Enterprise AI pilots often demonstrate that a model can perform a task without proving it can do that task reliably, safely, and economically inside a real business workflow. The most common blockers are an unproven business case, unready data, security and model risks, difficult integration, infrastructure demands, skills gaps, and unclear ownership. Scale is more likely when teams design for production from the start, measure value against a baseline, and make explicit decisions about whether to stop, improve, or expand a pilot.
Why do enterprise AI pilots get stuck?
A pilot is usually bounded: a small group tests a capability under controlled conditions. Production changes the problem. The system must work with real data and existing applications, support actual users, meet security and governance requirements, and have people accountable for maintaining it. Scaling adds further demands: predictable costs, reliable operations, training, and a repeatable way to deploy the solution across teams.
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Survey findings illustrate the gap, but they do not amount to one universal failure rate. The studies below differ in their populations, geographies, dates, and definitions of testing, production, and scale.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Study | Reported finding | Scope and timing |
|---|---|---|
| Gartner, 2024 | 48% of AI projects reached production on average; the reported average time from prototype to production was eight months. Gartner classified 9% of surveyed organizations as AI-mature. | Survey conducted in Q4 2023 with 644 respondents in the United States, Germany, and the United Kingdom. |
| Concentrix and Everest Group, 2025 | Half of large enterprises were in GenAI pilot limbo; 27% had moved GenAI from testing to real-world implementation; 77% had scaled fewer than 40% of their GenAI pilots across the enterprise. | More than 450 enterprises worldwide; published November 12, 2025. |
| Deloitte AI Institute, 2026 report | 42% of respondents said their strategy was highly prepared for AI adoption, while preparedness was weaker in infrastructure, data, risk, and talent. | 3,235 senior leaders in 24 countries, surveyed in August–September 2025. |
Gartner’s 2024 survey also found that 49% of respondents named difficulty estimating and demonstrating AI project value as their primary adoption obstacle. The figure describes respondents to that survey, not the share of all enterprise AI projects that fail. Gartner associated AI maturity with a scalable operating model, AI engineering, upskilling and change management, and trust, risk, and security management.
#1 Best Overall
What are the common blockers—and how can teams address them?
1. The business case is vague or too narrow
A successful demo is not evidence that a solution is worth operating at scale. If the team cannot say which business outcome should improve, who owns that outcome, and how it will be measured, it is hard to distinguish a valuable result from an interesting experiment. Measuring only time saved can also miss costs and effects elsewhere in the process.
- Define the business problem and record a baseline before building. Choose measures that the process owner considers meaningful, such as throughput, error rates, service levels, or another outcome appropriate to the work.
- Estimate total cost of ownership, including the systems, infrastructure, integration, oversight, and ongoing support the production workflow will need.
- Set decision criteria in advance: what evidence would justify stopping, iterating, or scaling? Link continued funding to those measurable outcomes.
In Gartner’s 2024 findings, difficulty estimating and demonstrating AI value was the most commonly named adoption obstacle. A business case should therefore test both benefits and the costs of sustaining the system, rather than treating productivity gains as the only relevant return.
2. A prototype is mistaken for production engineering
A prototype can work without the capabilities needed to release, operate, and improve a service repeatedly. If deployment, monitoring, reliability, support, and ownership are deferred until after the demo, production requirements can force a major redesign.
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- Map the workflow and its dependencies before choosing a technical approach. Identify where the AI capability enters, what systems it must connect to, and what happens when it is unavailable or gives an unsuitable result.
- Plan for deployment, telemetry, reliability, and a named operational owner from the beginning. Decide how changes will be tested and released, rather than relying on a one-off handoff from the pilot team.
- Where multiple teams may reuse the capability, design interfaces and governance so it can be managed consistently. Concentrix and Everest Group recommend scalable API-first infrastructure, MLOps, telemetry, and policy-as-code governance as parts of a scaling approach.
Gartner identified AI engineering as one of the capabilities associated with AI-mature organizations. The practical implication is that a pilot needs an engineering path into ongoing operations, not just a model that performs well in a demonstration.
3. Data quality, access, or risk controls are inadequate
AI can magnify problems in the data and processes around it. Poorly governed or unsuitable data can produce unreliable results; broader access can also expose sensitive information or create new risks. For retrieval-based systems, the documents and other content made available to the model need scrutiny as well as the model’s outputs.
- Check data quality, lineage, access permissions, and suitability for the intended use before widening deployment.
- Assess bias and security risks, and decide how results will be validated in the workflow.
- Define controls for who can use the system, what information it can access, and how exceptions or questionable outputs are handled.
In the 2025 Concentrix and Everest Group study, respondents reported skills and expertise as a barrier at 56%, cybersecurity and model risk at 51%, and data integrity and bias at 47%. These are reported barriers in that study, not estimates of the proportion of all projects affected.
