Why AI deliverables should be judged by outcomes, not effort

A client receives two deliverables… Both solve the problem they were hired to solve. Both are accurate and useful, and they lead to the same business outcomes. The client is happy with the work and sees no meaningful difference in the results. Then they learn that one deliverable took 20 hours to create while the other took 20 minutes. Now the questions start rolling in: Was AI involved? Should the faster deliverable cost less? Is the person who completed it somehow less skilled because they found a way to work more efficiently? What’s interesting is that most of us have completely different reactions to AI depending on which side of the transaction we’re sitting on. We love using AI to save ourselves time, but many become uncomfortable when they’re the customer and discover AI was used to create something they purchased. View embedded content I recently ran a LinkedIn poll asking a simple question: If the outcome is great, do we really care how it was made? The responses reinforced something I’ve been thinking about for a while. The biggest objections people have to AI often have little to do with quality. The time vs. value fallacy I think part of the discomfort comes from the fact that we’ve spent decades connecting value to effort. Long hours feel valuable. Fast work feels suspicious. Struggle feels like expertise. The harder something appears to be, the easier it is to justify its price. The story is about a ship engine that stopped…

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