Gartner Survey Shows 31% of Chief Sales Officers Cited Difficulty Proving ROI of AI-driven Tools as a Top Challenge for Sales Objectives in 2026

As AI investment accelerates across sales organizations, chief sales officers (CSOs) are under growing pressure to show measurable business value from the tools and initiatives they are funding. Yet many still struggle to explain where returns are showing up, how to measure them credibly and what kind of impact they should realistically expect from AI in the near term. 

A Gartner survey of 227 CSOs conducted from August through September 2025 found that 31% cited difficulty proving ROI of AI-driven tools as a top challenge to sales objectives in 2026.

From the Gartner CSO & Sales Leader Conference in Las Vegas, we spoke with Sandhya Mahadevan, Sr Director Analyst in the Gartner Sales practice, about why AI ROI remains difficult to prove, what sales leaders get wrong when they rely on traditional ROI formulas alone, and how they can take a more effective approach to evaluating AI investments. 

Journalists who would like to speak with Sandhya regarding this topic can contact Elizabeth.Bishop@gartner.com. Members of the media can reference this material in articles with proper attribution to Gartner.

A: Many sales leaders still try to prove AI ROI as if value should appear quickly, clearly, and in familiar financial terms. However, AI rarely works that neatly, especially early on. Gartner research found 31% of CSOs said difficulty proving ROI of AI-driven tools is a top challenge to achieving sales objectives in 2026, which shows how widespread the issue has become.

AI ROI also depends on many conditions coming together at once. Leaders need the right use cases, realistic expectations, organizational readiness, broad adoption, dependable measurement and gains that add up meaningfully. Even when each of those factors appears reasonably strong on its own, the likelihood of all of them aligning at the same time can still be surprisingly low.

A: A more effective approach is to move beyond narrow, formula-only methods and take a broader view of how AI creates value over time. AI’s impact is often better understood in terms of how it improves productivity, reduces friction and inconsistency, expands the organization’s ability to execute at scale and, in some cases, supports longer-term innovation and growth.

Different AI investments generate different forms of value over time. Early AI use cases typically deliver improvements at the individual productivity level — for example, meeting summarization, drafting support or research assistance — where impact is most often reflected in time saved. However, translating time saved directly into cost savings is not always meaningful. 

Instead, leaders can assess AI’s contribution by linking these gains to previously identified productivity gaps across roles or workflows. As organizations mature in their AI adoption, more advanced use cases extend beyond personal efficiency to increase the overall execution capacity of the sales function. Realizing this type of value typically requires accompanying changes to processes and role design. At this stage, ROI shifts from user-level efficiency to organizational outcomes and can be evaluated through improvements in capacity, coverage, conversion or revenue growth.

A: The first step is to diagnose the ROI barriers that are actually within the leader’s control, like data maturity, user adoption and choosing the right use cases. For many CSOs, that starts with reviewing the current AI roadmap, clarifying what type of ROI they are pursuing and securing CFO support for the approach. Proving ROI is often as much about organizational alignment and expectation setting as it is about measurement mechanics.

Sales leaders should also have full visibility into two metrics: seller productivity and sales capacity. Having a reliable baseline is critical for validating where AI is improving existing performance and assessing where AI could expand commercial reach or create new value streams. They should encourage disciplined exploration of more disruptive use cases, with clear go or no-go criteria for experimentation. The goal is to build a holistic AI ROI narrative and growth story rather than relying only on isolated formulas.