How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

**Brian Scanlan** (0:00)
Suddenly, you started realizing that you have to think bigger about things, or that your imagination is now the barrier, not the tool.

**Claire Vo** (0:06)
How is this not happening in your organization? Like literally the physical limits of my ability to type code are unlocked by AI.

**Brian Scanlan** (0:14)
Today, we are seeing twice the number of throughput as we did compared to nine months ago on our engineering team. Now it's like, why can't it be 10x?

**Claire Vo** (0:21)
This is a little bit more of what my instinct tells me is possible, which is if you go all in, if you prepare your team, if you prepare your code base, I think your overall product quality is going to go up. I think your overall developer experience is going up. There's just so many good things that come out of using these tools and using them correctly.

**Brian Scanlan** (0:38)
Backlog zero is a realistic thing for teams to be able to go after. All the things that you wish you ever wanted to do, it's now just achievable.

**Claire Vo** (0:46)
I often advise a lot of CTOs and VPs of engineering when figuring out how to get their engineering team AI-pilled, say, everything you hate about the code base, go spend a month fixing and see how fast we can speed run that. That's gonna feel really good.

**Brian Scanlan** (0:59)
I've been having the most amount of fun in my career over the last three months.

**Claire Vo** (1:06)
Welcome back to How I AI. I'm Claire Vo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today, I am showing how Intercom 2ext, the number of PRs that their R&D department is shipping in just a few months. Brian Scanlan is a senior principal engineer at Intercom and he is going to show us truly all of their secrets to getting a large product and engineering organization cooking on Claude Code. Let's get to it. This episode is brought to you by Celigo. Every company today wants AI to improve how work gets done. The fastest way is building it directly into everyday business processes, automating employee onboarding, keeping customer data accurate, managing orders and inventory, or resolving finance and operations issues. When AI lives inside the flow of work, it can update records, trigger approvals, route work, and kick off the next step across systems. That's how teams operationalize AI and deliver measurable results. Celigo makes this possible. And now, with Celigo Aura, it's never been easier. Celigo Aura gives you access to the entire platform through natural language, connecting your systems and turning intent into action. All of it under your control.
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Brian, welcome to How I AI. Why I am so thrilled that you agreed to join the podcast is, I think, Intercom has done it, which is you all have met the moment in sort of two ways. One, clearly met the moment from a product perspective. We're one of the first companies that had, sorry, I don't want to say legacy business, but had a going concern business that saw AI coming and really transformed how your product worked for customers. And I'm a happy Finn customer. They did not tell me to say that. And then second, what we're going to talk about is the team at the moment in terms of really understanding AI was going to change how in particular product engineering and design orgs and engineering organizations were going to work.
And you just went full speed at changing how the team works.
What drove sort of the urgency around meeting the moment? How did that come to be? Was it a single person? Was it everybody? What was your experience?

**Brian Scanlan** (3:44)
I think in some ways it's been the easiest place to be driving the adoption of AI in engineering and product. Because we've focused the company so much on, our folks and product on adopting AI and being AI first and how we think about the product, future customer support and all that. And we also had very clear expectations. Like we've seen what's possible in the product space and it's just very clear and obvious to us as like connoisseurs of AI. It's like this is clearly going to be huge in engineering product and building.
And honestly, there's been a lot of impatience for like, why isn't this happening today? You know, if we go back a few years and cursors picking up a bit of business and the models are getting better, but it still wasn't transformative. It still wasn't like the whole business was changed and we're seeing vast amounts of extra productivity. We knew there was potential, but it still felt like we needed to have some sort of breakthrough moment or something needed to big had to happen for us to guess to the kind of huge velocity wins that I think now we're starting to achieve. That said, we still want more. You know, we're proud of where we're at, but we're not content with what we've achieved so far.