MIT research reveals 95% of AI pilots fail to deliver revenue acceleration. Tom breaks down why this isn't a technology problem but a scaling failure, and provides three critical questions to identify which pilots deserve investment.

Show Notes

Key Statistics

• 95% of generative AI pilots fail to achieve rapid revenue acceleration (MIT, 2025)
• 8 in 10 companies have deployed Gen AI but report no material earnings impact
• Only 25% of AI initiatives deliver expected ROI
• Just 16% scale enterprise-wide
• Only 6% achieve payback in under a year
• 30% of GenAI projects predicted to be abandoned by end of 2025

Core Problem: Horizontal vs. Vertical Deployments

• Horizontal: Enterprise-wide copilots, chatbots, general productivity tools
  
  • Scale quickly but deliver diffuse, hard-to-measure gains
• Vertical: Function-specific applications that transform actual work
  
  • 90% remain stuck in pilot mode

Three Critical Evaluation Questions

1. Does this pilot solve a problem we pay to fix?
2. Can we measure impact in terms the CFO cares about?
3. Does it require process redesign or just tool adoption?

Success Factors

• Empower line managers, not just central AI labs
• Select tools that integrate deeply and adapt over time
• Consider purchasing solutions over custom builds
• Be willing to retire failing pilots

This Week's Action Items

• Inventory current AI pilots
• Categorize as: scaling successfully, stalled but salvageable, or stalled and unlikely to recover
• Apply the three evaluation questions
• Identify specific barriers for salvageable pilots

Chapters

• 0:00 - The 95% Problem: Why AI Pilots Aren't Becoming Products
• 0:24 - The Research: MIT, McKinsey, and IBM Findings on AI Failure Rates
• 1:49 - Why Pilots Stall: Horizontal vs. Vertical Deployments
• 3:07 - What Successful Scaling Actually Looks Like
• 4:11 - Three Critical Questions to Evaluate Your AI Pilots
• 5:40 - The Permission to Stop: When to Retire Failing Pilots
• 6:45 - Action Steps: What to Do This Week