Does it seem as though no one in business is talking about anything but artificial intelligence these days? Perhaps so, but finance chiefs are warned to be careful about looking at AI spending as a single big strategy bucket.
According to a March 2026 survey of 100 CFOs by Gartner, acquiring and developing skilled AI talent will be the biggest challenge for finance chiefs over the next two quarters. It’s a notable finding, considering the current, deepening global disruptions and the consequent unpredictability of market conditions and shock events.
“CFOs and their teams have to actively work on handling volatility and shocks because on multiple fronts, this is the operating environment organizations are facing today,” said Mallory Bulman, senior director analyst at Gartner, speaking at the firm’s recent finance symposium in Sydney, Australia.
Gartner stressed that traditional scenario planning is losing effectiveness amid the swirling volatility, largely because it over-relies on static assumptions, over-emphasizes internal drivers and responds too slowly to external shocks.
So, is AI the answer? It could be — but what is “it”?
According to Gartner, CFOs who treat achieving an acceptable return on AI investments as a single challenge are not viewing the matter realistically. Rather, such investments are actually a portfolio of very different bets.
To be sure, finding and hiring AI talent is not easy, and it’s expensive. However, said Twisha Sharma, senior research principal with Gartner’s finance practice, “AI does not follow one cost curve, and it does not produce one uniform type of value.”
CFOs need to stop looking for a single ROI formula, Sharma said, and instead build a balanced portfolio that includes productivity use cases, targeted process improvements and selective transformational bets.
She advised that regarding different AI projects similarly to different types of travel could be a useful visualization tool. That is, “routine trips” are comparable to the automation of repetitive tasks like invoice processing, expense management and basic financial reporting. “Adventure trips” are like advanced analytics and predictive modeling, such as cash flow forecasting in FP&A or spend analytics in procurement. And “luxury expeditions” correspond to AI-driven innovation, like dynamic scenario planning or autonomous procurement.
“An AI portfolio should contain projects with routine use cases that automate repetitive tasks, those with more advanced use cases that improve analysis and decision making, and larger transformational use cases aimed at innovation or competitive disruption,” Sharma said.
Each use case will have different timelines, ambitions, risk profiles and ongoing costs, she noted. If finance teams don’t dissect cost models with precision, they will face budget surprises later.
Sharma warned that CFOs risk undervaluing AI if they focus too narrowly on immediate financial returns, such as revenue growth, cost reduction or cash flow improvement.
“Many AI initiatives create important nonfinancial value first,” she said. Such value includes better decision support, stronger business agility, wider organizational reach and innovation capacity, and even a shift in finance’s role within the enterprise, “long before those benefits are fully visible in the P&L.”
