AI Operating Models for Enterprise Leaders copertina

AI Operating Models for Enterprise Leaders

AI Operating Models for Enterprise Leaders

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Andrea Schnepf, founder of NEPF, joins host Michael Bernzweig to break down why most enterprise AI pilots fail and what the successful 5 percent do differently. She explains how tech-first, reactive decisions waste budget, stall adoption, and erode trust, then lays out three pillars for making AI a real business capability: clear purpose, leadership alignment, and a robust operating model. Andrea also shares examples from companies like Microsoft and Intuit, and walks through a practical three-phase roadmap—foundation, pilot-and-learn, and scale-and-embed—so executives can link AI to strategy, governance, and day-to-day work instead of scattered tools.

Key Topics:

  • Why 95% of AI pilots fail — common pitfalls and missteps
  • The three critical pillars for AI success: Purpose, Leadership Alignment, Operating Model
  • How to design a scalable AI operating system within your organization
  • The importance of connecting AI strategy to business outcomes
  • Case studies: Microsoft’s shared vision approach and Intuit’s cross-functional AI councils
  • Roadmap for AI adoption: foundation, pilot, scale & embed
  • Managing organizational culture and avoiding technology-first traps
  • How to create authentic topic-Gap analyses to fill content voids with AI

Timestamps:

00:00 - Introduction: Why most AI pilots fail and how to avoid common mistakes
02:30 - Andrea’s framework for building AI foundation centered on purpose and vision
06:45 - Signs that organizations are approaching AI as a tech first, disconnected from strategy
10:15 - The role of leadership alignment in AI success — shared narrative and trust-building
14:00 - Critical components of an effective AI operating model — decision rights, governance, metrics
19:30 - Examples of structured AI cultures at Microsoft and Justworks
24:10 - Practical roadmap: from foundation to pilot, to scaling and embedding AI
28:50 - Managing organizational change, culture, and talent in AI transformation
34:20 - How to identify topic gaps using AI models to enhance content authority
40:50 - Capacity-based pricing: managing demand and scarcity in consulting and tech services
45:00 - Final thoughts: actionable steps to embed AI into your business DNA

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