Category Archives: Technology

GEN-AI IS NOT EMERGENT … AND CLAIMS THAT IT WILL “EVOLVE” TO SOLVE YOUR PROBLEMS ARE ALL FALSE!

A recent article in the CACM (Communications of the ACM) referenced a paper by Dan Carter last year that demonstrated that the claims of Wei et.al in their 2022 “Emergent Abilities of Large Language Models” were unsubstantiated and merely wrong interpretations of visual artifacts produced by computing graphs using an inappropriate semi-log scale.

Now, I realize the vast majority of you without advanced degrees in mathematics and theoretical computer science won’t understand the majority of technical details, but that’s okay because the doctor, who has advanced degrees in both, does, can verify the mathematical accuracy of Dan’s paper, and the conclusion:

LLMs — Large Language Models — the “backbone” of Gen-AI DO NOT have any emergent properties. As a result, they are no better than traditional deep learning neural networks, and are, at the present time, ACTUALLY WORSE since our lack of deep research and understanding means that we don’t have the same level of understanding of these models, and, thus, the ability to properly “train” them for repeatable behaviour or the ability to accurately “measure” the outputs with confidence.

And while our understanding of this new technology, like any new technology, will likely improve over time, the realities are thus:

  • no amount of computing power has ever hastened the development of AI technology since research began in the late 60s / early 70s (depending on what you accept as the first paper / first program), it’s always taken improvements in algorithms and the underlying science to make slow, steady progress (with most technologies taking one to two DECADES to mature to the point they are ready for wide-spread industrial use)
  • the technology currently takes 10 times the computing power (or more) to compute “results” that can be readily computed by existing, more narrow, techniques (often with more confidence in the results)
  • the technology is NOT well suited to the majority of problems that the majority of enterprise software companies (blindly jumping on the bandwagon with no steering wheel and no brakes for fear of missing out on the hype cycle that could cause a tech market crash unequally by any except the dot-com bust of the early 2000s) are trying to use it for (and yes, the doctor did use the word “majority” and not “all” because, while he despises it, it does have valid uses … in creative (writing, audio, and video) applications [not business or science applications] where it has almost unequalled potential compared to traditional ML designed for math and science based applications)

And the market realities that no one wants to tell you about are thus:

  • former AI evangelists and some of the original INVENTORS of AI are turning against the technology (out of a realization that it will never do what they hoped it would, that its energy requirements could destroy the planet if we keep trying, and/or that maybe there are some things we should just not be meddling with at our current stage of societal and technological evolution), including Weizenbaum and Hinton
  • Brands are now turning against AI … and even the Rolling Stone is writing about it
  • big tech and companies that depend on big tech (like Pharma) are starting to turn against AI … and CIOs are starting to drop Open AI and Microsoft CoPilot because, even when the cost is as low as $30 a user, the value isn’t there (see this recent article in Business Insider)

Now, the doctor knows there are still hundreds of marketers and sales people in our space who will consistently claim that the doctor is just a naysayer and against progress and innovation and AI and modern tech and blah blah blah because they, like their companies, have gone all in on the hype cycle and don’t want their bubble burst, but the reality is that

the doctor is NOT against “AI” or modern tech. the doctor, whose complete archives are available on Sourcing Innovation back to June 2006 when he started writing about Procurement Tech, has been a major proponent of optimization, analytics, machine learning, and “AI” since the beginning — his PhD is in advanced theoretical computer science, which followed a math degree — and, after actually studying machine learning, expert systems, and AI, he used to build optimization, analytics, and “AI” systems (including the first commercial semantic social search application on the internet)

what the doctor IS against is Gen-AI and all the false claims being made by the providers about its applicability in the enterprise back office (where it has very limited uses)

because the vast majority of the population does not have the math and computer science background to understand

  1. what is real and what is not
  2. what technologies (algorithms) will work for a certain type of problem and will not
  3. whether the provider’s implementation will work for their problem (variation)
  4. whether they have enough data to make it work

and, furthermore, this includes the vast majority of the consultants at the Big X and mid-sized consultancies who graduate from Business Schools with very basic statistics and data analytics training and a crash course in “prompt engineering” who can barely use the tech, couldn’t build the tech, and definitely couldn’t evaluate the efficacy and accuracy of the underlying algorithms.

The reality is that it takes years and years of study to truly understand this tech, and years more of day-in and day-out research to make true advancement.

For those of you who keep saying “but look at how well it works” and produce 20 examples to prove it, the reality is that it’s only random chance that it works.

