Eighty-one percent of C-suite leaders say their companies are at least a year away from seeing “meaningful returns” from artificial intelligence investments beyond efficiency gains, PwC found in a recent survey.

The findings, part of a broader look at executives’ views on policy, risk and growth 15 months into the second Trump administration, place CFOs at the center of the next phase of corporate AI adoption, according to Dan Priest, PwC’s U.S. chief AI officer.

As companies move beyond pilots and productivity gains, finance leaders will need to bring discipline to spending decisions by backing a select number of “high-impact initiatives” and ensuring those investments translate into “measurable business value, not just incremental efficiency,” he told CFO Dive.

Despite the delayed payoff, AI investment momentum remains strong. PwC found that most organizations plan to maintain or increase spending on AI during the next year.

CFO Dive: According to the report, 81% of business leaders say their organization is at least a year away from seeing “meaningful” AI investment returns beyond efficiency. What is preventing organizations from realizing meaningful AI returns sooner?

Dan Priest: Most organizations are seeing what we’d expect at this stage of AI adoption: efficiency gains first, with transformation taking longer to materialize. Meaningful returns require a deeper level of commitment than many companies have made so far, in part because AI is still often used to optimize existing ways of working rather than fundamentally redesigning them.

Many companies are also stuck between pilot and scale. They’ve proven AI can deliver value in pockets, but haven’t yet embedded it into core workflows or rethought how work gets done across the business. Early efforts tend to focus on cost and productivity, while the larger opportunity lies in reimagining end-to-end workflows as well as products, services and customer experiences. Realizing that opportunity requires more than deploying new tools. It calls for changes to processes, roles, partnerships and decision making.

Ultimately, the bigger constraint is organizational, not technical. While data, tech, and talent are still maturing in this new AI-powered world, the real unlock comes from leadership driving transformation at scale and ultimately broader reinvention.

CFO Dive: How are companies defining “meaningful returns”?

Dan Priest: Companies are increasingly defining “meaningful returns” as outcomes that go beyond efficiency gains and start to materially impact the business. That includes new revenue streams, improved margins, faster innovation cycles, or measurable changes in customer experience. It’s less about doing the same work cheaper or faster, although those are valuable benefits, but evolving what the business can do and how it competes is the ultimate prize.

Most organizations today are still seeing value in the form of productivity and cost savings, which tend to be incremental. The bar for what’s “meaningful” is rising and reflects the need for enterprise-level impact. 

CFO Dive: What distinguishes the small number of companies expecting returns sooner from the vast majority seeing a longer timeline?

Dan Priest: The companies expecting faster returns are doing a few things differently. First, they’ve invested in the basics in targeted ways, mainly around the models, data maturity, AI skills, and program execution. We see these attributes being strongly linked to better outcomes. But beyond that, they’re treating AI as a way to transform the business, not just optimize it in pockets. They’re redesigning workflows, embedding AI into decision-making, transforming value pools and looking for ways to create new value, not just cutting costs.

Just as important, these companies are far more deliberate in where they focus. Leading organizations are guided by a clear strategy that gives teams confidence in picking their spots by focusing AI investments on the highest-value opportunities where they’re positioned to be successful, rather than spreading efforts across disconnected pilots.

By scaling what works and embedding AI into core decision-making, they’re able to focus on areas where they can create differentiated value, not just keep up with the market.

CFO Dive: The research also found that 74% of business leaders plan to begin or increase AI investment over the next 12 months. Why are most companies increasing AI investment even though 81% say meaningful returns are still more than a year away?

Dan Priest: About 20% of companies are achieving outsized benefits. While this is a minority percentage of firms, it means AI works when used the right way. These are valuable proof points that investors, boards, and management teams pay attention to. And they create an imperative for everyone else to figure it out. Not investing is not a good option.

So companies are increasing AI investment because they feel a real strategic imperative to keep pace. There’s a recognition that AI is going to be central to how businesses compete going forward. So even if the returns aren’t immediate, companies are building those capabilities now.

While there are still concerns about ROI, there are enough compelling proof points that organizations are willing to keep investing despite that uncertainty. The real challenge is making sure those investments actually differentiate the business, rather than just keeping up with everyone else.

CFO Dive: What should CFOs prioritize in 2026 to avoid underdelivering on AI investments?

Dan Priest: CFOs should start by tightening the link between AI investments and business outcomes, not just efficiency. Cost savings show up early, but they can quickly become expected or competed away.

That puts CFOs in a key position to drive focus and alignment by backing high-impact AI initiatives and making sure those efforts translate into measurable business value, not just incremental efficiency.

The priority is to define clear value cases tied to growth, margin expansion, or better decision-making, and to be disciplined about where to scale versus where to stop. Spreading investments across too many pilots is one of the fastest ways to underdeliver.

They also need to fund the full equation, not just the technology. The biggest returns come when AI is embedded into core workflows, which requires investment in data, process redesign and, most importantly, the workforce.

Human talent — capable engineers, deep domain specialists, effective change leaders, and strategists who know how to connect this all back to the way companies compete and win — is the most critical success factor.