Category Archives: rants

While Your Supply Chains Are Impacted by War, They are Not At War!

And just because autonomous AI has become a standard tool of the current conflicts, that doesn’t mean that autonomous AI should be a standard tool in your supply chains. AI, defined properly, most definitely should, but not autonomous AI. And even then, only with human oversight!

This rant is inspired by THE PROPHET who tells us that The War in Iran is an AI War. Your Procurement and Supply Chain War Should Be as Well. And, despite parts of it appearing in LinkedIn comments, it is being expanded and reposted now to emphasize our previous article (on Friday) that essentially stated YOU SHOULD NEVER TRUST YOUR AI.

First of all, procurement and supply chain management isn’t a war. It’s a tense conflict between buyer needs and supplier leverage, but not a war.

Secondly, the fact that “AI never stops for a coffee break or to complain about leave not being granted.” is not on its own a valid justification for using it.

Because, by the same token, it also doesn’t care if a strike accidentally hits a school and murders hundreds of innocent children. (Al Jazeera, BBC, and Haaretz)

Nor does it care if multiple civilians get killed in a drone strike just to relieve a human soldier of a guilty conscience as they didn’t order the killing of the target and make the decision that resulted in civilian deaths. (NPR, The Guardian, The Times of Israel)

Given that AI has no ethics and no real intelligence to evaluate a situation beyond data it is provided and the question it is asked, is it really good enough to plan an operation on its own? I’d say it is not. (And also that it was applied without a full understanding of its weak points and how to use it properly.) (And if you want a great post about how critical human command decisions are, check out Michael Salehi‘s post and how the right decision always requires judgement, experience, and accountability — which an AI does not have.)

This is why Anthropic wants some safeguards, why you should too, and why you should be just as careful about where and how you use it in your supply chain. There are two realities with AI:

Properly applied augmented intelligence is a gift from heaven.

If you take the augmented intelligence approach, it can process all the data, give you recommendations, give you a synopsis of the reasoning, and allow you to dig into that reasoning, ask questions about risk and indirect ramifications, and explore the broader picture when you need to.

AI is not human, not ethical, not flawless, and not responsible.

You still need to review the synopsis, dig in when something appears to be off (and even if it’s just an uneasy feeling — your “intuition” can often be just as valid as the AI output), and verify the decision. And often these tools will allow what would take weeks to be done in minutes. But sometimes you’ll find there isn’t enough data, and you won’t be able to act confidently right away.

Now, THE PROPHET didn’t like my response, and countered with a number of questions, which I gladly answered and will repeat here because two of those questions missed the point, and including them helps illustrate what the real questions are.

“Would you take action?”: Yes!
(I don’t care if you agree or disagree with my viewpoint, or THE PROPHET‘s viewpoint, as this is not the point.)

“Would you use all tools available?”: YES!
(Again, I don’t care if you agree or disagree with my viewpoint, or THE PROPHET‘s viewpoint, as this not the point either.)

“Would you trust the tools blindly?“: No!

“Would you rush them into deployment without proper field testing and safeguards?”: NO!

That’s the point. All the hype and promises are resulting in an implicit trust of AI when it should be “Trust … But Verify!“. It’s usually the omission of just one extra step, which is usually just a few minutes of extra human review, that is the difference between success and accuracy vs. failure and widespread destruction. And this is true both in war and in business decisions that impact your supply chain.

This is why I continue to so strongly caution against the use of “autonomous AI” when it is largely built on systems that are flawed at the core, where hallucinations are part of the core function, and one subtle change in a prompt or query can result in a completely different output.

The reality is that, while you need modern tech platforms, constant intelligence monitoring, and pre-defined mitigation strategies just to survive, you usually don’t need AI. (Or at least not the “AI” they are selling … which, as you guessed, isn’t “AI” at all.)

