Course Objective & Scope
There is often a disconnect between the metrics technical teams track—such as F1 score, latency or groundedness—and the cost savings or revenue gains executives expect. This course teaches how to build an integrated measurement framework that connects technical performance to operational indicators and business outcomes.
It covers leading and lagging indicators, quality and safety metrics specific to generative AI and agent workflows, user adoption and trust measures, and baseline collection methods. Working on cases adapted to the organisation, participants produce a KPI tree and a measurement scorecard complete with pilot pass/fail thresholds.
Who Should Attend?
- Business unit leaders and process owners accountable for AI outcomes
- Product managers, business analysts and project managers
- PMO and finance specialists setting shared targets with data and AI teams
- Customer experience and operational excellence managers
Key Takeaways
- Build a KPI tree linking business impact, process performance, user adoption and model quality
- Select appropriate quality and unit-cost metrics for generative AI, RAG and agent projects
- Plan a reliable baseline measurement before launching an intervention
- Define quantitative thresholds that trigger scale, pivot or stop decisions after a pilot
- Design a concise KPI dashboard blueprint for both executives and delivery teams
Curriculum
The Four-Layer KPI Hierarchy in AI Projects
- Financial and strategic business outcomes (ROI, revenue, unit cost, NPS)
- Operational process metrics (cycle time, first-time-right rate, escalation rate)
- Linking leading technical indicators to lagging business outcomes
Translating Model, RAG and Agent Metrics into Business Terms
- Precision, recall, hallucination rate, context relevance and task completion rate
- Latency, token consumption and cost per completed task
- How quality degradation translates into rework and error cost
User Adoption and Human-AI Collaboration Metrics
- Active usage, suggestion acceptance rate and edit distance
- Tracking human-in-the-loop escalation and override rates
- Detecting fallback to legacy workarounds and measuring user trust
Baseline Measurement and Value Attribution
- Rapid baselining techniques when historical process data is incomplete
- A/B testing, shadow mode and pre/post comparisons
- Isolating AI impact from seasonal or operational changes
Success Thresholds and Stage-Gate Criteria
- Minimum acceptance thresholds for PoC, pilot and production stages
- Non-negotiable guardrail metrics: safety, privacy and SLA breaches
- KPI scorecard template and review cadence
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