Category Archives: AI

Outcomes is a Dirty Word! Part II

And you shouldn’t have to hear it!

The word of the day is still outcomes, and, no matter where it’s used, it’s still a dirty word.

Yesterday we gave you many examples of where outcome-based pricing has become the norm which includes, but is not limited to:

  • GPOs
  • Recovery Audit Firms
  • AI-first services-as-software
  • Big Consultancy projects

and where every single situation the entire point of the “outcome”-based sales pitch was just a ploy to convince you to pay more for less because

  • suppliers will happily match GPO prices for reasonable commitments as they have to pay the GPO a 1.5% to 3.0%+ administrative fee to get that business, and, moreover, at the head of the tail you can always get as good, if not better, prices using a tail-spend sourcing solution that automates 3-bids-and-a-buy RFQs and auctions (in a standard format that allows suppliers to automate bids) … and this solution often costs a fraction of what you will pay the GPO based on transaction fees (and then the additional savings from being able to quote every category at the head of the tail and not just what the GPO offers adds up to a greater savings)
  • proper retail-centric e-Procurment augmented with supplier and product management could prevent 90%+ of overpayments to begin with (and Lavante, Inc. proved that over a decade ago — why else would PRGX have acquired it and taken it off the market)
  • for every reliable AI-first services-as-software solution (as we all know that hallucinatory Gen-AI enables and amplifies fraud, security risks, bad decisions, etc.), there is a traditional SaaS alternative for a fraction of the price that does the same thing if you can do without the natural language chatbot interface and a slick UX
  • once a consultancy gets you on outcome-pricing, they are going to focus on projects where they know you are doing particularly poorly, employ junior grunts with five year old playbooks guaranteed to increase efficiency and reduce costs (because you are way above market average cost or way below market average efficiency), and use AI to generate their reports and strategy presentations (and hope the junior grunts both do their job and catch all the hallucinations in the prepared documents)

But, as we said in our last post, that’s not the worst of it.

The worst part of all these “outcome”-based pricing offers is that they are masquerading the grift that keeps on taking! (Which is something any American reading this should be quite familiar with by now!)

It’s not the overcharging that is the most insidious part of “outcome”-based pricing models, it’s what’s behind them.

  • GPOs want you to turn over more and more and more of your procurement to them because, the more you turnover, the more you reduce staff, and the more dependent you become … locking you in for years to come as your fees skyrocket to the point where you’re paying more to them then it would cost you to buy a modern sourcing to settle solution (that supports regular and semi-automated tail procurement and a couple of buyers [who will simply review any tail-spend awards that are new or out of bounds compared to past awards and select the suppliers for regular sourcing events, which the platform will automate until award time])
  • recovery audit firms want you believe only they can keep millions in your pockets and software will never solve the rampant overspend the suppliers siphon out of you, will do anything they can to further the narrative that you’re going to lose millions without them, that you shouldn’t even try to improve your procurement processes, and it’s best to just turn more spend over to them … again locking you in for years and years when you could be taking steps towards reducing your overspend to almost 0 with the right technology, processes, and senior category managers preventing that overspend from ever happening
  • AI-first service-as-software firms want you to go all-in on their service, fire your buyers, and believe that only their tech can get stellar results before compute costs go through the roof, the AI bubble bursts, and/or everyone realizes that the whole thing is being orchestrated by the Wizard of New Oz, it’s a bigger circus than anything P.T. Barnum ever managed to assemble, and when the curtain closes, all you’ll be left with is empty pockets (and, when you’re not looking, just like the auto-classifiers of old, they will throw as many Another Intern at the problem as required to ensure you succeed)
  • the consultancies don’t want you do anything yourself because once you realize that, if you hire qualified people and installed modern systems, you can do it just as good yourself, do it for less, and save a lot of money … so they will try to keep up the savings and strategy show as long as they can

In other words, the whole goal of “outcome”-based pricing is to take away your self-sufficiency, capability, and even knowledge and ensure your entire existence is 100% dependent on them. That way, they stay super profitable at your expense with the grift that keeps on taking!

At the end of the day, the only vendor who won’t price on outcomes is one that knows they can’t actually deliver any, even with fakery, because any vendor who can will find a way to use this trend to inflate prices and grift your hard earned gains!

P.S. You shouldn’t be surprised. It’s the same old story with a new name. It’s been going on since the first modern Procurement solution hit the market.

Outcomes is a Dirty Word! Part I

And you shouldn’t have to hear it!

The word of the day is outcomes, and, no matter where it’s used, it’s a dirty word.

