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The rapid adoption of AI has created a new challenge for organizations: not simply choosing the best tools, but understanding what they already have, why they have it, and whether it is delivering meaningful value. As experimentation expands across teams, organizations can quickly find themselves with overlapping capabilities, unclear ownership, inconsistent governance, and limited visibility into what employees are actually using.
This session will explore a practical framework for moving from AI experimentation to intentional AI portfolio management. We’ll look at how leaders can assess their AI landscape, distinguish valuable experimentation from unnecessary duplication, establish clearer ownership and governance, and evaluate tools based on business need, adoption, and measurable value. The goal isn’t necessarily a smaller AI stack. It’s a more purposeful one.
Attendees will learn how to:
- Assess the AI ecosystem: Map tools, agents, content, workflows, and overlapping capabilities to understand what exists and where rationalization is needed.
- Evaluate value, not hype: Start with the business problem and define the outcomes that determine whether an AI investment is actually working.
- Make intentional investment decisions: Determine what should scale, coexist, consolidate, or retire based on business need, adoption, overlap, governance, and value.
- Establish ownership and governance: Define who owns AI solutions, their underlying knowledge, decision rights, and ongoing lifecycle management.
- Govern without stopping experimentation: Create enough structure to scale successful AI while preserving room to test, learn, and innovate.
Sabina Goldberg
AI Enablement
Microsoft