AI is quietly rewriting the rules of product ownership.

As models shift from supporting decisions to actively shaping outcomes, the traditional software roadmap begins to break down. Plans become probabilistic. Performance may improve - but accountability becomes harder to define.
When outcomes drift, costs rise, or tradeoffs surface, leadership teams are left asking a new question: who actually owns the result when the model is making the decisions?

This session introduces the concept of Algorithmic P&L - a new way of thinking about ownership in AI-driven products where financial outcomes, user experience, and system behavior are increasingly determined by learning systems rather than deterministic plans.
Drawing on real-world patterns from large-scale AI platforms such as search, ads, and marketplaces, the talk examines how responsibilities across product, engineering, and data science collide - and why legacy ownership models no longer hold.

Rather than focusing on governance frameworks or org charts, this talk surfaces the leadership gap that emerges when models, not humans, effectively own the roadmap. Attendees will leave with a clearer mental model for assigning accountability, making decisions, and leading teams when outcomes are driven by algorithms rather than specifications.

What you'll takeaway from this session -

- Why AI-driven products create ownership and accountability gaps at scale

- How probabilistic systems break traditional roadmap and planning assumptions

- What leadership teams must rethink when models - not plans - shape outcomes