2026 customer service planning series: Vol. 04

Once you’ve defined the right roles on your team, you need an operating model that makes progress an integral part of how things work and keeps the AI Agent improving over time.

At Intercom, we use a simple mantra to guide how we think about this: “The first time you answer a question should be the last.”

This is part four of our five-part series on customer service planning for 2026. We’ll be sharing all five editions on our blog and on LinkedIn.

If you’d rather have them emailed to you directly as they’re published, drop your details here.

We’re trying to build an operating model where every resolution improves the system, so that fewer issues repeat, quality compounds, and support becomes more scalable over time.

Getting this right takes intentional design. It takes clear ownership, guardrails that let you move quickly without risk, a way to feed insights back in, and a culture that embraces and celebrates the work, not just the outcomes.

Let’s break that down.

1. Start with clear ownership

One of the most common reasons AI performance plateaus is ambiguity.

When no one owns how the AI Agent performs, feedback gets lost, issues linger, and improvements stall.

High-performing teams assign a single owner who’s responsible for making the AI Agent better by:

Reviewing resolution trends and identifying where the system is underperforming.

Making targeted updates to content, configuration, and behavior.

Coordinating with product and engineering on systemic blockers.

Setting improvement priorities, targets, and timelines.

That owner (often referred to as the AI ops lead) typically sits within support operations or grows out of an existing role. The title or team they sit on isn’t important. What matters is that they take clear ownership and have the authority to drive change.

Real-world example

At Dotdigital, AI performance plateaued after a strong start – resolving around 2,800 conversations per month for three consecutive months. To drive resolution rates up, the team created a dedicated support operations specialist role, filled by an experienced agent with deep product knowledge. This person will focus on refining snippets, improving content, and enhancing the AI’s resolution capabilities.

2. Make iteration fast and safe

As the AI Agent handles more volume and complexity, change might start to feel risky. And when teams hesitate to make changes, performance stalls.

That’s where lightweight governance comes in: a clear way to keep iterating without bureaucracy or endless approvals.

The teams that have developed a good rhythm with this put a few principles in place:

Everyone knows which changes need review, and which don’t.

Decision-makers are named.

Updates are tested (lightly but reliably) before they go live.

Feedback flows through one place, so it’s seen and acted on.

Progress happens on an agreed schedule (weekly reviews, monthly checkpoints, quarterly planning, etc.) not just when someone has time.