Q: We hear a lot about AI efficiency, but you’ve coined the term 'AI-Psychosis' in your work. What does this look like in a boardroom setting?

AI-Psychosis is the state where an executive team loses the ability to distinguish between their own strategic intent and the output of a model. It manifests as a feedback loop: an algorithm provides a recommendation, the team assumes it’s objective because it’s 'data-backed,' and the decision is ratified without the healthy skepticism that characterizes effective leadership. It is the erosion of professional intuition.

Q: Is 'automation bias' the primary driver here?

Absolutely. Research from Nature Scientific Reports (2024) confirms that humans have a psychological tendency to trust machine suggestions even when they are demonstrably incorrect.^[1] When a high-performing CEO sees a 50-page summary generated by an AI, the cognitive load of verifying that data against reality is often too high, so they skip it. That is where the trap is set.

Q: You’ve stated that leaders must treat AI as an advisor, not an oracle. How do we practically enforce that boundary?

Treat AI as you would a junior analyst who is incredibly fast but lacks context. If that analyst provided a high-stakes recommendation, you wouldn't sign off on it without asking, 'What are the assumptions here?' and 'What data is missing?' You must formalize this. If your AI isn't showing its work—its provenance and its logic—it isn't an advisor; it’s a black box.

Q: The EU AI Act is now putting a spotlight on transparency. How should that change internal corporate policy?

The EU AI Act mandates human oversight for high-risk systems.^[2] For a business, this isn't just a compliance hurdle; it’s a leadership mandate. You should adopt a 'Human-in-the-Loop' protocol for any decision that impacts capital allocation, talent management, or market positioning. If a human cannot explain *why* the machine reached a conclusion, that decision is effectively un-governed.

Q: Critics argue that manual verification slows down decision-making. In a competitive market, isn't speed the ultimate currency?

Speed is a currency, but accuracy is the capital. If your AI-accelerated decision is based on biased training data that reinforces your own existing blind spots, you aren't moving faster—you are just driving off a cliff at 100 miles per hour. Rigorous verification is not a delay; it is a risk-mitigation strategy that protects the enterprise from catastrophic errors.

Q: How can executives audit their own decision-making processes for hidden algorithmic bias?

Start with a 'Red Team' exercise. Take a major strategic decision supported by AI and force your team to argue against it using only data that the AI did not consider. If the AI’s logic falls apart under external scrutiny, you have your answer. You are looking for 'algorithmic drift'—where the model begins to prioritize the patterns it finds most frequently, rather than the ones that are most relevant to your business goals.

Q: Is it possible that human intuition is actually more biased than the machine?

That is the classic retort, and it’s partially true. Humans are riddled with cognitive biases. However, the goal isn't to choose between human error and machine error. The goal is a synthesis. The machine identifies patterns; the human identifies context, ethics, and long-term strategic nuance. The moment we outsource the latter, we lose the accountability.