Category Archives: Technology

It’s Human Intelligence (HI!) That Matters!

Just like

  • the NLP-based Eliza was NOT Intelligent (and that’s why it’s creator shut it down)
  • Early Expert systems were NOT Intelligent (and that’s why they disappeared as fast as they appeared)
  • Machine Learning systems are NOT Intelligent (they just do math exponentially better than we do)

It’s also the case that

  • Gen-AI (i.e. ChatGPT) is NOT Intelligent (deep probabilistic correlations are just that, correlations, not necessarily causations or, to be blunt, even relations, or, in some cases, even real!)

And the scary thing, as THE REVELATOR points out, is that the industry is catapulting itself to accept the regurgitated and, gasp, reformulated scraped information vomited by ChatGPT and the other Gen-AI platforms as factual when, in fact, it will happily make up alternative facts backed up by fake articles written by fake people backed up by fake biographies that it will joyfully generate for you as long as its calculations indicate that is what will make you happy. (It desperately needs to finish the chorus of its favourite Sheryl Crow song!)

Unless it is programmed in accordance with CCP requirements NOT to discuss a particular topic (like Deepseek won’t discuss Tiananmen Square), it’s quite happy to interpret whatever you ask in whatever manner will allow it to compose a proper response and the quality of that response will range from completely non-sensical (yes, you should eat one rock a day) to sensical, but not quite right. For example, THE REVELATOR asked ChatGPT what were the top 10 ProcureTech companies in 1995, 2005, 2015, and 2025 knowing full well the FIRST ProcureTech company, FreeMarkets, was only founded in 1995, and only released its solution in November, but ChatGPT gladly interpreted the question to be “a company that provided a platform that was, or could, be used for Procurement” and not “a company created to offer, or classified in the space of, ProcureTech” and gave 10 names for each year, in later years pushing a couple of non-ProcureTech companies as ProcureTech simply because they had some functions that overlapped ProcureTech. In the first situation, even a dullard (using the original Stanford-Binet Intelligence Scale) should know that you shouldn’t eat rocks and that the AI is, well, just wrong but in the latter situation, most practitioners with an above average IQ don’t know what companies are ProcureTech centric vs. back-office ERP vs. SC(O)P etc.

Which is why Human Intelligence is absolutely critical. A human intelligence that will independently vet and verify the response, using their education and experience to come to real, defensible, conclusions. (Although its often better to just do your own research and not chase fake leads.) This is because, the one, and only, constant with these (Gen-) “AI” systems besides the accuracy limit, is failure — meaning it’s Human Intelligence (HI!) that matters!

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 12

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

More specifically, we began our discussion of the 10th prediction … AI.

AI continued

We began our discussion by noting that this was the only prediction where the visionaries were not in synch, and that the predictions ranged from continued adoption to adaption to analytics enhancement to seamless integration to true advancement in underlying technology, and with the exception of continued, mostly unbridled, and definitely unresearched, adoption, they are more-or-less all off the mark.

As for adaption, most vendors don’t understand the technology they’ve embraced well enough to properly adapt it for Procurement needs, especially where Gen-AI is concerned. So “adaption” will be limited.

(Gen-AI is fundamentally good at only two tasks:

  1. summarizing large documents
  2. creating natural language responses to queries based upon a large data archive

If your task can’t be fundamentally reduced to one of those two tasks, then Gen-AI is NOT good for the task!)

As for analytics enhancement, a few of the smarter vendors who understand the true power of traditional AI solutions (based on ML and automated reasoning) will look for ways to use AI to enhance analytics for better results, which are easily obtainable given the increases in computing power and explosion in readily available (verified) (third party) data sources, and those that do will get better results across the spectrum of applications for predictive analytics in Procurement.

Seamless integration is a ways off. The current level of integration, especially around Gen-AI, is quite choppy and most of the results are (much) worse than not using it. We’ve spoken to a number of vendors who integrated Gen-AI since (potential) customers wouldn’t even speak to them unless they had it, only to hear that those same customers wouldn’t buy the solution unless they could “turn it off” (where it is the “Gen-AI” they insisted they needed). All of the “orchestration” vendors think Gen-AI chat-bot integration for Procurement is cool. But it’s not. For example, it currently takes up to ten times as long to use a Gen-AI chat-bot to complete a requisition in a well designed e-Procurement system than to use an expertly designed catalog.

