Introduction to AI in Business
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.
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.
Why CEOs Continue to Invest in AI
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.
Challenges in Scaling AI
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 to experiment on their own; whether to build in-house systems or rely on vendors; and how much waste is acceptable while capabilities are still forming.
Managing Infrastructure Costs
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 increases, so does scrutiny. Boards, regulators, and internal audit teams are asking harder questions. In response, many organisations are tightening control. Decision rights are shifting toward central teams, AI councils are becoming more common, and projects are being linked more closely to business priorities.
The Wall Street Journal reports that companies are moving away from loosely connected experiments toward clearer goals, measures, and timelines. This can slow progress, but it reflects a growing belief that AI should be managed with the same discipline as other major investments.
Importance of AI Governance
This shift marks a change in how AI is treated. It is no longer a side effort or a curiosity. It is being brought into existing operating and risk structures.
Expectations Are Being Reset, Not Abandoned
Importantly, the persistence of AI spending does not signal blind optimism. Instead, it reflects a reset in expectations. CEOs are learning that AI rarely delivers immediate, sweeping returns. Value tends to emerge gradually, as organisations adjust workflows, retrain staff, and refine data foundations.
Rather than abandoning AI initiatives, many enterprises are narrowing their focus. They are prioritising fewer use cases, demanding clearer ownership, and aligning projects more closely with business outcomes. This recalibration may reduce short-term excitement, but it improves the likelihood of sustainable returns.
What CEO AI Strategy Signals for 2026 Planning
For organisations shaping their plans for 2026, the message for every CEO is not to retreat from AI, but to pursue it with more care as AI strategies mature. Ownership, governance, and realistic timelines matter more than headline spending levels or bold claims.
Those most likely to benefit are treating AI as a long-term shift in how the organisation works, not a quick route to growth. In the next phase, advantage will depend less on how much is spent and more on how well AI fits into everyday operations.
Conclusion
In conclusion, while AI spending continues to rise, CEOs are becoming more cautious and realistic about the returns on their investments. They are focusing on long-term strategies, governance, and infrastructure costs, and are resetting their expectations about the pace of progress. As AI becomes more integrated into core operations, organisations will need to adapt and evolve to maximise its potential.
FAQs
Q: Why are CEOs continuing to invest in AI despite uneven returns?
A: CEOs see AI as central to long-term competitiveness and are worried that cutting back now could weaken their position later.
Q: What are the main barriers to scaling AI pilots?
A: Issues with data quality, system links, security controls, and regulatory requirements, as well as unclear ownership and decision-making processes.
Q: How are infrastructure costs affecting AI returns?
A: Infrastructure costs can outpace the benefits delivered by AI tools, particularly in the early stages, forcing tough choices about centralisation, vendor reliance, and waste acceptance.
Q: What is the shift in AI governance, and why is it important?
A: The shift is towards tighter control, clearer goals, and more discipline in AI management, reflecting a growing belief that AI should be managed like other major investments.
Q: What does the future of AI in business look like?
A: The future of AI in business will depend on how well organisations can integrate AI into their everyday operations, with a focus on long-term strategies, governance, and realistic timelines.
