Only 13% of CIOs reported their enterprise has realised significant or transformational value from AI so far, according to a new survey from Gartner, which adds that while AI use cases have accelerated over the past two years, the likelihood of use case success remains one-in-five.
Alicia Mullery, vice-president analyst at Gartner, says: “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.”
The Gartner 2027 CIO and Technology Executive Agenda survey of 2 002 CIOs and technology executives from May to July 2026 found that vanguards – defined as CIOs who are delivering better performance on business outcomes while also reporting high CEO-perceived AI value – are more likely to establish stopping rules at two points in the lifecycle: pilots and production.
Defining these guidelines becomes essential as organisations rapidly transition from initial experimentation to active deployment across GenAI, traditional AI, and AI agents.
Organisations with guidelines for determining when an AI pilot should not move to production are 2.3 times more likely to be vanguards, while those with guidelines for abandoning low-value AI initiatives already in production are 2.6 times more likely to be vanguards. However, only 31% and 17% of CIOs, respectively, reported having these practices in place.
“Establishing and adhering to these criteria is a team sport,” says Mullery. “CIOs need business partners who are willing and ready to walk away from low value investments.”
CIOs must be responsible for AI economic stewardship
Funding for AI is increasing, and 58% of CIOs said they have increased pressure to deliver cost improvement from AI. At the same time, 51% of CIOs expected AI to increase the total cost of ownership of technology solutions (see Figure 1). CIOs need to help their organisations govern AI economics across the full lifecycle, not simply approve an initial technology purchase.
Changes in Technology Funding, 2026 to 2027
“Many organisations continue to apply funding and governance models designed for traditional technology investments such as cloud and SaaS,” says Mullery. “Unlike traditional technology costs, which are largely predictable, AI costs are probabilistic and determined at run time. As enterprise technology funding shifts towards GenAI and agentic AI while investments in legacy infrastructure contract, CIOs will need new approaches to funding, governance and cost accountability.”
CIOs must bridge the IT workforce gap by expanding what humans do
Organisations with explicit plans to redesign roles for a human-agent workforce are 2.4 times more likely to be vanguards, yet a significant execution gap exists, as only 4% of CIOs reported having such plans in place.
“While many leaders assume AI will primarily serve to reduce technology headcount, the true vanguard differentiator lies in workforce redesign,” says Mullery.
This workforce transformation provides the essential foundation for understanding headcount trends: as AI lowers the cost and effort required to produce technology, overall demand for technology work actually increases. This creates a dual workforce shift where IT headcount can grow – with 40% of respondents expecting to increase IT headcount in 2027, particularly in cybersecurity and data and analytics (see Figure 2), while roles simultaneously compress and expand.
Expected Changes in Headcount within IT Functional Sub-teams Within 12 Months (Percentage of Respondents)
“Rather than maintaining static job definitions, vanguards recognise that as AI takes on specialised tasks, the boundaries between established technology jobs begin to blur,” says Mullery. “For example, designers can increasingly perform testing activities, while testers participate in design. Consequently, organisations will require fewer distinct role types, even as the span of responsibility within each role expands.”