Category Archives: AI

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.

Phil’s new HfS Services-as-Software FlyWheel Is Right On the Mark From a Customer-Centric Viewpoint

… but hides the full support required on the back-end!

This is important to point out for two reasons:

  • Gen-AI Hype-mongers will use this as another excuse to claim most white-collar functions will be entirely eliminated when, in fact, it strengthens the need for true back-office white-collar workers and real software engineers
  • Expert human support becomes more critical at each stage of the process (while bit pushers became less and less useful)

But let’s backup. In his most recent piece where he (re-)introduced the SaS Flywheel, Phil made one critical statement which is constantly overlooked by the industry: Stop treating FDE as optional: Your AI Flywheel will not spin without it.

As Phil astutely points out: the hard question nobody is answering is this: who actually wires AI into your live systems, governs it in production, and makes it keep working when the AI software vendors leave the room. The answer is, of course, your Forward Deployed Engineer (FDE) — and if your transformation strategy does not have it, you are building an AI theatre, not an AI operating model. (Which, FYI, is what most companies are building — and, as Stephen Klein astutely points out, putting on puppet shows. Great for entertainment, but not so great for getting anything done. Especially since they all overlook what AI can actually do.)

Now, a forward deployed engineer alone will not get you out of pilot purgatory, but it is an essential condition — just like you can’t climb out of a deep wide hole with smooth 90° vertical surfaces on all sides without a rope or a ladder, you can’t fly your way out of a pilot without a working plane, which you don’t have without an engineer to keep it running.

As Phil continues, FDE is not implementation – it is the engineering layer that makes AI governable this is because FDE teams build ontologies that reflect how the enterprise actually operates, wire models into real data with real permissions, and design the governance architecture that keeps autonomous systems accountable, which will, and for quite some time into the future, wire in non-overridable human oversight, approval, and review.

Phil goes on to list a few key things that LLMs cannot do on their own. (It’s in no way a complete list, but hopefully enough to get executives questioning all the AI-BS form the AI-Hype-mongers presenting grandiose claims that likely won’t be a reality within most of our professional life-times. Even better, Phil points out that Agentic AI without FDE governance is not transformation. It is risk accumulation!, and points out five key requirements of workable AI that can’t be achieved without an FDE. (There are more, but again, these should be enough key points to help executives realize that not only are LLMs sorely insufficient for almost every task they are being promoted for, but they aren’t even usable at all without the help of a FDE team.)

Phil also does us a great service by pointing out that while vibe coding creates velocity, FDE prevents it from becoming chaos — which is what happens every single time you employe vibe coding without FDEs (and a real engineering team — but we’ll get to that).

Vibe coding is simultaneously one of the biggest boons to software development and the greatest destructors, especially since it is almost universally misunderstood and misapplied. For example, while Phil’s statement that business analysts can express intent and receive working agent code in return is technically correct, it’s not practically correct. That’s because vibe coding produces code that is insecure, inefficient, and not appropriate for enterprise software. In fact, just about every startup that tried to launch an enterprise app on vibe-coding alone have lost hundreds of thousands (or more) attempting to do so — see this great post from Alex Turnbull.

Vibe Coding is super useful because, with the help of an FDE team with a good business analyst, the end user organization can quickly create functional prototypes that demonstrate precisely what they are looking for, which are much more powerful functional specifications than traditional functional specification documents with text descriptions of required functionality and powerpoint mockups. Plus, these prototype specifications can be created in a fraction of the time. But that’s all they are, prototypes. Real applications still need to be built by real software engineering teams who can build optimized, bug-free, secure code — vs. unoptimized, buggy (especially at the boundaries), and insecure code regularly generated by AI-based vibe coding tools (where, depending on what source you access, 53% to 78% of code generated has serious security issues).

In other words, it’s a great article, from a customer-centric viewpoint and written for customer executives. From a back-end, provider perspective, it’s missing one key step — the development step that takes vibe coding prototypes and produces real (AI-backed) enterprise applications.

Moreover, it centralizes the FDE activities when, in reality, they are ongoing throughout the entire cycle.

  1. they activate, and put the foundation in place
  2. they train the users on how to properly use the LLMs for accelerated research and are always on call for help
  3. they maintain the orchestration layer, and improve (and correct) it as necessary
  4. they work with the end users to vibe code prototypes
  5. they work with the development team to build the next generation (or iteration) of the enterprise apps in the SaS model

In other words, AI can enhance SaS, but it cannot replace the need for skilled humans on the provider side (for development, implementation, maintenance, and improvement) or the buyer side (for process definition, improvement, decision criteria, etc.).

At the end of the day, AI can only replace bit-pushers who do tactical data processing tasks which should have been automated by machines 30 years ago (when it was promised), but it can’t replace anyone who needs to make a (strategic) decision. This is true regardless of the model, and the right model, like Phil’s SaS flywheel, actually exemplify the need for the right, skilled, talent.

Dear Graduate, Don’t Skip the Internship … You Need a Gateway to an Apprenticeship!

