Rolling out an AI Agent doesn’t just change how your team works – it changes who your team is.
I learned that in the crucible of a fast-moving launch. Before we launched Fin publicly, our Support team became its first alpha/beta tester and we had to move fast. No roadmap. No step-by-step guide. Just a powerful new technology, and a steep learning curve.
That experience is exactly what led us to create The AI Agent Blueprint – a resource we wish we’d had when we were starting out, and one we hope will give other support teams a clearer path forward.
Looking back, I won’t lie and say I was cool, calm, and confident about how to do this – I was nervous as hell. I had no idea how to implement an AI Agent and ensure it resulted in huge cost savings and stellar customer experiences.
We had older machine learning technology available to us (shout out to our first-gen chatbot, Resolution Bot), but as a complex software business, we really only used it for basic FAQs. In all honesty, we still had a way to go – both in using automation more effectively and in making the chatbot experience actually enjoyable for our customers.
So why the urgency?
When ChatGPT burst onto the scene nearly three (!!) years ago, Intercom’s Machine Learning team immediately spotted the opportunity and dived into building the world’s first (and objectively best) AI Customer Service Agent.
Suddenly, we were being asked to pilot this brand new technology with real customers and go all in ASAP. Because we were selling this powerful new functionality, we had to use it ourselves and show it off in the best possible light so customers would want to use it too. #nopressure
There was no playbook, just a lot to figure out. As a product management leader, I had to switch into rigorous product discovery while staying execution-minded.
How do we do a phased rollout, but scale very quickly?
How do we QA Fin’s responses and make continuous improvements?
How will we produce and manage all the content Fin needs?
What will we do about all the outdated content we already have?
What are the success metrics now? Should they be different to original Support KPIs?
Who’s responsible for the success metrics? Who manages this newcomer to our team?
It was daunting. We had to take a brand new technology, figure out how to use it, build a team around it, and move at breakneck speed to implement every new feature that rolled out. It was ambiguous, fast-moving, and a massive lift.
But we got there and the results speak for themselves: Fin is now resolving over 75% of our inbound support volume.
That outcome didn’t happen by accident. We embedded forward deployed engineers with Support, treated our AI Agent like a product creator in its own right, and used gen ai for product prototyping to tighten our iteration loops. We prioritized a customer support AI strategy that balanced containment with quality: containment rate, CSAT on AI-resolved conversations, first-response latency, and recontact rates became our core scorecard.
That success led to real change for me and my team: new roles, new responsibilities, and new career paths. I now run a whole new function that didn’t exist before: AI Support. We’ve created new and elevated roles like Conversation Designers and Knowledge Managers. Fin hasn’t just changed how we support customers – it’s transformed the structure of our team and the trajectory of our careers.
And now, we’re helping our customers do the same.
In all transparency, if I hadn’t been this close to the work, I might have waited to see how generative AI played out before committing. I might have waited for a blueprint for how to deploy and scale an AI Agent. I wish I had something like that when we got started, or even later when we had a solid foundation but needed to scale our AI strategy.
How much less scary would it be to implement an AI Agent if something like that existed?
Whether you’re just getting started or already using AI in some way, you’re not early anymore—and you shouldn’t have to figure it all out alone. Strong product management leadership, a clear change plan, and tight feedback loops are what separate experiments from outcomes.
That’s why we created The AI Agent Blueprint – a practical map for launching and scaling AI in support. It brings together everything we’ve learned from our own journey, and from working closely with our customers who are doing the same.
If you’re ready to operationalize gen ai in support, align on the right metrics, and redesign roles for the future, this blueprint will help you move from pilots to pervasive impact with confidence.
Inspired by this post on The Intercom Blog.
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