Speaker

Luke F. Walton’s Sessions

2:00 p.m.–2:45 p.m. PT — Wednesday, February 24, 2027

Panel: What Humans Own Now: Taste, Judgment and Creative Direction in an AI-Executed Org

If AI can execute almost anything, human value moves to what it can't do: taste, judgment, and direction. This panel digs into the parts of the job becoming more important, not less — setting the standard, making the calls models can't, and steering creative vision when production is nearly free. A thoughtful conversation for leaders rethinking what to hire for, and what to develop, in an AI-executed org.

10:00 a.m.–10:45 a.m. PT — Thursday, February 25, 2027

Case Study: Agentic GTM in Practice: From Copilots to Delegated Workflows

Agentic AI becomes useful when it stops being a chat interface and starts completing real work. Giving a model tools is the easy part. The harder questions are operational: What should an agent actually own? Which steps should remain deterministic? Where does model judgment belong? What boundaries should be enforced in code rather than left to a prompt? When should a human intervene? And how do you know whether delegation is producing better business outcomes rather than simply more output?

Drawing on his experience operating AI inside Amazon and building production AI systems and software at Surmado, Luke F. Walton will break down a practical model for moving go-to-market work from AI assistance toward supervised and delegated execution. He will ground the framework in real operating patterns from global paid media and from Surmado, where an AI agent is entrusted to operate and change customer-facing production websites. The session will show how to decompose work, separate deterministic logic from model judgment, set permissions and escalation paths, enforce critical boundaries structurally, preserve reversibility and human oversight, and evaluate whether the resulting system is actually worth delegating to.

Key Takeaways

Learn how to break a GTM workflow into deterministic steps, model judgment, and human decisions
Examine what changes when an AI system is trusted to operate a customer-facing production asset instead of just recommending actions
Determine when to enforce a boundary in code, architecture, permissions, or CI rather than relying on prompt instructions
Calibrate the right level of AI autonomy based on stakes, reversibility, observability, and escalation paths