4. Legacy integration and infrastructure make scale harder
A pilot may avoid the systems that make the real process work. Production often depends on connecting to existing applications, permissions, data stores, and operational workflows. Infrastructure complexity can also make cost and performance harder to predict as usage grows.
- Map required systems, owners, and dependencies early, then estimate integration effort before calling the pilot scalable.
- Include infrastructure and operating costs in the business case. Check how cost and service demands could change if more users or workflows adopt the capability.
- Separate a model’s demonstrated capability from the readiness of the surrounding environment: a good result in a test does not establish that integrations, infrastructure, and support are ready for broad use.
Concentrix and Everest Group reported legacy integration as a barrier for 41% of respondents and infrastructure complexity for 34%. Everest Group also identifies cost predictability as a scaling challenge; these findings underline why technical feasibility alone is not a deployment plan.
5. People and process are treated as afterthoughts
Adding AI to an unchanged process can leave users unsure when to rely on it, who is responsible for a decision, or what to do when the system is wrong. Adoption also depends on whether affected teams have the skills and support to use the capability appropriately.
- Name a business owner, an executive sponsor, and the people responsible for operating and supporting the solution.
- Train affected users on the intended role of the system, its limits, and how to escalate problems.
- Review whether the work itself needs redesign: clarify which tasks AI supports, which decisions remain with people, and how exceptions move through the process.
Gartner associated AI maturity with upskilling and change management. Deloitte’s 2026 report emphasizes holistic work redesign and active senior leadership in governance, rather than treating adoption as a software rollout alone.
6. Each pilot starts from scratch
When teams cannot reuse validated components, operating practices, or lessons from earlier deployments, every new pilot repeats setup work and governance decisions. Reuse does not mean copying a solution blindly: teams still need to check whether its data, risks, integrations, and workflow fit the new setting.
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Concentrix and Everest Group recommend an operating model that supports scaling across the enterprise. OpenAI’s 2025 report describes reusable assistants that encode institutional knowledge and automate workflows through internal integrations. That is a provider-reported example, based on de-identified aggregated usage data and a survey of 9,000 workers across almost 100 enterprises; it is not independent proof that every organization should adopt the same product or architecture.
Best Value
How should an organization choose which pilot to scale?
Prioritize use cases by comparing the factors that determine whether a promising result can become a sustainable workflow. This is a decision checklist synthesized from the recurring barriers in the cited studies, not a validated universal scoring formula.
- Business value: Is there a clear process owner, a measurable baseline, and an outcome that matters to the business?
- Total cost: Have integration, infrastructure, oversight, and ongoing support been considered alongside expected benefits?
- Data readiness and risk: Are the necessary data accessible and suitable, and can privacy, security, bias, and validation requirements be addressed?
- Integration: Which systems and teams must be involved to make the capability work in the real process?
- Operating demands: Who will monitor, support, and improve the solution after launch?
- Workforce impact: What training or work redesign will users need?
- Governance fit: Can the use case meet the organization’s controls for access, risk, and accountability?
- Reuse potential: Could the components or workflow help other teams without assuming their requirements are identical?
What does a practical pilot-to-production path look like?
- Define the problem and baseline. Agree on the business outcome, the process owner, and how current performance is measured.
- Check feasibility in the real workflow. Assess data, permissions, risk controls, required integrations, infrastructure, and user needs before treating a demo as a deployment candidate.
- Build for operation, not only demonstration. Plan deployment, monitoring, support, reliability, and the process for testing and releasing changes.
- Test with intended users and controls. Validate the capability in the workflow, train affected users, and determine how human judgment and exceptions will be handled.
- Review evidence against the criteria. Compare outcomes and total costs with the baseline and the decision thresholds set before the pilot.
- Stop, iterate, or scale deliberately. If the outcome is weak, risks remain unresolved, or operating costs outweigh the value, revise or stop. If the evidence supports expansion, assign ongoing ownership and adapt the deployment to each new workflow rather than assuming a pilot transfers unchanged.
What the survey numbers mean for decision-makers
The figures can help frame the scale of the challenge, but they should not be combined into a single success or failure rate. Gartner’s survey covered respondents in three countries and reported production rates for AI projects; Concentrix and Everest Group reported findings about GenAI adoption among large enterprises worldwide; Deloitte surveyed senior leaders across 24 countries about preparedness. Their measures and samples differ, and the reported percentages answer different questions.
The consistent management lesson is to treat scale as an operating and organizational challenge as well as a technical one. A pilot is ready to advance only when the expected value, data and risk controls, integration path, operating ownership, and workforce needs have been addressed well enough for the intended workflow.
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