48% of enterprises say their revenue data isn’t ready for AI, as CIOs emerge as new strategic partners in revenue growth
A staggering 87% of enterprises missed 2025 revenue targets despite record levels of AI investment, according to new research from Clari Labs, the data and AI research division of Clari + Salesloft.
The findings expose a growing disconnect between AI ambition and data readiness: Nearly half of enterprises (48%) say their revenue data isn’t AI-ready, and 42% still lack formal governance frameworks to ensure accuracy and control. The risk extends beyond stalled adoption. Companies increasingly base critical business decisions on data that may be incomplete or unreliable. As Revenue AI advances faster than the infrastructure supporting it, CIOs face mounting pressure to modernize their foundations — or risk AI investments that fail to deliver meaningful business impact.
The CIO’s New Mandate: AI-Powered Revenue
Clari Labs surveyed 400 CIOs, CROs, and RevOps leaders at North American enterprises with 1,000+ employees to explore how CIOs are reshaping enterprise revenue with AI. The data shows that even as CIOs emerge as strategic partners in the revenue process, fragmented systems and weak governance continue to stall progress.
“We’re watching revenue evolve into one of the most disciplined systems inside the enterprise,” said 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.”
Key Findings
- AI Investment Is Outpacing Revenue Data Readiness
Despite record AI spending, 87% of enterprises still miss their revenue targets (among enterprises not using Clari + Salesloft). AI acceleration has outpaced enterprise data governance and control. More than half (55%) report conflicting pipeline signals from disconnected data sources, and nearly half (48%) admit their revenue data isn’t AI-ready. As a result, 57% say AI agents still haven’t been fully deployed across revenue operations.
- CIOs Emerge as a Strategic Revenue Partner
Once focused on infrastructure, CIOs now sit at the center of enterprise growth. Nearly two-thirds (64%) 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.
- Data Governance Becomes a Revenue Strategy
CIOs are moving from managing systems to shaping revenue data strategy. Nearly all (96%) of revenue leaders say CIO involvement improves forecast accuracy, and 89% expect their influence to expand even further next year as enterprises prioritize unified data models, control, and accountability.
- Weak Data Governance Erodes Forecast Confidence
Without consistent oversight, data inconsistency becomes a significant barrier to precision and predictability. Over one-third (39%) of organizations recalibrate forecast models only weekly or monthly, and 42% still lack formal governance frameworks to ensure data consistency and accountability.
- RevOps Missing From the Revenue AI Equation
While 91% of IT teams lead AI training and data preparation, only 29% of RevOps teams are top contributors. This imbalance between technical enablement and business execution leaves many AI projects detached from go-to-market realities — limiting adoption, impact, and trust across the field.
- Trust, Not Tech, Defines AI Outcomes
Closer CIO–CRO alignment is becoming the norm, with the majority (61%) now meeting daily or weekly to stay synchronized on revenue data and forecasting. Yet alignment alone isn’t enough. Nearly half (46%) cite accountability and trust as the biggest challenges in these engagements, reinforcing that success depends on shared ownership of revenue systems, data, and outcomes.
From AI Experimentation to Revenue Impact
Enterprises have moved beyond experimenting with AI and now demand clear revenue results. This research underscores why AI cannot compensate for fragmented signals or ungoverned data. Predictable revenue performance requires a disciplined operating model — one that standardizes revenue data, governs AI models, and aligns execution across sales, operations, and technology. When those foundations are in place, AI becomes a true force multiplier for productivity and growth.
Forrester’s Total Economic Impact™ study validates the shift from experimentation to measurable AI returns. Enterprises operating with unified, governed revenue data achieved up to 96% forecast accuracy, increased renewal rates by 20 points, and unlocked millions in expansion and retention value — delivering a 398% ROI and $96.2 million in realized benefits over three years.
Enterprises that invest in revenue data discipline are equipping sellers, managers, and executives to operate with greater confidence and precision — turning AI ambition into predictable growth.