Category Archives: rants

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.

Analytics Is NOT Reporting!

We’ve covered analytics, and spend analysis, a lot on this blog, but seeing the surge in articles on analytics as of late, and the large number that are missing the point, it seems we have to remind you again that Analytics is NOT Reporting. (Which, of course, would be clear if anyone bothered to pick up a dictionary anymore.)

As defined by the Oxford dictionary, analytics is the systematic computational analysis of data or statistics and a report is a written account of something that has been observed, heard, done, or investigated. In simple terms, analysis is what is done to identify useful information and reporting is the process of displaying that information in a fancy-shmancy graph. One is useful, one is, quite frankly, useless.

A key requirement of analysis is the ability to do arbitrary systematic computational analysis of data as needed to find the information that you need when you need it. Not just a small set of canned analysis on discrete data subsets that become completely and utterly useless once they are run the first time and you get the initial result — which will NEVER change if the analysis can’t change.

Nor is analysis a random AI application that applies a random statistical algorithm to bubble up, filter out, or generate a random “insight” that may or may not be useful from a Procurement viewpoint. Sometimes an outlier is indicative of fraud or a data error, and sometimes an outlier is just an outlier. Maybe the average transaction value with the services firm is 15,000 for the weekly bill; which makes the 3,000 an outlier, but it’s not fraud if the company only needed a cyber-security expert for one day to test a key system — in fact, the insight is useless.

As per our recent post on a true enterprise analytics solution, real analysis requires the ability to explore a hunch and find the answer to any question that pops up when it pops up. To build whatever cube is needed, on whatever dimensions are required, that rolls up data using whatever metrics are required to produce whatever insights are needed to determine if an opportunity is there and if it is worth being pursued. Quickly and cost-effectively in real-time. If you have to wait for a refresh, or spend days doing offline computation in Excel to answer a question that might only save you 20K, you’re not going to do it. (Three days and 6K of your time from a company perspective is not worth a 20K saving if that time spent preparing for a negotiation on a 10M category can save an extra 0.5%, which would equate to 50K. But if you can dynamically build a cube and get an answer in 30 minutes, that 30 minutes is definitely worth it if your hunch is right and you save 20K.)

Analysis is the ability to ask “what if” and pursue the answer. Now! Not tomorrow, next week, or next month on the cube refresh, or when the provider’s personnel can build that new report for you. Now! At any time you should be able to ask What if we reclassify the categories so that the primary classification is based on primary material (“steel”) and not usage (“electrical equipment”); What if the savings analysis is done by sourcing strategy (RFX, auction, re-negotiation, etc.) instead of contract value; and What if the risk analysis is done by trade lane instead of supplier or category. Analysis is the process of asking a question, any question, and working the data to get the answer using whatever computations are required. It’s not a canned report.

Analytics is doing, not viewing. And the basics haven’t changed since SI started writing about it, or publishing guest posts by the Old Greybeard himself. (Analytics I, II, III, IV, V, and VI.)

Advice For Dealing With The PROCUREMENT STINK from Leading Consultants!

Last week, the doctor asked fellow niche/independent consultants as to how we can help to dispel the PROCUREMENT STINK which is permeating the space as a result of poor choices, bad information, and sometimes bad actors, which include the reasons we described in that article as well as many more.

Why? Because it’s going to take a collective effort among analysts, consultants, and vendors to dispel the stink permeating the Procurement space, and no one on his or her own will have all the solutions. As expected, some of the greats chimed in with their thoughts and ideas and these thoughts and ideas need to be given center stage, so this is what we’re going to do today!

James Meads

Clarity and transparency on your business model is key, especially if you have revenue streams from solution providers.

As Patrick Van Osta echoed in the comments, the uphill path to recovery, I feel, is for consultants to reclaim the position of sole trusted advisor, and there’s no way we’re ever going to be trusted advisors if we are not clear and transparent in our operations and goals. If we’re hiding our intentions, or upsides, how will the client know whether or not our goals actually align with theirs?

Joël Collin-Demers

I’m 100% on-board with the need for transparency and taking decisions based on what’s best for the client long term. Your job is to make yourself redundant as soon as possible!

In Procurement, there’s always another project. ALWAYS. You don’t have to milk one for life, with your help and guidance, you can open the client’s eyes as to not only how much there is to do, but how much they can do better, for a great ROI, with your help. Just like there’s well over 25,000 (or 35,000) species of fish in the sea, there are tens of thousands of unique aspects to Procurement in a modern enterprise. And just like you have to know where to fish, what hook to use, and what bait to use to catch a type of fish, you need to know the equivalents for each category, methodology, and process.

Jon W. Hansen

Practitioners stop looking at technology as the “silver bullet” solution but instead focus on doing the real and hard work while Solution providers stop selling shiny paper and “falling in love” with your own technology. … and us consultants have to help the practitioners do the work, understand what they need, and steer clear of the vendor with the shiny new tech (that doesn’t actually do anything [more than cheaper, proven tech]).

Paul Martyn

Consultancies (and their clients) need to Provide performance based compensation with uniqueness. For example, provide specialist consultants with compensation that includes equity. In short, align compensation to customer value (revenue growth and retention). Because, right now, most of the good consultants that can generate the ROI a client should expect are not incentivized to do well on point-based projects (like an Affordable RFP), but instead are incentivized to work on, and sell, long-term “solution” oriented consulting that lines the firm’s (and not the clients’) pocketbook (i.e. keep doing the fishing vs. teaching the client). As a result, most of the good consultants move out of the roles they are needed in to the roles they are incentivized to take.

