Revolutionizing Customer Support with Language Models

In our post-ChatGPT world, few companies have moved to integrate generative AI as quickly as Intercom. Mere weeks after ChatGPT first went viral, Intercom had its AI-powered chatbots in the hands of beta testers, and it's continued to iterate on its products since.

That's why I was eager to talk with Fergal Reid, the VP of AI at Intercom and one of the company's key champions of generative AI. Before leading AI at Intercom, Fergal co-founded an ML startup, Synference, which was acquired by Optimizely. We had a chance to talk about Intercom's history building with AI, and how this new technology shift is going to impact customer support reps (for better or worse).

In addition to discussing the future of customer support, we discussed Fin AI Copilot, a new AI product from Intercom that's launching today. Like Intercom's existing AI, Fin AI Copilot can pull from internal and external documents, knowledge bases, and other content. But it can also learn from and reference past conversations, and has been built to improve agent interactions, not replace them.

Three key takeaways

Intercom is expanding from chatbots that talk to customers to copilots that augment agents. The company's first AI product, Fin (now Fin AI Agent), was designed to chat with users about your knowledge base before escalating to human support staff. Now, Intercom is bringing AI capabilities to the support staff themselves, with new features that can write answers, reference past conversations, and supercharge support staff.

This framing of a copilot for support really stuck with us. It's a tool. It's got to empower you. It's got to make you faster, more efficient, more effective. And we have for years been really deeply looking at what's the job a support agent does.

So often a support agent, especially if they're new or maybe they're getting a question they haven't gotten before, they look at question that comes in and they're like, I don't know what to do here. And then they have to go and read the company documentation, or they have to go and they have to search, they have to go into intercom, they have to search and they have to find the time their more experienced colleague answered this question a week or two before.

And we were like, can we use an LLM to do an end run around that? Can we use LLM to just make that experience really seamless?

The earliest adopters of generative AI are companies that were both already experimenting with machine learning, and had executive buy-in to move quickly. At Intercom, Fergal's team was able to deploy the first ChatGPT-enabled features within 7 weeks of ChatGPT's launch. That wasn't an accident - they had been playing with this technology for years, laid the groundwork with the rest of the organization.

When ChatGPT came out, that was at the end of November and we had features live with our own internal CS team by the holidays that year. ... I'm lucky that we have a very experienced team of some really great folks here who I guess had been in the space for a while. And then we just, we had the executive support we needed you know, because we had done the groundwork because we had talked a lot about how, "Hey, we think to something disruptive here," but we could get the alignment we needed internally to just force velocity of a project like that.

Bringing AI to customer support could create more jobs, not less. We've discussed this point in the context of software engineers, but my conversation with Fergal echoed a lot of the same points when it comes to customer support. When each CS rep is much more valuable as a result of AI, does that result in more reps or less? We don't yet have a clear answer.

While we don't know how this plays out, one thing we're really confident of at Intercom is that, customer support Is not close to servicing all the demand. There is huge latent demand. Like we see this in every industry study we do. Every time we talk to end users, they want customer support to be dramatically better, faster, friendlier than it currently is.

So many customer support experiences are really terrible. And so there's this absolutely latent demand for drastically more customer support. I have to believe that for many businesses, if it becomes way cheaper per customer support rep to deliver great customer support, there's tons more demand there to service.

And three things you might not know:

* Intercom's AI chatbot, Fin, has a 42% resolution rate on average (and up to 80% resolution rate in the best cases).

* When ChatGPT first launched (and even when GPT-4 launched), RAG wasn't a thing for the very first companies trying to build with it - they were inventing the techniques from scratch less than a year ago.

* Klarna, a “buy now pay later” company, has said their AI customer support agent does the work of 700 full-time employees.

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