. All a supplier has to do is lie on a form or get a third party to (electronically) sign a false form (with a small bribe), and, voila, the AI thinks the supplier meets all the requirements. As long as the supplier is the lowest cost and/or highest score on other metrics (which can be achieved through the submission of false data that matches what the algorithm is looking for), it gets the award. And the taxpayer suffers.

Taking this one step further, if awards come with an up-front payment, all a foreign actor has to do is register a fake front company on American soil, bribe third parties to help it submit a lot of false forms, game the system, get the award, get the up-front payment, wire it to an untraceable offshore account, and disappear and if that up-front payment is millions of US dollars, its easy money. Now, if the government is smart and insists that there is no payment until delivery, depending on what that delivery is, if cheap knockoffs can be produced at a fraction of the price (that don’t have the reliability, lifespan, etc.), then this trick could be used, and then, after a few large shipments are delivered, and before the poor quality products break down, the supplier could all of a sudden close shop and disappear. If this doesn’t work, if the foreign actors are training their AI to generate realistic looking data to be fed into America’s AI, it’s just a matter of faking a delivery receipt to accompany an invoice for goods not delivered, getting that first payment, and then disappearing. This is just the tip of the iceberg of obvious fraud opportunities (and every worst case hypothetical situation in your espionage movies and books will come to pass, and more).

In other words, only bad things will happen if you try to deploy AI “agents” to do a human’s job!

We need to stop this ridiculous focus on AI Agents and instead focus on AI helpers. We need to end these bullsh!t claims that we are going to achieve full artificial intelligence and instead focus on augmented intelligence and build tools that enable white collar workers to become super human in their jobs and do the work that used to take ten people. Because that IS possible today (and has been for a while, especially since that was the route we were going down before “chat, j’ai pété” came along with its false promises of artificial intelligence, reasoning, etc.).

All we have to do is, for every problem, apply our human intelligence (HI), design, or redesign, a the process to solve it so that all of the tactical data processing (the thunking the machines can do a Billion times better than us) is separated from the strategic decision making (the thinking the machine cannot do) and the machine automatically does all of the data processing and thunking that needs to be done at each step so that we have the knowledge (processed data) we need to make the right decision (and a well designed interface that allows us to quickly absorb the summary, identify factors that might change the typical decision, and dive into the knowledge and underlying data) and be confident in it.

In other words, we shouldn’t be doing the same analysis and running the same reports over and over again, the machine should automate all of that [as well as various outlier analysis] and present us with the summary, whether it is typical or atypical, the decisions and actions we typically make in similar situations, and the results typically achieved. In many cases, a well-designed process and properly encoded knowledge will result in the machine making the right suggestion, and all we will have to do is verify a suggestion. When it’s wrong, the system should still have the appropriate decision encoded as an alternate the majority of the time, and we should just have to select that. And in the exceptional situation we never thought of, or for which it has no data, we will still be able to alter the process, encode our reasoning, and recode the system to suggest the right action the next time the situation arises, meaning that we will not only start off being ten times as productive, but get more productive over time.

The only real constraints we have are on the data we can leverage due to

  1. the lack of good, clean, verified data (and AI will NOT fix that) in most organizations (private and public)
  2. the lack of proper tools to do an office job in the modern age!

For example, if you give me the right modelling, analytics, optimization, and RPA tools, I can leverage ALL the data at my disposal to arrive at the optimal decision (given the time to do so). But how many Procurement personnel have access to all of these tools? Moreover, what percentage of those personnel would know how to fully leverage those tools (considering you need advanced degrees in mathematics and computer science to do so today). And what percentage still would have the time to do so? The percentage can be expressed by a single digit in industry (if you round up). It’s worse in government! But properly designed tools that embed best practice and human intelligence on top of these tools and bring the knowledge requirements down to what an average Procurement professional has would allow them to be ten times as productive in their analysis and make the right decision every time.

Moreover, the compliance slowdown that people are grumbling about is due to lack of good tools (RPA platforms that walk the users through the process) and people to do the work that HAS to be done manually. (And AI is NOT going to fix the fact that health, safety, quality, and oversight inspectors, where you don’t have enough qualified people to begin with, can be fired in droves and further increase backlogs.)

And guess what? We still handle unstructured data better than AI as some of the BS it continues to spit out in what they call “edge cases” is astounding! (the doctor really hopes the maverick doesn’t go mad in his conversations with DeepSeek — it almost drove the doctor mad just reading them!)

In other words, the core of any business function MUST continue to be HUMANs applying HUMAN INTELLIGENCE (HI!), and modern technology must AUGMENT (not replace) every function. Properly (human) designed and (human) implemented systems that use the right Augmented Intelligence technology (not the hype of the day) to supercharge a human-driven process can make the human easily ten times more efficient in some cases. (But left to their own devices, interacting AI agents will, more-or-less, as Meta found out in multiple forays last decade and this decade, self destruct.)

Why Are There So Many Undifferentiated ProcureTech Startups That Still Don’t Solve My Problems? Part III

After all, with over 666 solution providers out there, I should be in solution utopia, right?

In Part I, we said there are three big big reasons for this, they are people-centric, and they all start with F!

  1. Founders
  2. Financiers
  3. Fashionistas

We also discussed Founders in Part I and Financiers in Part II. Today we will discuss the Fashionistas.

Now the doctor knows what you’re saying: “Wait, What“? What do Fashionistas have to do with ProcureTech? And the answer is, well, just about everything unfortunately.

