Background

Questions

1. Intercom has been positioning pretty hard on AI, and it's interesting to see that high-level conviction around it. How important is it that Intercom became founder-led again to make this big push around AI? Eoghan, we'd love to hear about your return to the CEO role over the last 8 months and how the AI focus came about.
2. Let’s dive into Fin, your AI bot for customer service. Can you go deep into what the core problem is for customers and how AI is the solution to that?
3. Do you intend to really, really focus on automated responses that improve the speed of response, or do you intend to also offer an option with a human in the loop? And how do you win the trust of customers so that they feel confident having Fin responding automatically on the site directly to customers?
4. Fin was built on GPT-4. Can you tell us about the process of building Fin—key learnings around hallucinations and the shortcomings of GPT-3.5?
5. Per what you said, are data, feedback loops, and SaaS the three main components of building an AI application? Do any of these take real major precedent? Is the data component by far the most important? How do you think about that?
6. Can you talk about the go-to-market strategy around Fin—keeping it in beta, in waitlist, to build anticipation and at the same time be able to fine-tune it behind the scenes?
7. Is percent resolution the main metric you use to evaluate product market fit? How do you decide that you're basically good to go?
8. Philosophically, we've only been talking about OpenAI, GPT-3 and 3.5 and 4, ChatGPT. Did you look at Anthropic, Bard, or others? Do you have an eventual trajectory to give yourself optionality around the different LLMs versus being an OpenAI shop or an OpenAI partner? How have you thought about that?
9. Can you talk about the previous era of chatbots and what worked, what didn't work, and how does that set us up for the current moment that we're in?
10. How does customer service change in a world where 80% of queries are automatically answered by AI? What does the team construction look like? How are you imagining that?
11. One of the things that's mixed into this question is Intercom's business model—pricing per seat SaaS—and how if customer service teams shrink, that’s something where you're disrupting your own model. Do you anticipate having usage-based models based on tokens? Is that something that’s going to be part of every AI-powered SaaS company's business model? How do you think about the pricing part?
12. Can you talk about interoperability versus vertical integration? Are there benefits for having your help docs and your chatbot both powered by Intercom?
13. How do you think about how AI enables Intercom to go up against an incumbent like Zendesk on customer service? What can Intercom do that Zendesk can't? How do you think about the positioning against an Intercom?
14. Is building a customer data platform (CDP) important when we talk about bringing more user data in so that the AI can be powered off of and personalized based on user data?
15. Is there a plan for integrating into the customer’s actual database to extract that information? How do you think that that information makes its way into the AI bot?
16. How are you thinking about marketing automation today? Is it essentially some form of proactive customer support? Have you played around with AI applications, in those use cases, and are there any promising results?
17. What are the core differences between the support use case with LLMs vs. LLMs with “marketing automation”?
18. How has Intercom’s pricing evolved in the last eight months since Eoghan's return? What are the plans for pricing, and how do you use that competitively to avoid the bottom-up disruption from many folks who are building for AI customers?
19. If everything goes right for Intercom over the next five years, what does it become and how has the world changed?