You all know that where DEI is concerned, especially in North America, it’s a dirty word. As @Jason Busch will explain in detail at every opportunity, DEI has replaced “equal opportunity”, but unlike properly applied equal opportunity, which took us two steps forward, DEI, or at least its “outcome”-focussed interpretation, has taken us two step backs.

These days, everything has to be measured, and the belief is that if you don’t meet the goals for whatever racial/religious/women/minority metric your organization has defined to be an appropriate racial/religious/women/minority mix for your organization, then you aren’t diverse, equitable, and inclusive and, therefore, you should go out and immediately hire the racial/religious/women/minority employees you need to meet the metric. Merit be damned. No longer is it the most qualified resource, where someone of a minority is hired when two or more applicants are otherwise equal, it’s the most qualified resource of the identified minority, who might not be at all qualified for the job! It’s the token black employee taken to a whole new level! Not only does it reward incompetence, but it insults minorities who study and work hard to be just as competent, if not more competent, than their white male counterparts.

But I digress — we already know outcomes is the dirty word of DEI. But what you don’t know is outcomes is a dirty word across the business, wherever it is used – and Procurement is no exception! Why? It’s only become the popular battle cry since the Age of (BS) AI, whereas its prior use was been limited to situations where the consultancy, vendor, or analyst firm could hide the darkness and venom that the word contained.

More specifically, until recently, outside of DEI, outcome was primarily the verbiage of GPOs, who were doing their best to convince you to turn over a significant percentage of your procurement to them, or recovery audit firms, who were doing their best to convince you their services were the only way to recover your money that your suppliers were assuredly screwing you out of.

But they reality is that they’ve been both misleading you since the get-go. Sure a GPO can get you better prices than you can get on the long tail with their volumes, but that’s only true for the long tail. Moreover, the reality is that the costs aren’t that much less, if any less, than what you could negotiate on your own if you did a winner-takes-all long-tail RFQ to a MRO, office supplies, electronics supplier who could meet the volume across your long-tail needs, especially since that GPO is charging the supplier an administrative fee of up to 3%, and they’d happily give you the same price to NOT have to pay that fee! Add to that the GPO is charging you for their services, and you’re not saving much. Plus, when you work your way up to the head of the tail, you are definitely in 3-bids-and-a-buy RFQ or auction territory, and the application of a well designed tail spend sourcing solution will save you just as much as a GPO, IF NOT MORE!

Moving to recovery audit firms, their outcome-based pitches sound great, as you only pay their 33% if they recover the money on your behalf and fatten your bank account, but here’s the thing. If you had a properly designed retail-focussed e-procurement solution that integrated supplier and product management, did m-way matches, and prevented payments where you didn’t have good receipts that matched the invoice that matched the PO where the prices matched the contract, rejected duplicate invoices, tracked rejected units and associated credits, applied those credit notes against future orders (with the matching product), etc., you could prevent all of those overpayments in the first place — despite the fact that all the recovery audit firms tell you that overpayments (and their services) are unavoidable.

But there are more, and more modern, examples. The worst is AI-first services-as-software vendors convincing you that you should pay based on “outcomes” instead of on a traditional SaaS pricing model. Their rationale? The majority of SaaS tools that you are paying for aren’t offering you immediate, measurable, savings and, therefore, are too expensive. But if you paid for software based on “outcomes”, you’d have measurable value and you could claim the fee was worth it. And the argument sounds convincing, even if it’s complete and total bullshit. The purpose of most software is to increase efficiency, not save money. That’s the value.

And when the real reason they are pushing outcome-based pricing is that they can’t afford to sell based on a SaaS model because the compute costs of their BS AI-first are too high to cover on traditional SaaS pricing — even though there is a traditional A-RPA SaaS application that does everything their app does for a fraction of the cloud and compute cost, as long as you don’t need a fancy-smancy natural language interface or a slick UX. In other words, if they were honest about the true value of their application, they could never charge enough to cover their costs and would be out of business yesterday.

A second, more modern, example is the big consultancies taking a queue from their GPO, Recovery Audit, and now AI-first services-as-software peers and trying to justify their highly inflated pricing (which has skyrocketed over the last decade as they became the go-to firms for all big tech strategy). Especially since it’s the only way they can overcharge for projects where they are primarily deploying a multitude of AI agents (which we know produce utter garbage, just look at the Deloitte fiascos in Australia and Canada) and juniors that they hope will catch and clean up all of the hallucinations in the deliverables. (Because if they charged based on what the tech and juniors were worth, in a climate where no one wants to pay inflated rates for consultants for projects with potentially guaranteed return, they wouldn’t be able to maintain their high rates.)

