The issue is not ambition, it is operating model design. Most Customer Success organizations were not built to absorb continuous signals, dynamically reprioritize work, or adapt decisions in real time. AI exposes that structural limitation quickly.

AI is everywhere in the post-sale conversation right now. Every Customer Success leader I speak with is being asked some version of the same question: “What are we actually doing with AI?” For most teams, the honest answer is still, “We’re experimenting.” And that’s not a failure, it’s reality.

While there’s no shortage of AI content, there is a shortage of practical, peer-level guidance specifically for post-sale leaders. That gap is why I’m creating the AI in Post-Sale Council.

Nearly everyone is using AI in some form: teams are experimenting, tools are being rolled out, and productivity gains are starting to show in day-to-day work. And yet, when the conversation turns to outcomes leaders are actually accountable for (retention, expansion, cost to serve, and customer experience) the impact of AI is far less clear. 

Nearly everyone is using AI in some form: teams are experimenting, tools are being rolled out, and productivity gains are starting to show in day-to-day work. And yet, when the conversation turns to outcomes leaders are actually accountable for (retention, expansion, cost to serve, and customer experience) the impact of AI is far less clear.