It is impressive how AI can do everything, but at the same time, finding the right place for it is incredibly difficult. Anyone building…

It is impressive how AI can do everything, but at the same time, finding the right place for it is incredibly difficult. Anyone building with AI faces the paradox of choice with every new interaction. Even in established use cases like coding assistants, there are still countlessly subtle ways to get it wrong.

In my view, the ideal use of AI isn't as flashy or polished as tech giants portray. It sits in our routine human-in-the-loop workflows inside the platforms we build. AI doesn't need to be perfect; it just needs to match the accuracy of an average human performing the task. This is about scale, not flawless execution.

Your top domain experts act as the guides, defining best practices and pointing out which shortcuts work. Breaking down high-level human responsibilities into small, structured tasks is what software engineering has always been about. Now, we use AI to run those steps.

Take vendor onboarding as an example. Is selecting a supplier purely about price? In reality, we need to:

• Parse unstandardized documents: read scanned or legacy Articles of Association to extract Partners and Ultimate Beneficial Owners (UBOs) despite massive format variance.
• Evaluate risk severity: read legal case summaries to determine whether an entry presents genuine reputational risk or mere administrative noise.
• Clean complex datasets: determine if "Linkana Tech" and "Linkana S.A." refer to the same entity across duplicate databases by matching addresses and context where exact string matches fail.

Each of these steps can be assisted by AI, and in well-structured environments, fully automated. The real magic happens when this human-in-the-loop mechanism bridges two systems: the moment a budget is approved, the payment system finalizes the transaction, triggers the vendor, and notifies the rest of the supply chain.