Published on the 08/09/2026 | Written by Heather Wright

Efficiency gains won’t come free…

AI might be sold as a productivity engine, but a new Bain & Co analysis warns that the technology is also creating a significant new layer of spending that organisations will need to manage carefully.

In a new brief, FinOps for AI: From Managing Costs to Maximising Value, Bain & Co says AI is driving a new wave of technology investment that extends far beyond model and token costs, increasing pressure on already stretched tech budgets.

“AI layers a new, highly variable cost base onto technology budgets, often with limited visibility into where the money is going and what value it’s creating.”

It’s forecasting that by 2035, IT costs could increase by 75 percent in organisations that enable AI.  (That figure, however, is drawn from a ‘typical’ US$10 billion consumer packaged goods company.)

And it won’t be courtesy of poor cost controls either, with the consulting firm warning that even organisations which successfully manage tech spending should still expect costs to rise significantly as AI adoption increases, making disciplined investment decisions more important than ever.

“AI promises significant gains in productivity, innovation and growth,” the brief says. “But it also layers a new, highly variable cost base onto technology budgets that are already under pressure, often with limited visibility into where the money is going and what value it’s creating.”

The brief argues that focusing on token costs alone substantially understates the true cost of AI adoption, with AI increasing spend across multiple parts of the tech stack, including infrastructure, software, cybersecurity, data management and new operating models. Large language model workloads in particular are increasing demand for cloud, compute and software licensing.

“Token costs are only one part of the cost equation,” the brief notes, while acknowledging the growing numbers of companies burning through their annual token budgets by the middle of the year – “You’re not alone.”

Greater architectural complexity as AI models and agents are layered into already fragmented technology environments will increase integration efforts, governance requirements and operational dependencies and risk, while faster technology lifecycles will make investments obsolete faster, increasing the cost of keeping pace and cybersecurity investment is also increasing. Data and governance requirements will also increase adding to the investments required, while organisations are also being forced to absorb the costs of reskilling talent, redesigning processes and decision rights and operating legacy and AI-enabled models in parallel during the transition.

Cost, and cost cutter

For tech leaders, that’s creating a dilemma with pressure to deploy AI to improve productivity and drive business outcomes, while simultaneously controlling costs and maintaining governance.

But while Bain & Co describe AI as ‘the largest emerging source of technology cost’, they also say it is ‘the most powerful tool available to control those costs’ – enabling better visibility on spending through automatically classifying invoices, mapping ledger entries and identifying shadow IT; optimising infrastructure and helping rationalise applications; delivering software faster and creating more productive operations.

“Senior executives must deal with both realities at once,” the report notes. “Left unchecked, rising demands for compute, data, software, security and talent will erode margins; deployed deliberately, the same technology can simplify the estate, automate work and reset productivity.”

The challenge, Bain argues, is that many organisations still lack sufficient visibility into how AI spending translates into business value. That’s a view shared by Gartner, with a recent report showing 11 percent of organisations are ‘entirely unaware’ of what their function spent on AI in 2025 – but nonetheless are continuing to pump money into AI.

Bain says to meet the challenge, leading executives are extending FinOps beyond cloud cost management into a discipline for governing AI investment, linking consumption, technology costs and business outcomes in near real time.

Spend smart

The firm argues the goal should not necessarily be reducing tech spend.

“In this environment, the goal isn’t necessarily to reduce technology spending. It’s to direct capital investment toward the AI deployments that will create the greatest business value.”

Not every tech investment deserves protection, the report notes. “Where differentiation is real, protect investment. Where commodity delivery is sufficient, mandate efficiency.”

Technology costs need to be treated as a capital allocation decision, the company says – rather than an operational line item to be managed by IT. “It is one of the largest and most consequential investment decisions many companies make. Boards, CEOs and CFOs should oversee technology costs with the same rigour applied to capital allocation, requiring clear visibility into spending, AI investments, value realisation and emerging risks.”

Transparency across tech spend, including shadow IT, is the starting point, connecting costs to business outcomes. “Without transparency, cost reduction efforts often destroy value rather than create it.”

Bain & Co also recommends designing a ‘self-funding AI investment engine’ reinvesting AI-fuelled savings from the likes of application rationalisation and productivity gains, back into the highest-value AI opportunities.

It’s also urging companies to build a capability, not a program, saying tech cost reduction programs often deliver short-term gains only to see costs rebound within a few years. “Break this pattern by embedding real-time cost visibility tools, agile funding methods and ongoing accountability structures into the operating model. The goal is a permanent capability, not a one-time result.”

Winners ahead

The findings echo Bain’s earlier research into enterprise AI spending. In a separate survey of more than 400 technology leaders, Bain found 69 percent expected AI spending to increase by more than five percent as organisations accelerated adoption. The firm also warned that AI introduces additional complexity through faster technology cycles, more demanding architecture requirements and new operating models.

Taken together, the reports suggest that while AI may ultimately improve efficiency, organisations should not expect lower technology budgets in the near term. Instead, they are likely to face growing demands for investment as AI workloads scale and become operationally embedded.

For business leaders, Bain’s message is straightforward: The issue is no longer whether organisations will spend more on AI, but whether they can clearly identify which investments are generating business value.

“Companies that treat cost optimisation as a source of growth capital can reinvest savings into the AI capabilities that create competitive advantage,” the report says.

As AI spending moves from experimentation to enterprise scale, the winners may not be those spending the most, but those with the clearest understanding of what their AI investments are actually delivering.