GTM Atlas — Let AI own, not assist

Author: Rati Zvirawa, Senior Director of Product at Intercom / Fin
Source: atlas.attio.com/let-ai-own-not-assist
Date: May 6, 2026

Who is Rati Zvirawa

Leads product in the AI group at Intercom and the team behind Fin — the Customer Agent that started in support and now runs end-to-end conversations for sales teams too.

Core Thesis

In 2026, the buyer has changed. The seller hasn't. Prospects arrive with more context — they've done the research, compared you to competitors, they have high intent and deeper questions. The moment they try to start a real interaction, they hit the traditional inbound experience: contact form, chatbot, queue.

The first thing most teams do with AI is close that gap by accelerating what they already have. Co-pilots to make the sales team faster. That's one side of the coin.

The other side is asking what whole parts of the conversation an Agent can own outright — first message through to paid customer — and how the human roles get redrawn around it.

The first path gets you a slightly more efficient web team and sales team. The second is a transformation. Not assist. Not augment. Own.

Take the first path and you end up with a collection of small Agents bolted onto different parts of the funnel. You haven't transformed anything. You've just added tooling.

Key Frameworks

Handovers Rebuild Relationships; Agents Preserve Them

No matter how much context you hand over from one person to the next, there's something inherent in that handover where the relationship has to be rebuilt. The context may transfer. The relationship doesn't.

When an Agent is continuing the conversation instead of handing it off, there's no rebuild. The relationship is maintained. The customer is served throughout. The handover, when it does happen, only happens at the moment it's most impactful for the customer and the business.

What an Agent Needs: Playbook + Knowledge + Data

Playbook: your sales strategy in natural language. Not a structured workflow — a set of goals the Agent understands and executes against, the same way you'd brief a new rep on who routes to self-serve vs mid-market vs enterprise.

Knowledge: grounding in truth about your product — pricing plans, feature docs, battle cards. When a prospect asks about a competitor, the Agent has the right answer in the moment.

Data flowing both ways: context pulled in from research on the open web and from your CRM at the start of a conversation, and written back at the close so your team has a clean source of truth.

Metrics Break. That's the Point.

The web team measures clicks to trial and has yearly goals tied to revenue. Put an AI Agent at the top of the funnel and fewer people click trial. More people are having conversations. How do you resolve that?

The instinct is to protect the old metrics. The better move is to step back: customers are going through the journey differently now. How do I measure success in that new journey? What signals actually matter?

The new metric still ties back to revenue. It just isn't the old one.

One customer thought the Agent had broken their MQL numbers, only to realize their MQL definition wasn't consistent across their funnels to begin with. The Agent didn't break the metric. It surfaced that the metric was already broken.

Redeploy Your Most Curious

Pluck whoever on your team has the most energy and curiosity, take them off their current role, and give them autonomy to build — ignoring the shape of the existing funnel entirely. Give them a budget that lets them drive revenue and lose a little along the way.

Treat this as a transformation, not a small adjustment. Build new roles around it — people analyzing performance, making sure your Agent adheres to strategy, adjusting as the business changes.