In the latest episode of the RevOpsAF podcast, Guillaume Jacquet, CEO and Co-Founder of Vasco, joined host Matt Volm to explain why AI agents often fail at revenue forecasting and why the fix lies squarely in the hands of RevOps teams.
Jacquet argued that the core problem isn't AI hallucination—it's a lack of proper data grounding. He shared a startling example: an AI agent confidently reported that revenue was on track, while the company was actually missing its target by 21%. The disconnect, he said, stems from AI models building reports on broken data foundations.
To bridge this gap, Jacquet introduced a four-part framework that can boost AI accuracy from as low as 11-30% up to approximately 98%. He emphasized that operators should treat AI agents like new hires, not simple tools. Just as a new employee needs clear processes, clean data, and oversight, AI agents require robust RevOps structures to deliver reliable results.
The episode underscores a growing trend: as companies deploy AI for revenue intelligence, the role of RevOps becomes critical in ensuring the AI is built on accurate, well-governed data.
"The problem isn't hallucination. It's grounding."
Key takeaways from the discussion:
- AI agents fail when they lack access to clean, unified revenue data.
- A four-part framework can dramatically improve AI accuracy.
- Treating AI agents as hires—with onboarding and governance—is essential for success.
The full episode is available on the RevOpsAF podcast channel.