post sale teams

When leaders ask, “What impact is AI having?” The honest answer is often: “I know it’s helping, but I can’t fully quantify it yet.”

That’s normal. Most AI adoption starts where risk is low and workflows are internal. That’s exactly where it should start. The challenge is translating invisible productivity into visible business outcomes.

When leaders ask, “What impact is AI having?” The honest answer is often: “I know it’s helping, but I can’t fully quantify it yet.”

That’s normal. Most AI adoption starts where risk is low and workflows are internal. That’s exactly where it should start. The challenge is translating invisible productivity into visible business outcomes.

I recently walked into my garage and straight into a puddle. This unexpected moment changed how I think about customer education and how customers use AI, here’s how: water behind the washing machine. The basin filling up. No obvious cause.

You’ve probably seen Matt Schumer’s post, “Something Big Is Happening.” It struck a chord because AI is advancing faster than ever, and the gap between perception and real capability keeps growing. For post-sale leaders, the question is not whether AI matters. It’s whether your operating model is evolving fast enough to turn AI into measurable business outcomes. That’s the real inflection point and the truth about AI post-sale leaders need to understand. 

In many organizations investment in AI continues to increase, but only a small percentage of teams have moved beyond experimentation into consistent, outcome-driven execution. The gap isn’t effort, it’s operational design. The capabilities required to experiment with AI are not the same as those required to operationalize it across a team or organization.

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.