Category Archives: AI

Scientists Take Us One Step Closer to AI-Led Destruction

Is it just me or does it take a special kind of idiot to watch Terminator 2 and say to himself “unstoppable shape-shifting robots would be so cool!”?

Scientists Just Created Shape-Shifting Robots That Flow Like Liquid and Harden Like Steel

Especially when Hugo Drax is executing his world domination plan to ensure his AI-backed SkyNet project gains global dominance!

I guess they figured a certain President wasn’t getting the job done fast enough …

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.)

Pretty Fly That Gen-AI!

To the tune of Pretty Fly for a Rabbi by Weird Al.
[Which is to the tune of Pretty Fly (For a White Guy) by The Offspring.]

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!
And all my rabbis say it’s pretty fly that Gen-AI!

Meccha Gen A I, meccha technologica!

Our space has had a fair share of tech hype in the past
But most of it was nudnik and none of it would last
But our new tech’s real kosher, we think it’ll do the trick
I tell ya, it’s to die for, it really knows its shtick

So how’s by you? Have you seen this tool?
Reads the Reddit, does its own synopsis too
Working’ like a dog at a synagogue
It scans all day, it scans all day

Just type “Vay is mir!” and it will kick into gear
And bring you lots of fear and tell you the world’s end is near
Just grab your shelter kit and
Hey! Hey! Do that Prepper thing!

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!
And all my rabbis say it’s pretty fly that Gen-AI!

It learns from the forums, not just any will suffice
It has to find extremist views ’cause that’s what masses like
Doesn’t care if it’s a fact, as long as you come and play
Hallucinations plenty, just eat one rock a day!

People used to scoff, now they say “Mazel tov!”
It’s so attractive ’cause they think they can lay you off
It computes non-stop and outputs slop
What’s not to like? What’s not to like?

For endless days, it works without complaint
And never asks for pastrami on white bread with mayonnaise
Just grab your vacation thong and
Hey! Hey! Do that Celeb thing!

When it’s computing your new payroll, now that you shouldn’t miss
It’ll happily add a Billion when the dot it skips
It’s got a lot of chutzpah, and it really is quite daft
The coders pay the moyl and then you just get the shaft!

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!

Meccha Gen A I, meccha technologica!

It’s doin’ well, I gotta kvell
The yentas love it, even shicksas think it’s swell
For your mental health, it is top shelf
It don’t care if you go and off yourself

Yeah it calls the shots, we really bow down lots
Oy gevalt, I’m so ferklempt that I could plotz!
Grab your pasta strainer
Bow down to the sphaghetti god!

Put on your pasta strainer and
Hey! Hey! Join the FSM!

Dedicated to THE PROPHET.

You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

This originally posted on March 22 (2024).  It is being reposted because we need solutions, Gartner (who co-created the hype cycle) published a study which found that Gen-AI/technology implementations fail  85% of time, and its because we have abandoned the foundations — which work wonders in the hands of properly applied Human Intelligence (HI!).  Gen-AI, like all technologies, has its place, and it’s not wherever the Vendor of the Week pushes it, but where it belongs.  Please remember that.

Open Gen-AI technology may be about as reliable as a career politician managing your Nigerian bank account, but somehow it’s won the PR war (since there is longer any requirement to speak the truth or state actual facts in sales and marketing in most “first” world countries [where they believe Alternative Math is a real thing … and that’s why they can’t balance their budgets, FYI]) as every Big X, Mid-Sized Consultancy, and the majority of software vendors are pushing Open Gen-AI as the greatest revolution in technology since the abacus. the doctor shouldn’t be surprised, given that most of the turkeys on their rafters can’t even do basic math* (but yet profess to deeply understand this technology) and thus believe the hype (and downplay the serious risks, which we summarized in this article, where we didn’t even mention the quality of the results when you unexpectedly get a result that doesn’t exhibit any of the six major issues).

The Power of Real Spend Analysis

If you have a real Spend Analysis tool, like Spendata (The Spend Analysis Power Tool), simple data exploration will find you a 10% or more savings opportunity in just a few days (well, maybe a few weeks, but that’s still just a matter of days). It’s one of only two technologies that has been demonstrated, when properly deployed and used, to identify returns of 10% or more, year after year after year, since the mid 2000s (when the technology wasn’t nearly as good as it is today), and it can be used by any Procurement or Finance Analyst that has a basic understanding of their data.

When you have a tool that will let you analyze data around any dimension of interest — supplier, category, product — restrict it to any subset of interest — timeframe, geographic location, off-contract spend — and roll-up, compare against, and drill down by variance — the opportunities you will find will be considerable. Even in the best sourced top spend categories, you’ll usually find 2% to 3%, in the mid-spend likely 5% or more, in the tail, likely 15% or more … and that’s before you identify unexpected opportunities by division (who aren’t adhering to the new contracts), geography (where a new local supplier can slash transportation costs), product line (where subtle shifts in pricing — and yes, real spend analysis can also handle sales and pricing data — lead to unexpected sales increases and greater savings when you bump your orders to the next discount level), and even in warranty costs (when you identify that a certain supplier location is continually delivering low quality goods compared to its peers).