With just a bit of simplification, we can describe these LLMs as essentially just super sophisticated deep neural networks with layers and layers of nodes that are linked together in new and novel configurations, with more feedback learning, and structured in a manner that gives them an ability to “produce” responses as a collection of “sub-responses” from elements in its data archive vs just returning a fixed response. As a result they can GENerate a reply vs just selecting from a fixed one. (And that’s why their natural language abilities seem far superior to traditional neural network approaches, which need a huge archive of responses to have a natural sounding conversation, because they can use “context” to compute, with high probability, the right parts of speech to string together to create a response that will sound human.)

Moreover, since these models, which are more distributed in nature, can use an order of magnitude more (computational) cores, they can process an order of magnitude more data. Thus, if there is ten to one hundred times the amount of data (and it’s good data), of course they are going to work reasonably well for expected queries at least 95% of the time (whereas a last generation NN without significant training and tweaking might only be 90% out of the box). If you then incorporate dynamic feedback on user validation, that may even get to 99% for a class of problems, which means that it will appear to be working, and learning, 99 times out of 100 instead of 19 out of 20. But it’s NOT! It’s all probabilities. It’s all random. You’re essentially rolling the bones on every request, and doing it with less certainty on what a good, or bad, result should look like. And even if the dice come “loaded” so that they should always roll a come out roll, there are so many variables that there are never any guarantee you won’t get craps.

And for those of you saying “those odds sound good“, let me make it clear. They’re NOT.

  • those odds are only for typical, expected queries, for which the LLM has been repeatedly (and repeatedly) trained on
  • the odds for unexpected, atypical queries could be as low as 9 in 10 … which is very, very, bad when you consider how often these systems are supposed to be used

But the odds aren’t the problem. The problem is what happens when the LLM fails. Because you don’t know!

With traditional AI, you either got no response, an invalid response with low confidence, or a rare (compared to Gen-AI) invalid response with high confidence, where the responses were always from a fixed pool (if non-numeric) or fixed range (if numeric). You knew what the worst case scenario would be if something went wrong, how bad that would be, how likely that was to happen, and could even use this information to set bounds and tweak the confidence calculation on a result to minimize the chance of this ever happening in a real world scenario.

But with LLMs, you have no idea what it will return, how far off the mark the result will be, or how devastating it will be for your business when that (eventually) happens (which, as per Murphy’s law, will be after the vendor convinces you to have confidence in it and you stop watching it closely, and then, out of the blue, it decides you need 1,000 custom configurations of a high end MacBook Pro in inventory [because 10 new sales support professionals need to produce better graphics] in a potentially recoverable case or it decides to change your currency hedge on a new contract to that of a troubled economy (like Greece, Brazil, etc.) because of a one day run on the trading markets in a market heading for a hyperinflation and a crash [and then you will need a wheelbarrow full of money to buy a loaf of bread — and for those who think it can’t happen, STUDY YOUR HISTORY: Germany during WWII, Zimbabwe in 2007, and Venezuela in 2018, etc.]). You just don’t know! Because that’s what happens when you employ technology that randomly makes stuff up based on random inputs from you don’t know who or what (and the situation gets worse when developers [who likely don’t know the first thing about AI] decide the best way to train a new AI is to use the unreliable output of the old AI).

So, if you want to progress, like the monks, leave that Genizah Artificial Idiocy where it belongs — in the genizah (the repository for discarded, damaged, or defective books and papers), and go find real technology built on real optimization, analytics, machine learning, and AI that has been properly researched, developed, tested, and verified for industrial use.

Proper Project Planning is Key to Procurement Project Prosperity! Part 2

In Part 1 we noted that we wrote about the importance of Project Assurance, and how it was a methodology for keeping your Supply Management Project on Track, ten years ago and that this typically ignored area of project management is becoming more important than ever. Given that the procurement technology failure rate, as well as the technology failure rate as a whole, hasn’t improved in the last decade, and is still as high as 80% (or more) depending on the study you select, that’s a problem. Especially when, for many companies, theses projects typically start in the million dollar range. (Even if the annual license is only 100K, by the time you multiply that by 3, the minimum term any vendor will give you, the annual maintenance fee by 3, and then add the implementation, integration, training, and ongoing integration maintenance costs and ongoing training costs, it’s well over 1M.)

But we also noted whereas there might have been a time when this was enough to tip the odds of success in your favour, it’s not quite enough anymore. Given the complexity of modern procurement (which hasn’t had as many complex problems to deal with simultaneously in over two decades) and modern technology (which is now AI enabled, AI backed, AI powered, AI enhanced, and or AI driven, even if it isn’t), when most organizational users are still struggling with basic technology (not enabled, backed, powered, enhanced, or driven by [fake] AI bullcr@p).

