A recent survey by PwC shows that 81% of C-suite executives say their companies will need at least another year to see "substantial returns" from artificial intelligence (AI) investments—that is, gains beyond efficiency improvements. The survey is part of PwC's broader study of corporate executives' views on policy, risk, and growth 15 months into the second Trump administration. Dan Priest, PwC's U.S. Chief AI Officer, believes this result places CFOs at the center of the next phase of enterprise AI adoption.

As companies move beyond pilots and productivity gains, financial leaders need to bring discipline to spending decisions, support a few "high-impact projects," and ensure these investments translate into "measurable business value, not just incremental efficiency gains," Priest told CFO Dive.

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

The following is a Q&A between Priest and CFO Dive reporter Alexei Alexis via email, covering PwC's research findings. The conversation has been edited for clarity and brevity.

Q: The report shows that 81% of business leaders say their organizations need at least another year to see "substantial" returns on AI investment beyond efficiency. What is preventing organizations from achieving substantial returns more quickly?

Dan Priest: Most organizations are currently seeing what we expect at this stage of AI adoption: efficiency gains come first, while transformation takes longer to materialize. Substantial returns require a deeper commitment than many companies have made so far, partly because AI is still often used to optimize existing ways of working rather than fundamentally redesigning them.

Many companies are also stuck between pilots and scaling. They have proven that AI can create value in pockets, but they have not yet embedded it into core workflows or rethought how the entire business operates. Early efforts often focus on cost and productivity, but the bigger opportunity lies in reimagining end-to-end workflows, as well as products, services, and customer experiences. Seizing this opportunity requires not just deploying new tools, but also changes to processes, roles, partnerships, and decision-making.

Ultimately, the bigger constraint is organizational, not technological. While data, technology, and talent are still maturing in this new AI-driven world, the real unlock is leadership driving scaled transformation and, ultimately, broader reinvention.

Q: How do companies define "substantial returns"?

Dan Priest: Companies are increasingly defining "substantial returns" as outcomes that go beyond efficiency gains and begin to have a material impact on the business. This includes new revenue streams, margin improvements, faster innovation cycles, or measurable changes in customer experience. It's no longer about doing the same work at lower cost or faster speed—though those are also valuable gains—but about what the business can do and how it competes, which is the ultimate prize.

Currently, most organizations still see value in the form of productivity and cost savings, which are often incremental. The bar for "substantial" is rising, reflecting a demand for enterprise-level impact.

Q: What distinguishes the few companies expecting faster returns from the vast majority seeing longer return cycles?

Dan Priest: Companies expecting faster returns do a few things differently. First, they invest purposefully in foundational elements, mainly around models, data maturity, AI skills, and project execution. We see these attributes strongly correlated with better outcomes. But beyond that, they treat AI as a way to transform the business, not just optimize in pockets. They redesign workflows, embed AI into decision-making, shift value pools, and seek to create new value, not just cut costs.

Equally important, these companies are much more deliberate about focus. Leading organizations are guided by a clear strategy, giving teams the confidence to choose priorities, concentrate AI investment on the highest-value opportunities where they have the conditions to succeed, rather than spreading efforts across disconnected pilots.

By scaling what works and embedding AI into core decisions, they can focus on areas where they can create differentiated value, not just keep pace with the market.

Q: The research also found that 74% of business leaders plan to start or increase AI investment in the next 12 months. Why are most companies increasing AI investment, even though 81% say substantial returns are still more than a year away?

Dan Priest: About 20% of companies are achieving outsized returns. While that's a minority, it shows that AI works when used correctly. These are valuable proof points that investors, boards, and management teams pay attention to. They also create urgency 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 urgency to keep pace. There's recognition that AI will be central to how companies compete in the future. So, even if returns aren't immediate, companies are building these capabilities now.

Despite lingering concerns about ROI, there are enough compelling proof points that organizations are willing to continue investing amid uncertainty. The real challenge is ensuring these investments truly differentiate the business, not just keep pace with everyone else.

Q: What should CFOs prioritize in 2026 to avoid AI investments underdelivering?

Dan Priest: CFOs should first tighten the link between AI investment and business outcomes, not just efficiency. Cost savings show up early, but they can quickly become expected or be offset by competition.

This puts CFOs in a critical position to drive focus and alignment by supporting high-impact AI projects and ensuring these efforts translate into measurable business value—not just incremental efficiency gains.

Priorities are defining clear value cases tied to growth, margin expansion, or better decision-making, and maintaining discipline between where to scale and where to stop. Spreading investment across too many pilots is one of the fastest ways to underdeliver.

They also need to fund the full equation, not just technology. The biggest returns come from embedding AI into core workflows, which requires investment in data, process reengineering, and most importantly—the workforce.

Talent—including capable engineers, deep domain experts, effective change leaders, and strategists who know how to connect it all to how the company competes and wins—is the most critical success factor.