Introduction

ServiceTitan CRO Ross Biestman reveals how AI reshaped their $860M ARR sales engine and customer operations. This is AI beyond chatbots.

What Problem It Solves

Most sales teams distribute leads unfairly and dispatch jobs inefficiently. ServiceTitan fixed both by using AI to match the right rep or technician to the right opportunity, boosting performance and customer outcomes.

Quick Summary

ServiceTitan didn’t just “add AI features” like most SaaS companies. Instead, they rebuilt their sales and operations around AI. Internally, they created a merit-based lead distribution system where reps earn better leads by consistently performing well. Instead of static territories or round-robins, AI looks at quota attainment, efficiency, and quality to assign leads. This system creates constant improvement, like a Premier League relegation-promotion model for sales reps.

Externally, they solved the trillion-dollar dispatching problem for contractors. Their AI dispatching tool chooses the best technician for each job by weighing skills, location, workload, and revenue potential. This lets customers scale without adding costly dispatch staff while improving close rates and customer satisfaction.

At the heart of this strategy is a simple principle: focus on customer outcomes first. AI adoption was once scary, but measurable gains in efficiency and profitability made it essential. ServiceTitan’s playbook proves AI will soon be as expected as mobile or cloud – table stakes, not a bonus. The real advantage goes to early adopters who redesign processes with AI at the core.

Key Takeaways

• ServiceTitan used AI to scale from $30M to $860M ARR.
• Internal AI: lead routing is based on rep merit, not round-robin or territory.
• External AI: automated dispatching optimizes technician use and revenue.
• Competitive pressure and ROI drove rapid customer adoption.
• AI will soon be standard - the edge comes from using it early and deeply.
• Vertical SaaS companies have unique AI advantages thanks to domain knowledge and data depth.

What To Do

• Rethink AI beyond product features - use it to redesign your business processes.
• Build systems that create compounding advantages over time.
• Hire real AI expertise early; don’t try to “fake it” with generic add-ons.
• Anchor AI adoption in solving customer problems with clear ROI.
• Act now - AI will be table stakes soon, so build your edge while competitors are still experimenting.

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