Start with the work. End with evidence.
AI initiatives often begin with a tool and search for a problem afterward. CLEAR reverses that sequence. It begins with the outcome and lived workflow, makes the proposed change visible early, and treats adoption as an ongoing learning system.
- C
Clarify the outcome
Define the business or human outcome before selecting a tool.
- Name the workflow and its owner
- Establish the current baseline
- Choose one observable outcome
- L
Locate the friction
Study where effort, delay, inconsistency or information gaps occur in the current work.
- Map the current workflow
- Talk to the people doing the work
- Separate symptoms from root causes
- E
Experiment visibly
Make the future workflow tangible with a small, testable prototype.
- Prototype the whole experience
- Use representative tasks and information
- Test value and risk early
- A
Adopt responsibly
Give people clear roles, guardrails, review points and space to build confidence.
- Define human oversight
- Document appropriate use
- Train inside the real workflow
- R
Review and reinforce
Measure the result, learn from actual use and keep improving the system.
- Track outcomes and adoption
- Collect qualitative feedback
- Improve or stop based on evidence
Before scaling, ask five questions.
- Can we name the outcome and its baseline?
- Did we observe the real workflow and its users?
- Has a representative prototype demonstrated value?
- Are ownership, oversight and acceptable use clear?
- Will evidence determine whether we improve, scale or stop?
How to reference this framework
Dabhi, Mitesh. “The CLEAR AI Adoption Framework.” MiteshDabhi.com, 10 July 2026. https://www.miteshdabhi.com/ai-adoption-framework