Enterprise leaders are pressing ahead with artificial intelligence, even as early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns.
This tension highlights where many organisations now sit in their AI journey. The technology has moved beyond trials and proofs of concept, but it has yet to settle into a reliable source of value. Companies are operating in an in-between phase, where ambition, execution, and expectations are all under strain at the same time.
Spending continues, even as returns lag
AI budgets have climbed steadily across large enterprises over the past two years. Competitive pressure, board oversight, and fear of being left behind have all played a role. At the same time, executives are more open about the limits they are seeing. Gains often show up in pockets rather than across the business, pilots fail to spread, and the cost of connecting AI systems to existing tools keeps rising.
A Wall Street Journal survey of senior executives found that most CEOs see AI as central to long-term competitiveness, even if short-term benefits are hard to measure. For many, AI no longer feels optional. It is treated as a capability that must be developed over time, rather than a project that can be paused if results disappoint.
That view helps explain why spending remains steady. Leaders worry that cutting back now could weaken their position later, especially as rivals improve how they use the technology.
Why pilots struggle to scale
One of the main barriers to stronger returns is the jump from experimentation to day-to-day use. Many organisations have launched AI pilots across different teams, often without shared rules or coordination. While these efforts can generate insight and interest, few translate into changes that affect the wider business.
Reuters has reported that companies trying to scale AI frequently run into issues with data quality, system links, security controls, and regulatory requirements. These problems are not only technical. They reflect how work is organised. Responsibility is often split across teams, ownership is unclear, and decisions slow down once projects touch legal, risk, and IT functions.
The result is a pattern of heavy spending on trials, with limited progress toward systems that are embedded in core operations.
Infrastructure costs reshape the equation
The cost of infrastructure is also weighing on AI returns. Training and running models demands large amounts of computing power, storage, and energy. Cloud bills can rise quickly as usage grows, while building on-site systems requires upfront investment and long planning cycles.
Executives cited by Reuters have warned that infrastructure costs can outpace the benefits delivered by AI tools, particularly in the early stages. This has forced tough choices: whether to centralise AI resources or leave teams…
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