AI infrastructure costs force companies to cut weak bets

AI infrastructure costs are forcing companies to cut weak bets

AI infrastructure costs are rising because agentic systems and autonomous AI agents create more queries, more data processing, and tighter low-latency demands, according to CIO Dive. Gartner data cited in the report says global AI spend is set to nearly double in 2026, partly because infrastructure demand keeps climbing. For enterprise leaders, the real decision is no longer whether to fund AI. It is which projects deserve more capital and which should be stopped before they consume more budget.

Key takeaways

• Agentic AI, software that plans and executes tasks on its own, is increasing query volume, data processing needs, and low-latency requirements, according to CIO Dive.
• Global AI spend is set to nearly double in 2026, according to Gartner data cited by CIO Dive, with infrastructure demand as a major driver.
• Only 8% of enterprises say their data is ready for AI, according to a recent Dun & Bradstreet survey cited by CIO Dive.
• Only 38% of leaders say they have seen significant ROI from AI investments, and nearly one-quarter have reduced or paused underperforming initiatives, according to CIO Dive.

Why are AI infrastructure costs rising so fast?

Agentic AI, software that plans and executes tasks on its own, raises costs because it multiplies workload behind the scenes. Tech leaders reported more queries, more data processing needs, lower latency expectations, and higher infrastructure costs tied to agent use, according to CIO Dive.

That matters because the economics change as soon as AI systems move from occasional analysis to continuous execution. CIO Dive, citing Gartner, said global AI spend is set to nearly double in 2026, so infrastructure is becoming a board-level budget line rather than a pilot expense.

Source: CIO Dive

Why do most AI programs hit data problems?

Most programs stall because the data foundation is not ready, not because the model is weak. A recent Dun & Bradstreet survey found only 8% of enterprises say their data is ready for AI, according to CIO Dive.

CIO Dive also reported that project failure rates have jumped in recent years because of poor data readiness rather than technology limitations. Fewer than one-quarter of tech leaders in Ocient’s survey said they can support growing AI workloads without significant changes, which means scaling up often requires more than a bigger budget.

Source: CIO Dive

Where are companies actually seeing value from AI?

The strongest value is operational efficiency, not employee productivity, according to CIO Dive. A recent SAP report had similar findings: the technology is helping employees create insights, make decisions, or interact with customers more than it is driving cost efficiency or productivity on its own.

That distinction matters for finance leaders. If AI is mostly improving decisions and workflows, the business case should focus on cycle time, service quality, and better judgment. If a project is pitched mainly as a productivity boost, the board should ask for a clearer operating result.

Source: CIO Dive

How are leaders deciding what to cut and fund?

Leaders are pruning portfolios instead of blindly adding projects. Only 38% said they have seen significant ROI from AI investments, according to CIO Dive, and nearly all are planning to improve the bottom line by cutting what is not working.

Nearly one-quarter said they have reduced or paused underperforming initiatives, while about 45% have increased investment in successful ones. Security risk, financial return, accuracy, and cost at scale now carry nearly equal weight, and companies are adding governance requirements and AI usage and spending controls to keep costs in check.

Source: CIO Dive

What to do now

For finance, operations, and technology teams, the message is simple: treat AI like a portfolio, not a trophy case. Fund the use cases that can prove value at scale, and stop feeding the ones that only look promising in a demo.

• Map every AI pilot to a measurable business outcome, such as cycle time, service quality, or cost at scale.
• Review data readiness before approving new use cases, including access, quality, and how much cleanup is needed.
• Set spending and usage controls for agents and other workloads that can expand quickly.
• Pause or sunset initiatives that have not shown clear ROI, then redeploy budget to the strongest cases.
• Add security, accuracy, and governance checks to the approval process, not after launch.

Frequently asked questions

Why do agentic AI systems raise costs so quickly?

Agentic AI systems raise costs because they do more work behind the scenes. According to CIO Dive, tech leaders are seeing a higher volume of queries, more data processing needs, low-latency demand, and higher infrastructure costs tied to agent use. That makes each new workflow more expensive to run at scale.

Source: CIO Dive

What does it mean that only 8% of enterprises say their data is ready?

It means most companies are not yet ready to deploy AI broadly without extra work. According to CIO Dive, a recent Dun & Bradstreet survey found only 8% of enterprises say their data is ready, and project failure rates have jumped because of poor data readiness rather than technology limitations.

Source: CIO Dive

How should a company decide what AI projects to keep?

CIO Dive says leaders are weighing security risk, financial return, accuracy, and cost at scale almost equally. A practical approach is to protect the projects with clear ROI, pause the ones that are underperforming, and raise the bar on governance and spending controls before adding more pilots.

Source: CIO Dive