Category Archives: Market Intelligence

M&A Mania is Coming Again … but will it be the same as last time?

the doctor agrees with THE PROPHET that M&A in Procurement, Supply Chain and Finance Tech is Back On For Q4 and 2025, because M&A Mania is part and parcel with the The Marketplace Madness that the doctor told you is coming back in May. The only question is, will this M&A cycle look like the last few during Covid (when every investment firm had to have an online collaboration platform, since they couldn’t do business in person, and an online e-Payment FinTech solution, since they still needed to make, and most importantly receive, payments) and in the late 2010s when companies were getting scooped up left, right, and centre. It was kind of like that first year in Chemistry where you were told to look to your left, look to your right, and look in the mirror and realize that only one of you would survive the end of the course (except the odds had worsened and there was only a 1/6 chance that any of you would be left standing at the end of the M&A cycle and less than a 1/9 chance that more than one of you would be left standing).

But first, let’s review THE PROPHET‘s reasons why:

Reduced interest rate climate coming
Not necessarily in your country, but in the US and a few other major investment markets, and for global funds, that’s enough.
Valuations back up (including a recent one)
the doctor is seeing a bit of this beyond just over-hyped fake-take and (now failing daily) Gen-AI, which indicates a return to value for real solution capability that solves real problems, and not just glam UX or tech buzzwords, could soon be coming.
Dry powder is the size of an ammo depot
And this is a rather conservative estimate. Broaden your definition of our Source-to-Pay space, and it could go well beyond the 666 providers in the mega-map.
Constrained target/asset pool to pursue
Too many providers not focussed on Gen-AI bullcr@p were not (well) funded and in need of funding to grow and too many providers who raised too much on Gen-AI bullcr@p blew too much on failed dev and marketing and need someone to infuse them with fresh funding while taking in the reigns and refocussing them on core problems.
No clear leader in many markets
Even if you constrain by target enterprise size, vertical groupings, and module, you’re usually looking at over a dozen vendors. Too many. By core module alone, you’re usually looking at over eighty (80) potential providers.
Counter-cyclical sector defensibility as a hedge
Most definitely. the doctor has always said the best time to develop/expand is on the verge of a coming financial or supply chain crisis, and it’s even better if it corresponds with the end of a hype-cycle (when everyone realizes that grandiose claims are just that, claims, and usually not realized and it’s time to return to the next generation of tried and true technology).
Times of increasing global uncertainty favours supply chain, supply and supplier risk management
Yes, and this will be constant for years. The outsourcing crisis the doctor and a handful of others have been predicting for over a decade (which is why he was telling you to near-source and home-source in the late 2000s) materialized during COVID, anti-globalization is at a high not seen in the remembered lifetime of most of the global population (and increasing by the day), we likely haven’t been this close to World War III since the cuban missile crisis of 1962 (since the Soviet radar malfunction of 1983 was caught by an alert Soviet air defence forces officer) putting global political tensions at a near all time high since World War II, ever increasing natural disasters and supply shortages are escalating costs at levels of inflation not seen since the 1970s, and in some markets, since the late 1920s (and the Depression era), and it’s just doom and gloom all around. Only our space has the tech to combat this.
Corporate spend flowing into tech, not new jobs
This is unfortunately true since

  • most executives don’t realize that tech only increases productivity and success in the hands of a human, it doesn’t replace them (since Aritificial Idiocy can’t even replace real idiocy, how can you expect it to replace Human Intelligence [HI!])
  • big companies don’t like high fixed costs, and the see people has the highest fixed cost
  • the dream of the new robber baron billionaires is to replace people with machines, which they think will help them realize their vision of constantly increasing profits from constantly increasing revenue (from a workforce that never needs to take a break) at a constantly declining cost to serve (not possible, but that’s their dream)
Nearly all big tech firms (ERP, business applications and stack) aside from SAP have not made any material moves yet — and will need to at some point
You can’t wait for a lumbering giant … by the time they buy someone, it’s ready for sunset. Remember IBM and Emptoris? A sad end to the APE circus! That means that the time to strike as an investor is before they awake!

