Kenji Hayward

My honest perspective on AI adoption in support is that success has far less to do with the technology itself and far more to do with how you bring your team along. AI introduces real change to how support work gets done, and if the team doesn’t understand the “why” or see how it helps them, adoption will stall or quietly fail. Leaders need to be explicit about what success actually means, whether that’s faster resolution, better consistency, less cognitive load, or more time spent on meaningful customer interactions, and measure against that, not vague ideas of “innovation.”

I also think support leaders too often jump straight to customer-facing AI use cases before getting their internal foundations right. There’s a temptation to lead with deflection or automated responses because they’re visible and exciting, but that’s often skipping steps. In my experience, the biggest early wins come from internal applications: AI for analyzing insights, surfacing trends, running QA at scale, summarizing conversations, or helping agents navigate complexity faster. The boring stuff is exciting, because it compounds quietly and builds trust.

Those internal use cases do two critical things. First, they make agents’ day-to-day work better, which earns buy-in instead of resistance. Second, they help leaders understand where AI is actually reliable and where it still needs guardrails. By the time you move to customer-facing implementations, you’re doing so with confidence, data, and a team that already sees AI as a tool rather than a threat.

If there’s one thing other leaders should watch out for, it’s treating AI as a shortcut instead of a system. AI won’t fix unclear processes, inconsistent quality standards, or misaligned incentives. But when you start with clear goals, invest internally, and bring your team along for the journey, AI can meaningfully raise the bar for both agent experience and customer experience.