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

#TheAITable #EnterpriseAI #AIReturns #AIGovernance #DataStrategy #AILeadership #AIROI #AIstrategy

Dia Adams sits down with Max Gokhman (Head of AI at Franklin Templeton) to break down the hard truths of enterprise AI implementation. Moving beyond the hype, they explore why corporate culture is the ultimate bottleneck to AI adoption, how organizations are actually measuring AI ROI, what AI job loss could look like in the future, and the critical need for model explainability and governance in automated decision-making.

Discover what it takes to transition from experimental AI chat interfaces to secure, autonomous enterprise workflows without falling into common productivity and security traps.

Key Takeaways:

• Why cultural adaptation is a prerequisite for successful enterprise AI deployment
• How to measure real AI success and corporate productivity beyond vanity metrics
• The vital role of model explainability and zero-trust governance in AI decision-making
• Navigating the build vs. buy dilemma for scalable enterprise AI infrastructure

Chapters:

00:00 Merging AI with Human Expertise

08:11 Measuring AI Success in Enterprises

11:05 The Future of AI and Productivity 14:57 Understanding AI's Limitations

18:25 Navigating AI Risks and Governance

22:18 Ethics and Morality in AI Decisions

26:31 Model Explainability and Trust

29:31 Building vs. Buying AI Solutions 32:47 Transitioning to Autonomous AI Systems

35:43 AI Pet Peeves and Concerns

#EnterpriseAI #AIGovernance #ArtificialIntelligence #DataStrategy #AIAdoption #TechLeadership #MaxGokhman #DiaAdams #TheAITable

In this episode of The AI Table, Dia Adams sits down with Jenna Zhang, the first University Chief Data & AI Officer in the midwest, to unpack the shift transforming education: the jump from legacy data stacks to real-time, agentic AI. They break down why traditional data governance fails in autonomous environments, how to bridge the communication gap between data leaders and the C-suite, and where the biggest innovation opportunities lie for modern enterprise leaders.

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#aistrategy #AgenticAI #DataGovernance #EnterpriseAI #EdTech #TheAITable #aigovernance

Takeaways

• Shift from data for humans to machines
• Importance of data governance in AI

Chapters

• 00:00 The Evolution of Data Management
• 02:02 Understanding Agentic AI
• 03:33 Defining True Data Products
• 05:45 Persuading Leaders on Data Value
• 08:02 Navigating AI Pressure as a Leader
• 09:43 Transforming Raw Data into Value
• 12:28 Universal Standards for Data Integration
• 14:00 Real-Time Data Pipelines in AI
• 16:21 Investing in Agent-Ready Data Products
• 21:03 Accountability in Data Ownership
• 23:37 Encouragement for Future Builders