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!

Procurement Needs to be Sharper and Consequential …

… but will you force it that way with (Gen) AI or because of (Gen) AI and the ridiculous claims the hype around it is making?

We’re wrapping up our first Garry week (as there may be more to come, especially if we can lure Garry back from the world of Architectural Design to Procurement … after all, software offerings like Programa really need a good Procurement module and a good leader like Garry to help them build it) with his post on how AI will force Procurement to become smaller, sharper and more consequential.

Which is true, as long as you accept that consequential won’t always be a good thing if you blindly use (Gen) AI or blindly ignore (classic) AI and f*ck up royally. But let’s backup.

As Garry astutely notes, AI frees Procurement from administration the same way a gym membership frees you from being unfit. It depends on whether or not you use it, and how. And, if you use it, as Garry points out, it depends on whether or not Procurement uses any additional time gained to do more of the same, or redesign the profession. (There’s also the possibility you ban all AI, even classic AI proven to be good, dependable, and hallucination free.)

Garry argues Procurement will become smaller (even though Procurement is usually understaffed as it is) because most coordination work can now be done by technology (AI not needed, just reliable middleware 3.0, also known as orchestration). There will be no tolerance for statements that “we need three people to do this” when the organization sees peers apparently doing it with one. (Not necessarily done well, but done.) And attempts to defined headcount by creating (unnecessary) governance will fail, as people will just continue to route around as much governance as they can, like they have always done.

It will become sharper because, despite the fact that the key to success is good processes, process competence is not rewarded — only commercial judgement. Good Procurement organizations will focus on finding professionals that understand irreversibility and second (and third) order consequences, who know how deep they have to investigate before making a decision, will quickly research to that depth (and only that depth), and quickly give you a “yes”, “no”, or “this is complicated — I need this much time to give you a authoritative answer”.

And one way or the other, it will be more consequential because, as Garry implies, and I clarify — Procurement now sits dead center in organizational strategic risk. It chooses the supplier, the carrier, the route, the chain, and the contract. All of which are now major risks across all organizations. Every day, another decision made by Procurement is a Board-level risk … and if it’s made by AI, it can be a devastating one.

Garry argues that future procurement organizations, and leaders, will be different. Not just processes, but decision architects. Not just cost avoidance, but risk-and-trade-off masters. Not just gatekeepers (where the gate must be kept locked where regulatory compliance cannot be broken), but “standard-based enablers”.

But there won’t be as much divergence as Garry indicates there might be. Procurement will only reach this level of effectiveness if they put a proper end-to-end decision enablement (not making) system architecture in place that implements and orchestrates best-in-class technology that captures best-in-class processes and supports end-to-end automation potential wherever the risk is acceptable for the platform to do so — including not only the ability to automatically stop, raise an exception, and include a human with expert judgement in the loop, but the ability to “learn” from that decision, encode a new pattern, and ensure the same type situation is automatically handled the same way in the future so that every system interaction removes the need for a future system interaction, allowing people to focus on tasks only people can do. (i.e. Adaptive Robotic Process Automation, or ARPA. Not necessarily Gen-AI. Classic ML will do just fine!)

Everyone, even those focussed on negotiation and relationship management, will make heavy use of systems — the only difference is what systems a Procurement professional will use in the majority of their system interactions. Back office people will focus more on modern risk-aware and trade-off aware sourcing and procurement systems which support advanced analysis, optimization, multi-objective cost vs risk vs quality trade offs, etc. Relationship managers will focus on third party financial and risk ratings, regional and natural disaster risk, performance, and quality data, interpolations, and projections as well as (critical/impact) spend (level) and distribution to judge the supplier’s overall performance and spend their time in risk analysis and performance tracking systems with an occasional spend dashboard. And so on. Processes that ensure all critical data, risks, and compliance requirements are captured are key, and so are the systems (automated to the extent possible) that encode them. Procurement will depend on these systems. The difference is how much manual work they will be doing in the systems vs using the analysis and guidance that comes out of the systems to make good judgement based decisions.

Procurement Doesn’t Need An AI Good or Bad Debate …

… because there’s always clearly one winning side (classic, tested, reliable, known confidence) vs. the other losing side (Gen-AI, experimental, hallucinatory, unknown dependability) …

but, as Garry points out in his Tuesday Afternoon post, they do need to know whether or not they can use it, what they can use, how they can use it, to what extent they can depend on it, and whether or not they’ll be in trouble for using it (or not) if they follow the rules and something goes wrong … especially when they are told late on a Tuesday afternoon to just git ‘r done on a last minute task that has to be done before they leave (and you haven’t provided them proper systems to get the task done).

