Individual
The person tries AI on small tasks and compares the result with their own judgment, without a stable pattern yet.
You are learning where AI helps, where it misleads, and what should remain human. The experiments are useful, but use is still discretionary and controls are not yet repeatable.
The person tries AI on small tasks and compares the result with their own judgment, without a stable pattern yet.
People are using AI in pockets and discovering where shared inputs, review, and handoff standards are needed.
Pilots are underway. The organization is learning where policy, workflow, and risk boundaries need to exist.
Read this as a condition in a particular workflow, not as a permanent label.
Experiments have a defined task, expected outcome, and reason AI might be useful.
Users are testing which sources, context, and constraints improve results.
First checks are emerging, but the depth of review depends on the person or situation.
AI assists selected steps, while the surrounding workflow remains largely manual.
People recognize that a human remains accountable, but disclosure and escalation are inconsistent.
Attempts are compared informally to learn what helps, fails, or should remain human.
Collecting experiments without decision criteria. A pilot should teach you whether to adopt, adapt, or stop a pattern.
A bounded pattern can be repeated, its failure modes are known, review is consistent, and handoffs carry enough context for another person to check the work. Continue to Experimenting, or take the assessment.
Use the AAOS stages to turn a promising experiment into a grounded, validated workflow.