One person now manages RevOps, enablement, data analysis, and CRM administration—functions that previously required 10-15 people—by orchestrating AI agents. This demonstrates a massive leap in productivity and operational leverage made possible by AI.
To create autonomous AI agents, first break a workflow into stages. Manually verify the quality of each stage's output. Once you trust the end-to-end process, package it as a recurring, proactive "skill" that requires only occasional check-ins.
For complex tasks, don't rely on one AI model. A "model council" approach queries multiple models (e.g., Claude, Gemini, ChatGPT) simultaneously, then synthesizes outputs to show agreement, disagreement, and unique findings for more robust decisions.
Use AI with connectors to file storage (Google Drive) or a CLM (Ironclad) to automatically read contracts. The agent extracts key data like amounts and dates, then creates or updates opportunities in your CRM, eliminating a highly manual RevOps task.
Instead of just reporting customer feedback, use AI to analyze transcripts and emails to generate a dashboard that assigns specific, actionable next steps to relevant teams. It answers "What should we do about it?" for product, enablement, and marketing.
As teams adopt AI, individuals create disparate workflows, leading to inconsistency. Solve this by building an organizational skills library. Vetted, high-performing AI workflows are shared, ensuring everyone uses the best-in-class process for common tasks.