Add add the following:

  • money has been idling in these funds from lack of investment over the last couple of years (as they got antsy last year with the predicted recession and the SVB failure and the fallout of both), and their investors aren’t happy
  • many of the more progressive funds have realized that fintech is useless if there’s no money moving through it, which means you have to look for broader business solutions that can assure the flow of money as well as information
  • companies are starting to realize that ridiculous 10X, 15X, 20X valuations are a thing of the past (or at least until we get a whole new generation of freshly minted investors who didn’t bother to study their history, like the new generation of founders that didn’t study theirs) and that if you can get a solid 5X to 7X valuation (which is the most a company can expect to realize at an aggressive 40% annual growth rate, which is the most they can hope to realistically support) for tech, that’s great, and this makes acquisitions a lot more attractive than during the last cycle when you’d have to bid 10X on something that might not scale as an investor just to get invited to the table

The M&A market is returning. But there will be some differences this time. The last two times it was valuation run up until the money ran dry or there were no companies left that were worth it. This time will be more reminiscent of the first M&A Mania to hit our space in the late 2000s and it will come with a little kiss, like this:

1. Valuations will be more realistic.

As simply stated, 10X, 15X, 20X growth doesn’t happen in five years for anything but a Unicorn, and even then it’s rare, and investors aren’t going to pay this any more. That being said, they will invest for value and firms who focussed on building real solutions, not slick UX with no substance, will be valuated quite well (at first).

2. The cycle will have 3 parts.

2A. Existing Growth Opportunities

Look for PE firms to buy suites or modules that can be sold and grown stand-alone or as complementary solutions to offerings in their stable. The market for these solutions could mature quickly as the Gen-AI and intake hype cycles crash and the global situation destabilizes and risk-focussed Sourcing and Procurement become paramount. This will be done at fair to very good valuations, depending on the offering and the financial situation of the firm being acquired … those that can wait and play the field will get better valuations.

2B. Fill the Gaps

As new competitors enter the scene, existing providers with aging tech are going to want to counter them and will start buying up point-plays to fill the gaps. This will take two forms.

  1. stable, stand-alone players who can survive without investment will wait for the right offer, get a very good to great valuation, and survive relatively unscathed in personnel and offering (and will continue to be available standalone for some time)
  2. cash-crunched desperate players who won’t survive long without a cash infusion will be bought in a fire sale, folded in quickly, and only key personnel will remain

2C. Liquidation Opportunities

Everyone loves a steal, err, deal. Investors included. As companies start to run out of money left, right and centre because they were underfunded (and struggled to compete with the overfunded overhyped companies) or overfunded and burned money like it grew on Central American fruit trees that produce two healthy crops a year, investors and buyers will be looking for companies with pieces of tech they can use to enhance their offering for pennies on the dollar. These companies will be broken up across talent and technology, with the acquirer keeping only what they want.

Questions to Ask Your Optimization Vendor

This is an update of a post that originally ran way back in 2007. Yes, two, double-o seven. Seventeen years ago. It is being updated because

  1. it needs a re-posting
    (as very few of you will find it that deep in the archives)
  2. most of the vendors originally mentioned are gone

However, if you read, and remember, the original, you’ll realize that, like my article where the doctor goes mental on optimization myths (which was recently shared on LinkedIn), it doesn’t need much updating and what was written seventeen years ago is still valid to this day. (When you write to inform vs. to create meaningless buzz, it really does stand the test of time.) Let’s begin.

Not all optimization vendors are equal … and, more importantly, not all vendors that claim to have strategic sourcing decision optimization (SSDO) actually have it (since the underlying algorithms and model needs to meet a stringent set of requirements to be true SSDO), with some systems, to this day, barely qualifying as decision support. Thus, since the need for optimization is as desperate as it has ever been with costs again skyrocketing, risks rising rapidly, carbon control being critical, and supply assurance necessary for sustained operations, it’s time to make sure you know how to qualify a potential provider. This means you need to not only understand the basics of what SSDO does (see the archives), but also how to distinguish between the relative strengths and weaknesses of the different offerings, as well as how much strength you really need.

You need to buy optimization at the strength, and usability level, that you need — especially if the vendor is pricing it according to its power, or computational requirement. And while there is no such thing as too much, the reality is that a 95% solution is often more than enough as the entire point is understanding the optimal solution against each dimension (cost, risk, carbon), the cost of compromise between the trade-offs, and the cost of going with a preferred, versus calculated, vendor award. And doing this for EVERY sourcing event. Once you factor in enough discounts and constraints, it’s almost impossible to calculate the best award in a spreadsheet, and the insight of what you could be spending, versus what you are, how low your risks could be, versus what they are, and how much you could alter your carbon footprint, vs what your footprint is today, is invaluable. Even if you never select a recommended solution, the key is understanding how good your (preferred) award actually is.

Before we get to the (starting) question list, it should be pointed out that it’s almost impossible to cover every question, as many of the questions you should be asking depend on the answers you receive to your first few questions, but the question list below is a good starting point.