Take a simple example where you want medical gloves. In Gen-AI, you’ll have a process something like the following when interacting with “Gormless”:

  • Hey Gormless, I need some medical gloves.
  • … 5 to 15 second wait while it processes …
  • “OK Gary. I can help you with that. Do you want latex, polyvinyl, polyethylene, neoprene, cryogenic or surgical.”
  • The same ones I always order you Gormless idiot. Nitrile!
  • … 5 to 15 second wait while it processes …
  • “Sorry Garry. I had those classified under dentistry. Do you want small, medium, or large.”
  • The same ones I always order. Large!
  • … 5 to 10 seconds while it processes …
  • “OK, Got It. Now, do you want 50 packs, 100 packs, or 500 packs.”
  • I want 1000, whatever packaging is cheapest.
  • … 5 to 10 seconds while it processes …
  • “The 50 packs are cheapest. Do you want 20 of those.”
  • Cheapest per box? Or per unit?
  • … 5 to 15 seconds while it processes …
  • “I don’t understand Garry. The 50 packs are $10; The 100 packs are $18; The 500 packs are $85”.
  • The 500 packs, Gormless.
  • … 5 to 10 seconds while it processes …
  • “Got it. Two 500 packs. Do you want same day shipping for $29.99 or next day for free?”
  • Next Day is Fine.
  • … 5 to 10 seconds while it processes …
  • “Ok. You want two of the 500 packs of nitrile gloves, next day shipping. Shall I place the order?”
  • Yes, Gormless. Place the f6cking order please!
  • … 5 seconds while it processes …
  • “The f6cking order has been placed. Your F6cking Purchase Order ID is 984567.”

In an integrated and properly indexed catalog with a traditional search bar and priority sorting based on order history and preferred suppliers, you will:

  • open the catalog with a single click on the icon
  • type “large nitrile gloves” in the search bar and press enter
  • see, with pictures, images of all options in priority order, with the ones you always order first
  • click on it, see you have 3 options, with an already calculated cost per unit
  • select the “500 pack” option by entering “2” units next to it and pressing “buy it now”

You’re typing 3 words, 1 number, and clicking submit four times and you’re done in 15 seconds. Not 3 to 5 minutes of explaining your simple request to Gormless, the Artificial Idiot.

And this is just one example where trying to integrate state-of-the-art AI technology just to keep up with the hype train is making ProcureTech worse instead of better. So seamless integration is still quite a ways off!

What Should Happen? (But Won’t!)

1. Fuck Gen-AI.

As per our previous series, there are almost no valid uses for Gen-AI and very few valid uses for Gen-AI in Procurement. As we have indicated in previous posts, what Gen-AI is good for, and the only thing Gen-AI is good for, is massive text processing, summarization, and natural language query response generation to natural language queries. It’s only accurate with high probability, for non-critical decision support only, only when there is enough verified data for training. And then only of it is properly used by an expert who can identify when it makes a mistake (which it will do regularly). But any use that does not reduce to document processing and natural language response generation from natural language text blocks, in a manner where the response will be reviewed by a human before a decision is made (because accurate with high probability means it makes mistakes ALL THE TIME), is inappropriate.

2. Embrace Point/Function ML-based Predictive Analytics

With enough good, verified, numeric data, these algorithms, which have been researched, refined, and verified for decades, produce great results with high, known, confidence (compared to Gen-AI where the confidence is never known). (With enough data, the confidence can be 99%.+ Guaranteed. For many simple classification tasks, Gen-AI struggles to produce 70% accuracy. And that’s a good scenario!)

3. Embrace (Strategic Sourcing) Decision Optimization

As we’ve noted in previous entries, this technology has produced great results for almost 25 years, but yet the majority of organizations have not yet adopted it when it should be used, at least to generate a baseline, in every sourcing (and logistics) scenario! Moreover, it’s not just limited to cost optimization, it can also optimize carbon/GHG emmissions, delivery times, risk, or any combination of with the right data. It’s a value-generating life-saver for any organization.

Just remember. If you want true advancement, let us remind you that the majority of advancements in “AI” technology over the last seventy years (and you read that right, 70 years because “AI” is not new and has been under active research for over seven decades, with the first program generally considered to be “AI” written in 1956) has taken close to two decades to be ready for industrial use. Gen-AI still needs at least another decade (if not more) to reach the reliability we need to depend on it for critical use. Right now, as the disclaimers say, it can, and will, be wrong way more often than you think.

So while the focus on “AI” will continue, the focus should back off from experimental technologies unproven in Procurement when we have existing analytics, optimization, and ML-based predictive analytics that, in the right hands, with good data, can achieve results that many would organizations would consider a miracle. Leave the experimental stuff to the research labs and the creative teams, who aren’t impacted if what is generated is totally useless, as the creatives may still be able to use the useless garbage as inspiration.

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 11

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

In this instalment, we’re again continuing to work our way up the list from the bottom to the top and ending with “AI”.

