If you've wondered what AI means for your career in customer success, start here. The hosts of the Customer Success Meetup open Moment 2026 with an optimistic argument: as intelligence gets cheaper, customer context rises in value, and the bottleneck has moved from building AI to making it work for real customers. John Gleeson makes the case, then Junan Pang (Fin) and Monica Perez (Lovable) share what that shift looks like inside their own companies. It sets up everything the rest of the day explores.

Most CS teams are still running the old motion (adoption dashboards, login-based health scores, quarterly check-ins) against a new scoreboard, where consumption pricing makes value realization the revenue model itself. At Lovable, the fastest company to reach $400M in revenue under that model, the CS team stopped asking "are they using it?" and started asking "if we disappeared tomorrow, does something break?" They call the answer load-bearing: less than 1% of what customers build clears the bar, but it's the one leading indicator of both retention and expansion, so they tied the whole team's goals to it, shifting from nurture to deployment motions and adding forward-deployed engineers alongside advisory CSMs. The playbook that got you hired won't keep you employed, and that CS isn't being disrupted but promoted, because proof is now the only differentiator.

The generalist CSM model, built to absorb everything from onboarding to renewal to executive relationships, can't hold up under bigger books, board-level ROI pressure, and AI adoption, so leading companies are replacing it with specialists: technical advisors, digital program leads, and hybrid advisors (sometimes Forward Deployed Engineers) while AI works as a true retention layer that detects risk, builds Day 1 onboarding playbooks, and handles long-tail renewals. The real question for post-sale leaders is no longer whether AI changes the role, but how to set CSMs up for the high-value work that remains now that AI covers the rest.