1. Does the product meet the four criteria for strategic sourcing decision optimization?

  • Sound & Complete Mathematical Foundations : such as MILP solutions based on simplex, branch and bound, and interior point algorithms as many simulation, heuristic, and “AI” algorithms DO NOT guarantee analysis of every possible solution (sub)space given enough time, and, thus, are not “complete” in mathematical terms (and if they incorporate Gen-AI, they aren’t even “sound” in that they may not even compute an award that satisfies the constraints!)
  • True Cost Modelling :
    that supports tiered bids, discounts, and fixed cost components — the model must be capable of supporting all of the bid types being collected, as well as the cost breakdowns
  • Sophisticated Constraint Analysis : at a minimum, the model must be able to reasonably support generic and flexible constraints in each of the following four categories
    • Capacity / Limit: allowing an award of 200K units to a supplier who can only supply 100K units does not make for a valid model
    • Basic Allocation: you should be able to specify that a supplier receinves a certain amount of the business, and that business is split between two or more suppliers in feasible percentage ranges
    • Risk Mitigation: you should be able to force multiple suppliers, geographies, lanes, etc. to mitigate those risks without specifying specific suppliers, geographies, lanes, etc. to take advantage of the full power of decision optimization
    • Qualitative: A good model considers quality, defect rates, waste, on-time delivery, etc., and must support qualitative factors and minimum and average scores across the award
  • What-If? Capability : The strength of decision optimization lies in what-if analysis. Keep reading.

2. Does it support the creation of multiple what-if scenarios per event?

Furthermore, does it simplify the creation of these scenarios? The true power of decision optimization does not lie in the model solution, but the ability to create different models that represent different eventualities (as this will allow you to hone in on a robust and realistic solution), to create different models off of a base model plus or minus one or more constraints (as this will help you figure out how much a business rule or network design constraint costs you), and to create models under different pricing scenarios (to find out what would happen if preferred suppliers decreased prices or increased supply availability).

3. How fast is it for different average model sizes?

And can performance be tweaked? Optimization takes what it takes. That being said, if one solution takes an average of 1 hour for an average scenario, and another solution takes 10 minutes, all things being equal, if you have compressed sourcing cycles, the 10 minute solution might be better. Emphasis on “might”. This is only true if the faster solution is of the same quality – some models, and some solvers, sacrifice quality and accuracy for speed. The best solution will let you trade off “tolerance” and accuracy for speed. Sometimes it’s easy to get within 1% or 2% in a few minutes, even though that last 1% or 2% could take hours. On a model with low total savings potential, getting within 1% may be enough. And when trying to hone in on the right what-if scenario, it’s nice to get within 1% quickly and then allow the right scenario to run to completion over lunch (or if its a huge model, over night) after you’ve quickly analyzed half-a-dozen scenarios and settled on your preferred scenario. Thus, tweaking ability is very important.

4. Is it “true” real-time or “near” real-time?

Thanks to significant advances in processor and hardware performance as well as off-the-shelf optimizer technology (like IBM ILog’s CPlex), it’s now possible to rapidly re-build and re-solve even very large models using off-the-shelf modeling languages in seconds, allowing for e-auction tools that keep the model relatively moderate in comparison, and presolve with seed bids (current prices, market prices, last quotes), to incorporate decision optimization in real-time by simply updating a few parameters and re-solving the model every (few) parameter(s) update (depending on model-size) on a high-powered multi- core server with an appropriately configured and optimized solver (which can spin off copies and have each processor work on a different subspace). However, if the approach the product takes is to rebuild and resolve the model on every update, that’s not real-time, that’s near real time, and the slowdown could be significant for large models. (To clarify further, real-time optimization requires the ability to merge model construction and model solution in such a way that a new bid can be introduced as a parameter change that does not require the optimizer to rebuild the sparse model matrix and start the solution process over from scratch.)

5. Can you describe two or three scenarios you have encountered where you could not model the situation exactly?

And, more importantly, how did you work around the issue, and how accurate was the final result. The real world is messy, compared to models that are clean, only so much data is available, and math can only model as much as the minds who created the model could conceive. As a result, no optimization model can handle every real-world scenario 100% accurately. If a vendor representative says so, he’s either lying through his teeth or not competent enough to be selling the product. (Note that: I’ll have our optimization expert get back to you on that is a good answer from an average sales representative.) This is about the only way to get a decent idea of how appropriate the tool is for you. If the scenarios were complex and the constraints based on business rules you hardly ever, or never, use, then the solution is probably okay for you. If the scenarios were simple and the constraints based on business rules you use all the time, it’s probably not the tool for you.

6. Would you be willing to demo your solution to, and answer questions from, our consultant who understands both our needs and decision optimization technology?

