The Hidden Cost of AI Is the Work–Feedback Loop

If AI were as easy to use as many posts suggest, it wouldn’t require this many posts explaining how to use it.

Over the past months, a clear pattern has emerged in AI-related articles in blogs, podcasts and posts on social media, especially on LinkedIn. The focus is no longer on spelling correction or summarization. Those use cases are commodities by now. Instead, the attention has shifted to much bigger promises: AI for Product Management, HR, Marketing, process automation, even strategy.

What is revealing is not the ambition behind these promises. It is the amount of guidance required to make them work. There are pages-long blog articles and long youtube videos explaining all necessary steps and controls required to make AI “successful” — without acknowledging what this complexity implies. And the authors don't even recognize it.

Most of these articles are not describing simple tools that teams can pick up and use. They describe carefully constructed setups that rely on elaborate prompt instructions, chained interactions, manual validation steps, and repeated human intervention to prevent subtle but costly errors. The more ambitious the use case, the more scaffolding appears around the AI.

These details matter, because they reveal something fundamental. This kind of AI usage is not a plug-and-play accelerator. It is exploratory work. The outcomes are uncertain, variance is high, and feedback is delayed. Progress does not come from execution speed, but from repeated inspection and correction.

This is where the real cost of AI adoption sits. Not primarily in tokens. Not in tooling licenses. The dominant cost is hidden in the work–feedback loop.

Someone tries an approach, inspects the output, adjusts the prompt, adds another safeguard, reruns the process, and evaluates again. Each cycle looks small in isolation. Together, they form a slow and expensive learning loop that is easy to underestimate, especially when the initial demos look impressive.

The paradox is that the more guidance a system needs, the more carefully we should ask where real feedback actually enters the system. Who notices that something is wrong? How quickly does that information affect the next decision? And how expensive is the correction once a mistake slips through?

Before adopting AI, it is worth being explicit about what you are trying to achieve. Are you running an experiment to explore a problem space and generate insight? Or are you trying to accelerate an existing workflow that already works reasonably well?

Both are valid goals, but they have very different economics. In experimental settings, slow and costly feedback loops are often acceptable because learning itself is the objective. In operational workflows, however, those same loops can quietly erase the promised efficiency gains.

In many cases, AI does not reduce the cost of work. It shifts it. And unless the surrounding work–feedback loop is deliberately designed and measured, that shift often makes work more expensive long before it ever gets cheaper.