Enterprise leaders are pressing ahead with artificial intelligence, even as early results remain uneven. Reporting from The Wall Street Journal and Reuters indicates that most CEOs expect AI spending to continue rising through 2026, despite ongoing challenges in linking those investments to clear, enterprise-wide returns.
This disconnect highlights where many organisations currently stand in their AI journey. The technology has moved beyond trials and proof-of-concept stages, yet it has not fully matured into a consistent source of measurable value. As a result, companies are navigating an in-between phase—one where ambition, execution, and expectations are all under strain simultaneously.
Spending continues, even as returns lag
AI budgets have risen steadily across large enterprises over the past two years. Competitive pressure, board-level oversight, and concerns about falling behind peers have all contributed to this momentum. At the same time, executives are becoming more candid about the limitations they are encountering. Productivity gains often appear in isolated pockets rather than across the broader organisation, pilot projects struggle to scale, and the cost of integrating AI systems with existing enterprise tools continues to climb.
A Wall Street Journal survey of senior executives found that most CEOs view AI as central to long-term competitiveness, even as near-term returns remain difficult to quantify. For many leaders, AI no longer feels optional. Instead, it is increasingly treated as a core capability that must be built and refined over time, rather than a discrete project that can be paused if early results fall short.
That perspective helps explain why AI spending remains steady. Many leaders fear that pulling back now could weaken their competitive position over the long term, particularly as rivals become more effective at integrating and scaling the technology across their operations.
Why pilots struggle to scale
One of the main barriers to stronger AI returns is the transition from experimentation to day-to-day use. Many organisations have launched AI pilots across individual teams, often without shared standards or central coordination. While these initiatives can generate insight and early enthusiasm, few lead to changes that meaningfully affect the wider business.
Reuters has reported that companies attempting to scale AI frequently encounter challenges related to data quality, system integration, security controls, and regulatory compliance. These obstacles are not purely technical. They also reflect how work is structured inside organisations, where responsibility is fragmented, ownership remains unclear, and decision-making slows once projects involve legal, risk, and IT functions.
The result is a familiar pattern: sustained spending on trials, but limited progress toward AI systems that are fully embedded in core operations.
Infrastructure costs reshape the equation
The cost of infrastructure is increasingly weighing on AI returns. Training and operating models require significant computing power, storage capacity, and energy, all of which drive expenses higher. As AI usage scales, cloud costs can escalate rapidly, while building on-premise systems demands large upfront investments and extended planning cycles.
Executives cited by Reuters have warned that infrastructure spending can outpace the value delivered by AI tools, particularly in the early stages of deployment. This imbalance is forcing difficult trade-offs: whether to centralise AI resources or allow teams to experiment independently; whether to build in-house platforms or depend on external vendors; and how much inefficiency is acceptable while AI capabilities are still taking shape.
In practice, these decisions are shaping AI strategy as much as model performance or use-case selection.
AI governance moves to the centre of CEO decision-making
As AI spending rises, so does scrutiny. Boards, regulators, and internal audit teams are asking tougher questions about value, risk, and accountability. In response, many organisations are tightening control. Decision-making authority is shifting toward central teams, AI councils are becoming more common, and projects are being more closely aligned with core business priorities.
According to The Wall Street Journal, companies are moving away from loosely connected experiments and toward initiatives with clearer goals, defined metrics, and realistic timelines. While this more disciplined approach can slow early momentum, it reflects a growing consensus that AI investments should be managed with the same rigour as any other major strategic initiative.
This shift marks a broader change in how AI is viewed inside organisations. It is no longer treated as a side project or experimental curiosity. Instead, AI is being integrated into established operating models, governance frameworks, and risk management structures.
Expectations are being reset, not abandoned
Importantly, the continued commitment to AI spending does not reflect blind optimism. Instead, it signals a reset in expectations. CEOs are increasingly recognising that AI rarely delivers immediate, transformative returns. Meaningful value tends to emerge over time, as organisations adapt workflows, retrain employees, and strengthen their data foundations.
Rather than abandoning AI initiatives, many enterprises are sharpening their focus. They are concentrating on fewer, higher-impact use cases, establishing clearer ownership, and aligning projects more tightly with measurable business outcomes. While this recalibration may temper short-term excitement, it significantly increases the chances of achieving sustainable, long-term returns.
What CEO AI strategy signals for 2026 planning
For organisations shaping their plans for 2026, the message to CEOs is clear: do not retreat from AI, but pursue it with greater care as strategies mature. Ownership, governance, and realistic timelines now matter far more than headline spending figures or bold public claims.
The organisations most likely to benefit are those treating AI as a long-term shift in how the business operates, not a shortcut to rapid growth. In this next phase, competitive advantage will depend less on how much is invested and more on how effectively AI is embedded into everyday operations.
