Diagnose adoption of an AI-assisted field-service workflow
Create segmented ADKAR diagnoses, choose matched support and design a bounded reassessment plan with safety and privacy guardrails.
32 minutes
Scenario
A maintenance company has launched a mobile workflow that uses an AI assistant to propose inspection steps. Training and account activation are high, but correct use differs sharply by role, connectivity and exception type.
- Your role
- Change lead responsible for safe adoption without employee surveillance
- Method
- ADKAR Change Model
Evidence pack
Launch activity
94% of technicians attended training and 82% activated the mobile account.
Awareness check
Only 36% can explain the operational and safety reason for replacing the spreadsheet.
Experienced technicians
They understand the reason but object that the app removes a justified stop-and-escalate judgement in unusual equipment conditions.
Practice evidence
68% pass the happy-path simulation; only 41% complete the workflow correctly when connectivity drops.
System conditions
Contractors cannot access the coaching channel, and two regions lack the permission required to save an offline exception.
Reinforcement
Supervisors still accept the old spreadsheet, while the current speed metric excludes time spent recording exceptions.
Safety and authority
Technicians remain accountable for safety and must be able to reject an AI recommendation and escalate to a human engineer.
Privacy boundary
The works council permits aggregated role-and-region adoption evidence but not individual motivation scores.
Constraints
- Preserve human stop, override and escalation authority.
- Do not infer motivation, loyalty or emotion from digital activity.
- Use only the minimum aggregated data allowed by the privacy boundary.
- Do not remove the old path until a safe offline fallback is verified.
Case steps
Work through each prompt using the evidence pack. These guided cases support self-directed practice; server-scored attempts are not available yet.
Define the observable change behaviour, separate delivery, adoption and outcome evidence, and choose meaningful segments.
Identify the earliest evidence-backed barrier for at least three segments and state confidence and missing evidence.
Choose one matched response for each diagnosed segment and identify the system constraint its owner must remove.
Design Reinforcement without coercion: align measures, supervisor behaviour, feedback and the legacy path.
Define a bounded test, adoption measure, safety guardrail, privacy rule, evidence date and reassessment decision.