CFOs may be overestimating their AI maturity

More than a third of CFOs and VPs consider their AI maturity "leading." The problem is, only a fraction of managers agree.

AI maturity is the single strongest predictor of revenue growth, according to the Pigment Uncertainty Index. Companies at the highest maturity stage grew 18.1% year over year – nearly three times the rate of early-stage organizations (6.2%). 

But knowing where your business actually sits on that curve may be harder than it looks. The same study found that, the more senior a finance leader is, the more likely they are to overestimate it.

If senior leaders are overestimating their current AI capabilities, the strategic decisions they're making may be based on capabilities their teams don't yet have.

The Pigment Uncertainty Index covers financial performance, AI adoption, and planning confidence across 2,000 finance leaders in the US, UK, France, and Germany. 

Explore the Q1 2026 report → 

Senior leaders and their teams aren't operating in the same reality

Throughout the Uncertainty Index, seniority correlates with a more favorable view of AI's impact, higher planned investment, and fewer perceived barriers to success. Senior leaders report seeing greater benefits from AI across productivity, decision quality, and planning confidence. These leaders plan to increase their AI budgets by larger margins, while also being less likely to identify obstacles like data quality issues, lack of internal expertise, or cultural resistance as significant challenges.

This pattern is consistent with what researchers at the Wharton School found when they surveyed business leaders at U.S. companies with revenues above $50 million. Nearly half of executives (45%) reported significantly positive ROI from their initial AI investments. Among middle managers, that number dropped to 27%. On the question of whether their organization was adopting AI faster than competitors, 56% of executives said yes. Among middle managers, only 28% agreed.

The question is whether senior leaders are seeing something on the outside that their teams haven't caught up to yet or projecting an internal reality that isn't fully there.

The biggest AI gains happen where leaders tend to see themselves: at the top of the maturity curve

For most of the maturity curve, AI delivers real  improvements, like reducing manual work and increasing productivity. Although these gains are exciting, they show up consistently at every maturity level, which is precisely what makes them a poor signal of how far along you actually are. Feeling like AI is working and having AI work at scale look identical from a distance. 

But the real transformative gains come later. Quality of decision-making jumps 19 points between advanced and leading organizations. Confidence in planning outcomes climbs 21 points between developing and leading. A finance team that crosses that final threshold works in a fundamentally different way.

Leading firms are the only cohort whose actual growth exceeded expectations, with every other group falling short of their own projections. A CFO who believes their organization is already at the leading edge is planning for outcomes that only leading firms will deliver. Their teams are experiencing 50% decision quality, but the investment is sized for 69%.

The urgency driving AI investment may also be inflating leaders' view of their readiness

Senior leaders are dramatically more likely to say that uncertainty has increased over the past six months. Among VPs and CFOs, 51% say uncertainty is “somewhat” or “much higher.” This drops to 43% for directors and 36% for managers and senior managers.

The same leaders who feel the most uncertain about the external environment are also the most confident about their organization's internal AI capabilities. One explanation is structural. Executives tend to use AI for high-level synthesis, strategic drafting, and decision support, which are tasks where the technology performs well. Middle managers deploy it in messier territory, such as workflows built over years, teams with uneven technical comfort, and outputs that have to be consistently right. When a tool fails, typically only one group has to cope with the aftermath. 

Senior leaders may have a clearer view of macro-level threats that haven't yet translated into operational disruptions, which heightens their sense of urgency around AI as a response. But that urgency may also be inflating their assessment of how far along their organization actually is.

For leaders feeling both of those pressures simultaneously, the instinct to move faster is understandable. The risk is that accelerating based on an inaccurate picture of your starting point leads to investments that don’t land where intended.

What gets measured shapes what is reported

Most organizations have an understanding of whether AI tools are being used, but access data alone can't tell you whether those tools are changing how work gets done. A high adoption rate can coexist with minimal impact on planning quality or decision speed. When leadership interprets the former as evidence of the latter, misalignment deepens.

The Wharton researchers found that, over the past year, nearly two-thirds of executives say they have become "much more positive" about generative AI. Among middle managers, only 39% say the same, and middle managers are 64% more likely than their senior colleagues to describe themselves as "cautious." That's not opposition to AI. It's a signal that the ground-level experience is different from what's being reported up the chain.

Finance leaders need to create structured channels for teams to report on what's working, what isn't, and where the distance between executive expectations and day-to-day reality is widest. The Wharton study calls these "upward feedback loops," which it treats as a precondition for transformation. The goal isn't to temper executive ambition so much as it is to calibrate that ambition to the actual starting point. That means adopting feedback infrastructure that surfaces how AI is actually being experienced at the operational level. This kind of visibility is especially important given how directly the data links maturity level to outcomes. 

Organizations that invest based on an accurate understanding of their current state are better positioned to sequence their efforts effectively and prioritize the capabilities that will unlock the most value.

An honest starting point is the most valuable asset in an AI strategy

Senior leaders in finance are more optimistic, more anxious, and more likely to plan larger investments and less likely to hear from the people carrying those investments out. That combination makes accurate self-assessment harder, and increases the cost of getting it wrong.

While it’s true that AI is reshaping functions like forecasting and go-to-market strategy, the people making the biggest bets on AI may have the least accurate view of where their organization stands. Getting it right starts with an honest internal assessment to test strategic optimism against what's happening on the ground.

For finance leaders who want to understand whether their view of their organization's AI maturity aligns with reality, Pigment's AI Maturity Assessment offers a structured way to benchmark your current state. 

If you’d like to explore the full dataset behind these findings, the Q1 2026 Pigment Uncertainty Index is available now.