Category Archives: AI

There’s a Reason that AI Talk at (Procure)Tech Events is All Talk and No Substance

A recent rant on LinkedIn noted that, at DPW (and other events), there was “Tons of talk about using AI, not so much about how to procure it, scope it, contract it, measure it, commercial agreements, which suppliers are best for which usage.”

Well, there’s a simple reason for that. And it goes as follows.

It’s the tech-du-jour. More specifically, the hype-du-jour. As a result you can forget any advice on how to:

–> Procure It

No one really knows who has what, or what it actually does, what it’s really worth, how to properly cost it, how to compare offerings and offers. So how can they tell you how to procure it?

–> Scope It

According to the hype it’s your new employee that does everything for every one, despite the fact that it hallucinates more times per second than an LSD junkie does in a lifetime. Without knowing what it does or where it goes, they can’t tell you how to scope it!

–> Contract It / Commercial Agreements

Even the vendors wrapping someone else’s model don’t really know what it does, what they can guarantee, what they can’t, or who is really liable (although courts are starting to make those decisions). So no one knows how to contract it.

–> Measure It

Whereas we have tried-and-true mathematically sound measures for traditional deep neural networks where you can get accuracy ranges with confidence ranges, when it comes to LLMs, no one has a f*ck1ng clue how to measure them. Random tests by random humans judged by random people with random definitions of accuracy is not a measure. And I’d have more confidence in a decision made by a Koala. At least it’s cute!

–> Assign It

If we can’t even measure what it does, do you think we can measure how well the vendor pushing it really understands it? Definitely NOT!

And that, in a nutshell, is why there’s a lot of hot air and nothing of actual substance in all the AI discussions. Which you should avoid anyway. Because you don’t want tech with a 6% success rate [McKinsey, MIT], which is half the general success rate of new tech installations (now that tech failure rates have reached an all time of 88% [Bain]).

You cannot export-control math!

A truly brilliant observation by Mr. Stephen Klein in a recent post on how China May Be The Only One With Mythos because they may have saved its responses (and thus figured out how to replicate it).

(Gen-) AI LLMs are just mega math models. Really big mega math models with probabilities being computed on top of probabilities being computed on top of probabilities in force-feedback loops that reinforce its computations (which could be brilliant deductions or hallucinations that equal the acid high of the most LSD addicted junkie on the planet).

And since the outputs are dependent on the equations that define the model and the training data, if someone can recreate the training data they can reverse engineer the equations from the output if they are sufficiently adept at mathematics, or at least a close proximity.

Which means that blocking off access to those who can evaluate and improve the model will not help if those you don’t want to have the model already have it.

But this isn’t a post about the blocking of AI models (because I personally think that’s great), but a post about what happens if you try to hide your capabilities behind “proprietary algorithms based on math” assuming that no on else can recreate it if you don’t talk about it and that the IP alone justifies an unreasonable price for your product or valuation for your company.

Every country has mathematical geniuses, and more than one can come up with the next iteration of a mathematical theory at about the same time. Maybe only one gets remembered (Newton vs. Leibniz), but it doesn’t mean they didn’t both invent the same concepts at about the same time (in calculus).

And the more you trump it up, the more it entices someone to tear it down. (And figure out how you built it.)

On its own, the math alone is not the advantage you think it is. There are a lot of free scientific papers with great math. In fact, more than enough to create your own Claude, DeepSeek, Gemini, Grok, etc. But most people can’t because it’s not just the equations, it’s the parameters, the training data, and the implementation.

With regards to the implementation, just because you have server racks that can do trillions of calculations per second, that doesn’t mean you can code inefficiently. For example, an average token output by Claude requires billions of operations, and an average question posed to Claude will require 1,000 to 10,000 tokens to answer, for 1 trillion to 1 quadrillion calculations for an output. A poorly designed model could require 10, 100, or 10,000 times that.

The same goes for classical machine learning or optimization algorithms. Good implementations will require billions of calculations. Bad, trillions to quadrillions to quintillions. Responses go from real-time to hours to days to the computations never end.

Then there is the training data. You can’t judge a model implementation unless you have good training sets that are representative of real-world problems. Optimizing for theoretical problems that don’t exist in the real world isn’t helpful and may, in fact, lead to a worse solution that not even testing it at all!

