AI agents are rapidly becoming the “front door” across channels, and many organizations report early adoption while still figuring out scale. At the same time, tools like Cursor and Lovable are compressing build cycles, enabling competitors (and non-traditional entrants) to ship convincing product surfaces and workflow automations in weeks—not quarters—raising the bar for what “table stakes” looks like. 

In B2B, customer experience is no longer “funnel → product → support” as separate worlds. It’s one continuous system where marketing promises, sales motions, onboarding, in-product activation, and service outcomes compound—or collide. Product’s job is to orchestrate that system and to defend the moat when AI makes the interface easy to copy. 

1) The Experience-to-Outcome Loop (EOL): A practical model for connecting upstream intent signals (campaigns, social, web, CRM) to in-product high-value actions and then to downstream service/resolution. This reframes “CX” as an operating system for revenue, retention, and cost-to-serve—not a department.

2) The Roadmap Influence Playbook (beyond Product): How product leaders earn prioritization from Marketing, Sales, Ops, Risk/Legal, and Support using three mechanisms: (a) shared outcome metrics (activation, time-to-value, renewal risk, cost-to-serve), (b) “journey ownership contracts” that name decision rights, and (c) an instrumentation-first cadence so debates are settled with end-to-end evidence rather than functional anecdotes.

3) AI Moats vs. AI Mirrors: A crisp way to decide what to build when competitors can clone features quickly. “AI mirrors” are copyable surfaces (chatbots, summarization, generic copilots). “AI moats” are durable advantages: proprietary data loops, trust and compliance, domain decisioning, and workflow integration depth. The talk outlines a prioritization rubric to shift investment toward moats without slowing delivery.

4) Safe Speed: shipping faster without losing trust: Why agentic experiences increase both capability and risk (quality, security, compliance), and how to implement governance patterns—human-in-the-loop controls, auditability, and guardrails—so teams can move at “AI speed” responsibly.