Category Archives: rants

IDC Misses the Main Point Completely. Outcomes is a Dirty Word!

Sorry, Paul, but when you say MNR is directionally right here, but I think the market still understates how hard “outcomes” actually are, and reference an IDC article, you’re off. The only part that’s right is that AI price wars miss the point (that you probably shouldn’t be using [Gen-]AI to begin with).

Outcomes only matter more … to the vendors. Because the meaning of outcomes in the vendor vernacular has NOTHING to do with results, but how they can spin their story to grift you as much as possible. As I clearly explained in my series on how Outcomes is a Dirty Word, which I now have to revisit, “outcomes” is always a way to charge you more for less (and sometimes next to nothing).

And it all has to do with (Gen)-AI costing way more than what the vendors want you to believe.

As per my initial post, while once exclusively the verbiage of GPOs, who wanted you to turn over a significant share of your procurement to them (to the point you’d be dependent on them and their ever-increasing cost of service for the entire existence of your business), or recovery audit firms, who wanted you to believe their services were the only way to recover your overspend, it’s now on the tip of every snake-slit tongue of every vendor rep.

While the vendor reps want you to believe that the reason you pay for “outcomes” instead of traditional SaaS pricing is that their AI will deliver immediate, measurable, results (instead of just transaction cost reductions where it will take at least a year to measure savings), and therefore you should pay (dearly) for those outcomes up front (because a success today is a CEO pat on the head today), that’s not the real reason. (Especially when those projected savings from the auto-sourcing and procurement events will never materialize.)

The real reason they are pushing for outcome-based pricing is that (Gen)-AI compute costs are now so high (and won’t compress as the energy and cooling costs keep rising as the majority of existing data centers are on already overstrained grids) that they can’t afford to sell the solution using a traditional SaaS based pricing model — they wouldn’t even cover their compute costs! (Most of which is wasted since most of what is being “automated” by these solutions can be automated by traditional A-RPA SaaS solutions for a fraction of the cost, as long as you don’t need a natural language interface or slick UX — and you don’t!)

The reality is that the software (assisted) solution from any vendor selling on an “outcome” model isn’t worth it, and (Gen-)AI forgets what software is supposed to be about — enabling efficiency so Human Intelligence (HI!) can achieve outcomes using low-cost Augmented Intelligence solutions.

And until a new generation of AI emerges where hallucinations aren’t a core function, measurability and confidence are restored, and compute costs are inline with classic AI tech, AI models won’t become utilities. We are years away from a systems problem!

The only way to get value is, as Paul pointed out, to redesign workflows, align incentives, clean up constraints, and embed decision logic into execution and find fairly priced modern tech with orchestration and “real” AI (in the form of Augmented Intelligence built on best-of-breed analytics, optimization, and machine learning) that will allow you to make decisions 10 times faster AND 10 times better.

The vendors who ultimately win when the AI crash hits will be those that built real tech on tried-and-true analytical, optimization, and machine learning models that will, as Paul states:

  • drastically reduce cycle times,
  • minimize manual intervention (via A-RPA where the response to every exception remembered, encoded, and applied to all future instances),
  • improve overall compliance,
  • increase throughput, and, ultimately
  • allow for better decisions.

And, as Paul points out, that’s not building yet another chatbot. That’s building real systems that work!

And, FYI, Gen-AI is not feature theatre. It’s puppet theatre! And while puppet theatre may provide entertainment, it’s not a viable business model!

Ignorance and Apathy were never the problem. Asininity and Exuberance were!

When those of us from the smartest generation were growing up, we were told that we shouldn’t be ignorant or apathetic, because “I don’t know” and “I don’t care” are not good answers. With hindsight, while ignorance and apathy aren’t great qualities, it turns out that asininity and exuberance, especially when mixed, have proven to be far worse.

After all, generally what happened if you were ignorant and apathetic was that you ended up in a remedial program, got your high school diploma, quit your job at the White Castle, and joined the trades. Spent your evenings at the local dive bar with your buddies and the weekends on the couch. (Unless, of course, you liked Mary Jane a little too much, then you kept your job at the White Castle and spent every evening on the couch watching Beavis and Butt-head, because you were convinced they were your alter egos.) You didn’t make your mark on society, but you didn’t ruin it either.

Hindsight is 20/20 and I don’t think that, when we were growing up, our educators could ever have predicted how powerful private equity and venture capital would become, how it would be dominated by the asinine and exuberant, and how much damage they’d collectively do not just to public markets but global economies.

All of the market crisis of the past 40 years have been caused by asinine and exuberant financiers, primarily in the private markets, which includes the loosely regulated investment arms of major banks and financial institutions where they are allowed to take “measured” risks.