The real differentiator is the expertise both in the implementation of solutions based on math and deep knowledge of the domain the solution is for. That can’t be recreated by mathematical ability alone and requires experience. And that can be export controlled (and represents the real value).

AI That Makes Recommendations Makes You Dumber, NOT Smarter!

Still too many posts about how great (Gen) AI is (in the age of LLMs).

They tell you truths:

AI can process all of your spend in seconds and find anomalies.

AI can collect all of the relevant market data and find opportunities.

AI can review large amounts of text and find risks or unfairly onerous contract clauses.

AI can track your SaaS utilization and ensure you are not being overcharged.

Yada Yada Yada.

All true, all fine.

But then the blasphemy starts. (Where I’m using the word in the context of the profane for the humanity bashing that it is.)

AI is great because it can recommend the spend “opportunities” you should pursue … and even automatically generate sourcing events for you.

AI can find the lowest cost when you need to spot buy and automatically buy/cut and send the PO for you.

AI can tell you what clauses to take out, what clauses to edit, and what clauses to add and automatically suggest the edits and write the new clauses for you.

AI can automatically enable and disable user accounts/seats, compute capability, application instances, etc. and save you money.

Now, theoretically, it can do all this. But practically, when it does so, it costs you money, capability, and you humanity.

When it selects opportunities, generates an event, and selects suppliers, it does so on a cost and historical utilization basis, with vacuum forecasts and whatever specs it can find. It doesn’t look at associated logistics costs, lead times, quality levels, service costs, certifications, safety, or anything else that is critical. You teach it cost, the metrics dictate that the CFO only cares about what shows up in the P&L, and you get the lowest cost piece of cr@p on the market. No big deal until customers get so fed up they start leaving, unless, of course it was the bolt holding the axels together on the bus that regularly drives the cliffs of the local mountain range or the door on the Jet you cram hundreds of passengers into.

When it selects the lowest price, there’s no guarantee the product will arrive on time or meet all of your requirements (because you just specified the cheapest card stock, but didn’t specify white and got pretty pink; 32 GB DDR chips, but didn’t specify they were for laptop upgrades and got server RAM; specified DBA, but didn’t specify Oracle and got someone who’s only ever used SQL Server).

When you tell it to slash your SaaS and Cloud costs, it happily deletes all the C-Suite accounts because they only log in once a month, cancels your vulnerability scanning service, because that costs way too much for something that happens only monthly, and deletes your main production database (because it cost way more than the QA database). And yes, plenty of news stories where it has already done all this.

When it scans the 60 page contract behemoth from the supplier, it overlooks the clause that transfers all liability to you for their AI failures (because you deployed the product) because you trained it on contracts where you transfer all liability of use of the equipment you create to your customers (because they use the product and accept not to use it beyond your specifications). Then when the AI accuses your best supplier of submitting fraudulent invoices, automatically files a report with the bank, which in turn freezes the suppliers accounts, which blocks all automated payments, which results in their energy supply being turned off for non-payment, which brings down their production line, which costs the supplier 2 million dollars, which results in them suing you … guess who’s on the hook when the court says “you can’t say the AI is responsible”?

But that’s just the direct costs.

The indirect cost is that it’s making you stupid.

The problem is this. Most of the time,

  • the opportunities will be real, not the most significant, but real, and the recommendations will be “good enough” that an average human won’t feel it worth the effort to qualify and/or improve
  • 80% of tail is usually well-defined cookie-cutter finished products/basket services and there will be enough description in the catalog/e-Pro system for the AI to get it “good enough” that the org can make it work
  • the security settings and “manual overrides” will usually prevent exec accounts and production instances from being deleted, and its ITs job to deal with the odd glitch, so who really cares
  • the missed risk won’t materialize in 95% of contracts, and usually not in the first 6 months

So people quickly stop questioning, start trusting, and then start blindly depending on it. The systems are allowed to do whatever they want as long as they aren’t creating fires worse than what the humans are already dealing with. And even as performance degrades, requirements change, or system costs escalates, nothing is checked, and as slightly worse decisions are constantly reinforced and bad data piles up, the organization is sitting on a ticking time bomb. Which is going to go off. The only question is how much damage it is going to do.

