How Dialpad Leveraged Its Own AI to Achieve $300M ARR at SaaStr AI Day

Building a successful AI strategy requires prioritizing data governance and responsible risk management from the outset. Rather than immediately pursuing complex model development, organizations should leverage in-domain data to refine accuracy and utilize "red teaming" to identify system limitations through adversarial testing. An iterative investment path—starting with Retrieval-Augmented Generation (RAG) and progressing toward fine-tuning—allows companies to maintain control over costs and throughput while scaling effectively. At Dialpad, processing over 8 billion minutes of conversation has enabled the development of specialized AI tools that enhance human efficiency in sales and contact center workflows. Ultimately, the goal of integrating generative AI is to provide actionable insights and time-saving automation that augment human capabilities rather than replacing them, ensuring that businesses can maintain high-quality communication standards at scale.