How Intercom rose from the ashes by betting everything on AI | Eoghan McCabe (founder and CEO)

How a Late-Stage SaaS Reinvented Itself With An AI Agent

Intercom’s transformation into an AI-first business is a study in urgency, cultural reinvention, and product clarity. Owen McCabe describes a near-death moment for a business that had plateaued in growth, and how a six-week internal prototype built on GPT-3.5 became the seed for FIN, an AI agent that now resolves customer issues at scale and is on a trajectory to exceed $100 million ARR. The story is less about a sudden miracle and more about a ruthless set of choices: pick a lane, simplify pricing, hire expert talent, and remake your culture around velocity and accountability.

From plateau to hypergrowth: why timing and desperation matter

The company was already mature and profitable but drifting into low single-digit growth. Public market revaluation and consistent declines in net-new ARR created a strategic imperative: either adapt decisively or continue decline. The launch of accessible large language models provided an immediate technical opening. Because Intercom had an existing AI team and a massive corpus of customer interactions, that six-week experiment quickly scaled into a product with clear unit economics and rapid customer traction.

Outcome-based pricing and the 99-cent ticket

Instead of wrestling with complex metered models, the team pursued outcome-based pricing tied directly to the value delivered: ticket resolution. That led to a simple, transparent price point that aligned cost with customer outcomes and removed barriers to adoption, even when early unit costs were unfavorable. This pricing clarity helped repair customer trust after years of convoluted plans and established an easy-to-understand commercial path for FIN.

Founder-first decisions: culture, values, and a trimmed organization

The turnaround was driven by a top-down reset. Leadership cut nonessential projects, focused investment heavily on AI, and rewrote company values to reward resilience, high standards, and shareholder value. A formalized performance and behavior grading process led to significant turnover—roughly 40%—but produced an organization aligned with the new mission: build and scale an agent that automates customer experience with human-quality results.

• Be explicit about the problem you will solve and focus your strategy on that lane.
• Align pricing to measurable outcomes so customers can see the return on investment.
• Use culture and values as a practical tool to recruit and retain the team you actually need.

Agents, jobs, and the future of enterprise work

Owen frames CX as the largest operational area in many companies—sales, service, success—and therefore the most immediate target for automation. He expects agents to pervade other repetitive, operational domains like accounting, onboarding, and contract review, and predicts a flatter, more efficient organizational shape where humans supervise and manage agent workflows. While some roles will shrink, the broader outlook is that better, less demeaning work will emerge and consumers will benefit from lower-cost, higher-quality service.

Practical takeaways for established product teams

If legacy companies want to survive the AI wave, they must move from incremental experimentation to full commitment: hire top AI talent, empower young engineers who move fast, and be willing to take short-term pain for long-term strategic clarity. Don’t half-commit with surface-level features—rethink pricing, operations, and the product’s core value proposition.

Intercom’s transition shows that transformation is messy and often unpopular, but it can deliver outsized returns when anchored to a clear problem, an outcome-based commercial model, and a culture willing to make hard trade-offs. The result is an agent-based customer experience that can be more personal and effective than legacy human-run workflows, and a company repositioned for fast growth and market leadership.