Lesson Content

From experiment to operation.

Experimenting with AI means trying AI on individual tasks, evaluating whether it works, and deciding whether to continue. Operating with AI means AI is integrated into defined, documented business processes with consistent quality standards – and those processes improve over time.

The transition requires three things:

1. Documentation: the workflow is written down, not just practiced
2. Quality standards: the review criteria are defined, not just intuitively applied
3. Improvement loop: there is a regular review of whether the workflow is working and how to make it better

Documenting an AI workflow.

A documented business AI workflow covers:

• Task: What the workflow produces (e.g., after-service customer emails)
• Trigger: When the workflow is initiated (e.g., service completion recorded in scheduling system)
• Setup: What Claude context is used (Project with service descriptions, prompt template)
• Steps: Who does what, in what order
• Review standard: What the output must achieve before use
• Owner: Who is responsible for the workflow's quality and maintenance

Written in plain language, this fits on one page per workflow. It is a recipe – anyone who needs to run the workflow can follow it without the inventor being present.

Establishing the improvement loop.

Review each operating AI workflow monthly for the first three months, then quarterly:

• Is the output quality still meeting the review standard?
• Has the workflow's context (pricing, services, policies) changed – requiring prompt update?
• Is there a common edit pattern across outputs that could be addressed in the prompt?
• Are there adjacent tasks this workflow could expand to cover?

A twelve-month AI expansion roadmap.

Month 1-3: First workflow embedded and operating Month 4-6: Second workflow added (using three-criteria framework again) Month 6-9: First workflow expanded to adjacent tasks; third workflow added Month 9-12: Team AI habits embedded; four to five core workflows documented and operating

This pace feels slow in month one. By month twelve, you have a genuine organizational AI capability – multiple documented workflows, a trained team, and an improvement process. This is the compound interest model for AI adoption.