How Dialpad achieved $300M ARR by building its own in-house AI

Dialpad has achieved $300M in Annual Recurring Revenue (ARR) by developing its proprietary artificial intelligence technology stack over seven years, processing more than 8 billion minutes of customer conversations.

Core strategy and approach: Dialpad’s success stems from a methodical, long-term approach to AI implementation that prioritizes practical use cases and measurable outcomes.

• The company began by identifying specific problems to solve around conversation intelligence rather than implementing AI for its own sake
• Their strategy combines both third-party APIs and custom solutions, demonstrating that AI implementation isn’t a binary build-or-buy decision
• The company’s massive dataset of processed conversations provides a significant competitive advantage in developing specialized AI solutions

Data management and governance: Proper data handling has been fundamental to Dialpad’s AI development strategy.

• The company emphasizes the importance of establishing data governance protocols from the beginning
• Their approach includes clear policies for data usage, privacy, and model training
• The infrastructure is designed to accommodate varying regulatory requirements across different regions

Measurable business impact: Dialpad’s AI implementation focuses on delivering quantifiable value to customers.

• Customer success metrics show up to 20% efficiency gains in workflow improvements
• Real-time AI features, such as AI Recaps, provide immediate value by distilling key information from calls
• The company employs comprehensive telemetry and observability tools to track accuracy and impact

Human-centric approach: Dialpad’s AI strategy emphasizes augmenting rather than replacing human capabilities.

• AI coaching features enhance sales and support team performance
• Automation focuses on routine tasks, allowing human agents to handle more complex interactions
• The company actively tracks how AI tools improve human agent performance and job satisfaction

Technical implementation insights: The company’s technical strategy balances immediate needs with long-term scalability.

• Early decisions about real-time versus batch processing significantly impact cost and performance
• Domain-specific data enables optimization for particular use cases while maintaining general capabilities
• The combination of real and synthetic data plays a crucial role in ongoing AI development

Future outlook and market implications: The next phase of AI development in customer communications will focus on enhancement rather than replacement of human interactions.

• Complex voice communications in contact centers are expected to remain partially human-operated for at least the next two years
• Development efforts will concentrate on creating more sophisticated tools to support human agents
• Companies building their data foundations now, even while using third-party tools, will be better positioned for future AI advancements