Over the course of 2026, I’ve talked to over 1,000 CFOs, and one trend keeps striking me: confidence is slipping. And this sentiment isn’t limited to those in my network. A recent survey by the Richmond Federal Reserve Bank found that CFO optimism about the overall U.S. economy has dropped to an average rating of 60.6 on a scale of 100. Persistent cost pressures, like the 3.5% June inflation rate, are a major reason why.
From what I’ve heard, these financial headwinds have snowballed throughout the course of the year, meaning they weren’t top of mind during H1 planning. Now, as these economic uncertainties become more permanent, CFOs are reprioritizing for H2 and determining the best way to navigate volatility while still delivering expected results. The first lever to do this is usually cost.
Fewer Resources, Same Expectations
The most obvious way to rethink financial plans is to evaluate what can be reallocated. In my talks with CFOs, the two most common cuts are usually:
- Cloud spend: Many finance leaders look to consolidate cloud spend across vendors so they can redirect that part of the budget toward other initiatives.
- Headcount: A lot of the CFOs I’ve spoken with have deprioritized headcount expansion. In fact, financial activities have shed 14,000 jobs as of July and have lost around 121,000 jobs since the sector’s high point in May of 2025.
The challenge with cutting these areas of spend is that they typically limit capacity in an otherwise demanding market. Economic conditions require finance teams to revisit assumptions and reforecast as soon as the market shifts. But the current reality, with fewer resources, is that by the time you report, the numbers are already stale.
In short, market volatility is creating an impasse. You need to reduce costs and deal with increased operational demands. Solve for one, and you make the other harder. That’s where most boards and CEOs turn to a different solution: AI.
Leadership is putting pressure on CFOs to improve productivity using these autonomous tools. This makes sense, in theory. Cut a few things here and there and use AI to supplement. Active AI use has already more than doubled over the past two years across the finance function. But that’s not what’s happening in practice.
AI’s Reality Check
Most financial organizations expect AI to fundamentally change workloads. The stark reality, though, is that only around 23% of finance organizations say their AI investments are exceeding expectations. A gap between what the technology can offer and how businesses adopt it drives this disconnect.
Certain finance processes follow rigid rules. Take reconciliations or consolidations – there is one clear answer and one right way to get there. And AI is not yet at that point. It’s probabilistic. If you ask the same question over and over, you’ll likely get a slightly different response each time. In finance, 99% accuracy is 0% trust. While the issue could seem like AI’s place in finance, it isn’t. It’s applying probabilistic AI to workflows that require deterministic outputs.
The other problem, aside from use cases, is setup. Organizations need the right data and governance infrastructure to power this technology. Right now, they have ambitions, hear the promises, and set unrealistic expectations, instead of standardizing the groundwork that supports AI.
As a result, organizations fail to create the capacity they need. Yet the expectations to do more with less and adopt AI remain the same. So, what’s really needed is a way for CFOs to operationalize it successfully – one that allows finance leaders to scale productivity while cutting unnecessary costs.
AI That Finance Can Trust
Out of all the CFOs I’ve talked to, the ones getting the most out of their AI investments have a few things in common:
- They are deliberate with adoption. CFOs should look for places that actually need AI. In other words, don’t fix what isn’t broken. If a workflow would benefit from AI, or if it requires that kind of speed to address market conditions, then add it. If it’s working, then leave it be.
- They develop governance guidelines and a single source of truth. The best AI is only as good as the data it can access. Implementing a governed data model, with the appropriate permissions, ensures the most accurate outputs with the lowest risk to the security of the organization’s financial information.
- They continuously explore future use cases. This means that as AI evolves, use cases should too. While AI is typically best at forming narratives, dissecting reports, and creating analyses, it may get to the point where it can accurately perform deterministic procedures. When that is the case, CFOs should jump on the opportunity.
A foundation like this one is non-negotiable for AI in modern markets. Done right, it gives finance teams capacity and meets leadership expectations. That’s what allows AI to move from answering questions to actually running background processes.
Turning Constraint into Capacity
Economic uncertainty isn’t going away. That means finance executives must adjust their H2 plans. While minimizing headcount and reducing cloud spending are popular approaches, they leave organizations with a different challenge: accomplishing the same work with less capacity.
In these cases, CEOs and boards often turn to AI. Yet deploying this tool effectively requires a change in processes. Enterprises need to define clear use cases, governance policies, and future workflow opportunities.
If finance leaders create this foundation, they can start meeting the expectations of the boards pushing for AI investments, while tackling economic volatility and scaling productivity. They’ll fundamentally change how their organization works.
ABOUT THE AUTHOR:
Alok Ajmera is President and CEO of Prophix.
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Tags: Accounting, cfo, economy, Technology