We told you we were going to dig into the project steps and help you understand what you need to do to get it as right as you can and greatly increase your odds of success. But first, there is one critical action you need to make that is common to all steps that is critical for your Procurement Project Prosperity and that is:

  • Engage an independent expert to guide you through the entire process and help where needed, including assurance.

As noted, this individual

  • cannot be an internal resource, even from a different department, as they are still subject to the internal pressures from the C-Suite (fast, cheap, etc.) that might be counter-productive to project success (that is critical for eventually obtaining the ROI you purchased the platform for in the first place)
  • cannot be a vendor representative as their only goal is to get you to buy more, or at least keep your subscription at the initial purchase level (which likely contained seats you never used, SKUs you don’t use enough to justify, and third party feeds/integrations you aren’t taking advantage of)
  • cannot be an implementation team representative, even if they are a third party consultancy, as the odds are that consultancy has a preferred partnership with the vendor and will be biased towards keeping the vendor and doing whatever is easiest (and thus most profitable for) the vendor to keep getting their implementation referrals

Now, what’s the difference between helping and pure assurance? In addition to making sure each step is accomplished effectively, this person is also guiding you through the creation of the necessary artifacts of each step to ensure success. This person is helping you define the goals, not just ensuring the goals are met. The person is simultaneously a project guide and a project evaluator, bringing the Procurement Best Practices and Technology Knowledge that your organization doesn’t have, and helping you identify the right intersection to take you forward on your journey.

And this goes well beyond just helping you write an RFP (although this is a key step, which is why the doctor has been telling you to get expert RFP help for your Procurement technology RFP for close to two decades, because a bad RFP is one of the leading causes of project failure).

This is because, as we noted ten years ago in our original Project Assurance Series (Part I, Part II, Part III, Part IV, and Part V), project success depends on more than just getting the technical specifications right. Project success also depends on getting the talent right — as it is the people who will have to use the new system. And project success also depends on getting the transition right —- if the changeover is not smooth, significant disruptions to daily operations can occur. And, equally important, they also depend on an often overlooked 4th “T” —- tracery. Organizational success depends on selecting a superior strategy and seeing it through until the desired results are achieved (or the organization changes the strategy). (And since you don’t know what you don’t know, the small cost of engaging an expert, relative to the overall project cost, will generate a return far, far greater than the technology ever will.)

Tracery, which stems from late Middle English, can be defined as a “delicate, interlacing, work of lines as in an embroidery” or, more modernly, as a “network”. Implementing a strategy requires effectively implementing all of the intersecting “threads” that are required to execute the strategy to success. If any one aspect is overlooked, the project can fail. And if you can’t even see all the threads, it should be easy to understand how most projects essentially fail as soon as they begin and why you need a master weaver if you want to beat the odds and actually succeed.

Come back for our next installment where we will dig into the six traditional project steps outlined in our original series and dive into what your independent, third party, Procurement technology project guide (who will be independent from you, your vendor, and the vendor’s third party implementation team) needs to do.

Procurement Organizations Need Automation, But that DOES NOT Necessarily Mean AI!

A number of leaders in our space, including Sarah Scudder in the comments to this post, have been noting to me that they are seeing AI resonate with companies of all sizes.

Sarah notes that:

1. She’s seeing AI agent automations resonate with smaller companies.

Smaller companies need automation desperately, but it’s important we educate smaller companies that doesn’t mean they need AI. We’ve had adaptive rules-based automation and tailored machine learning in this space for almost 20 years and they can get fantastic results without having to risk being pre-alpha testers for unproven AI while getting the solution they really need for a fraction of the cost of this new, relatively unproven, AI tech! (Remember, firms that dumped millions into this bandwagon need to recoup those millions fast before their investors abandon them, which means high prices for unproven tech!)

2. She’s seeing copilot intelligence resonate with bigger companies who understand risk.

Which makes sense for a small segment of the market who are ready for it because augmented intelligence and automated suggestions with yes/no approvals are great for organizations who

  1. understand risk and
  2. understand the categories/markets/domains they are applying the technology in, because a true expert will identify the 95% of the time it’s working just fine; the 3% of the time it’s probably okay (and not worth the effort to double check manually due to the risk threshold); and the 2% of the time they need to slam the breaks and take over.

However, that’s not a very large segment of the market. What most companies still need is better analytics, category intelligence, and guidance from category experts on how to use it and then where and when to integrate automation and co-pilot capabilities.

Furthermore, I’m also being told that:

3. Mid-Markets are looking for technology they can roll out to the organization at large to get tail-spend under control, manage intake, and/or relieve pressure on Procurement to focus on more strategic efforts.