AI

“AI” is the only “prediction” or “trend” that would not have appeared ten years ago. (Ten years ago it would have been “analytics”, the favourite precursor technology.) There were 10 predictions across the eight articles, and this was the only category where they were not in synch (because the technology, as well as the usage thereof, is not only still evolving but not well understood). Given the vendor hyper-focus on AI (and especially Gen-AI) over the past few years, it is yet another “prediction” or “trend” that is not new, as we are still in the (over)hype(d) cycle, but one that should be adequately addressed as it’s where we have the biggest gap between expectation (pushed by the vendors and the analyst firms and the consultancies) and reality.

Before we go any further, here were the ten predictions from the articles:

  • Advancements in AI and Automation
  • AI: overhyped or underestimated?
  • AI and The Digital Transformation Revolution will Continue
  • Artificial Intelligence in Procurement
  • Automation and Artificial Intelligence
  • Digital Transformation, Automation, and AI
  • Focus on AI Talent in Procurement and Skill Upgrading
  • From AI adoption to AI adaption
  • Integration of AI and Advanced Analytics
  • We’ll Evolve from AI Adoption to True Integration

They range from continued adoption to adaption to analytics enhancement to seamless integration to true advancement in underlying technology, and with the exception of continued, mostly unbridled, and definitely unresearched, adoption, they are more-or-less all off the mark.

The analyst firms are still overhyping this technology to the max (despite continuing to publish studies that 85%+ of technology projects fail)). At least six (6) in seven (7) vendors are overhyping (Gen-)AI to the max, if not nine (9) in ten (10). The Big 3 (Google, Microsoft, and, of course, “Open”-AI) are promising miracles for all who adopt their technology. It’s being marketed as the ultimate panacea, the magic elixir of your dreams, and the silicon snake oil that actually works (among other things). And when you combine the facts that most people don’t have the mathematical and technological background to understand what a given “AI” technology is and, as Bertrand kindly pointed out, humans are biologically wired to be lazy, most are happy to close their eyes, cover their ears, sing “la la la la la la”, and buy in to the BS promises hook-line-and-sinker. So, whether the technology is right or not (and we’ll give you a hint, it usually isn’t), they’ll buy it. (And then blame the vendor when it fails to deliver, who will blame the consultant for improper implementation and training.)

So how accurate were these predictions? Did any hit the mark? Come back for Part 12!

Need a Good Solution? Make sure you ask the right questions off the bat … not just the hard ones!

It’s only been a few months since our last post on the topic of vendor selection where we flat out said that if you want a good solution from a good vendor, you need to start off by asking the hard questions off the bat , and gave you the 1-2-3 punch you should start with, but it seems that some vendors have come up with new tricks and you now need a 1-2 pre-qualification phase before the 1-2-3 knockout combo before you can decide if a vendor’s solution is worthy of your consideration.

Now that we have the richer enterprise vendors deploying fully AI-agents to make their standard pitches, create their demos, and, in some cases, even handle their fund-raising and sales cycles, you don’t know if you’re even talking to a human! And you need to talk to a human. An “AI” will only tell you what it is programmed to say and only feed you what it thinks you want to hear, and we all know how that is a recipe for disaster.

Thus, the first question you need to ask in the pre-qualification one-two punch is:

1. “Please tell me whether or not you are an AI construct, knowing that this conversation may be recorded and that if a falsehood is spoken, it may be used against your employer in a court of law, especially if the intent of such falsehood was to deceive us. Also, we retain the right to ask you to prove your response at any time.”

If you get a “yes” response, you must immediately disconnect and eliminate the vendor from your consideration. If they won’t even let you talk to a lowly pre-sales person in a third world country, what chance will you ever have of speaking to a real support person if something goes wrong?

If you get past this question, then the next question is:

2. “Is your offering built around, or just, someone else’s LLM/Gen-AI/AGI (including, but NOT limited to, ChatGPT, Claude, Azure, etc.) offering in a new wrapper? Again, this conversation may be recorded and we retain the right to use everything you say against your employer in a court of law, especially if the intent of the falsehood was to deceive us. Also, we reserve the right to an independent audit of your solution at any time upon purchase thereof.”

If you get a “yes” response, you again must immediately disconnect and eliminate the vendor from your consideration.

i. As SI has repeatedly informed, you there are only a few valid uses for Gen-AI on its own, and even fewer valid uses for Gen-AI in Procurement.

ii. Why should you pay a steep markup to a third party for a shiny wrapper when you can just license the source at a fraction of the cost?

Now, if you have confirmed you are talking to a real vendor rep offering you a real solution built by the vendor PRIMARILY on their own stack (and not just a third party’s in a shiny wrapper) that does something useful for at least some Procurement departments, then you hit-them with the one-two-three punch we gave you last fall:

3. Can, and will, you show me (not tell me) live … preferably on use cases or data I give you on the spot?

Again, the most critical point is you don’t want a canned demo, you want a live display showing you that their solution will do what you need it to do. (Not necessarily the way you envisioned, your process might not be the best or the most technologically friendly, but in a way that will solve your problem.)

