AI Has a Cost Most Companies Aren’t Accounting for Yet. Here’s What They’re Missing.

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Key Takeaways Over-reliance on AI erodes critical thinking, judgment and neural engagement. When you stop exercising these skills, they don’t simply come back on command.

Organizations risk converging on a “machine mean.” If employees routinely accept AI’s first drafts and recommendations, companies can gradually lose their variance, dissent and unconventional ideas.

Data foundation and governance isn’t the real starting point — deciding how your people are allowed to think alongside AI is.

When someone talks about the true cost of AI, most of the words that pop into the head are token spend, GPU costs, model licensing, cost of having outdated infrastructure (and the associated cost of migration), training costs for people, etc. But they leave out one of the most basic and challenging aspects of AI cost.

I run a data and AI company. I’ve been telling enterprise clients for more than a decade that their AI problem is a data and governance problem, not a technology one (technology is and will always be an enabler). I still believe that. But I’ve started to think our industry skipped a step.

Let me call it a thinking problem for individuals and a governed change management problem at an enterprise level. Right now, it’s not about whether or not people will accept AI but more like how we ensure that the change is meaningful and that it is also beneficial to us as humans.

What AI does to your thinking

Many people remember this research by MIT’s Media Lab on the cognitive debt of using an AI assistant. The findings were and still are of an alarming nature. The report basically showed that people using only ChatGPT showed the least EEG (brain activity), with brain-only participants exhibiting the strongest. It clearly demonstrates what happens to a skill you stop exercising. Even if the skill is as simple as writing an essay. The neural pathways go quiet when you stop using them, and they don’t switch back on command.

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