When AI Stops Assisting and Starts Deciding

It is important to be critical about the adoption of AI in C-level roles when it starts being used as a substitute for expert judgment.

Today, we see executives using AI as if its recommendations were automatically correct, while operational teams often identify errors, loss of context, or unrealistic decisions. AI should be used to broaden the analysis, not to validate a strategy we have already decided to pursue. That is where the risk of creating a false sense of objectivity appears.

It is also concerning when AI-generated answers are used as the basis for technical decisions without considering how the business actually works. A recommendation may sound correct in theory and yet make little sense when applied to the reality of a company because of its processes, costs, structure, or customers.

AI can significantly improve decision-making capacity when it is used as support rather than as authority. For example, a sales director can use it to analyze sales, detect patterns of decline by region, identify accounts at greater risk, and generate different courses of action. But the final decision — for example, reducing a team, changing territories, or stopping an investment — should still belong to the executive, taking into account the human, competitive, and strategic context.

The idea of AI as a kind of “second brain for the CEO” is also beginning to emerge: a tool capable of processing information in real time, comparing scenarios, and challenging assumptions. The key point, in my view, is setting the limits: how much influence we allow it to have, how we supervise its recommendations, and who retains final responsibility.

There is another important change on the commercial side. AI is changing the way buyers research, compare, and validate suppliers before speaking with sales. In many B2B processes, a significant part of the research happens before the first commercial interaction, with greater weight placed on communities, peer validation, and AI tools. This is forcing CMOs, CROs, and GTM teams to rethink acquisition, content, and trust.

For that reason, the focus is becoming less about “which tool to use” and more about how the pressure to adopt AI can weaken critical thinking, professional judgment, and the quality of decisions.

I see two clear risks: on the one hand, poor implementations, pressure from the top, and poorly managed changes in GTM; on the other, something more structural: the risk of delegating organizational judgment to AI and falling into excessive enthusiasm around the technology.

The challenge for senior leadership is to define how far AI should be allowed to influence decisions without replacing judgment, context, and executive responsibility.

Some recommendations I consider important:

1. Use AI to broaden the analysis, not to confirm decisions we already want to make.

2. Ask AI to challenge assumptions and present alternative scenarios, rather than simply recommending one option.

3. Cross-check its recommendations with people who understand the operational, technical, and commercial context.

4. Distinguish between reversible decisions and decisions that are difficult to undo. The greater the impact, the greater the level of human involvement should be.

5. Do not treat a well-written answer as evidence that the answer is correct.

6. AI can recommend; the executive decides.

7. Review whether AI adoption is solving a real problem or simply responding to internal pressure, trends, or technological enthusiasm.

8. Measure afterward whether AI-assisted decisions actually improved results, quality, speed, or profitability.

And one important point: when an AI recommendation conflicts with the views of those closest to the operation, do not automatically assume that the AI is seeing something they are not. The opposite may be true: the AI may simply be missing context.