CFOs are being put in an unusual position. They are being asked to accelerate the adoption of AI while at the same time being expected to challenge its economics, manage its risks and demonstrate that the investment is creating real business value.
That tension is becoming increasingly visible. Deloitte’s 2026 CFO Signals survey found that 59% of CFO respondents identified the challenge of balancing pressure to deploy AI quickly with the need to manage risk as the biggest obstacle in building an enterprise-wide AI governance framework.
At the same time, the role of the CFO itself is expanding. IBM’s 2026 global study of finance leaders found that 62% said their responsibilities had grown to include enterprise technology or AI strategy leadership. Yet only a small proportion described their finance organizations as genuinely transformation-ready, with AI consistently embedded into workflows and decision-making at scale.
That gap between expectation and maturity may define the next few years for many finance leaders. It also raises a question that I think deserves more attention: what exactly should the return on AI be?
The easiest business case to build is productivity. Reduce the time required to produce a report, automate reconciliations, draft commentary, process invoices faster or allow managers to answer basic finance questions without waiting for an analyst to extract the data. All of these things matter, and many of them will generate meaningful savings.
But CFOs should be careful not to define the opportunity too narrowly around hours saved.
Gartner reported in 2026 that finance AI investments still lean heavily toward productivity, while a much smaller share are focused primarily on improving decision quality. That imbalance is understandable. Productivity is easier to measure. If a process takes ten hours today and two hours tomorrow, the benefit is visible.
The problem is that productivity eventually reaches a ceiling.
Imagine an FP&A team that reduces the monthly variance-analysis process from five days to one. That is clearly valuable. But now imagine that the faster analysis allows the company to identify margin erosion in a product family early enough to adjust pricing, renegotiate purchasing terms or change the production mix before the quarter closes.
The second outcome is fundamentally different. One makes the finance function more efficient. The other makes the business itself perform better.
This is why I believe finance has one of the most interesting opportunities in enterprise AI. The function sits unusually close to the decisions that determine the economics of the company: pricing, investment, inventory, working capital, hiring, capacity, procurement, product profitability, market expansion and capital allocation.
Finance sees the economic consequences of decisions made across almost every part of the organization. Historically, however, a significant share of the function’s capacity has been consumed by assembling information, extracting data, reconciling systems, rebuilding calculations, preparing presentations and explaining what has already happened.
AI has the potential to compress much of that work dramatically. The more important question is what finance does with the capacity it gets back.
If the result is simply that the same reports are produced faster, much of the opportunity will have been missed. The more valuable outcome is to move finance closer to the point where a decision can still be influenced.
That could mean detecting a margin issue while it is developing rather than discussing it during the following month’s review. It could mean identifying which customers, inventory movements or supplier terms are driving a working-capital deterioration rather than simply reporting that cash conversion has worsened. It could mean allowing management to explore several scenarios during a discussion rather than waiting days for analysts to build and reconcile different versions of a model.
This is where AI starts to become strategically interesting.
KPMG’s 2026 research on AI in finance points in a similar direction. Finance leaders report benefits not only in automation, but also in areas such as decision speed, decision quality and forecasting accuracy. That should influence how CFOs think about their AI portfolios.
There will always be a place for efficiency projects, particularly in transactional finance where processes are repetitive and the economics are straightforward. But some of the highest-value opportunities are likely to sit much closer to the decisions that determine revenue, margin, cash generation and return on capital.
The way we think about ROI therefore needs to mature as well.
Traditional technology ROI often begins with a relatively simple equation: implementation cost against labor savings. That remains important, but for AI it is incomplete.
There is efficiency value: how much work was eliminated or accelerated. There is capacity value: whether finance professionals are able to redirect time toward activities that matter more. There is operational value: improvements in collections, leakage, forecasting, close cycles, working capital or cost. And then there is decision value: whether the organization made a materially better decision because it had better information, analysis or insight at the right moment.
That final category is harder to measure, but it can be substantially larger.
A few hundred analyst hours have a relatively finite economic value. Avoiding a poor capital investment, identifying a profitability issue several months earlier, releasing unnecessary working capital or reallocating resources toward a better-performing market can create an impact that is many times greater.
This is where the CFO has an important role to play. The CFO does not need to become the chief AI scientist, but finance leaders increasingly need to become architects of how AI creates value inside the enterprise.
That means continuing to ask the questions CFOs have always been good at asking. What problem are we actually solving? Which economic outcome should change if this works? What evidence will tell us whether it did? What data and processes does the system depend on? Where must human judgment remain? What degree of error is acceptable? At what point should we stop funding something that produces impressive demonstrations but limited business impact?
AI does not make financial discipline less relevant. It makes it more relevant.
The latest adoption data illustrates why. Gartner reported in 2026 that the large majority of finance organizations have implemented or are planning to implement AI, yet only a small percentage currently report very high levels of impact. In other words, the technology is spreading faster than the value.
For CFOs, I do not think that should be interpreted as a reason to slow down. It is a reason to become much more deliberate about where AI is applied and what outcome it is expected to create.
Within a few years, almost every finance organization will use AI in some form. Simply having copilots, agents or automated processes will not be a competitive advantage for very long.
The differentiator will be whether an organization has built the processes, data, governance and management habits required to turn intelligence into action, and whether its people have learned how to use those capabilities to make better decisions.
There will inevitably be plenty of metrics around AI adoption: users, agents, automated processes, hours saved and models deployed. CFOs should monitor them, but none of those measures represents the ultimate objective.
The question I would keep coming back to is much simpler: are we making better business decisions because of this?
If the answer is yes, and those decisions are being made faster, with better information and greater confidence, then the economic value should follow. If the answer is no, we may simply be automating our way around the real opportunity.
