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Three lessons for creating a sustainable AI advantage
When ChatGPT launched in 2022, Intercom(opens in a new window) didn’t just watch the headlines—they mobilized. Within hours of GPT‑3.5's release, the customer service software company began experimenting, and just four months later launched Fin, their AI Agent that now resolves millions of customer queries each month.
That early momentum wasn’t an accident. As LLMs leapt forward, Intercom recognized that AI would reshape customer experience. Leadership acted quickly, spinning up a cross-functional task force, canceling non-AI projects, and committing $100 million to replatform the business around AI.
That decision sparked company-wide changes: reorganized product teams, a new AI-first helpdesk strategy, and a platform built to support Fin in handling high volumes and complex customer queries.
Below are three lessons from Intercom’s journey that any team—no matter where you’re starting—can put to work right now.
“AI-first has to be built in; you can’t bolt it on.”
Paul Adams, Chief Product Officer, Intercom
Lesson 1: Experiment early and often to build model fluency
Intercom tests models early, often, and learns deeply from their work.
The team began experimenting with generative models early, and their hands-on experience helped them map model limitations and spot opportunities. When GPT‑4 became available in early 2023, they were ready. Within four months, they launched Fin—and haven’t slowed down since.
“We were able to leverage GPT‑3.5 to have fluid conversations with glimpses of magic, but it wasn’t yet reliable enough to trust with our customers,” says Jordan Neill, SVP of Engineering. “Because we’d done the work, when GPT‑4 arrived, we knew it was ready, and we shipped Fin.”
Today, GPT‑4.1 powers a growing share of Intercom’s AI usage, including key logic inside Fin Tasks. The team also discovered that adding chain-of-thought prompting to non-reasoning queries closed performance gaps.
Lesson 2: Unlock speed with strong evaluations
Fin AI Engine™
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