A company solved its sales team's information gap by treating 25,000 hours of recorded Gong calls as the ultimate source of truth. This existing internal data, previously ignored, became the foundation for a company-wide AI automation strategy that transformed their go-to-market operations.

Critical buying journey insights are hidden in unstructured data like Gong transcripts. 2X CMO Lisa Cole notes that AI can surface mentions of communities, analysts, or even other AI tools that influenced a deal—signals invisible to traditional marketing attribution tools.

Ramp built an AI agent that sifts through Gong recordings, Salesforce notes, support tickets, and chats to answer any product question. This automates the work of an entire team, turning days of research into an eight-minute query to identify key customer pain points and roadmap priorities.

Upload call recordings or transcripts from tools like Gong or Fathom into an AI model. Ask specific questions like, 'Where was the most friction?' to identify disconnects you missed in the moment. Use this insight to craft hyper-relevant follow-ups that address the core misunderstanding.

Instead of direct API calls, build Model-Controlled Program (MCP) servers. They act as better guardrails for the AI, allowing it to interact with external data more effectively and even suggest novel use cases based on API documentation.

Use an AI agent to systematically analyze sales call transcripts. By automatically extracting and categorizing data like competitor mentions and objections into a structured format (e.g., a spreadsheet), product marketers can quickly identify trends and prioritize their roadmap and messaging.

An LLM analyzes sales call transcripts to generate a 1-10 sentiment score. This score, when benchmarked against historical data, became a highly predictive leading indicator for both customer churn and potential upsells. It replaces subjective rep feedback with a consistent, data-driven early warning system.

The context from daily sales and support calls is incredibly valuable but often ephemeral. A powerful, underutilized agent use case is to transcribe these calls and feed them to an LLM to automatically generate sales coaching notes, customer FAQs, testimonials, and even new keyword-targeted landing pages based on customer language.

Use AI tools to analyze sales call transcripts to see if new messaging is being adopted by sales and how it resonates with customers. By running prompts to check for specific keywords, you can quantify message adoption, discover what's working, and pinpoint areas where sales needs more training.