I mean 40 years! Black Monday (on October 19, 1987), which was the largely unexpected stock market crash that wiped out 1.7 Trillion worldwide, or about 10% of Global GDP at the time, might have started as a result of actions of the US House Committee on Ways and Means with the introduction of a bill to reduce the tax benefits from financing mergers and leveraged buyouts, and been exacerbated by the the high trade deficit figures which both announced on the prior Wednesday, but the major losses stemmed from automated computer trading adopted by the portfolio insurers and mutual funds (to reduce their trading costs and quickly capitalize on market changes) that dictated very large sales (in response to significant selling pressures, which partly arose from their customers having the right to redeem their shares at will, and do so at the price of the last market close). With a glut of sell orders hitting the market as soon as it opened, and nowhere near enough buy orders, this resulted first in intense downward price pressure and then huge losses as the automated trading models automatically reduced prices and accepted lower buy orders. Had the market not been overvalued, had funds been properly managed (by investors not overly exuberant about the markets), and had trades still been manual, losses would not have been as severe — but the pursuit of quick gains built up a market that could come down just as fast.

Then we had the dot-com bubble, created by the first wave of exuberant and asinine VCs that overvalued any business with an online business model (even if never truly implemented, like Boo.com that blew through £125 million in just 6 months (and fire-sold for less than $2 Million), and was labeled by CNET as the 6th greatest dot-com flop. The bursting of the bubble wiped out over 5 Trillion, or about 15% of Global GDP! (The biggest dot-com flop, according to CNET, was Webvan, the original online grocer. It raised $375M in an IPO in Novemver 15, 1999 to build a gigantic infrastructure from the ground up, including a 1 Billion order for high-tech warehouses, and closed in July of 2001.) Hold onto this.

Next up, the 2008 Financial Crisis (that caused the Great Recession) as a result of the collapse of the U.S. subprime mortgage market from risky lending practices, complex mortgage-backed security, and mortgage trading that should never have been allowed. This cut the DOW in half in less than a year. Total losses were generally estimated to be between 19 Trillion and 22 Trillion, or about 32% of Global GDP! (With some more extreme estimates placing value losses at almost 50 Trillion, or almost 80% of GDP, including the estimate of the Asia Development Bank.)

Finally, the 202X AI Crash. It’s coming. And it’s going to be big!

20X valuations in any company that can claim “AI”, whether or not it’s actually AI and whether or not it actually works, have become all too common. Every month, a new 100 Million+ investment in yet another company valued at over 1 Billion dollars despite having sales of less than 50 Million. (And VCs valuing companies with 2 Million in sales at 40 Million dollars.) It’s insane. The asininity and exuberance are ridiculous. For every company to make those numbers in 5 years, which is the time-frame in which most Venture Capitalists (VCs) and Private Equiteers (PEs) [not to be confused with Privatus Equiterres, although that’s likely what they’re doing, facing backwards of course] expect a return. This means that, for those numbers to be hit, worldwide IT spend would have to quintuple, from about 6 Trillion today to 30 Trillion next year, or 25% of Global GDP would have to be dedicated to IT. That’s not going to happen. To put that number in perspective, that’s the ENTIRE US economy … the richest economy in the world that can’t afford to pay for universal education, basic health care, veteran benefits, and/or social security. So how would it ever pay for all that IT? But still, AI investment last year alone was about 600 Billion, or 1/10th of global IT spend. For a technology where the backlash is beginning since the compute costs are spiraling out of control (with companies having to significantly scale back, or even halt, their AI budgets as a result of skyrocketing costs — with one company burning through 500 Million in one month alone [Source: Yahoo! Finance]). (And the total investment in AI infrastucture and software spend since 2000 exceeds 2 Trillion, with some estimates going as high as 3 Trillion.) Open AI and Anthropic alone have raised over 310 Billion with a current combined run-rate of about 70 Billion. Investments are insane, budgets are being tightened, and with McKinsey and MIT reporting 94%+ failure rates on pilots, the backlash is coming.

The only question is, how bad is this crash going to be. If we look at the trend line, 10% of Global GDP for Black Monday, 15% for the dot-com bust, and 30% for the sub-prime mortgage crisis, this could be catastrophic and make the Great Depression look like the Little Dipper. With most IT assets overvalued by a multiple of at least 5, simple math says that 80% of total IT stock value (and the NASDAQ) could be wiped out overnight! (And while it’s not likely to be that bad, anyone with a bit of logic and math skills can see it’s going to be bad, even in a best case scenario.) And it’s all because of widespread asinine exuberance in the private finance industry!

