Gartner: 31% of CSOs Say Proving AI ROI Is Their Top Challenge

What happened

A Gartner survey of 227 chief sales officers, conducted from August through September 2025, found that 31 percent cited difficulty proving the ROI of AI-driven tools as a top challenge to achieving their sales objectives in 2026. The findings were presented on May 19, 2026 at the Gartner CSO & Sales Leader Conference in Las Vegas by Dan Gottlieb, VP Analyst in the Gartner Sales practice.

The data arrives at a moment of acute tension for sales organizations. Enterprises are accelerating AI investment across revenue functions — tools for conversation intelligence, forecasting, coaching, and pipeline management are being deployed at scale — but the ability to credibly demonstrate that these investments produce measurable commercial outcomes is lagging behind adoption. As Gottlieb framed it during the conference, leaders need the right use cases, realistic expectations, organizational readiness, broad adoption, dependable measurement, and gains that add up meaningfully in order to successfully achieve AI ROI.

The 31 percent finding does not exist in isolation. It was released alongside a companion study showing that AI saves sellers an average of 4.8 hours per week, yet 72 percent of sales organizations fail to reinvest that time into high-value activities. Together, these data points describe a structural problem: sales organizations are deploying AI tools that demonstrably create capacity, but they lack the systems to convert that capacity into the outcomes required to justify continued investment.

Why it matters for practitioners

The ROI gap Gartner identifies is not an abstract measurement problem. It is a practical barrier that shapes budget decisions, headcount approvals, and technology procurement across the revenue stack. For sales enablement leaders, competitive intelligence teams, and revenue operations professionals, the implications are direct.

1. The burden of proof has shifted to enablement. When nearly a third of CSOs say they cannot prove AI ROI, the pressure flows downward to the teams responsible for operationalizing AI tools. Enablement leaders must now build measurement frameworks that connect AI-driven time savings and intelligence outputs to commercial results — win rates, deal velocity, average contract values, and pipeline conversion. Without these connective metrics, AI tool budgets become vulnerable in the next planning cycle.

2. Use-case specificity is the path to provable ROI. Gartner's conference highlighted five sales AI use cases that are producing measurable productivity gains: AI-powered account and buyer intelligence, lead management, deal orchestration, sales coaching, and pipeline and forecast management. The pattern is clear — organizations that deploy AI against specific, bounded workflows can measure the impact more credibly than those pursuing broad, undifferentiated "AI transformation" programs. Competitive intelligence for sales teams is a natural fit for this approach: competitive deal preparation is a specific activity with measurable downstream impact on win rates.

3. The ROI question reframes vendor evaluation. Buyers evaluating revenue intelligence platforms are increasingly asking not just "what does your tool do?" but "how do I prove to my CSO that your tool works?" Vendors that can provide built-in ROI measurement — usage analytics tied to deal outcomes, before-and-after benchmarks on competitive win rates, time-to-close improvements — will differentiate against those that stop at feature delivery. The 31 percent finding is a signal that procurement conversations are shifting from capability evaluation to outcome verification.

4. The measurement gap is a competitive enablement opportunity. If nearly a third of sales organizations cannot prove AI ROI, the organizations that can are creating differentiation at the leadership level. Competitive enablement programs that systematically track how competitive intelligence consumption maps to deal outcomes — battlecard views before competitive wins, intelligence briefings correlated with larger deal sizes — are building the exact measurement infrastructure that CSOs are demanding. The organizations that close the measurement gap first gain both internal credibility and external competitive advantage.

Key details

Survey: 227 CSOs surveyed August–September 2025
Presented at: Gartner CSO & Sales Leader Conference, May 19, 2026, Las Vegas
31% of CSOs cited difficulty proving ROI of AI-driven tools as a top challenge for 2026 sales objectives
4.8 hours average weekly time saved by AI per seller (companion study)
72% of sales organizations report low reinvestment of AI time savings (companion study)
2.2x more likely to exceed customer growth goals when time savings are reinvested
3.1x more likely to exceed lead-to-opportunity conversion goals when time savings are reinvested
5 AI use cases identified for improving sales productivity: account/buyer intelligence, lead management, deal orchestration, sales coaching, pipeline/forecast management
Key speaker: Dan Gottlieb, VP Analyst, Gartner Sales practice
73% of B2B buyers avoid suppliers who send irrelevant messaging (related conference finding)

Market implications

The 31 percent ROI gap identified by Gartner will shape technology procurement and vendor strategy for the remainder of 2026. Sales AI is no longer in the "should we adopt?" phase — it is firmly in the "prove it works" phase. This transition has significant consequences for how revenue technology vendors position themselves, how sales enablement teams justify their programs, and how competitive intelligence platforms demonstrate value.

For the revenue intelligence market specifically, this data creates both risk and opportunity. The risk is straightforward: if a third of CSOs cannot prove ROI on their AI investments, renewal rates and expansion deals will face scrutiny. The opportunity is that vendors who solve the measurement problem — who can demonstrate clear, auditable connections between their platform's outputs and commercial outcomes — will capture disproportionate market share from competitors that treat measurement as an afterthought.

The data also validates a structural shift in how competitive enablement programs should be designed. The era of competitive intelligence as a "nice to have" content function is ending. In a world where CSOs are demanding provable ROI from every AI investment, competitive intelligence teams must operate with the same outcome-orientation as demand generation or sales development: measurable inputs, trackable activities, and attributable revenue impact. Programs that can demonstrate specific, quantified contributions to deal outcomes — competitive win rate improvements, reduced time-to-close on competitive deals, larger average deal sizes when intelligence is consumed — will not only survive the ROI scrutiny but will be positioned as examples of AI investment done right.

Gartner's framing of the five productive AI use cases — account intelligence, lead management, deal orchestration, coaching, and forecasting — provides a practical roadmap for organizations seeking to close the ROI gap. Rather than deploying AI broadly and hoping the numbers work out, the data suggests that concentrated investment in specific, measurable workflows produces the provable outcomes that CSOs require. For competitive intelligence practitioners, this is a mandate to position CI as a specific, measurable use case within the broader AI portfolio — not a diffuse capability, but a targeted workflow with quantifiable impact on revenue.

Related resources

Sales Enablement — how enablement programs must evolve to close the AI ROI gap
Revenue Intelligence — the category facing the most direct ROI scrutiny from CSOs
CI for Sales Teams — practical guide for positioning competitive intelligence as a provable AI use case
Competitive Enablement — building measurement infrastructure that demonstrates AI-driven competitive impact