For technology leaders, 2026 has brought a difficult balancing act. Artificial intelligence is advancing at a remarkable pace, business teams want faster access to new capabilities, and senior management expects tangible returns on every major investment. However, expanding AI across an organisation demands more than simply deploying new technologies.
The issue came into focus at the CIO 100 Awards and Conference in September 2026, where discussions among India’s leading CIOs and CTOs reflected a wider industry dilemma. Organisations are moving at different speeds, with some already generating business value from AI deployments while others continue to test use cases and build internal capabilities.
The rush to adopt AI is understandable. Falling behind competitors carries its own risks. However, the pressure to keep pace can also lead to fragmented projects, overlapping investments and initiatives that struggle to move beyond pilot stages.
The next phase of enterprise AI will depend on how effectively organisations separate genuine business opportunities from technology-driven urgency.
Investment Is Rising, But Complexity Remains
Findings from Foundry’s AI Priorities Study, conducted in December 2025 among 538 IT decision-makers, illustrate the scale of enterprise interest.
The study found that 63% of respondents anticipated higher AI spending in 2026, compared with 53% in the previous year. Additionally, 97% had invested or planned to invest in tools to strengthen their internal AI capabilities, while 44% had implemented AI within business units or across their enterprises.
These numbers point to growing commitment. They also raise an important consideration: how prepared are organisations to turn investment into sustained operational improvements?
Integrating AI with existing systems, managing implementation costs and developing the necessary expertise remain significant hurdles. In the same study, 97% of respondents reported challenges with new AI initiatives, with infrastructure, systems integration and the expense of adding AI to existing technology environments among the leading concerns.
This suggests that the obstacles to progress often extend beyond the technology itself. Legacy systems, limited skills and competing business priorities can complicate even promising projects.
Why A More Selective Approach Matters
The appeal of adopting every emerging AI capability is easy to understand, particularly when competitors are announcing new deployments. However, organisations need to assess whether a proposed application addresses a meaningful business problem and whether the necessary foundations are in place.
Foundry’s research offers another useful indicator. The study also found that 37% of respondents were investing in internal AI capabilities, focusing on employee training and skills development. Meanwhile, 67% reported better results from industry-specific AI solutions than from general-purpose generative AI offerings.
The findings highlight the importance of context when selecting AI solutions. A specialised application designed around an industry’s workflows may offer greater practical value than a broader tool requiring extensive adaptation.
A defined corporate AI strategy can help establish these priorities. It gives business and technology teams a shared framework for evaluating opportunities, allocating resources and deciding which projects are ready for wider deployment.
The Growing Pressure to Prove AI’s Value
For CIOs, the pressure to demonstrate results is intensifying. Business leaders expect productivity improvements and revenue opportunities, while finance teams need evidence that technology spending is justified.
Foundry’s 2026 State of the CIO Study provides a snapshot of the returns organisations are reporting from AI initiatives.
Only 19% of respondents said at least 75% of their AI initiatives had met or exceeded ROI goals. Another 40% reported that between 30% and 75% of their initiatives had achieved those goals. Meanwhile, 18% said fewer than 30% had met or exceeded expectations.
The remaining respondents were divided between organisations without established metrics, at 10%, and those that considered it too early to assess results, at 13%.
These figures reveal a gap between the enthusiasm surrounding AI and the ability to demonstrate consistent returns. Some initiatives are producing results, but many organisations are still working to establish reliable ways of measuring their impact.
The challenge becomes particularly significant when pilot projects move into production, where infrastructure expenses, model usage, integration requirements and ongoing maintenance can influence the economics of deployment.
The Next Advantage Will Come From Better Decisions
Enterprise AI is entering a phase in which the quality of investment decisions will matter as much as the speed of adoption.
CIOs will need to bring together business strategy, technology readiness, workforce development and financial discipline. Projects should have clearly defined objectives, suitable performance measures and a credible path from experimentation to operational use.
Organisations must also prepare employees to work effectively with AI systems. Even well-executed AI deployments may fall short of expectations if organisations lack the necessary skills and processes to integrate them effectively into everyday operations.
The larger question for technology leadership is how to build an AI portfolio that delivers sustainable value rather than a collection of disconnected experiments.
As adoption expands, the organisations that make meaningful progress will be those that understand where AI can make a measurable difference, invest accordingly and remain willing to reassess their choices.
AI adoption continues to gather momentum. The more consequential race is to make those investments count.