A number of AI enthusiasts are advising soon-to-be and recent graduates to skip the internship and instead become proficient with AI because that’s how they are going to get a job. And, as you should know by now, it’s bullcr@p. Being able to write a prompt for a Gen-AI LLM that will return a convincing (but not necessarily sound) result is not going to get you a job. The only skill that’s going to get you a job is competence!

As with every over-hyped tech-du-jour that came before ([predictive] analytics, the fluffy magic cloud, SaaS, the WWW, etc), AI is not a silver bullet that’s going to solve all of an organization’s problems and grant magical status to those who have mastered it.

The only thing you’ll master with Gen-AI is the art of the con since whatever it spits out is so well written (compared to the average literary skill of an average high school, and even University, graduate these days) and so convincing that, without expert guidance, an average person is convinced that it must be right when they don’t know better. But that’s not a skill most organizations are going to hire you for (outside of sales and marketing), even if the organization is known for questionable ethics.

Organizations don’t need clueless idiots. They need experts who can assess situations, determine options, decide on the best option, and implement the decision. Someone who knows the analysis to run, the data to collect, the tools to use, the reports to create, the logs to keep, and the contracts to write.

And while you can’t graduate an expert, you can graduate with the skills to start you on the path to becoming one — the traditional skills of math, logic, critical reasoning, project planning, project management, and relevant domain knowledge — not creative crafting of perilous prompts for a flakey LLM that will eventually fail you no matter how much time and effort you put into that prompt.

And if you get get an internship and prove yourself, maybe that will lead to full time job where you can apprentice under a master in the real world and gain the experience you need to go from an adept (with the core knowledge and skills but not the wisdom needed to succeed in the real world) to practitioner (who has gained enough wisdom and experience to manage standard tasks and functions on their own, and who only needs guidance for new or complex situations not yet encountered) and, eventually, to expert where you become the new organizational mentor and the one that new hires turn to for help.

And organizations need (future) experts because only an expert knows when

  • it only has wrong/incomplete data (which will prevent an AI from ever working)
  • an analysis/outcome is wrong based on math fundamentals
    (and when an LLM-based AI multiplied by -1 because you told it to deliver savings vs. find the best opportunities based on price variability, lowest price, market trends, and differential analysis)
  • reasoning is correlative, not causative (which is a failure of not just LLMs, but many people as well)
  • an analysis is incomplete (because only they have specific insight that was not available to the machine or another analyst)
  • etc.

That’s why, if you want to become a true master of your craft, you need to forget the AI mastery and instead land an internship where you can apply the mastery of the real skills you learned in your degree program to stand out, get an apprenticeship, and learn how things work in the real world and acquire the real world mastery you need to get the job you want. Only then will you be able to work your way up to becoming the leader, and expert, you want to be.

There is no Artificial Intelligence (just Artificial Idiocy) and organizations will always need top talent. Automation, and well designed applications that solve real problems efficiently and effectively, will reduce the number of back-office employees that an organization needs and any employee who’s only skill is pushing bits will be eliminated. However, the need for talented employees will only increase to not only oversee the tools and handle the exceptions, but correctly analyze increasingly complex real-world situations and make the right decisions.

At the end of the day, AI tool mastery is meaningless if you can’t logically and holistically analyze the outputs with respect to math fundamentals and a real-world scenario!

This Should Be Obvious But Expert in the Loop …

… is Human in the Loop. Not another (AI) system in the loop, no matter how specialized that system is or how well it is trained!

The future is Augmented Intelligence, NOT Artificial Intelligence (which doesn’t exist and won’t exist any time soon until brilliant researchers come up with a few more insights that get us closer to understanding

  1. what intelligence actually is and
  2. modelling it.)

The algorithms might be getting more accurate in average use cases, but the illusion of intelligence, no matter how grand, is still NOT intelligence. (And, even worse, The Wizard of Oz has been replaced by a very poor digital facsimile.)

Done right, Augmented Intelligence will still let your organization reduce its non-value-add tactical workforce by 80% to 90% because the right tools will enable the strategic experts to be 3, 5, 7, and even 10 times as productive and oversee all the tactical work that needs to be done using an exception based approach where every instruction that is given forms a rule that allows the system to automatically deal with the same, and similar, exceptions should they arise again in the future in a predictable and repeatable fashion.

Instead of having to oversee a team of tactical grunts that just take up space (because they don’t have the education, experience, or raw capability required to make good strategic decisions, manage projects, and identify value), a strategic expert can instead focus her time on value-centric activities and training a protege or two who will be one that posses the right mix of EQ and TQ to grow into, and take over, her expert role (when she moves on and up).

In the near future, there will be no more bodies in seats just to push bits around, because that’s what software does best. Number crunching and thunking. NOT analyzing strategically and thinking. (I admit most humans don’t do that well either, especially these days, because they are too attracted to the principle of least action and/or enjoying the cognitive decline from ChatGPT, but those willing to practice strategic thinking daily still do it way better than a machine ever will based on our current approaches to AI). [And while there might be fewer of us each year that are willing to think, there are still enough of us to get the job done if you let us select tools that work. Not necessarily AI. Tools that work.]

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.)