What you do need to do is prepare for AI. If you do that, which involves:

  • getting your data under control
  • building an infrastructure for connectivity, process, and data integration
  • updating your processes for modern environments
  • training your talent accordingly

You will find that you have

  • put data at the core of not just category strategy, but overall operations
  • expanded your definition of risk to include price, partners, and related information flows
  • identified where automation fits; where optimization, analytics, and machine learning fits; and where “AI” doesn’t actually add any additional value
  • figured out that Employees backed by Augmented Intelligence and agents with escalating, but still restricted in critical situations, automation privileges as they learn from those human are best
  • developed a much better understanding of multi-tier exposure
  • begun the process of transitioning to a new, alert, organizational state where you are continually monitoring, optimizing, and re-planning your supply chain in response to emerging disruptive threats … and, as Koray Köse (who we may have to start calling The Oracle due to the insightful nature of his posts) points out, this is where you need to be

… and this is everything THE PROPHET says you need. Most importantly, all of this just might be accomplished without any modern AI (and definitely no BS AI Employees) at all!

What’s Wrong With 22% of Organizations? Why Do They Trust AI?

In a recent Horses for Sources Piece on The HFS AI Trust Curve: AI isn’t failing … leadership is, the byline is 78% of organizations do not trust their AI.

What the h3ll? 100% of organizations should not trust their AI when

  1. only 6% of organizations are seeing success (MIT, McKinsey) and
  2. there is no true Artificial Intelligence.

As a result, AI should NOT be trusted!

However, properly designed adaptive robotic automation, Machine Learning, and appropriately gated and guard-railed AI which sends exceptions for humans to deal with when the rules don’t cover the situation, the gaps are beyond what should be dealt with automatically with no approved precedents, and the only resolution you can trust is a human one is an AI that should be deployed since, while it might not be 100% perfect, it can still be applied with confidence as the guardrails will ensure no significant failures.

In other words, while I don’t agree that Agentic AI should be embraced to make decisions, because IBM had it right back in 1979:

a computer can never be held accountable, therefore a computer must never make a management decision
 

I do agree that the vast majority of back office tasks are just bit pushing and can be appropriately defined with flexible, parameterized rules, with machine learning that learns the tolerances over time, which means that agentic AI should be widely applied throughout a back-office, and that organizations that don’t embrace this level of AI are going to fall behind, but the trust in technology should not extend to decision making. Just decision execution.

And if 78% of organizations don’t trust their agentic systems to execute decisions, then that is a problem — they are going to fall behind, they won’t embrace SaS (Software as Services) where it makes sense, their overhead costs will stay high in a tight economy, and they’ll get crushed by the competition who will be able to be more competitive and actually sell in a tight economy.

In other words, despite HFS’ implications, organizations should NEVER trust Agentic AI to make decisions, but they absolutely need to trust the AI to execute the decision. If they don’t, they’re in trouble.

Part of the problem might be the framing of the last step of the current HFS Enterprise Adoption Journey.

Stage 1: Can the AI Model Work?
This is where you start. You have to find a viable model.

Stage 2: Do we Believe the Inputs?
This is where you progress to. You need valid inputs.

Stage 3: Will People Act on it?
This is the next step. If you don’t have organizational readiness, the initiative has failed before it begins.

Stage 4: Is the AI allowed to influence outcomes?
Since there is no such thing as Artificial Intelligence, and a computer should never make a decision, the AI should never be allowed to influence outcomes. It should INFORM outcomes. It’s a slight difference, but an important one. Moreover, it doesn’t really affect how the AI should be implemented. You’re still implementing with the goal that the AI will eventually automate at least 99% of all instances of the task(s) it is designed to execute, and the only difference is that you are deciding what to do with an exception and training the AI to execute your decisions, not being trained by it to accept anything as gospel that it recommends.

This minor change creates the trust matrix you adopt, and puts you on the path to proper Agentic AI automation that will allow your workforce to be up to 10X as productive. Augmented Intelligence, be it in-house or through SAS, is the true future. The tech is there for many tasks now, and you don’t have to wait for a promise that won’t materialize within our lifetime.

SaaS Discounts are Lies and Other Common Tricks and Traps You SHOULD NOT Fall For!

(These are also signals that you should run for the hills at their first utterance.)

In our last post on the subject we told you that If A SaaS Provider Offers You a 95% Discount you should

Slam the door, lock it; close the shutters, bolt them; don’t answer the phones, and rip the cables out of the wall; turn on the frequency jamming, and throw the cell phones in the Faraday cage; close the gates to the parking lot, and man security 24 hours. Because, no matter what they told you, the discount meant one of two things:

  1. the provider was trying to rip you off or
  2. the provider is in serious financial difficulty

And both are reasons NOT to do business with the provider.

