Intercom's decisive bet on AI

Co-founder Des Traynor takes us behind the scenes

👋 Hi, it’s Kyle and I’m back with a new Growth Unhinged, my newsletter that explores the unexpected behind the fastest-growing startups.

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Intercom is one of those companies that’s become a household name within software.

Founded in 2011, Intercom has raised nearly $250 million in funding. They’ve been named to the Forbes Cloud 100 for eight (!) consecutive years. The business generates “hundreds of millions” in annual revenue. You can see the product out in the wild at just about every up-and-coming startup.

Even still, Intercom was arguably the first major SaaS company to go all-in on AI along with a disruptive outcome-based pricing model, which they launched in early 2023. That was more than a year ahead of most competitors. Now even Salesforce is pivoting their AI pricing, positioning it as “a consumption-based model that aligns cost with business outcomes.”

Today, Intercom’s Fin AI agent is doing “tens of millions” in revenue and has quadrupled in size over the past year. Fin now autonomously resolves 56% of conversations for the average customer, more than double where it started (~25% resolution).

I’ve been a fan for a while and knew this was a story that needed to be told in Growth Unhinged. Thankfully, co-founder Des Traynor agreed. Des has been instrumental in shaping Intercom over the past 14 years and I asked him to unpack Intercom’s decisive bet on AI.

My top takeaways from our conversation:

1. It took a ‘war time’ CEO to be the first to go all-in on AI.

2. Each 1% improvement in Intercom’s resolution rate was hard fought with constant A/B testing. Most of the good AI tools are not simply thin ChatGPT wrappers.

3. Competition will no longer be about who has the most features. It’ll be about how well those features work.

4. The winners will be the ones who write the RFP for customers, teaching them how to evaluate and buy AI products.

5. If AI agents are doing the work for you, the pricing model needs to match. But prepare for potential trade-offs around attribution and predictability.

6. Users don’t necessarily see whether an AI agent is getting better. Educate them about improvements and empower internal AI champions.

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On being first to go all-in on AI

Rewinding back the clock to October 2022, Intercom co-founder Eoghan McCabe returned to lead the company as CEO. Eoghan made it clear that he was taking Intercom back to its roots with a singular vision: become the dominant platform in the customer support space, and redefine that space.

Less than two months later, on November 30th, OpenAI released ChatGPT for public use. By Monday Eoghan made a decisive call that Intercom would be all-in on AI to automate work. Their first modern AI release was on January 31, 2023. Fin came out (in beta) only six weeks after that.

I asked Des how such an established company could possibly turn the ship so fast? He attributed it to two things: (a) Eoghan being ready to be a ‘war time’ CEO and (b) having an established relationship with OpenAI prior to ChatGPT (via a product called Resolution Bot), which made Intercom well positioned to act quickly.

“With ChatGPT’s first release, all the chat immediately was about hallucinations,” Des recalled. “As a result, we decided to make our first release agent-facing and not user-facing.” These features, including things like conversation summaries and inbox improvements, proved to be extremely popular. The team immediately deprioritized other projects to push out AI inbox features with a focus on taking something that customers were already doing and automating it.

Then Intercom got access to GPT-4. “It was very clear that something was here, but also that you need to build a lot of software around GPT,” Des told me. This became the catalyst to go even bigger.

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Why most of the good AI tools are not thin ChatGPT wrappers

There’s a lot of dismissive online chatter about AI products being thin wrappers around ChatGPT. The implication is that they aren’t durable, will quickly get commoditized and could easily go to zero as LLMs improve. Des disagrees.

“There are genuinely thin ChatGPT wrappers where people build a thin shell around an API call, but most of the really good AI tools – like Granola, Lovable and Intercom – are not thin wrappers,” he emphasized.

Des says that with Fin there are 15 different sub-processes involved in answering a customer support question. “It’s not just given these docs, answer this question. It’s everything from asking who is Kyle, what screen is Kyle on, what’s the current state of Kyle’s account, what question is Kyle asking, what language does he speak, how urgent is it?”

Many incoming support questions don’t have a help doc about them. There’s a detailed reasoning and Q&A process that needs to happen in order to get it right. “Today, Fin is a long, complicated architecture of a bunch of stuff and each is extremely fine-tuned to exactly what it needs to do.”

Initially, Fin was at a roughly 25% resolution rate. It’s now at 56%. According to Des, “Each of those points was extremely hard fought with constant A/B testing.”

It’s worth noting that Intercom’s outcome-based pricing model – charging $0.99 per successful autonomous resolution – makes Intercom’s R&D team extremely motivated to fight for those basis points. Improvements to the resolution rate immediately flow through to revenue, making R&D a revenue-generation function. (Intercom also just released a bunch of new insights features designed to help customers drive continuous performance improvement.)

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