Which resonates, but, again, this is an area where AI is typically not needed. Catalogs, be they hosted, punch-out, hybrid, etc. with the ability to also request/book standard, pre-negotiated, services, easy search, and easy RFQ where there is no standard item but the buyer has budget authority, the vendors are preferred, and the amount doesn’t hit a threshold is often enough. Maybe a natural language search to find the right policy documents or bring up the right products or forms, but that doesn’t require modern AI either — we’ve had that for quite some time as well.

And, as Sarah implies, while organizations of all sizes need help to overcome their excessive workload and limited market insight so that they can prioritize risk management and mitigation in their procurement activities, this doesn’t mean they need AI. Automation yes, advanced technology a definite yes, but AI, rarely! Remember that when building and recommending ACTUAL solutions and not just buzzwords.

Proper Project Planning is Key to Procurement Project Prosperity! Part 1

Ten years ago we wrote about the importance of Project Assurance, and how it was a methodology for keeping your Supply Management Project on Track (Part I, Part II, Part III, Part IV, and Part V).

We told you that Project Assurance, which takes a proactive approach and tries to identify issues, and implement mitigations, before they arise, involves the organization periodically stopping to objectively assess project failure points as they arise, typically with the help of an outside third party who can be completely objective, to identify what is and is not being done well and what could cause failure later if not adequately addressed now.

In traditional Project Assurance, there are six health assessments at six critical points in every project (for each of the six initial project phases defined by the classic waterfall project methodology). In particular, there is an assessment at each of the following steps:

  • Strategy (Pre-Presentation)
  • Acquisition (Pre-Vendor Selection)
  • Planning (Pre-Design)
  • Design (Pre-Acceptance)
  • Development (Pre-Testing)
  • Testing & Training (Pre-Acceptance)

And that the right assurance expert can help you with

  • expectation management during the strategy development
  • narrowing the procurement gap during the acquisition phase
  • aligning the troops during the planning phase
  • delineate the disconnect during the design phase
  • evaluate for acceptance during the development phase
  • tame the transition during the testing and training phase

And we stand by these posts and the importance of a third party expert helping you with the assurance ten years later, because we feel that if more companies adopted the methodology, we might not be in the situation a decade late where we still have a ridiculously high failure rate in procurement technology projects (as well as technology projects as a whole), that, depending on the study quoted, still exceeds 80% in some cases.

But we also recognize that, given the complexity of both modern Procurement (which hasn’t had so many issues to deal with simultaneously in over two decades), and modern technology, project assurance isn’t enough to save a project that isn’t planned right from the get go. (You just don’t have time to identify and fix all the problems once things get underway and you have the army of grunts simultaneously doing the implementation, all the integrations, and training as they try to rush an enterprise project that used to take two years and get it done in 9 months so they can promise payback within a year (which never happens when they do this — but that would be a different rant).

So, in this short series, we are going to dive into the project steps and help you understand what you need to do to get it as right as you can and greatly increase your odds of success.

If You Still Don’t Believe That Gen-AI is Bad for Procurement …

Then maybe you should do the math.

It’s very expensive for what it doesn’t do. You can pay 10K a month or more just for a conversational interface to search your data or push data into your applications. For 10K a month, you can get a decent core P2P application or source-to-contract application that, well, actually does something.

It’s even more expensive to train these systems on your policies, connect them to your applications, test that basic requests generate reasonable responses, train it to guide your users to get to an eventual answer, and so on. This could easily be more than a year or three of license fees.

But the true costs are in the utilization. Every time a user asks a question, or responds to a question posed by the Gen-AI to try and elicit the users intent, it takes compute time. LOTS of compute time. At least 10X the compute time of a standard search engine or keyword based retrieval system. In some cases, 30X. (The wattage required is easily 10 to 30 times traditional Google search.) So if you’re a mid-sized organization with more than 1,000 employees, a portion of your cloud computing costs, which average between 2.4 Million and 6 Million a year (according to CloudZero), is going to increase 10X to 30X. Let’s say 5% of that was basic search and inquiry, 120K to 300K. Almost inconsequential. But multiply it by 10 to 30, and you’ve just added another 1 Million to 9 Million to your bill. Think about that.

That “low-cost” Gen-AI “chatbot” that makes enterprise search and application interface “easy” (but not as easy as a well designed workflow, FYI), that you think costs 10K a month after implementation, training, and most importantly, cloud computing costs could actually be costing you 100K a month (or even 500K). For what? A fancier Google?

As Procurement professionals, you can, and should, do the math. So even if you don’t believe the doctor when he says Gen-AI is a fallacy, then believe the math.

The math says Gen-AI is just NOT worth it.