Why are companies seeing massive productivity gains on paper, yet failing to see it hit the bottom line?

In this episode of The AI Table, we sit down with David Krause to unpack the AI Productivity Paradox—the glaring gap between perceived AI output and actual financial returns. If your board is asking for hard proof of AI ROI, this conversation is your blueprint.

We cut through the hype to explore why traditional metrics are breaking down, how to draw a hard line between human and machine output, and why proper governance and clear ownership are the ultimate differentiators between AI success and expensive shelfware.

Key Takeaways:

• The Earnings Gap: Why standard productivity metrics fail to capture true AI profitability.
• Accountability Frameworks: Defining who actually owns model performance and risk inside the enterprise.
• Governance at Scale: How to manage vendor models and structure AI steering committees that drive action, not bureaucracy.
• The Human Element: Why expert judgment and rigorous change management are more critical than ever in the agentic era.

Chapters:

• 00:00 — The AI Productivity Paradox
• 03:45 — Measuring True AI Impact
• 05:17 — Distinguishing Human vs. AI Actions
• 10:40 — Ownership and Accountability in AI
• 14:01 — Governance of Vendor Models
• 16:54 — The Role of AI Steering Committees
• 22:44 — Critical Evaluation in the AI Era
• 28:12 — The Importance of Expert Judgment
• 30:15 — Defining Business Metrics That Matter
• 34:01 — Change Management in Metrics
• 36:44 — Debunking Common AI Myths

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