Measuring impact of GenAI

About this Video

• Video Title: Measuring impact of GenAI
• Channel: Gooru Content
• Speakers: None specified in transcript
• Duration: 00:04:34

Introduction

This video discusses the challenges of measuring the impact of generative AI (GenAI) investments and proposes a framework for doing so. It emphasizes the need to move beyond anecdotal evidence and develop a robust measurement strategy to inform future investment decisions and demonstrate tangible business outcomes.

Key Takeaways

• Challenge of Measuring GenAI Impact: The difficulty in measuring GenAI success stems from evolving success metrics, isolating AI's impact from other initiatives, and quantifying intangible benefits.
• Comprehensive ROI Framework: A four-pillar framework is suggested: cost reduction (automation savings), productivity gains (increased output per employee), innovation value (new revenue streams), and experience enhancement (improved customer/employee satisfaction).
• Technical Performance Indicators: Key technical indicators include accuracy, precision (benchmarked against human performance), speed/efficiency (response time, resource usage), robustness, and reliability.
• Productivity Gains: GenAI offers significant potential for time savings by automating tasks, leading to error reduction and increased output. Quantifying these gains (e.g., annual savings per knowledge worker) strengthens the business case for investment.
• Tools and Techniques: The video recommends using A/B testing, time-motion studies, and performance dashboards to track and analyze the impact of GenAI.
• Action Plan: A three-step action plan is outlined: define business-aligned metrics (KPIs), implement continuous monitoring systems, and conduct regular impact reviews.