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Every major technology shift comes with a familiar temptation: take the new tool, attach it to the old process, and declare progress.

AI deserves a more thoughtful approach. The most meaningful gains often come from changing the workflow itself.

Start with the work

Before choosing a model or framework, map the actual work. Where do decisions happen? Where do people repeat themselves? Where does context get lost?

Those questions lead to better experiments than starting with a vendor demo.

Keep people in the loop

Automation should reduce toil without obscuring accountability. The right checkpoints depend on the consequences of getting something wrong.

Measure the outcome

Track cycle time, quality, adoption, and user confidence. A flashy proof of concept is not the same thing as a useful capability.

The goal isn’t to make every process look like AI. It’s to build better processes with the tools now available.