Category Archives: Technology

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!

There is NO Infinite Compression – The Latest DeepSeek Paper is BullCr@p!

Every decade or so, some idiots who never studied Huffman coding or Information Theory believe they have cracked the problem of infinite compression, and this linked paper is just the latest example of this lunacy. I really hope this was a joke paper authored by AI because it’s all bullcr@p!

On average, a text token in a LLM should require 20 bits or less (as 17 bits support a 129,000 word vocabulary) while a vision token can be 16,384 bits (based on 1024 dimensional continuous vectors) — because it takes a lot of bits to represent pixelation of a square in a 2-D image! This says you can store about 820 text tokens in the same space it takes to store one vision token. Or, you can store the entire text (lossless) in 48K, versus the 4M it would take to store the 250 vision tokens (using very lossy compression) that are required in the paper. Looks like a LOT of people can’t do basic math if this is being praised as revolutionary!

Moreover, the raw text, which maintains the full context if the tokens are kept in order, is not only fully lossless, but can be compressed using a modified Lempel-Ziv algorithm to take up an average of less than 2 bits per character (and achieve up to an 80% compression rate). Given that the average length of a word in average text is 5 characters, and a space is one character, 2500 words would be 15,000 characters, storable in 30,000 bits or a mere 4K! In other words, this paper is trying to pass off a ONE THOUSAND FOLD increase in space requirements as space saving! Pure lunacy!

In other words, if someone is claiming something too good to be true, it is! Don’t fall for it or the sure to follow claims that DeepSeek OCR is revolutionary because of this. (Since every document is different, you can’t imagine the true loss with a 90% vision token reduction!)

Dangerous Procurement Predictions Part II

As per our first post, 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. And to make sure you don’t fall for them and make bad decision based on them, we’re going to tackle some of the most dangerous predictions, which include predictions that look innocuous at first glance (like the last prediction on how a big legacy suite will go out of business) but hide the dangerous consequences of what will actually happen if a big suite finds itself in big trouble. Today we tackle the next four, and you can be sure this won’t be the last post in our series. Feeds are still being flooded with prediction posts, and I’m done ignoring the insanity.

4. The jobs market will be tough for the first half of the year, but will start to pick up in Q3 and Q4.

The job market is tied to the economy, and everyone predicts the job market will rebound when the economy picks up. But here’s the thing. Even when the economy picks back up, the job market never does quite as well as the last time. And the economy isn’t going to magically improve half-way through the year. This is the exact same thing we’ve been told the last two years, and it hasn’t happened.

First off, most of the first world economies around the world are flat, borderline recession, or in recession. Secondly, the only thing propping the US economy up right now is AI, and the money circles keeping it afloat as all the AI, Hardware, and Software companies keep moving the same money around investing in each other to keep each other afloat. If the bubble bursts, the US is in trouble, and the economy will quickly flush itself down the toilet. And the job market will go with it.

Considering only the big tech giants who have been hoarding cash for the last few years are in good shape, and everyone else is trying to conserve cash to survive not only the current market but a potential recession, the last thing they are going to do is hire unless absolutely necessary to fill a critical role as a result of a departure. Remember, they’ve spent the last two years using AI as an excuse to lay people off and are always looking for the next excuse to lay people off, not hire them!

Jobs will continue to be super scarce, and only the best will have a chance to land one.

5. We’re in the early stages of a broader pushback (against unnecessary upgrades or technology investments).

A few companies smartening up and saying no to forced big provider upgrades, eight (8) figure consultancy projects, and big Gen-AI investments is not pushback. There have always been a few leaders who have broken away from the pack, did the math, and made the right decisions, but the pack is still charging ahead on Gen-AI. Every big software shop except IBM (who hired a CEO who can actually do math) has invested heavily in Gen-AI, which still loses four dollars for every dollar of revenue, despite any hopes of a real return in the near future and a 94% failure rate.

Let’s face reality. I warned this space about The Vendor In Black nineteen years ago and how he always Comes Back sixteen years ago, no one took heed then, and no one is taking heed now. The business model of the enterprise software space, which has not changed for the two decades I’ve been covering it, is to solve the problem created by the old sh!t by selling the customers the new sh!t that comes with new problems so they can sell even newer sh!t in three years to fix those (and so on). Same old story. Only the vendor names change.

