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Companies are pulling back from some AI projects, but the available evidence does not show a broad corporate retreat from AI investment. The clearer pattern is a gap between rapid experimentation and enterprise-wide results: surveys report stalled pilots and canceled initiatives even as organizations continue investing and some leaders report positive returns.
What “pulling back” means in practice
A company can cancel a pilot, halt a rollout, or replace a model without reducing its overall AI budget. Those are project-level decisions, not proof that the company has abandoned AI. The surveys available here point to selective reassessment: many organizations struggle to move projects into routine use, while investment continues.
Gartner’s survey of 1,303 respondents at organizations with at least $50 million in fiscal-year 2025 revenue found that 22% had successfully scaled AI across multiple business units or adopted an AI-first approach. The survey ran from January through April 2026. Gartner also said investment was accelerating, making the finding evidence of a scaling gap rather than a spending collapse. Gartner’s 2026 findings do not establish that all organizations, or any particular company, are cutting investment.
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Moving from a pilot into daily work is hard
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 enterprise-wide. A promising demonstration may not survive the move into real work if it does not fit existing processes or produce measurable results. The Conference Board’s report describes adoption maturity, not a count of companies that have stopped spending.
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Some projects do not make it past the pilot stage
Orgvue’s 2026 release reported that 78% of surveyed organizations had projects that failed or remained stuck in pilot: 35% reported failed projects and 43% projects stuck in pilot. The survey was conducted in February and March 2025 and covered 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 reported that 57% of leaders said competitive pressure was the main reason their organization deployed AI. These are commissioned survey responses, not audited records of every AI project. Orgvue’s findings suggest that some organizations adopted before they had a clear case for how a tool would fit or pay off.
Abandonment figures measure different things
S&P Global Market Intelligence reported in 2025 that the share of companies abandoning a majority of AI initiatives before production rose from 17% to 42% year over year. In the same release, organizations said an average of 46% of projects were scrapped between proof of concept and broad adoption. The survey included 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 an average share of projects scrapped. They are not interchangeable failure rates. S&P Global’s survey release documents a substantial project-level culling, not a universal corporate exit from AI.
Do companies see a return on AI?
The answer depends on what “return” means and who is asked. EY reported in 2026 that 98% of surveyed senior leaders at AI-investing organizations had seen positive ROI from AI initiatives. Its release described four survey waves from April 2024 to October 2025, covering 534 U.S. senior decision-makers across listed industries. This is self-reported positive ROI; it is not an audit showing that nearly every deployment produced verified financial gains.
PwC reported a narrower result: 12% of CEOs said AI had delivered both cost and revenue benefits. That figure comes from PwC’s 29th Global CEO Survey and asks about two specific kinds of benefit. It should not be treated as a direct contradiction of EY’s broader question about whether leaders had seen positive ROI. EY’s AI findings and PwC’s survey coverage use different respondents and outcome measures.
As Robin Erickson, PhD, Head of Human Capital Research at The Conference Board, put it 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 tension is central to the story: enthusiasm and reported returns coexist with difficulty proving consistent, broad business impact.
Why organizations are reviewing deployments
Costs need to be visible and controlled
AI use can create ongoing costs that are easy to miss in a pilot. EY found that only 64% of its surveyed leaders said their organization actively monitored token use and had clear spending guardrails. The figure does not show that token costs caused cancellations, but it highlights a management gap: usage and spending can expand without clear oversight.
Reliability and vendor dependence matter
In separate 2026 executive research, IBM 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 responses underline why companies may reassess a deployment’s resilience and dependence on a provider, rather than simply ask whether the model works under normal conditions. IBM’s executive research reports survey findings, not a forecast that any particular vendor will suffer an outage.
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Governance and portability can affect outcomes
A separate IBM study found that organizations embedding controls directly into AI systems reported 25% fewer incidents than those relying on manual governance. Organizations designing for adaptable workloads and replaceable models reported 10% higher AI ROI in 2025. The 2026 survey covered 2,000 senior executives across 33 geographies and 19 industries. These are IBM-reported survey analyses, not guarantees that controls or model portability will produce the same results in every organization. IBM’s analysis supports treating governance and the ability to change models as part of deployment planning.
What the evidence says about AI and layoffs
AI is not established as the main explanation for workforce cuts. The Conference Board reported that 6% of surveyed organizations cited AI as a primary reason for layoffs. That is a specific survey response, not a measure of every company’s workforce decisions, but it cautions against assuming that AI is driving most corporate layoffs.
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How to decide whether an AI project deserves to continue
A useful review asks whether the project has moved beyond an impressive demo and earned a place in operating work. Evaluate each deployment against the same practical tests:
- Measured value: Define a baseline and a specific outcome before expanding. Track the result against that baseline rather than relying only on broad claims of positive ROI.
- Total and usage costs: Monitor recurring costs, including token usage, and set budget limits that trigger review before spending grows unchecked.
- Workflow integration: Check whether the tool fits the actual process and whether intended users adopt it. A pilot that does not change routine work has not demonstrated enterprise value.
- Governance and incidents: Build controls into the system where possible, and track incidents and escalation paths rather than treating governance as paperwork after deployment.
- Resilience and portability: Identify the consequences of provider outages and whether workloads or models can be moved or replaced if requirements change.
These tests do not guarantee success or identify one universally best AI platform. They make the decision concrete: continue, redesign, limit, or stop a project based on evidence about value, operational fit, cost, control, and dependency.
Are companies entering a broad AI retreat?
The available surveys do not establish a general retreat in corporate AI investment. They do show that many efforts fail to scale, some initiatives are abandoned before production, and organizations are confronting gaps in measurement, workflow integration, cost oversight, governance, and resilience. At the same time, Gartner described investment as accelerating, while EY respondents reported positive ROI under a broad self-reported measure.
These figures come from surveys with different samples, fieldwork dates, and definitions, so they cannot be combined into a single rate of AI adoption or failure. The strongest conclusion is narrower: companies are pruning or reassessing individual initiatives as they try to distinguish useful deployments from pilots that do not deliver or cannot be operated reliably.
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