Category Archives: Technology

In the Software World, It Is Never Build vs. Buy!

In a LinkedIn post, THE REVELATOR asks “Why is the build versus buy debate a moot exercise?”.

The answer to this question is super simple.

If you are NOT a software* company, it is NEVER build. NEVER, EVER. Especially since “Build” typically means outsourcing to a Big X who are typically specialist implementors, not builders, and will just have to outsource to a Dev Shop and add a high margin to manage that outsourced project for you IF they want to get it right. (Just Google “Accenture Hertz Lawsuit” to see what happens when they get it wrong, so the smart Big X really do add a layer between you and an outsource Dev Shop in South America, Eastern Europe, or India … and trust us when we say that the last option ain’t always great either!) In the end, the project will cost 5X to 10X, take significantly longer than you expect, and rarely deliver entirely what you want.

The debate today should be “assemble vs. buy”, because the most you should do is determine whether its best to go with one provider who provides some functionality across the board for a function, but maybe not as deep as you want in certain areas, or if you want to assemble a slew of best of breed modules that go deep everywhere you want deep. In the latter case, you are deciding whether you are going to select a slew of best of breed modules from a slew of vendors and oversee the integration yourself (one time cost plus incremental costs on the update of each component solution) or go with an “orchestration” solution (and its year over year SaaS fee) vs. just selecting one of the same old Big Suite providers that will handle everything (with a fee to match).

The only thing that remains correct about the “build” vs buy debate is that you need to maintain the “build” mentality, in that you may have to lego-block “build” from a collection of best-of-breed modular solutions. However, the “build” will never be a build from scratch, just a build from components, the same way we used to assemble our own desktop systems.

* and even if you are a software company, if the type of software needed is not the type of software you build, and there is a reasonable SaaS solution, you should go with that;

Yes, There is a Fork in the Road. Which Path Will You Choose?

THE REVELATOR asks What is the ProcureTech fork in the road.

The answer is easy! It’s the same fork every year. As a ProcureTech practitioner, you have two choices:

1. Take the shiny yellow brick road that the marketers are trying to lead you down with their fancy soundbite hogwash and promises that all your dreams will come true when the software is implemented (as long as you don’t ask to see the wizards behind the curtain creating and implementing the software until after the contract is inked and the payment made).

(But, of course, the wizard behind the curtain, like the Wizard of Oz, is nothing more than a conman and a simple circus magician. The yellow paint is fool’s gold. And the bricks are made of cheap plaster and can’t take much weight.)

2. Take the dark path through the forest where you’ll have to clear the brush and bramble yourself, learn how to be self sufficient, and, presuming you survive, come out stronger in the end for undertaking the journey.

Of course, this is a harder path, and you’ll have to work for it. You’ll have to do all of the work and follow all of the guidance in our 2025 is Just Another Year series that we just finished. More importantly, you have to be willing to admit that all the marketers are telling you lies, damn lies, and fake statistics produced by Gen-AI. That the promises from sales are all best case hypotheticals and not practical reality, likely assume you are getting functionality not yet developed, and that you have perfect, complete, data well beyond what you actually have. That the implementation will be more involved, take longer, cost more, and be much, much harder than they want you to believe. And integrations won’t be out of the box and will take a lot of custom jiggery. And then your data won’t be clean enough or complete enough or in the “expected format” and the data migration will be way more challenging.

Success will ultimately depend on you owning the system selection, implementation, integration, data migration, and training. You have to understand your needs, your systems, their integration requirements, your data, the cleansing and enrichment requirements, and the training your team will need. And you have to oversee the creation of the plans for each step of the journey. You can hire outside expert consultants … but you have to make sure they are experts and nothing is overlooked. It’s all on YOU. Just YOU.

And only one of these paths is the right fork, but guess which fork will be chosen by the majority of people?

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!