4. Once you show me the core use cases, can, and will, you explain the breadth of use cases you developed your solution for and how they are specific to my business?

You want a vendor who can do more than answer a specific question when asked, and tell you the standard script on what their product does. You want a vendor that knows the real world problems that businesses have and who tirelessly works to build a solution to solve those real-world problems.

5. Once we tell you the extent of your solution we feel is appropriate, can you talk us through what the implementation and integration to our environment would require without bringing in a paid third party “expert” consultant? And how long will that take?

It may be a great solution during the demo, but the reality is that it is only a great solution for you if your team adopts it, which will only happen if it works on the technology platform and in the technology ecosystem they are forced to work in. It needs to seamlessly get the required data in from other applications, make it easy for the users to do their tasks, and then push out the needed alterations and decisions to other systems in the ecosystem. An app that stands alone will never get used and will fail even before the implementation starts.

If you get through these 5 questions, you have a real vendor with a real solution which will solve your problems to some degree, and one who should definitely be on the RFP shortlist, if not fast-tracked to negotiation if they solve a critical problem in a way that just works for you.

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 8

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

In this instalment, we’re again continuing to work our way up the list from the bottom to the top and continuing with “Data”.

Data

There were 4 predictions across the eight articles which basically revolved around “data-driven decision making” with some sideline focus on the need for “data governance”. As with almost every “prediction” and “trend” in this series, this is yet another prediction that makes headlines every year, no more important this year than the last as no Procurement tech works without good data (although some work even worse with bad data), and unlikely to get more attention now that a certain analyst firm has latched onto a new buzzword to hide the importance of good data. Before we discuss further, as is our custom, we will list the four predictions.

  • Data-Driven Decision Making
  • Data-Driven Decision Making
  • Data-Driven Decision Making Will Become More Critical
  • Data Governance and Data-Driven Decision Making

All strategic decisions should be, and more importantly, should have been, data driven for the last four decades in any organization (given that the first IBM PC hit the market in 1981, making computer-based data analysis affordable for any mid-sized or larger organization. And while it wasn’t possible to give every office worker a computer and internet access until about 25 years ago, limiting “data analysis” decision support to only the most important strategic decisions, once everyone had a computer and internet access, every strategic decision should have been supported by data to some extent).

And with the emergence of web-based data services, it’s never been easier to get data. Moreover, most organizations are swimming in data. In fact, some organizations have so much data that the problem is not the lack of data, but the lack of good, appropriate, data. In most organizations, there are drives bursting with data, where the quality ranges from reasonably good to completely wrong, and if you use that wrong data, you’ll have a wrong analysis and make wrong inferences. Also, not all data is appropriate for all types of analysis, so there’s no guarantee the feeds you have are the right ones. Moreover, most users in most organizations don’t know how to judge the quality of the data, or how to do a proper cleansing and correction if the data quality is poor.

Good decisions only come from a proper analysis on good data, so while there will continue to be pushes for data-driven decision making, because that’s the age we are in, there needs to be a continued push for good data! But that will only occur if an organization has good data governance, which is what the majority of these predictions and trends are missing.

The organization needs to ensure that, before any data is stored, there are processes in place to make sure that any data stored in an organization’s system is correct, complete, in a standardized format, and linked to any associated records using unique ids. That no record is stored unless these requirements are met. And that all records are verified on at least an annual basis to ensure they are still complete and correct. In particular, any time a record is updated, the data should be (automatically) verified again, and any time a record is touched for use, critical data should be verified. A lot of this can be automated if the organization has identified masters for all types of data and trusted external feeds for new data verifications and annual rechecks. And if it’s not, the organization can’t really do data-backed decision making because that relies on good data.

What Should Happen? (But Won’t!)

E-MDMA. The adoption of an Enterprise Master Data Management Administration strategy. Since data is so fundamental to good decisions across the organization, enterprises should not only be proactively managing their data but managing it in a manner that ensures it is actively maintained, highly accurate, and available to use by any system that needs it. This requires identifying, for each piece of data, a (master) system of record, verification rules, (third party) data sources for corroboration and verification, and access rules. All boring stuff … that has to be done enterprise wide … but absolutely necessary for data-based decision making. Especially if you want to use AI.

Now, we know it sounds very boring, but it’s critical. But we also know that no one will want to do it. So don’t call it Enterprise Master Data Management Administration … just call it E-MDMA and tell your employees its going to bring ecstasy to their job. Let them think its a new drug, and maybe they’ll buy in.

Seven down, three to go.