Let’s face it -– just like the right decision optimization tool can deliver huge savings multiples on your investment (10X or more), the wrong tool will simply represent a six (or seven) figure cost that yields little return. If you can’t tell the difference, and there’s no shame in admitting you can’t if you’ve never used this type of technology before, then you should bring in a consultant who can to help you select the right technology, and ensure you are appropriately trained on it, until you are self sufficient and saving an average of 10% or more per project put through the tool.

7. Can we do a pilot project at-cost (or gain-share) before committing to a long term license?

If you like what you hear, but are still unsure, or are having problems getting the budget approved, a pilot is often the way to go! (Note that I did not use the word “free”!) If you’re not willing to sign a license, given the sophistication of this technology and the amount of effort the provider is going to have to allocate to support you through the pilot and ensure you are successful, you need to be willing to pay for services at a rate that is sufficient to cover the provider’s cost for the pilot -– especially considering that many of the companies that offer affordable optimization offerings are only able to do so because they keep their costs and overheads down.

How Dumb Is Your Company?

And, more importantly, will you be among the 20% who will be completely gone within two years (as per the doctor‘s predictions, and remember that he has been following this market for almost 25 years and seen all the ups, down, startup explosions, M&A manias, and the following implosions) or the 75% who won’t last in their current form (as per THE REVELATOR‘s predictions).

the doctor first asked this question to the space on November 7, 2008 when he saw the first implosion (which had all the signs of the first major enterprise back office tech implosion in the 2000 crash) coming (which wasn’t the last, as there was another one in the latter part of last decade that followed the next big wave of M&A and startup mania), but this time the forthcoming implosion looks to be the biggest our space has ever seen (and while the space is too crowded with vendors who aren’t actually providing any new, solid, innovative Procurement solutions, this implosion could also wipe out a large number who are, and that would not be a good thing).

So, it’s time to ask this question again, except this time we’re focussed entirely the vendors. Last time, it was directed at all organizations generically, including buying organizations that, sensing a market correction (which was worse than expected in 2008 and 2009, were putting off much needed Procurement technology purchases which could have saved their hides during the crash) as well as poorly run vendors. But this time, it’s all on the vendors and the investors (namely VCs and PEs investing way too much in companies without any real solutions, hoping to profit from the hype cycle before it crashes). So, without further ado, here are 10 of the most common dumb mistakes we’re seeing.

1. Doing Away With the Perks

Even if money is getting tight (or the PEs are telling you to tighten your belts because they just realized they aren’t going to sell low value solutions for a Million bucks a pop), this is the last thing you want to do. For an employee, it’s the first sign the company is in trouble and for a good employee who is talented and in demand enough to get a job elsewhere, the first sign to accept the next offer that is more-or-less equal to her current renumeration package.

2. Delaying Time-Saving Technology Purchases

Your developers, back office, sales, and marketing personnel need tools too, not just your potential customers. This doesn’t mean that you should buy the first tool they request, because if everyone is on a different tool you’re not achieving economies of scale and spending 30% to 40% more on SaaS than you should be, but that for every task they do regularly that they could do much faster with an appropriate tool, they get an appropriate tool. For e.g. SalesForce isn’t the only CRM, there are a lot of marketing tools for expediting content to multiple business and social networks, and a lot of back office suites that are quite affordable, especially in the small business / mid-size business market. You just have to take the time to look.

(As we all know, just like you’ll never get a Mega-S2P Suite in our space for less than 1M a year, you can get mid-market suites with all the functionality a mid market actually needs 90%+ of the time for less than 250K. The same holds true in other enterprise technology markets too.)

3. Postponing Actual New Product Development

Remember, business need actual solutions more than ever — and this doesn’t mean wrapping a shiny new third-party Gen-AI tool and claiming success. This means researching their problem, identifying actual process-bases solutions, and coding those processes (with configurable rules-based workflows) in an easy to use manner. Now, you can use ML/AI as appropriate to analyze data and trends, and even Gen-AI to summarize available natural language documents and data, and present these insights to a user as intelligent augmentation to help her make a decision, but the tool works without it in a way everyone can trust.

4. Strangling the Travel Budget

National and global business requires national and global travel. There’s only so much that can be done (or that old school business people will allow to be done) over Zoom and Teams. Now, this doesn’t mean that travel should be granted willy nilly for every prospect, conference, etc., but at key points during the marketing, sales, and implementation cycles, on-sites will be needed. (Nor does it mean that travel budgets should be fully unsupervised, for anything over a trivial amount, at least one other employee at an equivalent or higher rank should agree it’s worthwhile.)

