Only 7% of CFOs Report High AI Returns as 84% of Spending Misses the Mark

This imbalance is a fundamental reason the ROI gap persists. Many finance organizations are funding incremental efficiency gains while underinvesting in AI applications that are tied directly to revenue growth, strategic decision-making, and competitive differentiation . Gartner's message was blunt: CFOs have been mistaking AI deployment for value creation, and that framing needs to change urgently .

The three-phase roadmap for finance AI

To help CFOs escape the pilot trap and build toward meaningful returns, Gartner recommended a structured three-step approach :

1. Set the vision and assess maturity — Finance leaders must first define a clear vision for what an AI-enabled finance function should look like. That vision should answer three questions: what is the desired end state, how will AI help achieve enterprise objectives, and what specific value will AI deliver to the business? A maturity assessment then helps identify capability gaps that must be closed before AI can deliver that value .

2. Build the roadmap — Once the vision and maturity baseline are clear, CFOs should translate them into a concrete roadmap for finance AI adoption. This roadmap needs to span culture, governance, skills, and data—not just technology—and should identify a focused portfolio of use cases to prioritize, pilot, and eventually scale .

3. Execute and scale use cases — The final phase moves from planning into disciplined execution. Rather than chasing dozens of disconnected pilots, finance teams need to scale a smaller number of prioritized use cases that have a clear path to realized business value .

Beware the 'accidental factory' of too many pilots

One of Gartner's sharpest warnings at the symposium was aimed at a common failure pattern: the "accidental factory." This occurs when organizations treat AI as a collection of individual tools rather than an interconnected system, leading to uncontrolled pilot proliferation with no clear path to production .

The numbers underscore the severity of the problem. Data presented in Gartner-linked material during the symposium noted that 59% of AI initiatives fail to make it into production, leaving potential value locked permanently in the pilot phase . Instead, Gartner advised CFOs to limit active pilots, focus on use cases with accessible data and fast time-to-value, and build governed, integrated AI systems that can actually scale .

Don't wait for efficiency to chase higher-value outcomes

Perhaps Gartner's most counterintuitive piece of advice was that productivity-led efficiency use cases should not be treated as a prerequisite for pursuing higher-value AI outcomes. The firm explicitly urged CFOs to look beyond automation of existing tasks and invest directly in use cases tied to material business problems—even if those projects look riskier or harder to measure with traditional ROI formulas .

Speaking at the symposium, Gartner analysts told CFOs to stop looking for a single ROI formula and instead build a balanced portfolio of AI bets: productivity use cases that automate routine tasks, targeted process improvements that optimize specific workflows, and selective transformational bets that could reshape business models . The travel analogy Gartner used was memorable: routine trips (productivity gains), targeted expeditions (process improvements), and ambitious voyages (transformation) all belong in the portfolio, but they serve vastly different purposes and require different evaluation criteria .

The maturity framework beneath the roadmap

Underpinning Gartner's three-phase roadmap is a broader set of AI maturity dimensions that go far beyond simple adoption metrics. The framework covers seven capability areas: strategy, value, organization, people and culture, governance, engineering, and data .

For CFOs, the practical implication is clear. An organization cannot simply buy AI tools and declare maturity. Real progress requires systematic investment across all seven dimensions—building a business-aligned AI strategy, governing data properly, upskilling existing finance talent, and creating organizational structures that support AI at scale rather than in isolated experiments . The organizations seeing the strongest returns, Gartner noted, were those deploying AI intentionally across customer, product, and decision-making use cases, not those simply spending the most money .

The symposium's bottom line: finance has adopted AI faster than it has learned to profit from it. Closing the gap requires CFOs to rebalance spending, impose structure on their AI portfolios, and measure success by realized business outcomes—not by how many tools have been deployed.