You see, a fashionista is a designer of haute couture, and, today, haute couture is not restricted to clothes … it also breeds into tech, and while one might think it should be restricted to wearable tech, it isn’t. It’s any tech-du-jour that the fashionistas find cool. And, right now, they find FinTech cool. That’s why you see an over-focus on FinTech, of which ProcureTech is fast becoming the leading category, from (some of) the analyst firms and the influencers. Especially if that ProcureTech embraces the current haute couture hype of Gen-AI, whether or not the Gen-AI adds any value whatsoever. (After all, functionally, there is no difference between a $2K designer sundress and a $20 Walmart/Marks & Spencer sundress of the same size and thickness from a functional coverage/cooling perspective.)

Now the doctor knows that you’re probably saying: “so what, you don’t have to listen to them“. And that’s true. But the problem is, it’s mind boggling how many people do, especially at bigger companies. For example, if the ProcureTech player isn’t on a Gartner, Forrester, or IDC map, good luck even getting permission to invite the vendor for an RFI for a core system. We may have made it past the days of “you never got fired for IBM” but we still haven’t made it past the days of “you never got fired for buying the Gartner/Forrester/IDC” recommendation.

These big analyst firms, which feature the same big suites year after year after year with little to no change (because one of them will not include any vendor who is not a paying customer, another will go out of its way to redefine the inclusion criteria each update to what is absolutely minimal to allow for inclusion of all paying customers who want to be in the map [and, in the process, exclude as many non-customers as possible], and the third defines very restrictive criteria to keep the map size, and workload, down, and ends up with a client heavy map). A big company exec following these maps would think the space hasn’t changed in over a decade, even though the biggest change is that most of the companies in the map haven’t done any major innovation in a decade, and don’t appear to be focussed on it either (see the doctor‘s Sailing the Seven Seas Sans a Sextant? piece).

But this isn’t the extent of the problem these analyst firms pose. The real extent, as per our last piece where we noted that the financiers who over-invest and need to make their return pursue the increased pricing strategy (with no increase in underlying functionality or value) do so by ensuring that you are hit with a constant onslaught across all channels, making sure that their investment is hyped up by the big analyst firms and echoed by the Platinum/Diamond/Rhodium consulting and implementation partners. This means that the first thing these PE firms do is have their investment sign a six figure deal with one of these big analyst firms, pay that invoice promptly, make a vague promise to sign more in the future, and get the analyst firm to hype them up like mad. All of a sudden there’s a new Cool Vendor, a new Tech Sub-Category, or something where their investment gets hyped a lot even if it can’t yet make the major map (due to annual revenue, missing baseline functionality, etc.). They also ensure that their the lead influencer consultants at their Platinum+ Partners get these messages and reprints to echo and distribute.

And then, of course, there are the ever present influencers, who are often the biggest fashionistas of the space, with the large-ish followings who will have their newsletters sponsored by these firms, paid speaking engagements at upcoming customer events or conferences these firms sponsor, and other benefits in addition to first access to the firm’s messaging and content for redistribution. This will be especially true if the influencer doesn’t really know the ProcureTech landscape and how valuable a technology like Gen-AI really is to the product being promoted. This is because the less in-depth the influencer’s space and technology knowledge actually is, the greater the chance they will happily echo the firm’s marketing, meaningful or not, because, as one of the more astute readers commented on the doctor‘s Top 10 Ways to be a Procurement Influencer on LinkedIn!, they will happily self-censor their thoughts even without the marketers having to censor for them because they don’t want to cut the branch they sit on. (Or, for you Americans, they’ll happily parrot the message without question because they don’t want to bite the hand that feeds.)

And this is the third reason your ProcureTech solution doesn’t solve your problem, and that’s because the only solutions you hear about are the ones being over-marketed which may not even solve a basic problem for the majority of the customer base the advertising and marketing is (incorrectly) targeting.

In Conclusion

Any one of these F’s will result in a poor fitting solution for you, two of these F’s will result in a struggle to get a return that equals what you paid, and all three F’s will likely result in a disaster. And with over 666 companies, the sad reality is that it is a statistical guarantee there are way more companies that fall victim to all three Fs than you think, at least from your particular point of view.

Remember, even if a company has a good solution that works for the set of problems it was initially designed for (and received an unbiased write-up from an independent analyst), that still doesn’t mean it is right for you. As we have continuously pointed out in prior and forthcoming articles and LinkedIn Comments, YOU still need to make sure your problem aligns, the implementation option aligns, the integration with your systems is possible, the necessary data is available (or at least will be made available by IT), and that you have an independent project assurance expert who’s goal aligns with yours. Otherwise, you’re still falling for the fashionista’s fashion du jour, and failure is waiting around the corner.

Post Script

Please don’t interpret this series to imply that all founders or financiers are bad or that all influencers have evil in their hearts. There are some very good founders (who happily admit their shortcomings, seek out help, and step back at the right time). There are also some great PE firms that make it a point to avoid bidding wars and limit the multiple they pay to what they expect to make back without raising prices to unsustainable levels and then actually help the firm do better at selling. And some influencers honestly think the substance free content they push out is helpful. (It’s not, but they didn’t start to screw you over with bad recommendations, just to get famous and make their fortune off of being a famous influencer.)

However, as you have probably guessed by now, both sides of the coin exist. And the coin is definitely NOT weighted to come up heads anymore. It’s such a shame, shame shame.