5. Cutting 10% above the Board

Now, the Big X like to to this, but this is one of the reason the doctor keeps hearing examples of how their remaining AI and analytics teams are not delivering value relative to the price tag the Big X charge (relative to what mid-markets can charge and deliver). (Because the Big X kept hiring whomever they could during a tech boom and then kept cutting the worst as an ongoing “correction” to their over-hiring of under-skilled, under-educated, and/or under-experienced individuals, relative to the value they wanted to bring to the market, the best believe that just one mistake, or one bad quarter for their team, and they could get the axe no matter how good they are, so many left for what they perceived as better opportunities as soon as those opportunities came their way). That’s one of the two reasons the bloodbath started earlier this year (and is still ongoing). You can’t continue to charge 2X (or more than) the niche firms, especially if you have junior people who deliver 1/2 the value (or less) and expect customers to keep putting up with that, especially during non-growth and recessionary times. (So while this strategy is great for weeding out under-performers and bad apples in good times, in bad times it scares the top talent away.)  You have to charge less or increase value.

Only cut people who aren’t working out (and only after giving them time or support to find a job more appropriate to them elsewhere), and avoid hiring people who aren’t likely to fit in the first place!  (Side note: identify your core values and focus on that.  Just like there are situations when they should use Big X as a client, there are situations where they should use you as a client!)

6. Killing the Training Budget

In fact, you need to double or triple it. If you think that Gen-AI, intake-to-orchestrate, AI-backed/AI-driven/AI-enabled/AI-enhanced/AI-powered, supplier insights, or some other overhyped buzzword is the answer, then you don’t actually know what the majority of Procurement organizations need and what you should actually be building. So train your product managers on real Procurement practices and processes and how to do actual market research (or at least identify a niche consultant who can help them).

7. Shifting Focus from Infinite-Growth to Indefinite Belt Tightening

Just because you overspent on marketing hype and a sales force (who couldn’t sell because you didn’t actually have anything worth selling, or at least worth buying at the ridiculous price tag the investors hoped for), that doesn’t mean that you’ll survive if you just cut costs across the board. The only way to survive is to start building actual process-based solutions now that take a people and process centric first approach (what do our target users need to do everyday and how can we best enable that in an easy, minimal, step-by-step process with an intuitive UX), and educational messaging that will help hit this point home (and make your solution stand out from all the other hogwash that these businesses are fed up with hearing about).

8. Freezing the Marketing Budget

Just because you overspent like Montgomery Brewster in Brewster’s Millions and have nothing to show for it, that doesn’t mean you’ll do any better with $0 in the budget either. The key is to do consistent educational marketing that informs your audience not only that you exist, but on what your solution does and how it will help them solve their daily problems. And to do it through channels relevant to your industry, geography, and the communities these buyers are a member of. (Not one-time “look how great we are” conference booths that no one remembers, or one-time “groovy vendor” write-ups with limited reprint rights from overpriced analyst firms, or splashy advertising in the biggest publication you can afford.) Consistent, month after month education in small pieces such as short webinars or podcasts, bite-sized white-papers (with an e-book on your site if they are interested and/or for your sales people to use during a sales cycle), info-adverts in targeted publications. By the time the next budget season hits, you should be a name they know and trust because you took the time to learn about problems, instead of pushing magical solutions that will never work (the new silicon snake oil).

9. Stifling Real Innovation to Reduce Risk

Because optimization, machine learning, analytics, and other “real” methodologies that, with a lot of blood, sweat, and occasional tears, will actually produce solutions that actually work, is hard, requires top people (who command top salary), and has some risk (in that it could take a lot more time to get it right than you think — but at least you can get it right and it will work, as some of the best minds at the best companies in our space have demonstrated for over two decades). The biggest risk is not advancing towards a solid, trustable, usable, solution that the market will actually want!

10. Retreating into your Moated Castle

This is still the doctor‘s personal favourite. Often the first thing to go these days after the employee perks is the consulting budget — and it’s often by far the dumbest thing your average newly funded company can do (because, as has been repeatedly stated, just because you can sell an investor on what you think a buyer needs doesn’t mean you can sell a buyer, especially if you don’t really know!). Often the only way of introducing significant, meaningful, cost-saving revenue-generating improvements into your company is to bring in an outside consultant who specializes in one or more types of solution-based business innovation. A consultant who can tell you what technology roadmap is right for you, even during a recession. A consultant who can help you maximize your marketing budget. A consultant who can help you save money and avoid unnecessary costs in an intelligent, non-destructive, fashion. And a consultant who can keep you on the innovation path and out of the cost-cutting abyss that ultimately spells a cruel demise to what could have been a very successful business model with just a few tweaks.

And, FYI, the doctor has seen a lot of dumb over the years. That’s why he did a 5-part series on 15 common mistakes in hopes some of these founders would read it, reflect on it, and not make the same mistakes over and over again.

