The most effective AI adoption starts with clarity: what problems are we trying to solve, and what tradeoffs are acceptable? Many teams chase “deflection” and unintentionally degrade trust, so I recommend choosing a small number of outcomes (e.g., issue-level accuracy + time-to-resolution) and designing the system around them. AI in support is less about a model and more about an end-to-end workflow: knowledge quality, routing, guardrails, measurement, and continuous improvement. Start narrow. Pick a well-scoped use case with strong documentation and low downside if the AI gets it wrong, then instrument the experience deeply (containment quality, escalation rate, recontact, CSAT, and accuracy audits). The goal of the first phase isn’t scale; it’s learning, what your customers ask, where your knowledge base is weak, and where ambiguity triggers failure. Expand only when quality is stable. Accuracy and uncertainty handling are the differentiators. Retrieval and content governance often matter more than the underlying model. Keep a clear “source of truth,” ensure content owners maintain it, and design the assistant to ground answers in approved docs. When the system is unsure, it should ask clarifying questions or hand off gracefully, confident hallucinations are what kill adoption. Treat agents as part of the system, not a backup plan. Early ROI frequently comes from agent-assist: drafting replies, summarizing long threads, suggesting next steps, and auto-tagging, while agents retain final control. When deflection is the goal, ensure handoffs preserve context so agents can resolve quickly without restarting. Lastly, set governance early: define what AI can do, what requires confirmation, what it must not do, and how you review new intents/policies. Communicate to the team that AI removes repetitive work so humans can focus on complex, high-empathy support.