Unfortunately these aren’t the only tricks and traps you have to watch out for. Other common tricks and traps include:

  • 1. We will give you a 50% discount off of standard prices if you don’t do a bid and just award us the contract without going to market.
  • 2A. Since we lost the bid, you can have it for a 95% discount and a right to use your logo on our webpage …
  • 2B. … but note that, once the contract is signed, we have to right to reprice your entire enterprise deal based on the total number of associated members [including janitors, advisors, and part time contractors who will never use the software] in your organization on LinkedIn (if we’re charging by the seat) and/or average daily use in the prior month (based on CPU cycles and storage against our chosen enterprise averages). [This will probably quadruple the quote within a few months.]
  • 3. If you [still] don’t select us after we drop our price (multiple times), we will go straight to the CFO/CEO of your company to tell them YOU are an incompetent fool bribed by our competitor who is making a huge mistake.

Before you even think twice about their offer, you need to remember that expecting them to treat you well as a client after you sign the contract is akin to expecting your abusive significant other who beats you regularly in drunken fits to all of a sudden stop once you get married. (And yes, I went there. It’s the same rationalization. As per my last post, if they give you this much of a discount, they’re losing money until they can trigger price escalation clauses or change orders, and even then they might not break even on your account. As a result, it will be too costly for them to give you any support whatsoever and, thus, they will ignore you the majority of the time and treat you poorly when they do respond.)

While I shouldn’t have to state this again, all of these situations happen way too often in our industry when companies are struggling (due to taking too much investment at too high of a valuation which resulted in angry investors breathing down their neck with nooses in one hand and pitchforks in the other when they didn’t make ridiculous targets) or they hire that 1/20 pathological salesperson (with a great close record at his last job) who only cares about his* year end bonus and not about whether or not you actually get served once you’ve paid the bill.

* Yes I’m being sexist here as a man is 3 times as likely to be psychopath than a woman, and a salesperson in enterprise software is 2 times as likely to be a man. This which means that your chances of a being ripped off are at least 6 times higher (and I’d argue more) if the salesperson is a man. (I can’t speak for everyone, but like many who have been in the enterprise software space for 30 years, I’ve encountered my share of sleaze-bags and grifters, and, as you might have guessed, every single one of them has been a man — and, FYI, they don’t think much of technical people either!)

If A SaaS Provider Offers You a 95% Discount …

Slam the door, lock it; close the shutters, bolt them; don’t answer the phones, and rip the cables out of the wall; turn on the frequency jamming, and throw the cell phones in the Faraday cage; close the gates to the parking lot, and man security 24 hours.

No matter what they tell you, a 95% discount from a vendor always means a combination of EXACTLY two things.

  1. the provider was trying to rip you off (because they thought they could due to their customer portfolio, surging popularity, or your lack of market SaaS pricing intelligence) and
  2. the provider is in financial difficulty

That’s it. The only unknown is the weighting between those two realities (and just how severe the financial difficulty is).

They’re NOT giving you a huge discount because they want your logo or case study.
They might want your logo and case study, but a solid provider with a solid solution who creates a good relationship can certainly get it without 95% discounts — most customers who get real ROI from a solution offered at a fair market price are happy to give you a case study for the free publicity.

They’re NOT giving you a huge discount to prove value in exchange for future purchases.
Everyone knows there’s no guarantee those will happen, even if you get the full promised value of the solution. You might have no use for their other solutions. You might never need any additional seats.

And any other reason they can come up with is also a lie.

Unless the company is run by a bunch of cons where their entire business ethos is charge as much as you can for as long as you can until the market realizes how much they are being ripped off (and then the cons skip town), the only reason a company will offer that level of discount is because they are desperate to get a sale on the books because, if they don’t, someone is losing their job in the best case or the company is going bankrupt in the worst case. Either way, that’s not a vendor you want to be putting your faith in. You want honest companies who price based on actual costs with a fair markup and who are financially stable — not dishonest companies who price based on how much they think they can scam you while being on the verge of bankruptcy.

