According to a recent Clari Labs report, 67% of revenue leaders missed their 2024 targets. Yet 91% believe they’ll hit 2025. What’s behind the optimism? AI.
But AI alone won’t close the gap. Billions are being spent, but few are seeing meaningful impact because CROs and CIOs aren’t aligned. Trusted data, connected strategy, and unified execution are the missing links.
Much of this confidence is due to investments in artificial intelligence, with organizations allocating billions annually to AI. But it’s estimated three-quarters of these companies have yet to realize the full value of their investments. Put another way: simply bolting on AI to an existing solution won’t magically make it run more efficiently, despite promises to the contrary.
The potential for AI to transform revenue is massive — enabling smarter prioritization, early risk detection, and enhanced forecasting based on critical patterns. Yet, realizing this potential increasingly requires deep strategic alignment between Chief Revenue Officers (CROs) and Chief Information Officers (CIOs). Leading organizations are witnessing these executives collaborate to create a new paradigm: revenue growth powered by data-driven workflows, AI capabilities, and comprehensive Revenue Context that tracks activities, timing, and outcomes.
As CIOs assume more active roles in driving revenue outcomes, they face critical questions:
- What is the current state of enterprise AI adoption for revenue operations?
- Where are industry peers encountering challenges, and where are they succeeding?
- What foundational elements are truly necessary to scale AI for revenue growth?
To address these questions, Clari Labs conducted comprehensive research across hundreds of major enterprises to understand the current state of AI adoption at the enterprise level.
Align Now or Fall Behind
CROs need insights. CIOs own the infrastructure. But if they’re not working from the same playbook, AI becomes a fragmented experiment, not a driver of revenue. Deep alignment and collaboration between CROs and CIOs is fundamental to unlocking AI's full potential for revenue growth and enterprise transformation.
This partnership ensures AI strategies align with business objectives, establishes necessary data infrastructure, enables successful implementation, and delivers sustained long-term value. These findings indicate that AI adoption lags at many companies precisely because CIOs and CROs operate in silos rather than as strategic partners.
CROs leveraging AI agents to do more analysis and reporting need reliable data, putting pressure on the CIO to provide a new level of data efficacy to their organization. Revenue teams are also facing an ultimatum — use AI or fall behind competitors.
As modern enterprises continue to scale their revenue operations, the CRO and CIO need to create a unified strategy that effectively leverages AI agents supported by trustworthy data to drive revenue.
How the CRO and CIO can Drive Predictable, Repeatable Growth
While CROs and CIOs bring more AI agents and assistants to their teams, leaders across their organization are doing the same. As more AI tools that track marketing activity, manage accounts, create workflows and support day-to-day functions are brought on, organizations face two major challenges:
- Ensuring trustworthy data for revenue forecasting
- Seamlessly integrating technology at scale
Historically, these two areas have been owned by the CIO, and relied upon by the CRO. But they must become a priority for both functions as they are crucial for driving growth and scaling the business. If the organization lacks a single source of truth, it’s difficult for CROs to trust in the data they’re using for revenue forecasting. This is where it’s essential for the CRO and CIO to have an aligned AI strategy in order l to maximize predictable and repeatable growth.
Simply put, CROs need a single source of trusted data to make AI agents and assistants work. The CIO needs to facilitate that data source and enable pipeline visibility.
Achieving Revenue Context to Maximize AI Value
Many enterprises face a lack of insight into the context around both successful and unsuccessful deals. Contextual data is crucial to understanding what is working, then repeating that success — especially when it comes to AI.
Clari Labs’ recent report, The State of Revenue, 2025: Insights from 10 Million Opportunities found that enterprises implementing AI close new logo deals 20% faster than they did two years ago. Leaders across enterprises are taking note, but they aren’t always able to immediately onboard new AI tools.
A few of the challenges business leaders face in regards to AI adoption:
- Insufficient, or inaccurate data to train genAI tools
- Lack of trust in AI from frontline sellers
- A reliance on generic, rather than bespoke, AI solutions
CROs and CIOs are coming together to drive a new approach to enterprise selling. One that is driven by data, repeatable workflows, and Revenue Context which unifies critical revenue signals from every source — CRM, email, conversations, data warehouses, customer
support, marketing automation, ERP, and more. However, this requires enterprise-wide adoption, not a patchwork of products being used in silos.
Creating a strong culture of AI adoption starts with the CRO and is facilitated by the CIO. As revenue operations and IT continue to converge, these departments share more responsibility for driving the impact of AI on revenue. Enabling AI-first capabilities such as guided selling, intelligent sales assistants, and autonomous forecasting will set businesses apart.
Using AI to Drive Growth at Scale
While some organizations face adoption challenges, many are bringing on so many AI tools and agents that AI use becomes more of a disconnected group of experiments, rather than a system that drives growth and can scale with the business. Scaling AI requires a solid data foundation, consistent standards for quality and security, strict governance, and finally alignment with revenue targets.
- Data foundation: A recent Clari Labs report found that 67% of revenue leaders don’t trust their data. Finding a Revenue Orchestration Platform (ROP) with the capabilities necessary to create the data foundation that revenue leaders need to accurately forecast is essential.
- Consistent Standards for Quality & Security: To trust AI, you need to trust your data. These standards and safeguards are needed to ensure that AI is acting on clean, reliable, and secure data. It’s foundational—the technical baseline that enables trust and integrity in AI outputs.
- Strict Governance: AI doesn’t succeed without structure. Governance defines how your organization enforces processes, aligns cross-functional teams, and measures outcomes. When CROs and CIOs are misaligned, governance breaks down, leading to data silos, stalled execution, and AI initiatives that fail to scale.
- Alignment With Revenue Targets: AI is a strategic powerhouse when it comes to providing actionable information to revenue driving teams. Teams utilizing AI can move faster, but both the humans in the driver’s seat, and their AI assistants require Revenue Context to create a functional system.
Bringing together the CRO and CIO
How can the CRO and CIO come together in order to maximize the benefits of AI Revenue Orchestration? Starting with Revenue Context is the best way to create a foundation for CRO and CIOs to work from. Achieving that context sets the stage for alignment, scaling, and maximizing AI value.
Find out how global enterprises are closing the AI-to-outcomes gap with Revenue Context as their foundation. Download Revenue Context: The Missing Link for Enterprise-Scale AI.