There are more examples, but by now you should see the common theme. Which is simply this: “outcomes” is always a way to charge you more for less (and sometimes next to nothing) (just like DEI is an excuse to replace people with actual capability with people with next to no capability).

But the worst part, the blatant financial rip-off that always accompanies a (pure) “outcome-based” sales pitch isn’t the worst of it!

You Really Don’t Need to Read Another State of Procurement Report for Five Years!

Just read this 34 part series and you can ignore the 10+ surveys / studies / reports that will be collectively released by every major ProcureTech consultancy and analyst firm this year (which will likely include, but not be limited to: Capgemini, Deloitte, Everest Group, EY, Hackett, McKinsey, PwC, and many, many more)! We say this with certainty because we reviewed all of the reports they put out for the last 5 years and the vast majority of the content was the same year-after-year and firm-after-firm. You can practically count on any survey/study that tackles barriers, risks, and concerns to overlap with the following at least 80%, and that these will be the most significant barriers, risks, and concerns. In fact, in five years, only one concern will have changed, and that’s the tech-du-jour, because that’s all that was really different between 2025, 2020, 2015, etc.

You’re welcome!

You Don’t Need To Read Another State of Procurement Study for the Next 5 Years!

Top Barriers to Success

Breaking Down The Major Procurement Risks with High or Moderate Impact

Primary Concerns for Procurement Leaders

BONUS

If You Think You’re Ready for AI, You’re Not Ready for AI!

All of the Big Analyst Firms, Consultancies, and Vendors are telling you that you need AI, that it’s the only technology that’s going to allow you to get with the digital times, and that everyone else is using it, so you should too.

But the reality is that you probably don’t need AI, it’s not the only technology that can bring you up to date in the digital age, and while many people are using it, 94%/95% are FAILING.

The only hope you have to succeed is to be brutally honest, to ADMIT what you don’t know, that you’re only chasing AI because of FOMO and FUD, and that real progress has always been methodical and one step at a time.

More specifically, from where you are starting, not from where the market pretends you are.

The only organizations that have been successful at AI are those that:

  • honestly assess where they are today
  • determines their readiness for change
  • identifies the most time consuming processes they are willing to change
  • identifies the appropriate automation one process at a time, which is often just simple workflows/RPAs/built-in automations in existing platforms and other times ML/ARPA
  • monitors and tweaks them until they run smoothly and reliably
  • uses modern meta-workfows/ARPA/AI to connect the individual automations together where, and only where, it makes sense
  • only slaps guard-railed semantic tech / focussed SLMs on top to provide a natural language interface that processes inputs and outputs fixed action requests where appropriate

Successful companies don’t go all in an unproven tech, don’t try to do big bang projects (as that only results in big failures and sometimes the greatest supply chain disasters of all time), and definitely didn’t take the advice of the BIG X that promoted multi-year modernization mega-projects with no successes that they can point to.

In other words, the only companies that have succeeded with AI (the 5% to 6% depending on if you would rather go with McKinsey or MIT) are those that learned from the mega-ERP disasters of yesteryear and did a sequence of successive mini-projects that each built on the lass and slowly ramped to mega success.

In other words, they understand that you have to crawl before you can walk and walk before you can run. And if you can’t even crawl, you’re not ready to try and run at the Olympics, which is the level of tech maturity you have to be at to HOPE to succeed with AI.

Primary ProcureTech Concern: (Gen-)AI Integration/Impact

The non-stop hype coming straight from the A.S.S.H.O.L.E. is continuing to cause market confusion and utter chaos.

Why?

Gen-AI is on the concerns list because it’s the tech-du-jour. Five years ago it was (advanced) (predictive) analytics. Ten years ago it was the fluffy magic cloud. Fifteen years ago it was SaaS. Twenty years ago it was the World Wide Web. And so on.

But not one of these technologies, all sold as the panacea that would solve all your woes, solved your problems because all of the promised capabilities were just silicon snake oil, and Gen-AI is no different. The hype cycle may be slowly coming to an end, but it will quickly be replaced by Some-BS-World-Model-Adjacent-Agentic-AGI that will be sold as the AI that finally solves all your problems but, in reality, still won’t be anything close (but, if narrowly applied in the right domains where the client has sufficient data might actually work quite well … but won’t do anything reliably in general and the failure rate will still be 80%+, which is the average tech failure rate for the last 25 years … and SI knows, because the doctor has been following tech failure for over 25 years).