Fortunately, the corporate intelligence scale from 16 years ago doesn’t need updating. Start with 10 points and subtract 1 point for each of the above that you are currently doing (and be honest):

Score Rating Comments
10 Genius Congratulations! You are a true market leader.
9 Intelligent Quite Good! You’re best-in-class.
8 Smart Not Bad. You’re above average and on the road to stardom.
7 Average You’ve got some work to do, but if you set your mind to it, a bright future awaits. In fact, with the right effort, you just might have to wear shades!
6 Dull You’ve got your work cut out for you.
5 Deficient You’re handicapped, but if you’re handi-capable, with hard-work, perseverance, and a devout focus on change, you can be average in no-time!
4 Feeble You’re seriously lacking in corporate know-how, but if you open your heart to innovation, and bring in some expert consultants, you might just be able to get back on the right track.
3 Dumb You’re going to need a serious corporate make-over to survive. the doctor wishes you the best of luck!
2 Moron Find a Leprechaun! You’re betting on Lady Luck at this point!
1 Imbecile Start writing your corporate obituary. It’s just a matter of time.
0 Complete Idiot Congratulations! The Sourcing Maniacs lay their bells at your feet. It should be impossible to be this idiotic and still be alive (and you must have received an absolute shipload of private funding to still be around), but you’ve proven that nothing’s impossible. Have some bubbly before the money runs out.

For those of you who score 6 or below, please get help now to avoid being a casualty!

For those of you who score 3 or below, your theme song is still in the archives!

Why are there so many tech failures?

Those following along know that this is a primary concern of both THE REVELATOR and the doctor because, if we were truly progressing in technology, we wouldn’t still be seeing the same enterprise technology implementation failure rates of 80%+ that we saw two decades ago! (This is why the doctor decided to update, expand, and republish his Project Assurance series series from a decade ago. See Part 1, Part 2, and Part 3.)

THE REVELATOR asked this question again in his recent article on Why is AI such a hard sell?, in comments in my recent piece on Vendor Onboarding for Payment Assurance because it reminded him on how so many vendors miss critical solution elements required by the business in their technology-first push*, and in comments to his recent article on DPW & Comdex.

The answers are varied, and regardless of which one applies in the failure at hand, none of them are good. In fact, they are mostly so bad that THE REVELATOR, who is as fed up as the doctor with all of the sales and marketing bullcr@p, flat out stated in his most recent article that after 40-plus years, I say this with the deepest sincerity -– 90% of salespeople aren’t worth the gum stuck on the bottom of a shoe. And while the doctor would like to think the number wasn’t that high, given the failure rate, it can’t be that far off.

A lot of commentary as to why can be found in the comments to these (and other recent articles), but most of them revolve around the following reality (which the doctor also knows all too well with over 25 years in tech and Procurement).

At the majority of tech enterprises,

  • sales people are compensated on how much they sell, not how successful the solution is for the customer
  • sales people are pressured to hit numbers, or be cut if they have even ONE quarter in the bottom 10%
  • sales people don’t stick around long enough for success to matter — as THE REVELATOR has noted,
    • sales people could make a good living selling next to nothing for 18 to 24 months drawing a good 5-figure salary every month (once they made a few sales and had a “track record”) and then changing jobs as soon as they closed a few mega deals (which could sometimes net them a six-figure departure bonus)
    • sales people make more money by changing jobs just after closing a few F500 clients (and negotiating a bigger salary building on their recent high)
    • … and even more if they can do it during the rapid rise in spending (that translates into top engineers and top sales people at any cost) at the fore-front of a hype cycle (when early vendors believe they can make the biggest sales first if they just have the “best” sales people, defined as those who just closed the biggest deals at their last job with F500/G3000 customers)

It’s all about how much, how fast they can sell … not about actually selling a solution and making a client successful (and building a pipeline for upsell over time as they learn the customers’ business and create newer, better solutions for the clients who would happily fork over fistfuls of dollars to a vendor with a track record of delivering solutions that actually worked).

As to THE REVELATOR‘s paraphrased question with regards to why don’t these sales people care that the solution they are selling is going to fail, it becomes pretty obvious when you consider the above:

  • they aren’t compensated to solve customer problems; only to sell as much as possible as fast as possible and do so at ANY and ALL costs
  • if it’s a big enterprise suite deal with an F500/G3000 being implemented by a third party consultancy, chances are the implementation won’t even be finished before they move on to their next job (and if it fails, then it’s the consultancy’s fault for sending the B-team)
  • caring would weigh down on their conscience until they had to find a new occupation (and if they had no other significant skill, then what would they do?)

And if they are actually caring people?

