Is AI taking your job?

AI is moving quickly enough that even very large, sophisticated companies are still learning where it belongs.

That’s the useful lesson in Brian Solis's recent Forbes piece on Ford and AI. Ford invested heavily in AI and automation but needed to bring back hundreds of engineers and specialists for their capacity for judgement to find real quality. Klarna used automation to engage a mountain of support tickets, but needed the human touch to truly elevate the quality of their brand. The lesson is a wonderful dispelling of AI’s taking our jobs: AI may be amazingly capable, but human expertise supplies invaluable judgement.

Ford and Klarna learned that the technology works better when it’s paired with the people who understand the product, the edge cases, the history, and the consequences of being wrong. In other words, AI needed better context and better human review.

It’s not enough to invest in AI.
It’s knowing where to apply it.

AI can summarize, search, draft, inspect patterns, and speed up repeated work. Those are real advantages. But many business problems don’t live neatly inside the task. They live in the handoff between teams, the exception nobody wrote down, the customer promise that changes the meaning of the data, or the veteran employee who knows why one option looks fine on paper and fails in practice.

That’s where leaders can get into trouble if the first question is, "Who can this replace?"

Instead they should ask, where can AI help experienced people see sooner, decide better, and prevent more rework?

Small and mid-sized businesses need regular checks to examine the business holistically and see what’s working, what could be better, and how can we get good information in front of the people who can apply good judgement?

AI can be incredibly useful. But it does not remove the need to understand the work. It raises the value of understanding the work.

The companies that benefit most from AI will not be the ones that chase every new capability first. They will be the ones that know which problems are ready for automation, which ones need better process first, and which ones still depend on human judgment.

That’s the work before the tool.