Which you won’t be able to deal with when it does go off because you won’t have a clue what to do.

Every time you fail to question it, your cognitive skills atrophy.

Every time you fail to use a skill, your expertise dissipates.

Every time you fail to put the effort in, your problem solving stamina degrades.

Every time you fail to think about the situation, your knowledge erodes and you forget.

When they say your mind is a muscle, they’re not exaggerating. Bodybuilders don’t work out every day just to build, they do it because it’s a basic requirement to maintain what they have so their muscles don’t atrophy and their hard work dissipate.

The brain works the same way, when you don’t use it, it atrophies.

Numerous studies have shown what happens when you use AI even for the simplest of tasks. Even typing vs hand writing reduces brain power. Using it to edit reduces more. Using it to write reduces more. (15% or more — you’re effectively shaving 15 points off your IQ!) Using it for strategy gets to the point where you might as well be boarding the short bus and taking the remedial class, because in a matter of months you’ll have trouble keeping up with that!

So, the more AI you use, the dumber you get.

And that’s great?

Maybe if you’re a malevolent billionaire trying to dumb down humanity to the point that they are too stupid to question your true intentions, but otherwise …

AI Hasn’t Changed The Fundamentals Of Analysis — It’s Reinforced The Needs For It

AI is Not AI … And AI-Related Tech is Not Created Equal

Joël Collin-Demers recently made a post that correctly stated that real “AI solutions” tell you exactly what they do, in which sequence, and [help you] understand how it solves your exact problem. the doctor totally agrees. Otherwise, they are using buzzwords and trying to cash in on the hype to sell you old-school automation at best, or third party (Gen-AI LLM) wrappers at worst, and don’t have any real AI.

He covered ten different types of technology and attempted to capture the positives, the negatives, and the best uses therefore in source-to-pay. For a relative non-techie (compared to the doctor with a PhD, degrees in CS and Mathematics, and actual experience implementing everything but BS LLMs from scratch), he got a lot right. But he got a few things wrong. As a result, there was a need to correct him (in this post) and ensure the corrections have as much permanence as the original post.

We do recommend you read his original post, but for each tech addressed, integrate the correction below.

RPA – does not “break” when processes change; it breaks when you feed it bad data; when you change processes, it simply loses its usefulness until you change it to match the process — with a good RPA system, that shouldn’t be hard

Machine Learning – requiring clean data is NOT a bad thing; it ensures the algorithm “learns” the patterns you need it to learn to use it effectively

Natural Language Processing – doesn’t struggle with jargon, just context — you define the dictionaries, the grammar, the language — it’s accuracy boils down to that; if you are feeding in documents that use the same words/phrases in multiple contexts, it will always struggle with that to a point

Predictive Analytics – in lay terms, classical predictive analytics is essentially just multi-dimensional curve fitting based on the data available — when something has not been modelled, there is nothing the algorithm can learn to fit against — but to be fair, nothing you’ve listed will succeed with unprecedented events/data

Anomaly Detection – this is based on outlier detection, and well trained outlier detection does NOT have high false positive rates (which are no higher than false negatives), and any “wrong” classifications from a business perspective simply means that the definition of a valid transactions needs to be amended to reduce the “outliers”

Computer Vision – lighting is not as much of a problem as you think as most good algorithmic interpretations will always mathematically adjust the brightness and contrast to a consistent range in pre-processing, and sometimes even greyscale; angles are a problem, because if they can’t be determined, the right transform can’t be applied to appropriately orient the image to maximize identification likelihood

Optimization Algorithms – “requires precise problem definition” is not a bad thing, it’s a good thing — if you don’t have a precise problem definition, you cannot get a precise answer with any technique; one of the best uses is product mix, not just supplier portfolio

LLMs – not “can” hallucinate false info; “will” hallucinate false into — every single time, just a question of the degree; it’s “generative” AI which literally means it makes stuff up, and how accurate what it makes up is with respect to your problem depends on how it was trained, what was asked of it, and how you ask it … very unreliable all around

Pattern Based Recommendations – the whole point is to “filter” to what you would normally buy so that you don’t have to sort through everything, that’s not a problem

Reinforcement Learning – it does not require extensive time, it requires extensive data — computers process mathematical calculations billions of time faster than we do — it’s never time anymore!