Then they convince themselves the solution can be configured to work with the right tweaks, even if, in reality, it can’t.

So what is a buyer to do? What the doctor has been saying for years.

Their research!
And, most importantly, get unbiased third-party help with need identification, vendor identification, and proposal review!

Why, because, as the doctor has said many times, including in the comments in response to THE REVELATOR‘s comments, everyone needs to remember:

  • there are no silver bullet tech solutions
  • many “solution” providers riding the current hype cycle are just proffering a new form of silicon snake oil
  • some providers don’t have anything except this snake oil, and the minute the third party fails, so do they
  • relying on the wrong tech is dangerous, just like relying on airplanes made under poor quality control processes … you’ll get a few good flights out of them, and then the door will suddenly blow off as the landing gear falls off on the same flight, and then what do you do? (Unless you have ““Sully” at the helm, you pray to whatever deity you believe in, because at that point, there is nothing you can do.)

* whereas PaymentWorks, chronicled in that piece, started by identifying what their clients’ biggest business issues were, and solving that first — so while it’s not the broadest Supplier Management suite on the market, it is one that contains the necessary functionality to solve a very specific set of pain points that almost no other vendor does; which most of you should find shocking given that there are over 100 Supplier Management vendors, illustrating THE REVELATOR‘s comments that not enough technology providers put solving customer problems first).

GEN-AI IS NOT EMERGENT … AND CLAIMS THAT IT WILL “EVOLVE” TO SOLVE YOUR PROBLEMS ARE ALL FALSE!

A recent article in the CACM (Communications of the ACM) referenced a paper by Dan Carter last year that demonstrated that the claims of Wei et.al in their 2022 “Emergent Abilities of Large Language Models” were unsubstantiated and merely wrong interpretations of visual artifacts produced by computing graphs using an inappropriate semi-log scale.

Now, I realize the vast majority of you without advanced degrees in mathematics and theoretical computer science won’t understand the majority of technical details, but that’s okay because the doctor, who has advanced degrees in both, does, can verify the mathematical accuracy of Dan’s paper, and the conclusion:

LLMs — Large Language Models — the “backbone” of Gen-AI DO NOT have any emergent properties. As a result, they are no better than traditional deep learning neural networks, and are, at the present time, ACTUALLY WORSE since our lack of deep research and understanding means that we don’t have the same level of understanding of these models, and, thus, the ability to properly “train” them for repeatable behaviour or the ability to accurately “measure” the outputs with confidence.

And while our understanding of this new technology, like any new technology, will likely improve over time, the realities are thus:

  • no amount of computing power has ever hastened the development of AI technology since research began in the late 60s / early 70s (depending on what you accept as the first paper / first program), it’s always taken improvements in algorithms and the underlying science to make slow, steady progress (with most technologies taking one to two DECADES to mature to the point they are ready for wide-spread industrial use)
  • the technology currently takes 10 times the computing power (or more) to compute “results” that can be readily computed by existing, more narrow, techniques (often with more confidence in the results)
  • the technology is NOT well suited to the majority of problems that the majority of enterprise software companies (blindly jumping on the bandwagon with no steering wheel and no brakes for fear of missing out on the hype cycle that could cause a tech market crash unequally by any except the dot-com bust of the early 2000s) are trying to use it for (and yes, the doctor did use the word “majority” and not “all” because, while he despises it, it does have valid uses … in creative (writing, audio, and video) applications [not business or science applications] where it has almost unequalled potential compared to traditional ML designed for math and science based applications)

And the market realities that no one wants to tell you about are thus:

  • former AI evangelists and some of the original INVENTORS of AI are turning against the technology (out of a realization that it will never do what they hoped it would, that its energy requirements could destroy the planet if we keep trying, and/or that maybe there are some things we should just not be meddling with at our current stage of societal and technological evolution), including Weizenbaum and Hinton
  • Brands are now turning against AI … and even the Rolling Stone is writing about it
  • big tech and companies that depend on big tech (like Pharma) are starting to turn against AI … and CIOs are starting to drop Open AI and Microsoft CoPilot because, even when the cost is as low as $30 a user, the value isn’t there (see this recent article in Business Insider)

Now, the doctor knows there are still hundreds of marketers and sales people in our space who will consistently claim that the doctor is just a naysayer and against progress and innovation and AI and modern tech and blah blah blah because they, like their companies, have gone all in on the hype cycle and don’t want their bubble burst, but the reality is that

the doctor is NOT against “AI” or modern tech. the doctor, whose complete archives are available on Sourcing Innovation back to June 2006 when he started writing about Procurement Tech, has been a major proponent of optimization, analytics, machine learning, and “AI” since the beginning — his PhD is in advanced theoretical computer science, which followed a math degree — and, after actually studying machine learning, expert systems, and AI, he used to build optimization, analytics, and “AI” systems (including the first commercial semantic social search application on the internet)

what the doctor IS against is Gen-AI and all the false claims being made by the providers about its applicability in the enterprise back office (where it has very limited uses)

because the vast majority of the population does not have the math and computer science background to understand