6. We Won’t Buy Things; We’ll Orchestrate Ecosystems.

This prediction likely came straight from the A.S.S.H.O.L.E. and anyone who repeats it should be ashamed of themselves. There are no AI Employees. Claims to the contrary are false and anyone making those demeaning and degrading claims is simply dehumanizing you. And, as we have clearly explained, you definitely don’t want agentic buying because it will happily spend your money not only on stuff you don’t need but stuff that doesn’t exist and, if you’re super unlikely, stuff that is highly illegal. You need wood, it will buy up all the Minecraft wood because it’s cheap and call your problem solved. And that’s if you’re lucky. If you’re not, it will fulfill your resin need with an illegal purchase of hash (the drug) on the dark web (which is labelled resin so the poster can claim they never advertised an illegal drug). And so on.

Plus, as we have already noted, most of today’s “orchestration” platforms in Source-to-Pay are really ORCestration platforms and can barely connect a handful of major Source-to-Pay offerings. They’re nothing close to what is needed to orchestrate ecosystems.

7. Boards will Zero in on Supply Chain Security and Supplier Risk shifts from quarterly PowerPoints to continuous “signalops”.

Just like they won’t invest more in cybersecurity, they won’t invest more in supply chain security until they lose a shipment in the tens of millions. After all, they’ve got supply chain insurance, why should they care? Especially since their current security measures have been sufficient up until now.

But here’s the thing. When the economy goes down, jobs go down. And then two things happen. People get desperate and turn to crime. And criminals, when their investments in drugs, alcohol, gambling, prostitution, and other quasi-legal through illegal activities start losing money because unemployed people run out of money to spend on their vices, these criminals get desperate too — and high value theft becomes more attractive. A temporarily unguarded truck here. A container there. An entire warehouse. And so on.

If it’s critical raw materials they can move (like rare earths), in-demand finished electronics they can sell (like iPhones, where a single container will contain at least 20M worth), military equipment or weapon (component)s that are now in demand globally, they’ll take bigger and bigger chances, especially if there are weaknesses in security. It’s not just cyber attacks that are going to increase, it’s physical attacks, supply chains aren’t ready, and companies won’t even stop preparing them until they lose tens of millions, don’t recover it all through insurance, and risk losing their insurance entirely. No one likes the math of risk prevention because, when it works, you don’t see the return. Even though it’s so much cheaper than insurance! And that’s why, in the majority of organizations, nothing will change.

Primary ProcureTech Concern: Managing Digital Fragmentation / Digital Transformation

As per our previous concern on Technology Transformation, it’s time to be digital, but with digitization comes digital fragmentation, especially when you don’t fully understand what you’re doing.

Why?

Digital fragmentation increases the risk of IP/cyberattacks (which is one of your top risks) as each fragment presents its own unique weaknesses and opportunity for attack. Moreover, it explodes the tech execution support required and increases one of the largest barriers to organizational success.

Digital transformation is also a concern because organizations know we have reached the age of digitize or die, but the digitization project failure rate is at an all time high of 88%+ (and 95% if it’s AI-based for the sake of AI) and every digitization effort to date has just resulted in more digital fragmentation. (To the point that the average mid-size organization has over 600 SaaS subscriptions and some have over 1,000.)

Impact Potential

The impact potential depends upon the degree of fragmentation. How many software applications? How many different hosting platforms? How many data pools? The impact of data fragmentation can be low if there are a relatively small number of software applications, they are all AWS hosted, and there’s only one data warehouse/lake/lakehouse. Or it can be extremely high if there are 1,000 SaaS applications, they are hosted on half a dozen cloud stacks (AWS, Azure, Google, IBM, Oracle, and Salesforce), there’s a data warehouse/lake/lakehouse for each of the divisions, and so on.

Major Challenges/Risks

Cybersecurity
Every one of your SaaS applications provides an entry point into your organization if hacked. Every cloud provider provides multiple entry points if hacked. Your data warehouses provide a huge amount of data that can be used against you. These hack points are in addition to all of your internal servers / on-site applications, employee laptops and smart devices. An average organization these days is a cybersecurity nightmare and a hacker’s dream.

Data Integration
Chances are all of your applications have their own data models, own unique entity ids, and own standards for data access. Integrating your data across applications is a nightmare, forcing integration through data warehouses/lakes/lakehouses, which in turns creates a data replication and synching nightmare.

Data Maintenance
Not only is there the synching issue from the replication used to support data integration, but less used apps means there are less checks and updates for the critical data they are the master applications for, and data quickly becomes stale and out of date. And employees depending on that data and accessing it through the lake don’t know that, and can make bad buying and partnership decisions based on that.

Final Words

Managing digital fragmentation is not easy. In fact, it’s a nightmare because most organizations don’t have, and never had, Master Data Management (MDM) or a Master Data Governance (MDG) strategy.