CFOs can win by deploying AI strategically, Gartner says

Gartner said chief financial officers can gain a competitive advantage through strategic AI deployment rather than higher AI spending. The finding is based on an analysis of 101 companies identified as efficient growth leaders.

The research found that companies with stronger corporate performance were more likely than their peers to use AI across customer, product, and decision-making activities, rather than treating spending levels as the main indicator of success.

Michelle Carlsen, Director Analyst in the Gartner Finance practice, said the data showed a clear distinction between the level of investment and how organisations apply it.

"Simply spending more on AI does not, by itself, equate to better business outcomes," Carlsen said.

Gartner compared efficient growth leaders with a control group of companies with similar industry and revenue profiles. The biggest differences appeared in how the two groups framed and connected their AI investments.

Among the efficient growth companies in the analysis, 46% deployed AI across product innovation and sales, marketing, and customer growth, compared with 32% in the control group.

These businesses were more likely to link AI use cases across product development and customer-facing functions, creating reinforcing effects that were less evident in peers focused mainly on labour productivity.

By contrast, the number of AI use cases centred on efficiency and productivity was almost identical between efficient growth firms and the control group. That suggests automation alone did not explain the performance gap between the two sets of companies.

Carlsen said the central issue for finance leaders was the fit between AI investment and a company's main sources of growth and value.

"Organisations that outperformed industry peers on revenue growth, margin expansion and return on invested capital over the last 10 years were more likely than matched peers to frame AI as a growth engine and to connect AI use cases across product innovation and sales, marketing, and customer growth," Carlsen said.

Scale matters

The analysis also pointed to a sharper AI effect among smaller and mid-sized businesses. Efficient growth companies with less than USD $3 billion in revenue deployed twice as many AI use cases as comparable peers.

For companies with less than USD $10 billion in revenue, efficient growth businesses were 2.6 times more likely to use AI across product innovation, sales, marketing, and customer growth.

That pattern suggests smaller organisations may be using AI to extend their reach when resources are tighter. It also indicates that the link between AI deployment and business performance may be clearer outside the largest corporate groups.

Industry split

Sector structure also shaped the results. The strongest differentiation appeared in data-intensive industries such as technology and financial services, where AI can be embedded more directly into products, customer interactions and internal decision workflows.

In asset-intensive industries, the picture was different. There, AI delivered more in operational efficiency than in clear differentiation, making it more of a competitive requirement than a distinct edge.

That distinction matters for finance leaders deciding how to assess returns. In sectors where AI can be woven into products and customer activities, the benefits may extend beyond cost savings to include revenue and margin performance. In sectors with heavier physical assets, the case may rest more on keeping pace with rivals and improving operations.

Carlsen said finance chiefs should judge AI spending in a broader business context.

"The most important question for CFOs is not how much can the organisation spend on AI, but whether those investments are being deployed in ways that reinforce the business's core growth and value drivers," Carlsen said.

She added that the findings challenged the idea that productivity gains alone would create a lasting edge.

"Moreover, the near-identical amount of use cases for efficiency and productivity use cases between efficient growth firms and control peers suggests that productivity-focused AI investments alone do not explain performance differences, and that automation by itself is increasingly becoming table stakes rather than a durable source of advantage," Carlsen said.

The findings add to a wider debate among finance leaders over how AI budgets should be measured. As spending on AI tools rises across corporate functions, investors and boards are pressing companies to show clearer links between investment and business results.

Gartner's analysis suggests those links may depend less on the total budget and more on whether AI use is integrated across product development, customer growth, and decision-making processes. For CFOs, that shifts the conversation from spending levels to deployment choices across the business.

"For CFOs, the implication is to evaluate AI investments not only by the return of individual use cases, but also by how well those capabilities reinforce broader growth, product and decision processes across the enterprise," Carlsen said.