AI KPI Design and Performance Monitoring

Measure the value and efficiency gains delivered by GenAI tools.

Pillar

Process – Workflow, Governance, Risk & Efficiency

Overview

This course equips participants with the skills to design meaningful Key Performance Indicators (KPIs) for Generative AI initiatives and establish performance monitoring frameworks. It emphasizes measuring the real business impact and operational efficiency of AI deployments.

Learning Objectives

Participants will be able to:

• Identify relevant KPIs aligned with GenAI project goals

• Design quantitative and qualitative metrics to track AI performance

• Implement monitoring systems to continuously assess AI outputs

• Analyze KPI data to drive improvements and decision-making

• Communicate performance insights effectively to stakeholders

Target Audience

• AI program managers and analysts

• Business leaders and decision-makers

• Data scientists and engineers

• Operations and performance management teams

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies

• KPI design workshops

• Hands-on monitoring tool demonstrations

Materials Provided

• KPI design templates and frameworks

• Performance monitoring tool guides

• Sample reports and dashboards

• Certificate of completion

Outcomes

• Develop KPIs that accurately reflect GenAI value and risks

• Establish robust AI performance monitoring processes

• Use insights to optimize AI initiatives and resources

• Foster data-driven communication and accountability

Outline / Content

Day 1: Fundamentals of AI KPIs

• Importance of KPIs in AI projects

• Types of KPIs: business, technical, user experience

• Aligning KPIs with organizational objectives

Day 2: Designing KPIs for GenAI Use Cases

• Defining measurable and actionable KPIs

• Balancing quantitative and qualitative metrics

• Setting benchmarks and targets

Day 3: Implementing Performance Monitoring Systems

• Tools and technologies for AI monitoring

• Real-time vs periodic monitoring approaches

• Handling data collection and quality issues

Day 4: Analyzing, Reporting, and Acting on KPIs

• Interpreting KPI results and trends

• Reporting frameworks for stakeholders

• Continuous improvement through KPI feedback loops

• Workshop: Design a KPI framework for a GenAI project