Intercom's CEO predicts that companies will abandon separate AI agents for sales, service, and onboarding. A single, coordinated "customer agent" is necessary to avoid conflicting goals and create a seamless, high-touch experience for every user.
Counterintuitively, AI responses that are too fast can be perceived as low-quality or pre-scripted, harming user trust. There is a sweet spot for response time; a slight, human-like delay can signal that the AI is actually "thinking" and generating a considered answer.
AI's initial workforce impact is absorbing future hiring needs, not causing layoffs. Most support teams are so understaffed ("underwater") that AI simply helps them catch up with existing demand, allowing them to freeze headcount growth.
Before replacing human workers, AI expands the total addressable market by making services economically viable for previously unserved segments. For instance, Intercom customers now offer AI support to their free users, something they could never afford with human agents.
The vast majority of Intercom Fin's resolution rate increase came from optimizing retrieval, re-ranking, and prompting. GPT-4 was already intelligent enough for the task; the real gains were unlocked by improving the surrounding architecture, not waiting for better foundation models.
Competing in the AI era requires a fundamental cultural shift towards experimentation and scientific rigor. According to Intercom's CEO, older companies can't just decide to build an AI feature; they need a complete operational reset to match the speed and learning cycles of AI-native disruptors.
Despite mature backtesting frameworks, Intercom repeatedly sees promising offline results fail in production. The "messiness of real human interaction" is unpredictable, making at-scale A/B tests essential for validating AI performance improvements, even for changes as small as a tenth of a percentage point.
For specific, high-leverage tasks like conversation summarization and re-ranking search results, Intercom trains its own custom models. These smaller, fine-tuned models have proven to be cheaper, faster, and higher quality than using general-purpose frontier models from vendors.
Intercom priced its AI agent per successful resolution, aligning its incentives with customers. Though initially losing money on each resolution ($1.21 cost vs 99¢ price), efficiency gains made it profitable, proving outcome-based pricing can succeed for AI products.