FROM INSIGHT TO IMPACT: OPERATIONALIZING AI FOR PREDICTABLE REVENUE

Industry studies suggest nearly 87% of enterprises missed their revenue targets even amid record Al investments. In this candid conversation, Mike Meyer, Chief Information Officer, Clari / Salesloft explains why Al underperforms without a predictive revenue system, what it takes to move from pilots to production, and how leaders can turn Al from informational to operational.

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MIKE MEYER

CHIEF INFORMATION OFFICER, CLARI/SALESLOFT

"Enterprises haven't failed at AI innovation-they just haven't modernized revenue into a system that AI can reliably power."

Industry studies suggest that nearly 87% of enterprises missed their revenue targets. What's the biggest reason behind this gap?

The gap exists because most AI is deployed without a predictive revenue system underneath it. This system is a way of running revenue where data, workflows, and AI work together to con- tinuously anticipate risk, guide the right actions, and help teams make smarter decisions across the entire go-to-market process.

The survey shows that 87% of enterprises missed revenue targets despite record Al invest- ment because Al is operating on fragmented, ungoverned revenue data. Al can't predict or prescribe outcomes when it lacks revenue context, or the connection between actions, signals, and results across the revenue lifecycle. Without that context, Al produces insights that aren't action- able. Enterprises haven't failed at Al innovation; they just haven't yet modernized revenue into a system that AI can reliably power.

Are organizations treating AI as a technology experiment rather than a transformation-and how does business that impact ROI?