Vinnie Mirchandani

The web lulled a number of procurement (and IT) folks into expecting vendor, negotiation etc intelligence for cheap, if not free. Vendors are not afraid to spend on sales and marketing. Procurement needs to adopt a similar mindset to even the game.

The best things in life may be free, but the best things in business are not. (As the Arrogant Worms pointed out over three decades ago, you get NOTHING FOR NOTHING!) And if you don’t have the right tools that enable the right processes powered by the right intelligence, you’re not going to win the game. Remember that all of the best sports teams use high-tech sports tech backed by science and data analytics to help their athletes reach peak condition. Raw talent only gets you in the game. You need the right training to win, or, at least, the right guidance and tech to enable you as you learn.

There’s a lot of STINK out there now, but if you follow this advice, you’ll go a long way to removing it. After all, you can’t solve everything with a pressure washer.

Dear Marketer On a Budget …

It’s never quantity, it’s quality.

And audience matters!

  • The majority of people who follow a celebrity aren’t following because they want pitches.
  • The majority of people who follow a major influencer aren’t following because they jive with that influencer.
  • And those that follow a minor influencer are following for a reason and are generally of a certain consumer class (based on the common reason). Don’t ask a fashion influencer for low cost apparel to sell a high end luxury watch, and in our space, don’t ask an influencer whose only use for tech is to make brainless content for followers to consume to sell an enterprise product.

These are hard truths that have been the case since even before influencers, so the following linked post from Phoebe Sophia Russell from “In the Style” (on how 150,000 on a celebrity Instagram post only produced $800 in sales) didn’t surprise me. It’s like the new startup that forks over 100K for a big bash at ISM only to come back surprised when they didn’t even manage to get a single follow up demo scheduled.

Think back to the days when Oracle, SAP and IBM (and almost no one else) used to advertise everywhere, but see almost nothing for their stadium sponsorships, airline magazine ads, etc. All it bought them was name recognition — which was important IF you could get in front of a client who’d seen your business name (repeatedly) and not your competitors (and then instinctively thought of your company as successful), but they still had to get those RFPs and meetings, which means investing in traditional sales channels that would enable that. But that’s not a strategy the vast majority of companies can afford!

SI, which has been giving away free marketing advice (including great advice from Pinky and the Brain#) since it began (because ??? ?????? has no intention of being a marketer … but still knows what works*), including this piece on Marketing 101 which appeared with the FAQ in 2007, always advocated for intelligent spending for smaller companies which focussed on publications (on & offline), events, and thought leaders who had the necessary audience, even if it was small. 100 buyers who actually want the type of products covered by the publications, events, and thought leaders is better than 100,000 individuals who have zero interest.

And the good news is that, even though many marketers during the heyday of free money would say I was off my rocker, the best marketers today pretty much agree with me, include the Marketing Maven Sarah Scudder who has teamed up with Dr. Elouise Epstein (in their DualSource Discourse podcast) to help educate you.

(Which is great since there aren’t many of us left trying to … going back to when I started, it’s just Jason Busch, Jon W. Hansen, Peter Smith, and Pete Loughlin who haven’t given up. Fortunately, we were joined by Kelly Barner and Philip Ideson of Art of Procurement and now we have David Loseby, Tom Mills, Joël Collin-Demers, and James Meads as well … )

Focus, Audience, and Education matter!

* Every single sponsor of SI before ??? ?????? joined Spend Matters in ’16 (to ’22) [and suspended sponsorships] was acquired by ’19.

# The Brain Gives Pinky a Marketing Lesson
# Where Pinky and the brain devise a plan to market their strategy

PLEASE TELL ME: Why buys research cobbled together by “researchers” who don’t have a clue as to what they’re researching?

This press release just went live yesterday:

“Sourcing and Procurement Operation Software Industry Future Trends Analysis”

which announced a new “Sourcing and Procurement Operation Software market” research report from Orbis that opened with obvious (that we are a pivotal sector), stated a few more obvious facts around software delivery methods (could-based, traditional ASP based), broad market sectors (business, manufacturing, education, government, etc.), and top players that include:

  • GEP SMART – Source to Pay
  • Jaggaer – Source to Pay
  • Corcentric – Source to PayMENTS
  • Coupa – business spend management, sorry, margin multiplier maker based on Source to Pay

which are in every map, quadrant, wave, logo map, etc. … so no surprise there but …

  • Precoro – Procure to Pay

which only solves half the problem

  • Servicenow – workflow management
  • Kissflow – low code app development

which can build solutions, but doesn’t offer them out of the bark

  • Vendr – SaaS marketplace

where you can buy some of them

  • ClickUp – Project Management

which is not even remotely related to S2P at all!!! And if these are the top 9 vendors, I shudder at what other totally irrelevant, non-comparable, vendors were included!

A report such as this should ONLY include vendors that offer real, and core, Source-to-Pay functionality, and only if they break down the space into segments where included vendors are actually comparable!

And it shouldn’t be hard as there are over 600 such vendors in some core area of S2P … you don’t have to include generic workflow engines, project management, buy-an-app platforms, or generic project management just to hit 25!

Reports like these give analyst firms, and analysts, a bad name!

Why Won’t They Stop?!?