And never kid yourself that it’s worth the risk because all the company needs is a few deals and a right-size on its pricing because a company losing money can’t stay in business — and any piece of enterprise software fairly priced at 1M will cost the company offering it at least half of that sale price to adequately support. You need to keep two things in mind

  1. cloud compute costs are real and significant and, thanks to Gen-AI that is over-straining global compute infrastructure, rising year-over-year
  2. the development talent needed to maintain and secure your solution (and despite claims, Gen-AI can’t do either, especially since it typically makes your solution less secure) is not cheap either

So if you intend to have 10,000 users hitting the app daily and doing at least one compute-intensive task (and LLM queries are compute-intensive, at least 20X as compute intensive as a classic Google or Lucene search, and possibly 200X depending on what’s being asked), your provider’s cloud costs will be in the six figures — which means the 95% discount isn’t even covering their hosting costs and they are digging themselves into a deeper grave just by signing you!

How You Know Your Education System Is Broken!

Only 40% of employees say they’d be fine NEVER using AI again! (As per a recent Section AI survey in the Wall Street Journal of 5,000 white collar workers, as reported in a recent post by Stephen Klein who also noted that the majority of employees say it only saves them 2 hours or less per week. Furthermore, he also mentioned a Workday study that reported every 10 hours “saved” by AI resulted in 4 hours being lost due to required error corrections, flawed output revision, and necessary verifications, which means there aren’t much savings at all. [Specifically, for an average employee to actually save 10 hours, they’d have to save almost 16 hours, which would take them two months to achieve!])

Gen-AI is failing 94% of the time. It’s causing serious cognitive apathy and decreasing our IQs far beyond what Twitter achieved on its introduction (where it reduced our collective attention spans to that of a goldfish). It’s direct and indirect costs to run 8 hours a day are often more than to just hire another person (due to compute requirements that are 20X to 200X that of Google for a basic query, and the extreme amount of energy and water [for cooling] required on grids that are already stressed and ecosystems where fresh water is running out).

Chat-GPT. Claude. Grok. Rufus. Gemini. Meta. DeepSeek. Perplexity. Co-pilot. Poe. Le Chat. They’re all over applied due to over promises when they all have fundamental issues (like hallucinations) that cannot be trained out (as the issues are a result of their core design and programming), limited data sets (and now that AIs are being used to generate additional training data, performance is getting worse), limited guidance, and no guardrails.

There’s always been a time and a place for proper AI, but it’s not now, it’s not everywhere the investors losing Billions on Open AI and competitors are telling you, and it’s not the “AI” they are pushing.

Every time a new advancement in tech comes along, we always forget how long it takes to get from prototype to safe for unmonitored regular industrial and home use, be it hardware or software. With AI, it’s always been about two decades between a new algorithm being invented, and a production ready system with known performance, limits, and guardrails being ready for the mass market. In other words, this tech shouldn’t even be out of the research labs yet! We definitely shouldn’t have every major consultancy trying to push it as the cure-all for every problem throughout your entire enterprise. (Or new start-ups claiming they can offer you AI Employees!)

How many more examples of (silicon) snake oil do we need before we accept there is no panacea for all your ailments — be they physical, mental, or industrial — abandon this current iteration of Gen-AI, and go back to the targeted, mature, solutions that were finally ready for prime time (as we finally had enough processing power, data, and research behind us to deploy them with confidence)?

And even though the technology might work as much as 12% of the time, as per a PwC study that found that 12% of 4,454 CEOs surveyed reported both revenue gains and cost reductions, that’s not much of a validation of the technology — especially since those gains and cost reductions could have nothing to do with AI at all (and the pilot success of 6% from a recent McKinsey is a much more reliable metric here).

If you want real success, find a (A)RPA solution that works, lie its AI and buy it while you wait another decade for this technology to mature to the point its reliable, guarded, and safe for mass market adoption and widespread application. (Or wait for an AI-enabled SaS provider to come along who will do the 24/7/365 human monitoring required for you and make its software is usable and safe through this monitoring. Because all the current generation of LLM[-powered Agentic AI] tech is doing is increasing the need for human monitoring, not decreasing it.)