Insights

Practical thinking for responsible AI work.

Field notes, methods, and leadership perspectives from the continuing work of making AI useful, reviewable, and accountable.

Field note

The difference between AI output and accountable work

Why evidence, review, and acceptance criteria change what organizations can responsibly do with AI.

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Leadership

The AI-fluent manager does not need to be an engineer

A practical model for directing, supervising, and evaluating AI-assisted work.

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Methods

Design the workflow before choosing the automation

A better sequence for moving from an interesting AI capability to a durable operating practice.

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Apply the thinking

Find one workflow worth improving in practice.

Find your first workflow