Work-First AI principles

These are the principles I use when deciding whether an AI tool or workflow deserves a place in real work. They are designed for professionals, managers, operators, and business builders who want useful progress without AI theatre.

A prompt is not a workflow

“A prompt is not a workflow. A workflow has a trigger, an outcome, controlled context, a quality standard, and an owner.”

A good prompt can produce a useful draft. A workflow makes the complete job repeatable, reviewable, and accountable.

Make the complete job better

“AI is useful at work when it makes a repeatable job easier to check—not when it merely produces an impressive answer.”

Measure preparation, generation, review, correction, handoff, and follow-up. Faster output is not automatically better work.

Start with one consequential task

Choose a recurring job that is important enough to measure and safe enough to test. Define the outcome, source material, permissions, review standard, and stop condition before adding automation.

Keep judgment at the boundary

Name the decision that remains human: approving a customer commitment, sending an external message, changing a financial record, publishing a claim, or merging code. The AI system should produce a reviewable artifact and evidence.

Use TRY / SKIP / USE

TRY: Test a defined workflow beside the existing process.
SKIP: Avoid broad transformation projects with no named job, baseline, or owner.
USE: Keep a workflow when the complete job improves and its limitations are understood.

These principles are a living field guide. New examples should be backed by sources or clearly labeled as experiments and hypotheses.

About the author

Cihan Uygur is a practical AI-for-work coach focused on useful workflows, clear examples, and honest limits.