Only 13% of Chief Information Officers (CIOs) say their organisations have realised significant or transformational value from artificial intelligence (AI), despite the acceleration of enterprise AI use cases over the past two years, according to Gartner.
The findings from Gartner’s 2027 CIO and Technology Executive Agenda survey, which gathered responses from 2,002 CIOs and technology executives between May and July 2026, indicate that the likelihood of an AI use case succeeding remains at one in five.
The report highlights gaps in AI governance, investment discipline and workforce planning that could prevent organisations from translating experimentation into measurable business outcomes. Gartner identifies a group of CIOs, described as “vanguards”, who deliver better business performance while also reporting high perceived AI value among their CEOs. These leaders are more likely to establish clear criteria for deciding when AI initiatives should progress, be scaled or be discontinued.
“Volume is not value. AI is creating a reckoning for organisations that lack fundamental disciplines, such as defining use-case value ahead of time and establishing criteria for when to walk away,” said Alicia Mullery, vice-president analyst at Gartner.
AI pilots need clearer exit criteria.
The survey found that organisations with guidelines for determining when an AI pilot should not progress to production are 2.3 times more likely to be vanguards. Those with criteria for abandoning low-value AI initiatives already in production are 2.6 times more likely to belong to this group.
However, only 31% of CIOs reported having guidelines for stopping pilots from moving into production, while just 17% had criteria for discontinuing low-value AI initiatives already deployed.
As organisations expand the use of generative AI, traditional AI and AI agents, Gartner said CIOs need to establish these rules in collaboration with business stakeholders.
“Establishing and adhering to these criteria is a team sport,” Mullery said, adding that CIOs need business partners who are prepared to walk away from low-value investments.
The findings suggest that enterprise AI programmes need to be evaluated against predefined business objectives, with clear measures of value and explicit decisions about whether initiatives should continue. Without these disciplines, a growing number of pilots and deployments may not translate into meaningful business returns.
Rising AI costs increase pressure on CIOs.
The financial implications of AI adoption are another concern. Gartner found that 58% of CIOs faced increased pressure to deliver cost improvements through AI, while 51% expected AI to increase the total cost of ownership of technology solutions.
The report highlights the need to manage AI economics across the entire lifecycle, rather than focusing only on the initial cost of technology acquisition.
Unlike traditional cloud and software-as-a-service investments, AI costs can vary according to usage and runtime activity. As enterprises expand generative AI and agentic AI deployments while reducing investment in some legacy infrastructure, CIOs may need to revise funding models, governance frameworks and cost-accountability mechanisms.
Mullery said many organisations continue to apply funding and governance approaches designed for conventional technology investments, despite the different cost characteristics of AI.
For technology leaders, this means evaluating not only the potential productivity gains from AI but also the costs of operating, maintaining and governing AI systems as usage scales.
Workforce redesign remains a major gap.
Gartner also identified workforce transformation as a differentiator between organisations reporting stronger AI outcomes and their peers.
Organisations with explicit plans to redesign roles for a human-agent workforce are 2.4 times more likely to be vanguards. Yet only 4% of CIOs reported having such plans in place.
The findings challenge the assumption that AI adoption will primarily reduce technology headcount. Instead, Gartner said organisations may need to rethink responsibilities, job definitions and how employees work alongside AI agents.
As AI reduces the effort required for certain technology tasks, the overall demand for technology work could increase. The survey found that 40% of respondents expected their IT headcount to grow in 2027, particularly in cybersecurity and data and analytics.
The accompanying workforce chart on page three of the report shows that 56% of respondents expected headcount in data and analytics to increase, while 54% anticipated growth in cybersecurity. By comparison, 35% are expected to increase in both application and software engineering and IT core functions.
The report also points to changes within individual roles. As AI takes on specialised tasks, responsibilities across established technology functions may increasingly overlap, with designers undertaking more testing and testers participating in design activities.
Gartner said organisations would need to reconsider static job definitions as the scope of responsibilities expands, even if the number of distinct role types decreases.
Moving from AI experimentation to measurable outcomes
The findings place three priorities at the centre of the CIO agenda: disciplined decisions about AI investments, stronger oversight of AI costs and deliberate workforce redesign.
For enterprises, the challenge is no longer simply identifying potential AI use cases. It is determining which initiatives deliver sufficient business value, ensuring that the economics remain sustainable as deployments scale, and preparing employees for changes in how technology work is performed.
Gartner’s report, Bridging the AI Trough: 3 Gaps to Close for the 2027 CIO and Technology Executive Agenda, provides further analysis of the gaps technology leaders need to address as AI moves from experimentation towards wider deployment.