ServiceTitan’s CRO Ross Biestman reveals how they use AI to revolutionize both internal operations and customer outcomes – from Premier League-style rep relegation to automated dispatching that scales technician teams without adding overhead.
The AI Strategy That Scaled $30M to $860M ARR
While most SaaS companies are still figuring out how to sprinkle AI into their product features, ServiceTitan has been quietly using artificial intelligence to fundamentally redesign how they operate internally. The result? A merit-based sales organization that routes every lead to the rep most likely to close it, and customer-facing AI that’s driving real business outcomes across a $1+ trillion industry.
Ross Biestman, ServiceTitan’s CRO, shared their AI playbook at SaaStr Annual – and it’s unlike anything you’ve heard before. This isn’t about chatbots or content generation. This is about using AI to create systematic competitive advantages that compound over time.
The Internal AI Revolution: Merit-Based Lead Distribution
Throwing Out the Playbook on Lead Routing
Most sales organizations distribute leads using one of two methods: round-robin (everyone gets equal leads) or territory-based assignment (geographic or named accounts). ServiceTitan threw both approaches in the trash.
“We have this really unique way in which we think about customer acquisition,” Ross explains. “If a lead comes in that looks like a particular type of lead, it gets distributed to the account executive that has the highest propensity to close that type of business. So it’s not round-robin, it’s not named accounts – it’s based on merit.”
The Three-Dimensional Scoring System
ServiceTitan’s AI analyzes every rep across three critical dimensions each month:
1. Quota Attainment Pure performance against targets. Did you hit your number?
2. Efficiency Metrics Close rates by deal type, sales cycle length, and conversion rates at each stage. How effectively do you convert opportunities?
3. Quality Performance AI-powered analysis of pitch quality, demo effectiveness, and overall sales execution. How good are you at the craft of selling?
These scores combine to create a dynamic ranking system that determines pipeline allocation for the following month.
Premier League Relegation for Sales Reps
“It’s not unlike the premiership in soccer, this concept of relegation and promotion,” Ross describes. “If they perform really well, they get the best type of pipeline in that next month. If they perform poorly, they get relegated. If you get good, you got to stay good to be good.”
This creates a continuous performance pressure that traditional sales organizations lack. In most companies, once you’re assigned a territory or account list, you’re set for the year regardless of performance. ServiceTitan’s system means every month brings new opportunities to earn better leads – or risk losing them.
The Business Impact
The results speak for themselves:
- Higher overall team close rates (leads go to reps most likely to convert them)
- Improved rep performance through competitive pressure
- Better lead-to-rep fit based on historical data, not subjective assignment
- Continuous optimization as the AI learns which reps excel at which deal types
“We score the team based on their ability to hit quota, their efficiency defined by close rate, and the quality of their performance by leveraging tools that score their pitch and demo quality,” Ross explains. “This creates repeatability in your business and your go-to-market motion.”
Customer-Facing AI: Automated Dispatching at Scale
The $1 Trillion Dispatching Problem
ServiceTitan’s customers – plumbing, HVAC, and electrical contractors – face a complex optimization problem every day. When a customer calls with a problem, someone needs to decide which technician gets dispatched. Traditional dispatching relies on human judgment considering factors like:
- Technician availability
- Geographic proximity
- Skill set requirements
- Current workload
But human dispatchers make subjective decisions that often aren’t optimal for business outcomes.
AI-Powered Dispatching at Work
ServiceTitan’s AI considers multiple variables simultaneously to optimize dispatching decisions:
Geographic Efficiency Minimize drive time and fuel costs by routing based on real-time locations and traffic patterns.
Skill Set Matching Ensure the technician has the specific expertise required for the job type.
Close Propensity Route jobs to technicians with the highest historical close rates for that specific type of work.
Revenue Optimization Consider average ticket size by technician to maximize revenue potential per dispatch.
The Scaling Advantage
“This has been awesome for our customers because as they grow and they hire more technicians and they serve more parts of their respective markets, they can do so without having to hire the same level of dispatchers,” Ross notes.
The math is compelling: traditionally, dispatchers can effectively manage 15-20 technicians. With AI-powered dispatching, that ratio can increase significantly, improving unit economics while maintaining (or improving) service quality.
