AI Changes the Paradigm: Machines Work, Humans Guide
Why Your New AI Strategy Needs a Fundamental Change
Many companies are trying to define their AI strategy - what use cases to prioritize, which products to buy, and what talent to hire.
Amid inflated promises - and after the GPT-5 letdown - it’s fair to ask: does AI deliver real value? Is it still worth investing in, or did the bubble pop?
Yes, today’s systems are still a meaningful step forward. Agents aren’t truly autonomous and will not be for a long time; they’re not ready to run unsupervised or make decisions on their own. But they do raise the ceiling on automation. Coding makes this obvious: agents can provision projects from templates, draft and (after human review) run bash commands, generate code and tests, and accelerate routine work.
This is the paradigm shift I explore in this article: AI as practical, supervised automation that puts human in the role of observer and machine in the role of actor.
The key consideration for your AI strategy should be: this paradigm is shifting towards more automation. This is something you should understand first and foremost.
And it's shifting as follows:
Before, all processes were built with the human as the main actor (operator), and AI was at best there to support, if at all. Humans made all the decisions, organized data in ways best for humans, and built processes the way they found most convenient.
For example, John from legal would find it most convenient to keep his documents in his home folder, put his presentations on Teams, track his progress by sending emails to himself, etc. He also just knew how to do certain tasks as he relied a lot on subjective experience and common sense. He used AI only as support, for grammar correction or dashboard evaluation, all in all, he was the main actor.
Now, the paradigm is shifting. Essentially, we want to end up there where AI is the actor most of the time and the human is the observer. This is very far away and might not happen for decades - especially since human intuition remains irreplaceable in areas like ethical decision-making, creative problem-solving in ambiguous scenarios, or fostering empathy-driven innovation, where AI lacks the emotional intelligence or "gut feelings" that humans bring.
But the automation of tasks with AI, particularly in IT, is happening, and we should start by understanding what this paradigm shift means for us, focusing on hybrid models where AI handles routine tasks while humans provide oversight and unique insights.
Please note that this is a long-term shift that will not happen quickly, and there will be many pitfalls along the way. To achieve full automation, we will eventually encounter the “last mile” problem, well known from logistics, where progress slows down as we approach the final stage.
Let's break it down using the example of IT and coders - AI for coding and artificial languages is the area where I believe it creates the most value. This is where we'll see significant increases in developer productivity, changes in tasks, strengthening of the enablement role, and infrastructural shifts.
Are your processes ready to be automated?
Integrating agents into a flawed process will only create overhead and slow down human workers. If you want an “agentic” process, it must be redefined so it's suitable for AI as the main actor and humans as observers. This means the process should be easily formalizable, for example, through IF…THEN rules.
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