AI spending at record highs but enterprises still miss revenue targets
Despite record AI spending, 87 percent of enterprises still miss their revenue targets according to new research from Clari Labs, the research arm of Clari + Salesloft.
The findings suggest that AI acceleration has outpaced enterprise data governance and control. More than half (55 percent) report conflicting pipeline signals from disconnected data sources, and nearly half (48 percent) admit their revenue data isn’t AI-ready. As a result, 57 percent say AI agents still haven’t been fully deployed across revenue operations.
The research surveyed 400 CIOs, CROs, and RevOps leaders at North American enterprises and shows that even as CIOs emerge as strategic partners in the revenue process, fragmented systems, and weak governance continue to stall progress.
CIOs now sit at the center of enterprise growth. Nearly two-thirds (64 percent) of revenue leaders say CIO teams now lead the selection and implementation of forecasting and revenue tools, solidifying their role at the heart of revenue orchestration.
“We’re watching revenue evolve into one of the most disciplined systems inside the enterprise,” says Steve Cox, CEO of Clari + Salesloft. “AI doesn't just need data; it needs context. The winners of the next decade will be the companies that trade fragmented signals for a unified revenue truth. True revenue predictability depends on every forecast, deal, and action being grounded in trusted, governed data that is aligned across the CIO, CRO, and RevOps.”
Nearly all (96 percent) of revenue leaders say CIO involvement improves forecast accuracy, and 89 percent expect their influence to expand even further next year as enterprises prioritize unified data models, control, and accountability.
Closer CIO–CRO alignment is becoming the norm too, with the majority (61 percent) now meeting daily or weekly to stay synchronized on revenue data and forecasting. Yet alignment alone isn’t enough. Nearly half (46 percent) cite accountability and trust as the biggest challenges in these engagements, reinforcing that success depends on shared ownership of revenue systems, data, and outcomes.
However, without consistent oversight, data inconsistency becomes a significant barrier to precision and predictability. Over one-third (39 percent) of organizations recalibrate forecast models only weekly or monthly, and 42 percent still lack formal governance frameworks to ensure data consistency and accountability.
You can get the full report from the Clari Labs site.
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