Business Outcomes for Customers:
- Higher close rates (right technician for the job)
- Lower operational costs (fewer dispatchers needed)
- Improved technician utilization (optimized routing)
- Better customer satisfaction (faster response times)
The Strategic AI Philosophy: Customer Outcomes First
Why ServiceTitan’s AI Approach Works
ServiceTitan’s AI strategy succeeds because it’s built around a core philosophy that Ross calls “order of operations”:
“Every decision we make, every dollar that we fund raise, every product that we build, every person that we hire is solely focused on how can we create a better life for contractors and better business outcomes for their businesses. And then, and only then, if we do that, we in return will have an incredible business ourselves.”
This customer-first approach to AI development ensures they’re solving real problems rather than building technology for technology’s sake.
The Hiring Strategy That Enabled AI Success
Three years ago, ServiceTitan hired a CTO who previously ran the Einstein product at Salesforce. “This is something he’s deeply passionate about and has deep expertise in,” Ross explains. “We’ve been really intentional about AI in our product to deliver the best AI solutions to our end customers for quite some time – at least two years.”
The lesson: AI success requires dedicated expertise, not just good intentions.
Customer Adoption: From Fear to Embrace
Overcoming Initial Resistance
“Two years ago it was a little scary for them,” Ross admits about customer reactions to AI features. “But I think they are fully embracing that this is the way of the future.”
Several factors drove this shift:
Industry Consolidation Private equity has poured into the trades industry, creating pressure for operational efficiency and scalability.
Competitive Pressure As larger operators gain AI-powered advantages, smaller competitors must adopt or risk being left behind.
Proven ROI Customers see measurable improvements in close rates, efficiency, and profitability.
The Table Stakes Reality
Ross predicts AI will follow the same path as mobile and cloud technologies:
“No one calls a company a mobile company anymore. No one really says a cloud company anymore. Everyone has to have a multi-tenant cloud offering. Everyone has to have a mobile offering. Everyone will have to have an AI offering. In fact, in the end, we’re just going to be talking about companies.”
The implication: AI isn’t a differentiator forever – it becomes table stakes. The competitive advantage goes to companies that implement it earliest and most effectively.
Lessons for B2B Leaders
1. Think Beyond Product Features
Most SaaS companies approach AI as a product enhancement – adding smart features to existing workflows. ServiceTitan’s approach is more fundamental: using AI to redesign core business processes for both internal operations and customer outcomes.
2. Focus on Systematic Advantages
Rather than one-off AI implementations, ServiceTitan built systems that create compounding advantages. Their merit-based lead distribution gets better over time as the AI learns which reps excel at which deal types. Their dispatching AI improves as it processes more data about technician performance and customer outcomes.
3. Hire AI Expertise Early
ServiceTitan’s AI success stems from hiring dedicated expertise (their Salesforce Einstein alum CTO) rather than trying to build AI capabilities organically. The lesson: treat AI as a core competency that requires specialized talent.
4. Customer Outcomes Drive Adoption
ServiceTitan’s customer-first approach to AI development ensures they’re solving real problems. When customers see measurable improvements in close rates and operational efficiency, adoption follows naturally.
5. Prepare for AI as Table Stakes
Companies that view AI as a long-term differentiator may be disappointed. Like mobile and cloud before it, AI will become an expected capability. The advantage goes to early adopters who build AI-native processes while competitors are still experimenting.
The Vertical SaaS AI Advantage
ServiceTitan’s success with AI illustrates a broader principle: vertical SaaS companies may have inherent advantages in AI implementation.
Deep Domain Knowledge Understanding the specific workflows, pain points, and success metrics of a single industry allows for more targeted AI applications.
Rich, Structured Data Vertical SaaS platforms capture detailed, industry-specific data that enables more effective machine learning models.
Clear Success Metrics Industry-specific KPIs (like technician close rates or dispatch efficiency) provide clear optimization targets for AI systems.
Customer Intimacy Deep customer relationships enable better change management and adoption of AI-powered features.
The Future: AI-Native Business Models
Ross’s vision extends beyond current implementations: “We’re looking for opportunities to drive incremental leverage in their business that is not dependent on human-based labor, looking at every part of the funnel where they can compress cycle time, improve close rates, and increase average price per ticket.”
This suggests a future where AI doesn’t just enhance existing processes but enables entirely new business models – ones where human labor scales more efficiently and customer outcomes improve systematically.
For SaaS leaders, ServiceTitan’s AI playbook offers a roadmap: start with clear customer problems, build systematic advantages, hire dedicated expertise, and think beyond product features to fundamental process redesign. The companies that execute this approach earliest may build insurmountable competitive advantages in their markets.
The question isn’t whether AI will transform your industry – it’s whether you’ll lead that transformation or be disrupted by it.