Not only is Gen-AI no different than the previously over-hyped tech-du-jour offerings of the last two decades, but with a failure rate of 94%+ (McKinsey, and 95%, MIT), it’s arguably the worst yet! And, as per our predictions, it’s not going to get much better. If the failure rate gets as low as 90% this year, it will be the closest thing to a tech miracle that we can conceivably get. Like every other tech before, Gen-AI will only solve a relatively small set of problems.

Just like

  • The Web only solves remote connectivity
  • SaaS only allows solutions to be built in the cloud
  • Analytics only provides insight where you have the right, sufficient, data and the right algorithms to get useful insights
  • Gen-AI is just a next-gen probabilistic deep neural net that often does
    • better semantic processing
    • better search
    • better summarization
    • better potential pattern identification (but only if you can learn how to prompt it to do so and only if you have it trained on the right data subsets, not the entire web which is now more than half AI slop)

    but does so at the additional expense of

    • hallucinations
    • intentional falsehoods
    • thoughtless reinforcement
    • cognitive atrophy
    • etc. etc. etc.

As a result of this, as far as I’m concerned, the AI bubble can’t burst fast enough! It’s all hype, buzzwords, and hallucinatory bullcr@p. And, frankly, any (claims of) agentic AI built on it are fraudulent. (After all, we’ve already seen what happens when you let AI run your vending machine. The last thing you want is it buying for you!)

Especially when, on top of hallucinations, we have plenty of examples of:

We’ve said many times that LLMs are not helpful and ChatGPT (in particular) is not your friend, that if you have a headache you definitely shouldn’t take an aspirin or query an LLM, and that, frankly, you’d be better off with a drunken plagiarist intern because that’s the best case result from an LLM. Most are worse.

Frankly, it’s time to stop falling for the artificial intimidation, fight back against AI Slop, and remember cutting edge tech is NOT defined by the C-Suite or the incessant marketing from the A.S.S.H.O.L.E. that is targeting the C-Suite on a daily basis!

Impact Potential

Huge! Companies will continue to waste millions individually and collectively hundreds of billions on the next generation tech that, with a probability of 90%+, will generate a (huge) loss.

Major Challenges/Risks

The major challenge is not with the tech, it’s helping companies realize that they’re probably not ready for the tech. The reason that tech failure rate has averaged 80%+ over the last twenty years is that consultancies keep promoting, vendors keep selling, and companies keep buying advanced leading edge tech they are not ready for. The reality is that unless you are in the top 10% of buyers of tech, already on the latest tech, and have sufficiently mastered that tech, you are not ready for Gen-AI (which should not have left the research lab when it did and, in all honesty, should still be in the research lab since it still only works in a small number of well defined scenarios and is so bad that every year a couple of AI founders turn away from AI because of it — with Yann Lecun walking away from Meta and LLMs and reverting to world models, that can be thought of as next generation (Semantic) Web 3.0 models augmented with [deterministic and dependable] automated reasoning and, hopefully, very little dependence on hallucinatory probabilistic models [beyond what’s needed to semantically parse an input].)

The only place you should be using Gen-AI is where a non Gen-AI solution doesn’t exist, the task is well defined, and you can build a custom in-house model that works reasonably well in the majority of situations and that can be implemented with guard-rails. But that’s something you can only do if you have a high TQ (Technical Quotient) and have mastered last generation tech. Right now, you should be tripling down on E-MDMA and Advanced Analytics as this tech has improved to the point where it can allow you to optimize processes, spending, schedules, and anything else you can think of with high accuracy and low cost with basic analytics skills as so much comes pre-packaged and the visualizations and drill-downs are much more intuitive than they were a decade ago. Plus, these firms have figured out how to use multiple forms of AI to classify your data with high accuracy and minimize the work required by you to fix errors and reclassify to your preferred schemas. It’s literally drag and drop as compared to the complex rule-building that used to be required. In addition, you should be looking for the mature A-RPA (Advanced Robotic Process Automation) solutions that are highly customizeable and capable of “self-learning” such that the parameters that trigger exceptions will adjust over time based upon user acceptance or rejection of recommended actions and the platform will automatically encode new processing rules based upon the users’ actions on an exception. Much better than Artificial Iiocy that decides everything based on hallucinations.

THE FINAL WORD

If you haven’t mastered all of the tech that existed before Gen-AI, including classical machine learning AI that has been studied, optimized, and proven to work for over a decade, you’re not ready for Gen-AI, should treat it like the drug it is (as it does more damage to your cognitive abilities than many illegal drugs), and JUST SAY NO!