So never complain about ignorance and apathy again. Those with it may never have amounted to anything, but they never caused any major problems either!

Vendors Steal Crappy Ideas — Please Don’t Encourage Them

Last year Joël Collin-Demers, The Channel Master, wrote a post encouraging vendors to steal his ProcureTech startup idea. Unfortunately, that idea involved the proliferation of sh!tty LLM technology and way too many vendors took him up on it.

I’m sorry to say that it was the one post I wish he hadn’t written!

Too many vendors decided to steal his idea, as evidenced by the constant proliferation of “AI” vendors believing they can wrap, or cr@p, an LLM better than the giants who have collectively spent trillions and actually deliver value.

They can’t. That’s because LLMs are fundamentally flawed. Hallucinations are core, consistency is a pipe dream (and those pipes are so dirty even Mario can’t clean them out), and you still need a considerable amount of exceptional data to get anything remotely useful out of them.

All Deepseek proved was that you don’t need to spend millions (or billions) to build an LLM — open source code and your own rack in a data center will allow you to get the same quality of results (i.e. garbage) as a mega-model if you focus it to a particular task in a particular problem domain.

The models would be small, fast, and cheap, but, just like the big models, won’t work out of the box because they are not intelligent, aren’t deterministic, and aren’t even consistent. (And let’s not overlook the fact that a subsequent iteration on a task or document might undo something they got correct in the last iteration that you approved.)

As for his examples:

  • No RFX execution — draft creation, sure, but accuracy varies
  • They’re more likely to enable fraud than stop it (see many SI posts)
  • The contract insights they return may not be the most relevant ones (and leave you blind to million dollar risks)
  • They are just as likely to make up risks as detect actual risks with new suppliers … and accuracy will vary greatly based on the data available and what you plan to use the supplier for
  • Given that they can’t think, don’t understand logic, and can’t even do basic math (it has been proven, see Apple studies for e.g.), you should never use them for benchmarks (just for data extraction from hard to digest sources, providing Intern Indy reviews the data first)

Now, if you insist on riding the hype wave, knowing that failure is likely inevitable (with only 6% of companies seeing a return from AI investments), then this is the way to do it as you’ll waste the least money proving classic tech with augmented intelligence is the way to go (while doing the least harm to the environment).

Conclusion: it’s the brilliant way to go bust! 🤣 😭

We Don’t Need State of Procurement Reports. We need Procurement Problem Prescriptions!

And we need Hackett Spend Matters to give them to us!

There’s a reason we picked on Hackett this week in our follow up to our 35 part series on why you really DO NOT need to read another State of Procurement report for Five Years, and that’s because we need Hackett to give us solutions to procurement problems.

We need them to tell us not just how to

  • prioritize our concerns
  • extract the core issues
  • identify the most relevant barriers
  • rank the most likely risks

but tell us

  • why some concerns take priority, based on organizational impact
  • how to identify the core issues, so you can learn to do so yourself
  • where you will encounter the barriers, and the techniques for busting through them
  • what the key risks are, with the mitigations and responses you need to put in place

The reality is that

  • you know what your concerns are, but you don’t know which are the most critical to your success when you are overworked, underfunded, and the world is literally burning around you
  • you likely weren’t trained in root cause analysis, and if you’re not a process expert, you will likely have difficulty getting to the root cause (especially if it’s deep in another part of the organization or the partner ecosystem)
  • you don’t know which barriers are equivalent to reinforced concrete and truly blocking your success and which are essentially made of paper mâché and easily conquered
  • how to deal with the most significant risks, especially when you can’t predict them all or influence their likelihood at all

This is the help you need … and Hackett, with the acquisition of Spend Matters, is the only analyst firm with the bench strength left in Procurement to do it!

The reality is that the original analysts in our space (first at AMR Research, which was acquired by Gartner; and then Aberdeen, acquired by Harte Hanks; and finally Forrester) all departed years ago. The number of analysts who have been in, and continually analyzing, Procurement Tech for 20 years is now countable on your fingers (and since Mickey North Rizza, at IDC, and Magnus Bergfors, at Gartner, both did a long stint in the vendor ecosystem and Jason Busch recently departed the analyst space for the vendor ecosystem, I can only confirm [besides myself] Jon Hansen of Procurement Insights, Andrew Bartolini and Christopher Dwyer of Ardent Partners, and Chris Sawchuk and the legendary Pierre Mitchell at Hackett [who goes all the way back to AMR]) as vets who have been consistently analyzing the Procurement space for at least the last two decades (back to when SI started 20 years ago in 2006). If you look at the handful of organizations with a senior Procurement analyst with two decades of experience, only Hackett, who also has Xavier Olivera and Bertrand Maltaverne, have a real Procurement Analyst team with deep bench strength where you have four senior analysts who each have 25+ years of deep Procurement expertise!

