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Some organizations are turning off AI features after launch: in Applause’s 2026 survey, 44.1% of respondents said their organization had deactivated live AI features in the prior year because operating costs outweighed user value. That is evidence of reported deactivation—not proof that 44.1% of AI features fail, or that the features had all shipped exactly one year earlier.
What the available figures actually show
Applause surveyed 1,097 people working in software development, QA, data science, AI research, and product management. In that respondent group, 54.5% said their organization had released AI features, while 44.1% reported an organization-level decision to deactivate live AI features in the prior year because operational costs outweighed user value. These are self-reported respondent figures; they are not a feature-by-feature failure rate, and the survey does not isolate engineering teams or features launched exactly the year before.
Other studies describe different stages of AI work, and should not be added together as one retirement rate:
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- Before production: S&P Global Market Intelligence reported that the share of companies abandoning most AI initiatives before production rose from 17% to 42% year over year. Respondents also said an average of 46% of projects were scrapped between proof of concept and broad adoption. This is project attrition before production or broad adoption, not the retirement of a live product feature. The 2025 study surveyed 1,006 midlevel and senior IT and line-of-business professionals across North America and Europe. Source: S&P Global Market Intelligence.
- After deployment: Applause’s 44.1% result concerns respondents reporting live-feature deactivation in the preceding year, with cost outweighing user value cited as the reason. Source: Applause.
- Longer-term production: Gartner found that 45% of leaders in high-AI-maturity organizations said their AI initiatives had remained in production for at least three years, compared with 20% in low-maturity organizations. The 2025 survey included 432 respondents in the U.S., U.K., France, Germany, India, and Japan and was conducted in Q4 2024. This is a comparison by reported organizational maturity, not a direct measure of feature retirement or proof that maturity caused longer production. Source: Gartner.
Together, the findings support a measured conclusion: organizations report dropping some AI work before production and disabling some live features, while reported production longevity differs with AI maturity. They do not establish one universal AI failure rate.
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Why an AI feature can lose its place in a product
The running cost exceeds the value users get
A feature may attract interest at launch but still be expensive to operate or support. Applause directly links reported live-feature deactivation to operating costs outweighing user value. S&P Global also found cost concerns among reported adoption challenges. A useful accounting should include more than model inference: infrastructure, engineering maintenance, evaluation, incident response, and customer support can all affect whether the feature remains worthwhile.
Cost alone does not tell a team whether to retire a feature. The decision also depends on whether people use it for a meaningful task and whether the value can be measured. In a 2024 Gartner survey of 644 respondents in the U.S., Germany, and U.K., 49% cited difficulty estimating and demonstrating AI-project value as a primary adoption barrier. Source: Gartner.
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Quality problems emerge in real workflows
A feature can pass a demonstration and still fail to handle the context, inputs, or edge cases people encounter in daily work. Applause describes teams finding AI systems not ready after testing, including a chatbot delayed after tester feedback. Its Chris Sheehan, EVP, High Tech and AI, said: “Right now, many teams are struggling with internal workflows or finding that their data isn’t in the proper format for an agent,” Applause’s 2026 survey.
These issues can raise support costs and weaken user confidence even when the underlying model appears capable. A feature that regularly needs correction may transfer work to the user rather than remove it.
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Integration and data readiness are harder than expected
Connecting AI capabilities to an application involves more than calling a model. The feature must work with the product’s data, permissions, internal systems, and user flows. Gartner’s 2025 survey of 400 U.S. and U.K. software engineering and application-development leaders, conducted October–December 2024, found that 77% described building AI capabilities into applications as a significant or moderate pain point. Gartner analyst Jim Scheibmeir summarized the challenge: “Even with business leaders focusing more on this technology and despite the growing hype, execution is not easy.” Source: Gartner.
Trust, privacy, and security remain part of the product
Users need to understand what the feature does and have reason to rely on its output. Organizations also need to manage risks around data and security. Gartner identifies trust as a differentiator and reports data availability or quality and security among implementation barriers; S&P Global also lists privacy and security among common challenges. Those concerns can constrain adoption even when a feature is technically available.
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How to decide whether to retain, improve, or retire a feature
No cited source establishes a universal retirement threshold. Teams can make a more defensible decision by evaluating the feature against their own user, cost, quality, and risk evidence, then recording what would change the decision.
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1. Check for user value, not just availability
- Identify the user task the feature is meant to improve and whether it solves a recurring problem.
- Measure customer impact and meaningful use, not just launches, clicks, or access.
- Look for signs that users avoid the feature, abandon its workflow, or routinely redo its output.
Gartner reported that 63% of leaders in high-maturity organizations said their organizations used financial analysis of risk factors, ROI analysis, and concrete customer-impact measurement. That is a reported practice in this group, not evidence that those practices alone caused better outcomes. Source: Gartner.
2. Count the full cost of keeping it live
- Include inference and infrastructure spending, plus engineering upkeep, monitoring, evaluation, and support.
- Compare that cost with the feature’s measured user impact over a relevant period.
- Separate a fixable cost spike from a persistently uneconomic design; do not treat an expensive month by itself as proof that a feature has no value.
3. Test quality in the context where people use it
- Review errors, corrections, escalations, and failure cases in actual workflows.
- Check whether problems come from model behavior, missing context, unsuitable data, or a confusing interface.
- Determine whether a bounded improvement can reduce those problems without adding disproportionate cost or risk.
4. Verify the integration and risk controls
- Confirm that required data is available, appropriately formatted, and governed for the intended use.
- Review permissions, privacy, security, and what users are told about the feature’s limits.
- Assess whether the feature’s dependencies and operational responsibilities can be maintained reliably.
5. Choose a deliberate next state
Retain the feature when evidence supports user value and the organization can operate it responsibly. Improve it when a defined, testable change addresses a specific weakness. Disable or retire it when the ongoing cost, quality, integration, or risk burden cannot be justified by measured impact. Set the thresholds and review date locally: the available studies report outcomes and challenges, not a validated universal rule.
What organizational maturity adds
The Gartner production-duration comparison suggests that long-running AI initiatives are more common among organizations reporting higher AI maturity. Its accompanying finding that 63% of high-maturity leaders reported financial risk analysis, ROI analysis, and concrete customer-impact measurement points to practices worth examining, but the survey does not prove those practices cause longevity.
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