And that’s just the Procurement spend … it can also handle the supply chain spend, logistics spend, warranty spend, utility and HR spend — and while you can’t control the HR spend, you can get a handle on your average cost by position by location and possibly restructure your hubs during expansion time to where resources are lower cost! Savings, savings, savings … you’ll find them ’round the clock … savings, savings, savings … analytics rocks!

The Power of Strategic Sourcing Decision Optimization

Decision optimization has been around in the Procurement space for almost 25 years, but it still has less than 10% penetration! This is utterly abysmal. It’s not only the only other technology that has been generating returns of 10% or more, in good times and bad, for any leading organization that consistently uses it, but the only technology that the doctor has seen that has consistently generated 20% to 30% savings opportunities on large multi-national complex categories that just can’t be solved with RFQ and a spreadsheet, no matter how hard you try. (But if you want to pay them, an expert consultant will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at, say, 5K a day, there goes three times any savings they identify.)

Examples where the doctor has repeatedly seen stellar results include:

  • national service provider contract optimization across national, regional, and local providers where rates, expected utilization, and all-in costs for remote resources are considered; With just an RFX solution, the usual solution is to go to all the relevant Big X and Mid-Sized Bodyshops and get their rate cards by role by location by base rate (with expenses picked up by the org) and all-in rate; calc. the expected local overhead rate by location; then, for each Big X / Mid-Size- role – location, determine if the Big X all-in rate or the Big X base rate plus their overhead is cheaper and select that as the final bid for analysis; then mark the lowest bid for each role-location and determine the three top providers; then distribute the award between the three “top” providers in the lowest cost fashion; and, in big companies using a lot of contract labour, leave millions on the table because 1) sometimes the cheapest 3 will actually be the providers with the middle of the road bids across the board and 2) for some areas/roles, regional, and definitely local, providers will often be cheaper — but since the complexity is beyond manageable, this isn’t done, even though the doctor has seen multiple real-world events generate 30% to 40% savings since optimization can handle hundreds of suppliers and tens of thousands of bids and find the perfect mix (even while limiting the number of global providers and the number of providers who can service a location)
  • global mailer / catalog production —
    paper won’t go away, and when you have to balance inks, papers, printing, distribution, and mailing — it’s not always local or one country in a region that minimizes costs, it’s a very complex sourcing AND logistics distribution that optimizes costs … and the real-world model gets dizzying fast unless you use optimization, which will find 10% or more savings beyond your current best efforts
  • build-to-order assembly — don’t just leave that to the contract manufacturer, when you can simultaneously analyze the entire BoM and supply chain, which can easily dwarf the above two models if you have 50 or more items, as savings will just appear when you do so

… but yet, because it’s “math”, it doesn’t get used, even though you don’t have to do the math — the platform does!

Curve Fitting Trend Analysis

Dozens (and dozens) of “AI” models have been developed over the past few years to provide you with “predictive” forecasts, insights, and analytics, but guess what? Not a SINGLE model has outdone classical curve-fitting trend analysis — and NOT a single model ever will. (This is because all these fancy-smancy black box solutions do is attempt to identify the record/transaction “fingerprint” that contains the most relevant data and then attempt to identify the “curve” or “line” to fit it too all at once, which means the upper bound is a classical model that uses the right data and fits to the right curve from the beginning, without wasting an entire plant’s worth of energy powering entire data centers as the algorithm repeatedly guesses random fingerprints and models until one seems to work well.)

And the reality is that these standard techniques (which have been refined since the 60s and 70s), which now run blindingly fast on large data sets thanks to today’s computing, can achieve 95% to 98% accuracy in some domains, with no misfires. A 95% accurate forecast on inventory, sales, etc. is pretty damn good and minimizes the buffer stock, and lead time, you need. Detailed, fine tuned, correlation analysis can accurately predict the impact of sales and industry events. And so on.

Going one step further, there exists a host of clustering techniques that can identify emergent trends in outlier behaviour as well as pockets of customers or demand. And so on. But chances are you aren’t using any of these techniques.

So given that most of you haven’t adopted any of this technology that has proven to be reliable, effective, and extremely valuable, why on earth would you want to adopt an unproven technology that hallucinates daily, might tell of your sensitive employees with hate speech, and even leak your data? It makes ZERO sense!

While we admit that someday semi-private LLMs will be an appropriate solution for certain areas of your business where large amount of textual analysis is required on a regular basis, even these are still iffy today and can’t always be trusted. And the doctor doesn’t care how slick that chatbot is because if you have to spend days learning how to expertly craft a prompt just to get a single result, you might as well just learn to code and use a classic open source Neural Net library — you’ll get better, more reliable, results faster.

Keep an eye on the tech if you like, but nothing stops you from using the tech that works. Let your peers be the test pilots. You really don’t want to be in the cockpit when it crashes.

* And if you don’t understand why a deep understand of university level mathematics, preferably at the graduate level, is important, then you shouldn’t be touching the turkey who touches the Gen-AI solution with a 10-foot pole!