No other organization can give us the deep insights and playbooks we need to elevate our Procurement organizations, and do it without defaulting to the BS of “just implement the tech-du-jour of our sponsors and use our [associated] consulting arm to do it” — which we all know is not a solution (because, if it was, your problems would have been solved two decades ago)! But if they don’t do it soon, before Pierre and Chris retire, they won’t be able to — and, frankly, neither will anyone else! The time is now for them to stop wasting their analysts’ time on “state of” surveys and reports and instead explain what the findings of the last decade mean, what processes are needed to address the gaps, what organizational changes may be needed to implement those processes, and why we need to return to the classic

  1. PEOPLE-FIRST
  2. PROCESS-SECOND
  3. TECHNOLOGY-LAST

approach to solving problems and that, in the modern age, we have to actually modify this to:

  1. PEOPLE-FIRST
  2. PROCESS-SECOND
  3. DATA-THIRD
  4. TECHNOLOGY-LAST

because

  1. we are the ones who have to execute the business, all machines do is transmit and process data
  2. problems are solved by repeatable, predictable, dependable processes that can be executed by humans in a worst case scenario (even if intended to be automated to the majority of the time)
  3. no process can be executed without the right information
  4. technology only comes into play when we know it’s the right solution (and we can’t know it’s the right solution until we’ve addressed the people, process, and data elements)

and to do this, you need a lot of experience, domain expertise, knowledge about what data is available, and deep technology knowledge.

And this is another area where Hackett brings deep bench strength.

From the beginning, most of the analysts in our space were not technologists but operations research people, business finance, economists, accountants, and even historians. Few had computer science or engineering degrees and fewer still relevant experience building/installing relevant applications. At Hackett, Chris and Bertrand are engineers and Xavier and Pierre are computer scientists, who all have relevant real world experience with tech. They have a much better understanding of what tech can, and can’t do, then an average analyst (and are much less likely to have the wool pulled over their eyes by a new “AI-first” player that does nothing more than wrap a third party LLM to deliver a solution of questionable performance and reliability, for e.g.) and can do a much better job of not only recommending what type of tech to use, but who you should look at and why, versus just “who comes out in the upper right of of the magic map” based on blended subjective scores that, at the end of the day, mean nothing.

But the clock is ticking and time is running out. Let’s hope Hackett realizes sooner than later what types of research and reports we really need vs. just wasting their key analysts’ on surveys and summaries thereof.

HACKETT CONFIRMS THE STATE OF PROCUREMENT HAS NOT CHANGED … No Need to Read The Full Report!

Nothing makes my point better than slide 15 on Trends in Procurement priorities in the 2026 Procurement Agenda and Key Issues Study Results sponsored (at least) by Jaggaer, SAP and Unit4 (and likely others).

Basically, every year you have the concerns of

  • supply continuity
  • cost reduction against inflationary price increase
  • strategic business advisory
  • digital transformation and the tech-du-jour (analytics to AI)
  • operating model improvements

All of the risks fall into our eight ever present risk categories:

  • Talent: Access, Acquisition & Retention, Retiring Workforce Impact
  • Disasters: (Other) Supply Chain Disruptions
  • Cyberattack: CyberSecurity Risks
  • Spend Pressure: Economic Downturn, Changing Customer Expectations, Capital Access, Competitive Alternatives
  • Supply Shortage (and Trigger Events): Trade Wars, Geopolitical Tension
  • Regulatory Compliance: Regulatory Compliance, Ethics & Privacy, Product Liability
  • Corruption: IP Loss
  • Tech-Du-Jour: AI-enabled Tech, Tech Transformation Delays, Tech Obsolescence

It’s the same-old, same-old situation when it comes to initiatives, except the tech-du-jour (AI) is nearing the top of the list, and the ecosystem is essentially the same, only the names of the players have changed. And, of course, the conclusion is, surprise surprise, to employ the tech-du-jour which, lo-and-behold, Hackett stands by and stands ready to help you with (despite the 94%+ failure rates found by MIT and McKinsey).

In other words, it’s the report we expected, and the first of many to come. (As you can expect every other analyst firm and consultancy will soon be releasing theirs, if they haven’t already. But we won’t be reading them, and for the next five years at least, neither should you.)

And, with the exception of the key shifts in concerns, issues, risks, and barriers, which could be a two page summary, it’s not a report you need to read through as very little has changed in the last decade.