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.

Instead of relying on subjective feedback from account executives, Vercel uses an AI agent to analyze all communications (Gong transcripts, emails, Slack) for lost deals. The bot often uncovers the real reasons for losing (e.g., failure to contact the economic buyer) versus the stated reason (e.g., price).

Every customer call is a potential blog post. An AI workflow systematically redacts all sensitive and identifying information from call transcripts, then rewrites the core use-case discussion into an SEO-optimized article. This creates a scalable content machine fueled by real customer problems, generating thousands of posts.

A product manager with limited coding skills used Claude to generate a step-by-step guide and the final Python script to scrape 34,000 Reddit conversations. This allowed him to gather unfiltered, external feedback on a new product initiative, avoiding internal company bias.

The high-volume feedback during a mastermind "hot seat" can be overwhelming. A simple solution is to record the audio, run it through an AI transcription service, and generate a structured document. This creates an actionable summary, ensuring valuable insights are captured and not lost after the event.

A primary AI agent interacts with the customer. A secondary agent should then analyze the conversation transcripts to find patterns and uncover the true intent behind customer questions. This feedback loop provides deep insights that can be used to refine sales scripts, marketing messages, and the primary agent's programming.

Tim McLear's journey to automate metadata logging began with a single Python script. As its value became clear, he evolved it into a robust REST API service running on a dedicated machine. This service now handles various metadata tasks for his entire film production team, demonstrating a clear path from solo experiment to shared infrastructure.

Using plain-English rule files in tools like Cursor, data teams can create reusable AI agents that automate the entire A/B test write-up process. The agent can fetch data from an experimentation platform, pull context from Notion, analyze results, and generate a standardized report automatically.

Instead of guessing keywords, an LLM analyzes customer call transcripts to identify the exact terms customers use to describe their needs. These keywords are then automatically added to Google Ads campaigns, creating a closed-loop system that ensures marketing spend is aligned with the authentic voice of the customer.