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.

Dangerous Procurement Predictions Part III

As per our first two posts, if you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But the predictions are not only bad, they’re dangerous if you believe them. So we are continuing to lay bare the reality of the situation to make sure you understand that this year isn’t much different than last year, no miracles are coming, and only hard work and the application of your human intelligence are going to get you anywhere. Today we tackle the next three, and while we hope we’re getting close to the end of the series, we’re pretty sure there will be at least one more entry.

8. Global Trade Will Shift, Prioritizing Resilience Over Cost.

In the mid to long term some trade will shift to prioritize resilience, but most trade won’t. While defence procurements, critical mineral and material acquisitions for high-end electronics, and valuable commodities that can be traded like currency (such as gold, silver, platinum, diamonds, etc.) will be shifted for resilience, the reality is that, even with natural disasters, sanctions, trade wars, and actual wars, most companies aren’t going to make any changes to their supply chains (unless given absolutely no choice) because

  • finding new suppliers (in new countries) takes time and effort
  • qualifying new suppliers (in new countries) takes time and effort
  • identifying and contracting reliable carriers takes time and effort
  • building and securing new supply lines takes time and effort
  • etc.

and most companies are in constant fire-fighting mode, overworked, overstressed, and they just don’t have the time as long as the current supply chain, while strained, still works. Until their supply completely dries up, their primary production lines and revenue streams are threatened, and they have no other choice, they won’t change because they’ll keep telling themselves random natural disasters won’t impact them, the tariffs are only temporary, sanctions change with administrations, and wars eventually end.

9. Your employees will orchestrate outcomes.

Woody Woodpecker, take it away!

The level of talent needed to orchestrate outcomes is well beyond the average level of talent in an average (and even most above average) Procurement Department(s). There’s a reason that talent is a concern, a <href=”” target=_blank>top risk, and a top barrier for not just the last five years of studies and surveys, but at least the last ten. Talent has been scarce for a decade, and the situation is much worse since COVID. COVID saw many early retirements of the forced and chosen variety. Then the constant fears of recession saw more layoffs, starting with the highest paid (and most experienced) talent first. And you can be damn sure many of them are not coming back. We told you a year ago that talent is about to become scarce, and we’re sad to say we think we underestimated just how scarce talent is about to become.

And the reality is that only top talent can orchestrate outcomes. All the vast majority of talent can do is execute tasks one by one in a well-defined process. They can’t create new processes, and they certainly can’t define new outcome-centric processes on the fly. Especially when the ORCestration platforms they are given can’t even “orchestrate” a process to lead a mouse to the cheese it desperately wants.

10. New Year, New Me.

Who were you last year?

That’s right, the same person you are this year.

This BS lasts until all the bubbly you drank on New Year’s eve wears off, the rose coloured glasses go dim from the glare of doing the same damn thing as you stare at the same damn screen 12 hours a day, and you get overwhelmed with all the same tasks you were doing last year. Within two weeks at most, the new year, new me bullcr@p disappears with your last new years resolution and you’re just fighting to survive being overworked, understaffed, underfunded, and under-resourced, especially on the tech side (because the C-Suite wasted all the budget on a Big X Consultancy Gen-AI project that never even got to beta testing because the prototype phase never actually worked).

Most people won’t even make an effort to improve, which is the best one can hope for! (So if you have an employee who does, proactively give them a raise, any training they ask for, and keep them. Because, as per our response to the last false, and dangerous, prediction, talent is scarce and you should do whatever you can to keep whatever talent you have [instead of trying to replace it with fake AI that will never work fully autonomously].)

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!

Primary ProcureTech Concern: Weakness & Volatility in Emerging Markets / Trade Wars

Emerging markets are your future markets, and often the source of critical raw materials.

Why?

Given that a lot of outsourcing has been redirected to these “low cost” markets over the past two to three decades, any rapid increase in volatility becomes a significant concern, especially if the markets are not strong enough to weather the storm. A major event could wipe out an entire subset of the supply base literally overnight, greatly increasing supply shortages and increasing the market complexity. Or at least make it unsustainable, such as a 145% tariff on China which is the source of over $500 Billion dollars in imports into the USA.

Impact Potential

The impact of a “low cost” market becoming unavailable, or at least unsustainable, is moderate to severe, especially if all of your outsourced eggs are in the same country basket. One lesson that some companies haven’t learned yet is that dual sourcing is not reducing risk if the two sources of supply are in the same country (or the same small geographic region — because if you have two factories located 100 miles from each other on two sides of a border, guess what, one natural disaster can wipe them both out).

If your primary source of affordable supply is wiped out overnight, it could take months to identify a new source of supply and quarters to secure the supply and get your supply chain flowing properly.

Major Challenges/Risks

Foreign Market Predictions
It’s hard to predict what’s going to happen in a foreign market that you aren’t in everyday. You can follow economist predictions, follow currency trends, try to get a grip on the trade relations between that country and your home country, and so on, but it’s not easy. If you can predict early enough, you can take action. But if an administration, without warning, decides to drop 100%+ tariffs on your source of supply, you’re in trouble.

Alternate Sources of Supply
Sometimes there’s few sources of supply for a given material, part, or product outside of a given country that has a similar total cost of acquisition, especially if you aren’t sourcing at full volume. Identifying alternate sources of supply that you can switch to quickly can be quite a challenge.

New Market Identification
If the emerging market also happens to be one of your primary emerging sales markets, the hit from volatility can be quite significant if the volatility results in rapid inflation, job loss, or both and your sales start to drop rapidly.

Final Words

Given the globalization of today’s supply chains, where a product can depend on materials and parts from dozens of countries, weakness and volatility in emerging markets is a significant concern. And we have yet another (fourth) reason you need an economist!

Primary ProcureTech Concern: Tightening Credit Conditions

The world runs on money, regardless of what form it comes in. Gold, cash, or credit. Credit is particularly important because it helps an organization bridge between cash cycles.

Why?

If economic downturns or inflationary pressures arise quickly, then credit will also tighten.

Impact Potential

If the organization, or its suppliers, needs credit to produce and distribute the goods for sale, the lack of interim credit could lead to reduced inventories and sales and even bankruptcies.

Major Challenges/Risks

Economic Market Prediction:
Predicting whether the economy is going to grow, stay flat, or recess (or depress) is the first challenge, as that’s a leading indicator of credit markets.

Credit Market Prediction:
Based on the projected economic changes, predicting the base and prime rate changes, availability of credit, and the future cost to your organization and your primary suppliers.

Alternative Credit Sources:
If your primary sources are projected to become considerably more expensive or restrict credit access, can you identify alternate sources? Moreover, how much will those cost, how long to establish the relationships, and how reliable will they be?

Alternative Credit Arrangements:
If right now you are just using loans or lines of credit, maybe you need to consider early payment discounts, invoice factoring, or alternative supply chain based credit arrangements.

Final Words

Credit conditions depend heavily on economic conditions, so this is yet another reason you need a good economist.