Course Objective & Scope
Many organisations track AI adoption purely by seats assigned or monthly logins—vanity metrics that hide whether tools are genuinely changing daily work. This course teaches teams how to build a multi-layered AI adoption measurement system combining product telemetry, process cycle-time data and qualitative feedback.
The funnel from access and activation to habit formation, feature depth, output acceptance and business outcomes is worked through within strict employee-privacy boundaries. Participants design a complete adoption measurement framework and an executive dashboard specification for their AI tools.
Who Should Attend?
- Digital transformation, AI CoE and adoption managers
- Enterprise AI product owners, business intelligence and data analytics teams
- People analytics and learning & development (L&D) specialists
- PMO and finance leaders auditing the utilisation of AI investments
Key Takeaways
- Distinguish vanity metrics (assigned seats, raw prompt counts) from actionable adoption indicators
- Build a five-stage funnel covering access, activation, habit stickiness, usage depth and business impact
- Extract quality signals from output acceptance rates, edit distance and fallback behaviour
- Run cohort and unit-level usage analytics ethically without triggering employee surveillance concerns
- Design an adoption dashboard that flags stalled cohorts early and triggers targeted interventions
Curriculum
From Vanity Metrics to the Five-Stage Adoption Funnel
- Why assigned licences and raw query counts mislead decision-makers
- The 5-stage funnel: Access -> Activation -> Habit -> Depth -> Business Outcome
- Measurement differences between conversational assistants and embedded workflow AI
Habit Stickiness and Usage Depth Indicators
- DAU/MAU ratios, the 3+ active days per week threshold and cohort retention curves
- Separating shallow prompting from high-value multi-step, document-grounded or agentic usage
- Setting realistic usage-frequency benchmarks by job family
Quality, Trust and In-Process Impact Metrics
- Output acceptance rate, human edit distance and fallback-to-manual rates
- Triangulating user thumbs-up/CSAT feedback with objective eval scores
- Linking usage to process KPIs: ticket resolution time, proposal turnaround, first-time-right rate
Privacy, Ethics and Measuring Without Surveillance
- Aggregating at unit and cohort level under KVKK/GDPR rather than policing individuals
- Avoiding Goodhart's Law: why rewarding raw prompt counts backfires
- Communicating analytics transparently to preserve psychological safety
Adoption Dashboard Design and Intervention Playbook
- Designing three dashboard views: Executive Summary, Product Owner and AI Champion
- Diagnostic playbook: telling training gaps apart from poor model/data quality
- Workshop: building the metric dictionary and dashboard wireframes for the organisation
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