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AI Operating Model & Talent

Building the teams, capabilities, and operating model to scale AI​

About this category

Practical guidance for leaders on building the organization behind AI: designing operating models, structuring teams, developing talent, and creating the culture and accountability needed to scale AI across the enterprise.

Technology alone doesn't make an AI-enabled enterprise. It takes the right structure, roles, skills, and ways of working. This category is about how organizations set themselves up to deliver AI repeatedly and at scale, and how they build the people capability to sustain it.

What You'll Find Here​

  • Operating Model Design: Centralized, federated, and hybrid models, including centers of excellence and how AI teams work with business units.
  • Roles and Team Structures: Who does what across product, data, engineering, ML, risk, and business, and how to organize cross-functional delivery.
  • Talent Strategy: Hiring, upskilling, and retaining AI talent, and deciding when to build skills internally versus bring in partners.
  • Ways of Working: Delivery processes, platform and enablement teams, and how to move from experimentation to reliable production.
  • Culture and Change Management: Driving adoption, building AI literacy across the workforce, and helping people work effectively alongside AI.
  • Leadership and Accountability: Executive sponsorship, funding models, and clear ownership for AI outcomes.

What You'll Learn​

  • How to choose and evolve an operating model that fits your organization's size and maturity.
  • How to build the teams and skills needed to scale AI beyond a few champions.
  • How to structure ownership so AI initiatives don't stall between business and technology.
  • How to lead the people side of AI adoption.

Who It's For​

Executives, transformation and HR leaders, heads of data and AI, and technology managers building or scaling AI capabilities.