Some companies are shelving AI projects or struggling to move them beyond pilots, but the evidence does not show a broad corporate retreat from AI investment. The clearer pattern is selective reassessment: organizations are trying to separate useful, controllable deployments from experiments that have not demonstrated value or cannot be scaled.
Are companies pulling back from AI?
Some are pulling back from particular projects. Survey findings point to stalled pilots, failed initiatives and programs abandoned before production. At the same time, other surveys report continued investment and positive returns. Taken together, the evidence suggests a gap between rapid experimentation and reliable, enterprise-wide results—not a general collapse in corporate AI spending.
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The figures below come from surveys with different respondents, dates and definitions. They should be read as separate indicators, not combined into a single failure rate or adoption statistic.
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In a Gartner survey conducted from January through April 2026, 22% of 1,303 respondents said their organizations had successfully scaled AI across multiple business units or adopted an AI-first approach. The surveyed organizations had at least $50 million in fiscal-year 2025 revenue. Gartner also said investment continues to accelerate, making the low scaling share evidence of a deployment gap rather than a spending collapse. Gartner
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The Conference Board reported in 2026 that 60% of surveyed U.S. organizations had not moved beyond early AI adoption. Fewer than half were integrating AI into workflows, measuring its impact or embedding it across the enterprise. Those findings help explain why organizations may be reassessing individual deployments even as they continue to invest.
Surveys find projects failing or stuck in pilots
Orgvue reported in 2026 that 78% of surveyed organizations had AI projects fail or remain stuck in pilot: 35% reported failed projects and 43% reported projects stuck in pilot. The survey was conducted in February and March 2025 among 1,163 senior decision-makers in the U.S., Canada, U.K., Ireland, Australia, Hong Kong, Malaysia and Singapore; minimum organization sizes varied by market. Orgvue also found that 57% of leaders said competitive pressure was the main reason their organization deployed AI. These are Orgvue-commissioned survey results, not audited counts of all AI projects. Orgvue
A separate 2025 survey from S&P Global Market Intelligence found that the share of companies abandoning a majority of AI initiatives before production had risen from 17% to 42% year over year. Respondents also reported an average of 46% of projects scrapped between proof of concept and broad adoption. The survey covered 1,006 midlevel and senior IT and business professionals in North America and Europe. The 42% figure describes companies abandoning a majority of their initiatives; the 46% figure is the average share of projects scrapped. They are different measures, not two estimates of the same rate. S&P Global Market Intelligence
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A pilot can work technically and still fail as a business project. It may not fit everyday workflows, attract sustained employee use, show measurable improvement over the previous process, or justify its ongoing costs. The surveys do not establish one universal cause for project cancellations, but their findings point to several pressures behind reassessment.
Adoption can run ahead of evidence
Competitive pressure can encourage companies to launch AI experiments before they have defined a business problem or a way to measure success. Orgvue’s finding that 57% of surveyed leaders cited competitors as the main reason for deployment suggests that adoption is not always driven by a clearly established internal need.
Robin Erickson, PhD, Head of Human Capital Research at The Conference Board, said on March 31, 2026: “Use of the technology is happening very rapidly and there is massive investment behind it, yet the measurable impacts have yet to materialize.” The Conference Board’s findings reflect that tension: many organizations are experimenting, while fewer report integrating AI into workflows or measuring impact. The Conference Board
Costs and returns are difficult to compare
Different surveys ask different questions about whether AI is paying off. In 2026, EY said 98% of surveyed senior leaders at organizations investing in AI had seen positive ROI from AI initiatives. Its fieldwork ran in four waves from April 2024 to October 2025 and included 534 U.S. senior decision-makers across listed industries. That result is self-reported; it does not establish that most deployments delivered audited financial returns. EY also found that only 64% of those leaders said their organization actively monitors token use and has clear spending guardrails. EY
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PwC’s 29th Global CEO Survey, published in 2026, asked a narrower question: whether AI had delivered both cost and revenue benefits. Twelve percent of CEOs said it had. That result is not directly comparable with EY’s positive-ROI finding, because the populations and outcome measures differ. Positive ROI as reported by a leader is not the same as showing both cost and revenue benefits. PwC
Reliability and vendor dependence matter
Operational resilience is another reason to scrutinize deployments. IBM’s 2026 executive research found that respondents reported an average of six AI-related disruptions over two years; 81% said a seven-day vendor outage would cause severe or critical disruption. The survey included 1,000 senior executives across 16 countries and 17 industries. Those figures describe surveyed executives’ reported experiences and expectations, not a forecast that every organization will face the same disruption. IBM
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A separate IBM study of 2,000 senior executives across 33 geographies and 19 industries reported that organizations embedding controls directly into AI systems had 25% fewer incidents than organizations relying on manual governance. Organizations designing for adaptable workloads and replaceable models reported 10% higher AI ROI in 2025. These are IBM-reported survey analyses, not causal guarantees that a particular control or architecture will produce the same result elsewhere. IBM
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is corporate AI spending slowing down?
The available figures do not establish that overall corporate AI investment is broadly slowing. Gartner said investment continues to accelerate even though only 22% of its surveyed organizations had scaled AI across multiple business units or adopted an AI-first approach. The evidence instead supports a distinction between investment and execution: companies can continue spending while stopping particular pilots, narrowing use cases or demanding stronger evidence before expanding a deployment.
There is also a limit to what aggregate surveys can show. These reports do not provide robust, company-by-company documentation of recent reversals, so they cannot establish which named businesses have pulled back or how much they cut spending. Treat survey results as indicators of reported patterns, not proof of a universal corporate trend.
How to decide whether an AI project should continue
For a company reviewing a pilot, the practical question is not simply whether AI is popular or whether another organization reports positive returns. Assess the use case against its own baseline, operating costs and constraints before expanding it.
- Measure value against a baseline. Define what the process costs or achieves today, then identify the outcome the AI deployment is meant to change. Track the result rather than treating launch or usage as proof of value.
- Count ongoing costs. Include token use and set budget guardrails. EY’s survey found that only 64% of surveyed leaders at AI-investing organizations said they had active token-use monitoring and clear spending controls.
- Check workflow integration and adoption. Determine whether the system fits the work people actually do and whether it is being used consistently. A pilot that remains separate from the workflow has not demonstrated enterprise-wide adoption.
- Review governance and incident controls. Decide how the system is monitored and what happens when it makes an error or causes disruption. IBM’s survey analysis associates controls embedded in AI systems with fewer reported incidents than manual governance, but it does not guarantee the same outcome for every deployment.
- Test vendor resilience and portability. Consider the effect of a vendor outage and whether workloads or models can be replaced or adapted. IBM’s outage finding makes dependency a material operational consideration, not just a procurement detail.
These checks help frame a decision to scale, redesign or stop a project. The survey evidence supports the importance of value measurement, cost oversight, workflow integration, governance and resilience; it does not establish a universally best platform or a single ranking for those factors.
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