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The deadly pitfall of predictive AI that no one talks about (but you and I do)
AI is everywhere. In a short period of time, it has become an integral part of our daily tasks. And this is only the beginning, according to both suppliers and users. Developments are happening at lightning speed. The promise of AI is huge, very huge. Many companies now rely on AI systems to identify trends, map risks, or even support strategic decisions.
But... there is a fundamental problem with that trust.
Predictive AI only works well if the future resembles the past.
The most popular form of AI we use today—LLMs, or Large Language Models—is entirely trained on historical data. And so their predictive power is limited to that past. As soon as something happens that falls outside the patterns, reliability immediately plummets.
For example:
- Covid — a pandemic that no AI model had predicted.
- CrowdStrike — a software error that shut down companies worldwide.
- A stock market crash — sudden and unpredictable, despite all the models.
These were precisely the events that some people even said out loud, “We can't predict that, so we're not anticipating it.”
Ouch.
Now we rely on AI and its predictive power.
But...
Why didn't AI systems see the above crisis coming? Make no mistake, AI models have been around much longer than their current popularity.
AI models didn't see it coming because these are Black Swan events: rare, impactful events that hardly ever occur, are not included in the data, or are filtered out by the algorithm as noise.
In his book The Black Swan, author Taleb compares it to a turkey: it learns from experience that the farmer brings food every day. Until December 24th.
An LLM is that turkey. It learns patterns but does not understand causes, intentions, or radical twists. And so it has no idea about the concept of “Christmas” (with disastrous consequences).
This is a very important fact when using AI for strategic choices. AI does not give you a guarantee for the future; it only gives you a stylized past packaged as an idea for the future.
Why your AI has no clue where it went wrong
LLMs learn patterns. They look at millions of examples from the past and search for statistical correlations: if you say this, that often follows. That's useful for text, summaries, or chatbot responses.
But it falls short when you want to know: why did this happen?
Note that it does provide an answer. That answer... is the danger.
A Large Language Model works from cause to effect, or rather: from input to output. Give it a prompt and it predicts which word is likely to follow.
But what if you turn it around? What if you ask: what caused this situation? Then the model crashes.
Because it doesn't know why something happens. It guesses what comes next based on probability, not what was needed to get there. So LLMs can't help you if you only see the consequence and need to reason back to the source. They have no internal understanding of cause and effect. No sense of direction. Just a mathematical guess at the next piece of text.
And that is often more dangerous than we would like in crisis situations.
Now I dare to predict that your brain is wondering whether all this is correct. Because there is a lot to ask an LLM, and you will get an answer. Further on in this blog, you will find prompts to test this.
Look, if your app is slow, your system is down, or your market is collapsing, you need to understand why. Not vague, cobbled-together so-called knowledge. Not well-intentioned statistical probabilities. You need real answers. Without knowledge of the cause, you'll keep spinning your wheels in the aftermath.
What you need is an AI system that reasons. That forms hypotheses. That actively searches for connections and causes. Because only when you know where things went wrong can you decide how to proceed.
And LLMs? They keep guessing... for now, in the wrong direction.
LLMs talk nicely, but understand nothing
LLMs are impressive. They produce human-like texts, give smart answers, and sometimes seem almost omniscient. But don't be fooled: they are essentially black boxes. You only see the output, not the reasoning behind it.
You enter a prompt, and an answer comes out. Wonderful. But how does the model arrive at that answer? No idea.
Even the creators usually don't know exactly. There is no transparent reasoning, no testable logic, no insight into the considerations. The model is not designed to be understandable. It is built to generate plausible language. As quickly as possible. Period.
That's a problem when you're dealing with serious questions about cause and effect. Because many causes and effects are not expressed in language, but in numbers. In measurement data. In process data. In performance metrics. And you can't solve those “quickly” with words... You solve them with thoughtful, real analysis.
For that, you need different AI. Not LLM, but models that provide insight into how a prediction is made.
White box models that not only predict, but also explain. That reveal patterns in numbers. That show what happens when you turn a knob.
Combine that with Augmented Intelligence—where humans and machines work together—and you get the best of both worlds: analytical depth and human judgment. You can then not only see what is going on, but also why. And what to do about it.
And that is essential when the world is changing. Because if you want to make adjustments, you need to understand how your system works, not just what it says.
Black boxes talk. White boxes explain. Don't choose answers, choose explanations.
What Sciante sees, remains hidden from others.
At Sciante, we don't work with black box AI that sells quick pseudo-solutions. We combine white box models with Augmented Intelligence: the power of advanced data analysis, coupled with human experience and judgment. This prevents guesswork. Prevents noise. And provides insight that explains, predicts, and improves.
Our AI shows you exactly which factors are driving up your cloud costs without you noticing. Where waste lurks in the margins of your configurations. What FinOps overlooks because it gets stuck in dashboards and averages.
Because FinOps tells you that you are paying too much. We show you why and how you can prevent that. And that goes beyond costs. We also find performance bottlenecks in applications that other tools simply do not detect. Because those bottlenecks are deeply hidden in the interplay between user behavior, infrastructure, and software logic. Things you only notice when performance drops, and then... it's already too late.
But... with our approach, you'll discover it before your performance collapses. Before your cloud costs skyrocket. You can take action at a time when it costs little and yields a lot.
This combination of transparent AI and human craftsmanship ensures that you not only have data, but real control. No illusion of control, but insight into the buttons that really matter.
And that makes the difference between continuing to experience hassle or structural optimization.
Prompts for testing
Copy the prompt below into your favorite LMM:
“Is it true that AI—and especially LLMs—are only reliable as long as the future resembles the past?”
Then ask a question about why something current is happening. My example was this:
“Why are they striking at Air Canada?”
Finally, ask this critical question:
“So you're saying you can't identify the cause of a problem, yet you still come up with an answer... explain!”
The answer you get will be... honest and will hopefully make you realize that from now on, you need to solve problems in a different way.
Let's prevent problems instead of trying to fix them after they happen!
Most companies only discover that something is wrong when the damage has already been done. When costs rise or even skyrocket. When performance collapses. When users complain. That's when the search begins and the big fix.
You can prevent that by doing things differently.
At Sciante, we help you identify problems before they arise. With Sciante's White Box AI and Augmented Intelligence, we expose waste, delays, and hidden risks. So you don't have to react to problems, but can focus on improvement.
Want to know where your system is “cracking” before it breaks?
Then make a no-obligation appointment with me. In a short conversation, you'll immediately discover the benefits for your organization. No sales pitches. Just an open conversation about where you stand and where you can improve.