This is why, as per our last post, you need an AI Rulebook that can give your users guidance on what AI can be used where, who can use it, when, how, and why those rules are in place.

However, as Garry makes clear in his Tuesday post, your users need training on how to properly use the rulebook, and, more importantly, on how to properly think about AI and their use thereof. This posts, which builds on his ladder post (which, as we’ve noted, you don’t need if you use Busch-Lamoureux Exact Purchasing because that tells you how much “decision” authority can be turned over to a dumb machine vs. how much needs to be human judgement by default), puts together a routine for departments that don’t have a request type classified (and obviously don’t have the right systems in place because his examples of getting a contract deviation approved, a risk flag explained, or a supplier added should be a quick and easy process in your current systems assuming you have guaranteed access to legal/risk professionals for targeted questions on a daily basis, full system log access, and/or management has (pre)granted you override authority — because none of these examples require AI (and certainly not Gen-AI), and if you need to use AI, it’s demonstrating a failure of your Procurement leadership on expert and system selection, implementation, and/or utilization. (But we digress.)

When you feel you need to use AI because you don’t have the systems and expert access you need, and especially if you don’t have a good AI rulebook, then you need to go through Garry’s AI buying routine.

  1. Why are you using the AI?
    To quickly locate information (in an online help or policy guide) or help you support a decision. In the former case, use it without hesitation, click through to the source (so you don’t have to worry about hallucinations), and git-r-done. Move on. In the second case, slow down, think about its response, and continue to step 2. (And start by asking, does it make sense?)
  2. What is the decision that has to be made?
    Frame it in the shortest possible sentence. Is the clause acceptable to the organization in a signed contract? Is it okay to put the order through to the contracted supplier given the newly identified risk? Will adding this supplier violate any risk policies or compliance requirements? You’re much less likely to be swayed by LLM mumbo-jumbo when you analyze the response in response to your short and succinct question.
  3. How reversible is this?
    What happens if I get it wrong? If it’s a three year contract, that tells you that you can’t make the decision unless you have expert access and confidence as it’s not reversible. If it’s a new supplier that might not meet government compliance regulations, you can always ban them later, so there is short term reversibility, as long as a contract or payment doesn’t go through, so it really depends on how soon an order is going out and being fulfilled and how likely they could be in a risky area as to whether or not you can make the decision . If it’s an order to an existing supplier that just got flagged for a delivery or bankruptcy risk, you can always send the order to someone else in a few days if you need to and it’s completely reversible.
  4. What is the evidence bar to match the cost of failure?
    For reversible and low impact, which is what you’d find in the bottom-most octant of the Busch-Lamoureux Exact Purchasing framework, you can literally turn over the process to an AI, even a Gen-AI that hallucinates semi-regularly because it’s so easy to undo and having to deal with an exception on one in twenty decisions is just so much more efficient than making sure all 20 decisions are perfect. But if the impact is high and the decision irreversible, you don’t want to use AI for anything beyond helping you do your research and opinion-free analysis.
  5. Can the AI show its work?
    If not, you’re not using AI, it’s using you.
    (Because if you can’t question and verify it, then all you can do is follow it.)
  6. Who owns the judgement?
    What single person is responsible for the decision. Not a system. Not a committee. What person. You? Then you get to make the decision and accept the consequences. Your boss — then you need to get her approval, bringing your recommendation and reasoning, and then you can go forth and execute. Your boss’s boss — you can’t make the decision, but you can bring your boss all of your research and reasoning and she can choose what to push up the corporate ladder.
  7. Are All Exceptions Automatically Logged in Unalterable Audit Trails?
    Not just because exceptions are where trust is won or lost, but if AI is used at all, and the worst case happens, and you end up in court, you need that audit trail that shows you followed a process, a human was involved where necessary, the necessary risks were analyzed, and the decision, according to your processes, was just. If you don’t have that trail, you don’t use AI for any critical decision. Period.

That’s the process. And the reality is that if you have proper systems and processes installed, you are properly staffed and trained, and you are proactively planning risk mitigations, you’ll need to use AI a lot less than you think you will. (Unless, of course, you’ve already used it so much that the cognitive atrophy has progresed to complete brain fry and you don’t know how to think for yourself anymore.)