When AI Agents resolve the majority of customer conversations, the shape of your support team has to change. I’ve experienced this shift firsthand: the moment AI begins to carry the volume, your people must pivot from answering individual questions to engineering the system that consistently delivers quality outcomes.
The old tiered model built around queue management, handoffs, and volume-based productivity no longer fits. AI now handles the bulk of customer interactions, and that changes the role of your human team entirely. Responsibilities evolve, and success is measured differently. It goes beyond just adding automation to existing ways of working. You’re building an operating model that’s entirely new.
Most teams don’t hire a dedicated AI function from day one. They start by distributing a few critical responsibilities across existing team members, and formalize those responsibilities as AI becomes central to how support works. That’s exactly how I recommend getting momentum without over-hiring too early: prove value fast, name clear owners, and then scale.
Once you have executive support and a clear strategy in place, these are the four foundational roles we believe are key to getting AI off the ground in a meaningful way:
1. AI operations lead
Responsibilities: Owns day-to-day AI performance. Tracks quality. Tunes behavior. Prioritizes fixes. Drives iteration.
Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.
Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.
In my teams, this role becomes the heartbeat of AI performance—instrumenting quality feedback loops, triaging failure modes, and aligning fixes across product, data, and support ops.
2. Knowledge manager
Responsibilities: Owns macros, snippets, and help content. Maintains structured, accurate inputs the AI Agent depends on.
Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.
Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.
Every generative AI system is only as good as its knowledge. I’ve learned the hard way that inconsistent or stale content erodes trust—both for customers and internal stakeholders. A rigorous knowledge manager prevents that.
3. Conversation designer
Responsibilities: Designs how the AI Agent communicates by focusing on tone of voice, structure, handoff logic, and interaction flow. Tunes how responses feel.
Skillset/background: Background in content design, UX writing, or support enablement. Deep grasp of policy, CX standards, and conversational nuance.
Why you need this: This role ensures the AI Agent speaks like your brand – clearly, helpfully, and in line with customer expectations.
This is your brand’s voice in motion. A strong conversation designer sets the guardrails that keep interactions on-brand, compliant, and empathetic while still efficient.
4. Support automation specialist
Responsibilities: Builds workflows and backend actions the AI Agent can execute.
Skillset/background: Background in support engineering, systems, or tooling. Works closely with product and engineering teams.
Why you need this: Enables the AI Agent to take action – not just respond. This role translates customer intents into business systems.
In practice, this role unlocks the jump from “answering” to “resolving.” They wire up secure actions, map intents to outcomes, and partner with engineering to keep latency low and reliability high.
Introducing new AI-first roles doesn’t mean your existing functions disappear. But they do need to evolve. For AI to scale effectively, every function in your support organization must shift its focus from managing queue-level activity to improving the system’s performance:
Enablement trains human agents to work with the AI Agent: managing handoffs, tuning responses, and understanding how to give feedback that improves the system.
QA evolves from reviewing conversations to reviewing the quality of the customer experience and behavior of the AI Agent: where the AI succeeds, where it falls short, and how the system as a whole performs.
Workforce management plans capacity based on automation coverage, not just inbound volume.
You’ll also need a new kind of leadership to make this model work. The traditional support leader doesn’t map cleanly to an AI-first organization. You need a new layer: leaders who are part strategist, part operator. They roll up their sleeves to analyze the AI Agent’s performance, refine content, and debug handoffs, but they also coach the team through a new way of working.
This is the “player-coach model” – leaders who actively shape both the system and the people within it.
These leaders see the AI Agent as a teammate to manage, not just a tool to monitor. They can’t be purely people leaders or purely systems thinkers. They need to be both, and they’re emerging as a critical hire in support right now.
Some teams are restructuring their organizations around the AI Agent as a core product, not just a support tool. Here are some real-world examples:
At Dotdigital, a dedicated “Fin Ops” specialist role was created to refine content and improve AI performance.
At Clay, a dedicated GTM engineer role has been established as part of the ops team with a focus on making support more efficient at scale using Fin. Additionally, a support engineering function has been embedded directly in the CX organization to help reduce volume by fixing bugs and building internal tools.
Lightspeed created a dedicated Digital Engagement team to manage Fin’s optimization, and formalized a triangular model that brings together technical teams, frontline experts, and content specialists.
In my experience, the most resilient org designs align around three pillars: Human Support, AI Support, and Support Operations and Optimization. Each pillar carries distinct ownership yet shares accountability for AI performance. That structure keeps the team focused on outcomes over output and makes continuous improvement everyone’s job.
Once AI Agents handle most conversations, your team’s work moves from “answering questions” to “designing and improving the system that answers questions.” They become the force that steers quality, rather than the one that carries the volume.
This is why new roles are important. It’s not because they’re trendy, but because the performance of your support organization now depends on the performance of AI, and no AI Agent succeeds without clear ownership of content, behavior, workflows, and improvement cycles.
That’s the pattern we’ve seen from working with so many teams:
They name owners early.
They distribute responsibilities before they formalize them.
They anchor teams around AI outcomes, not ticket outcomes.
And they hire leaders who can manage both the system and the people.
If you take one thing away from this week’s article, let it be this: if AI is going to handle the majority of your customer conversations, your team needs to be designed to help it do that well.
Your roles, responsibilities, and leadership approach are now part of the architecture of AI performance.
Next week, we’ll go deeper into how these roles actually operate day-to-day – the workflows, responsibilities, rhythms, and collaboration patterns that make an AI-first support organization run.
Inspired by this post on The Intercom Blog.
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