  1. what is real and what is not
  2. what technologies (algorithms) will work for a certain type of problem and will not
  3. whether the provider’s implementation will work for their problem (variation)
  4. whether they have enough data to make it work

and, furthermore, this includes the vast majority of the consultants at the Big X and mid-sized consultancies who graduate from Business Schools with very basic statistics and data analytics training and a crash course in “prompt engineering” who can barely use the tech, couldn’t build the tech, and definitely couldn’t evaluate the efficacy and accuracy of the underlying algorithms.

The reality is that it takes years and years of study to truly understand this tech, and years more of day-in and day-out research to make true advancement.

For those of you who keep saying “but look at how well it works” and produce 20 examples to prove it, the reality is that it’s only random chance that it works.

With just a bit of simplification, we can describe these LLMs as essentially just super sophisticated deep neural networks with layers and layers of nodes that are linked together in new and novel configurations, with more feedback learning, and structured in a manner that gives them an ability to “produce” responses as a collection of “sub-responses” from elements in its data archive vs just returning a fixed response. As a result they can GENerate a reply vs just selecting from a fixed one. (And that’s why their natural language abilities seem far superior to traditional neural network approaches, which need a huge archive of responses to have a natural sounding conversation, because they can use “context” to compute, with high probability, the right parts of speech to string together to create a response that will sound human.)

Moreover, since these models, which are more distributed in nature, can use an order of magnitude more (computational) cores, they can process an order of magnitude more data. Thus, if there is ten to one hundred times the amount of data (and it’s good data), of course they are going to work reasonably well for expected queries at least 95% of the time (whereas a last generation NN without significant training and tweaking might only be 90% out of the box). If you then incorporate dynamic feedback on user validation, that may even get to 99% for a class of problems, which means that it will appear to be working, and learning, 99 times out of 100 instead of 19 out of 20. But it’s NOT! It’s all probabilities. It’s all random. You’re essentially rolling the bones on every request, and doing it with less certainty on what a good, or bad, result should look like. And even if the dice come “loaded” so that they should always roll a come out roll, there are so many variables that there are never any guarantee you won’t get craps.

And for those of you saying “those odds sound good“, let me make it clear. They’re NOT.

  • those odds are only for typical, expected queries, for which the LLM has been repeatedly (and repeatedly) trained on
  • the odds for unexpected, atypical queries could be as low as 9 in 10 … which is very, very, bad when you consider how often these systems are supposed to be used

But the odds aren’t the problem. The problem is what happens when the LLM fails. Because you don’t know!

With traditional AI, you either got no response, an invalid response with low confidence, or a rare (compared to Gen-AI) invalid response with high confidence, where the responses were always from a fixed pool (if non-numeric) or fixed range (if numeric). You knew what the worst case scenario would be if something went wrong, how bad that would be, how likely that was to happen, and could even use this information to set bounds and tweak the confidence calculation on a result to minimize the chance of this ever happening in a real world scenario.

But with LLMs, you have no idea what it will return, how far off the mark the result will be, or how devastating it will be for your business when that (eventually) happens (which, as per Murphy’s law, will be after the vendor convinces you to have confidence in it and you stop watching it closely, and then, out of the blue, it decides you need 1,000 custom configurations of a high end MacBook Pro in inventory [because 10 new sales support professionals need to produce better graphics] in a potentially recoverable case or it decides to change your currency hedge on a new contract to that of a troubled economy (like Greece, Brazil, etc.) because of a one day run on the trading markets in a market heading for a hyperinflation and a crash [and then you will need a wheelbarrow full of money to buy a loaf of bread — and for those who think it can’t happen, STUDY YOUR HISTORY: Germany during WWII, Zimbabwe in 2007, and Venezuela in 2018, etc.]). You just don’t know! Because that’s what happens when you employ technology that randomly makes stuff up based on random inputs from you don’t know who or what (and the situation gets worse when developers [who likely don’t know the first thing about AI] decide the best way to train a new AI is to use the unreliable output of the old AI).

So, if you want to progress, like the monks, leave that Genizah Artificial Idiocy where it belongs — in the genizah (the repository for discarded, damaged, or defective books and papers), and go find real technology built on real optimization, analytics, machine learning, and AI that has been properly researched, developed, tested, and verified for industrial use.