Business leaders are pushing ahead with the adoption of artificial intelligence, although early results remain mixed. Report from wall street journal and Reuters According to , most CEOs expect spending on AI to continue to increase through 2026, even though it is difficult to link AI investments to clear benefits for the company as a whole.
This tension highlights where many organizations are currently in their AI journey. Although this technology has moved beyond testing and proof of concept, it has not yet established itself as a reliable source of value. Companies are in an in-between stage, where ambition, execution and expectations are all under pressure at the same time.
Expenses continue even if revenue lags
Over the past two years, AI budgets have been steadily increasing across large enterprises. Competitive pressures, board oversight and fear of being left behind are all at play. At the same time, executives are becoming more open about the limitations they see. Profits often appear in pockets rather than across the business, pilots are not widespread, and the cost of connecting AI systems to existing tools continues to rise.
a wall street journal A survey of senior executives shows that most CEOs believe AI is central to long-term competitiveness, even though short-term benefits are difficult to measure. For many, AI is no longer an option. This is treated as a capability that must be developed over time, rather than a project that can be paused if the results are disappointing.
This perspective helps explain why spending has remained stable. Leaders worry that cutting now could weaken their position in the future, especially as rivals improve their use of technology.
Why pilots struggle to scale up
One of the main barriers to achieving greater benefits is the transition from experimentation to routine use. Many organizations launch AI pilots across different teams without shared rules or coordination. While these initiatives can generate insight and interest, they rarely lead to changes that impact the broader business.
Reuters Companies looking to scale AI report frequently encountering issues with data quality, system links, security controls, and regulatory requirements. These issues are not just technical. These reflect how work is organized. Responsibility is often divided between teams, ownership is unclear, and decisions are delayed as projects involve legal, risk, and IT departments.
The result is a pattern of costly trials and limited progress toward systems integrated into core operations.
Infrastructure costs reshape the equation
Infrastructure costs are also weighing on AI revenues. Training and running models requires large amounts of computing power, storage, and energy. Cloud prices can rise quickly as usage increases, but building on-site systems requires upfront investment and long planning cycles.
named the executive Reuters It warns that infrastructure costs can outweigh the benefits provided by AI tools, especially in the early stages. This forces companies to make difficult choices: centralize their AI resources or let their teams experiment on their own. Should you build your own system or rely on a vendor? and how much waste is allowed while capacity is still being formed.
In reality, these decisions, along with model performance and use case selection, shape your AI strategy.
AI governance at the center of CEO decision-making
As spending on AI increases, so will surveillance. Boards of directors, regulators, and internal audit teams are asking even tougher questions. In response, many organizations are tightening controls. Decision-making power is moving to a central team, AI councils are becoming more common, and projects are more closely tied to business priorities.
of wall street journal reports that companies are moving away from loosely coupled experimentation toward clearer goals, measures, and timelines. While this may slow progress, it reflects a growing belief that AI should be managed with the same discipline as other major investments.
This change signals a shift in the way we treat AI. It’s no longer a side hustle or a curiosity. It is brought into existing operating and risk structures.
Expectations are reset, not discarded.
Importantly, continued spending on AI is not a sign of blind optimism. Rather, it reflects a reset of expectations. CEOs are learning that AI rarely delivers big benefits right away. Value tends to emerge gradually as organizations adjust workflows, retrain staff, and improve their data infrastructure.
Rather than abandoning their AI efforts, many companies are narrowing their focus. They are prioritizing fewer use cases, demanding clearer ownership, and aligning projects more closely with business outcomes. This realignment may reduce short-term excitement, but increases the potential for sustainable returns.
What the CEO AI strategy means for your plans for 2026
For organizations planning for 2026, the message to all CEOs is not to back away from AI, but to pursue it more carefully as your AI strategy matures. Ownership, governance, and realistic timelines are more important than headline spending levels and bold claims.
Those likely to benefit most are those who view AI not as a means to rapid growth, but as a long-term change in the way their organizations work. In the next stage, the advantage will depend less on how much it costs and more on how well AI fits into daily operations.
(Photo courtesy of Ambre Estave)
See also: AI in 2026: The rise of autonomous systems will end experimental AI
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