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Businesses are spending on AI faster than many can prove it is creating value. The frustration is real, but “AI kind of sucks” is not a universal verdict: survey results range from abandoned projects and hard-to-measure returns to positive self-reported ROI from advanced deployments. The divide is often between trying a tool and integrating it into work in a way that produces measurable outcomes.
Why is AI not delivering ROI for businesses?
Because using AI, deploying it, and earning a return from it are different milestones. A prototype can work in a demonstration without fitting a real workflow. A production system can be used without improving a business outcome enough to justify its full cost. And time saved by an individual employee does not automatically translate into company-wide financial returns.
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Measurement is a major part of the problem. In a Q4 2023 survey of 644 participants from organizations in the United States, Germany, and the United Kingdom, Gartner found that 49% named difficulty estimating and demonstrating project value as a leading AI adoption obstacle. Gartner also reported that an average of 48% of AI projects made it into production, with the prototype-to-production journey taking eight months. Those are findings about AI projects generally, not a GenAI-only failure rate. Gartner’s May 2024 findings show why a successful pilot should not be treated as proof of business impact.
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The cost side also extends beyond buying or building a model. Organizations may need to prepare data, connect systems, train employees, monitor output, and manage privacy, security, and compliance. If a business case counts only possible productivity gains, it can overstate the return.
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Why do AI pilots fail to make it into production?
A pilot usually tests whether a use case can work in a limited setting. Production requires it to work reliably inside existing processes, systems, and controls. That shift brings operational demands a small demonstration may not expose: usable data, integration with business software, people who can maintain the system, and employees willing and able to use it.
In a 2025 KPMG survey of 100 U.S.-based C-suite and business leaders at organizations with at least $1 billion in annual revenue, 85% anticipated organizational data quality challenges, 71% cited data privacy and cybersecurity, and 46% cited employee adoption. The results describe this large-company respondent group, not businesses as a whole. KPMG’s January 2025 survey also found that 31% expected to be able to measure GenAI ROI in the following six months; none believed they had reached that point when surveyed.
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Roland Berger’s 2025 study offers a view from a different population: 150 executives at companies with more than 250 employees across five European countries and several industries. Only 27% said their company had fully integrated GenAI into operations and workflows. Respondents identified data issues (28%), integration complexity (25%), and difficulty finding AI and data experts (15%) as implementation challenges. These figures should not be ranked directly against KPMG’s: the samples, geography, and survey questions differ. Roland Berger’s study underscores that adoption involves organizational readiness as well as model capability.
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S&P Global’s 2025 survey reported that the share of organizations abandoning the majority of their 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. These are survey findings tied to those specific stages; they do not mean that 46% of all AI projects everywhere fail.
The same survey found that 46% of respondents whose organizations had invested in generative AI said no single enterprise objective had received a “strong positive impact.” That is not the same as saying those organizations got no benefit at all: the finding concerns the absence of a strong positive impact on any one objective. S&P Global surveyed midlevel and senior professionals in North America and Europe, so its results reflect those respondents’ reported experiences. Read S&P Global’s 2025 findings.
Is generative AI actually improving productivity at work?
Some deployments may improve productivity, but a claim about a useful tool or a scaled initiative should not be generalized to every pilot or every adopter. Deloitte’s 2025 survey reported that almost all respondents said their most advanced, scaled GenAI initiatives had measurable ROI; almost a quarter (20%) reported 31% or more. This is self-reported ROI for organizations’ most advanced scaled initiatives, not a result for all GenAI projects. Deloitte’s respondents were AI-savvy leaders involved in piloting or implementation, a group likely to differ from organizations still experimenting or struggling to deploy. Deloitte’s 2025 State of Generative AI findings provide a counterpoint to abandonment reports without canceling them out.
The contrast is useful: S&P Global’s figures track abandonment and whether respondents saw a strong positive impact across enterprise objectives, while Deloitte’s ROI result focuses on the most advanced scaled initiatives. They measure different things in different respondent groups. Together, they suggest that outcomes are uneven and that maturity matters—not that one survey proves AI is either broadly successful or broadly useless.
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What are the biggest barriers to using AI in a business?
- Data quality and access: AI systems depend on relevant, reliable information. Fragmented, outdated, or poorly governed data can undermine output before a model reaches a workflow.
- Integration: A useful system must fit the software and processes employees already use. Connecting it and keeping it dependable can take more work than building a prototype.
- Employee adoption and skills: A tool that staff do not trust, understand, or find useful will not become routine. Training and change management are operational needs, not optional polish.
- Privacy, security, and compliance: Organizations need to know what information is being used, where it goes, who can access it, and how output is checked against their obligations.
- Unclear value measures: If the team has no baseline or defined business outcome, it may be impossible to tell whether a system is worth its cost.
- Talent and ongoing ownership: Production systems require people to monitor, maintain, and improve them—not just launch them.
Gartner described AI-mature organizations as investing in operating models, AI engineering, upskilling and change management, and trust, risk, and security capabilities. These are reported differentiators, not a guaranteed recipe for success. As Gartner Senior Director Analyst Leinar Ramos put it on May 7, 2024: “As organizations scale AI, they need to consider the total cost of ownership of their projects, as well as the wide spectrum of benefits beyond productivity improvement.”
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How can a business tell whether an AI use case is worth scaling?
Before expanding a pilot, decision-makers can ask questions that connect the project to operational reality rather than the novelty of the technology:
- What outcome is the system meant to change? Choose a business measure—such as cost, customer experience, revenue, or risk—rather than treating “using AI” as the goal.
- What is the baseline? Record how the process performs before deployment so that any change has a meaningful comparison.
- Is it part of routine work? Distinguish a demonstration or isolated tool from a system integrated into the workflow and used consistently.
- What is the full operating cost? Include implementation, data preparation, integration, oversight, training, and ongoing maintenance alongside expected benefits.
- Who checks quality and risk? Define responsibility for errors, privacy, security, compliance, and escalation before the system handles consequential work.
- What evidence would justify a change in course? Agree in advance what would lead the organization to stop, revise, or scale the use case.
KPMG Vice Chair of AI & Digital Innovation Steve Chase argued in January 2025: “The dynamic nature of AI demands new ways to measure value—beyond the limits of a conventional business case. As leaders work to define the right metrics, those measures must be tightly aligned with the business strategy and should account for the cost of not investing.” The practical point is not to promise that every investment pays off, but to make the intended value and the evidence for it explicit.
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