Table of contents
- AI PoCs are easier to build. Scaling them is not
- The problem predates generative AI
- When an easy PoC leads to bad decisions
- A PoC should answer more than one question
- The operating model behind scalable AI
- Europe shows how wide the industrialization gap can be
- Every PoC should end with a decision
- Your PoC works. It still has not reached production
- Sources
A proof of concept (PoC) is a test used to determine whether an AI idea is feasible and has real potential. Today, it can be built quickly. The hard part begins after a successful experiment. Moving into production requires business ownership, integration into operating processes, risk controls, ongoing support, and a clear decision on whether the solution is worth scaling.
I have spent years working with organizations at different stages of AI adoption, from the first idea and the PoC through to running a solution in production. The same pattern keeps coming up. Teams are getting better at building demonstrators, working versions that show what a solution can do, yet they increasingly struggle to answer a simple question: what happens once the demo works?
Cloud services, ML platforms, AutoML, and foundation models have lowered the barrier to entry. They allow organizations to test ideas faster than they can build the capabilities needed to deploy them. The result is a growing number of demonstrators and a persistent gap between proof of concept and production.
In my view, that gap is one of the best tests of AI maturity. A company is ready to scale when it can choose the right problem, validate the value, and take responsibility for the entire solution, including what happens after launch.
AI PoCs are easier to build. Scaling them is not
There is no single reliable “global failure rate” for AI projects. Different studies measure different outcomes, so it makes more sense to compare the direction of travel than to search for one universal metric.
In 2017, Capgemini reported that only 36% of the organizations it surveyed had moved beyond the pilot or PoC stage. By 2020, the figure had risen to 53% (Capgemini Research Institute, 2020).
That improvement only reflects the first threshold: moving beyond the pilot stage. On a stricter measure, the result was much lower. Only 13% of organizations had deployed multiple AI use cases across multiple teams, reaching what Capgemini called “AI at scale.” The most common barriers were a shortage of experienced specialists, weak change management, and insufficient governance. This was before the current wave of generative AI.
Technology has advanced rapidly since then, but the last-mile problem remains.
A 2025 S&P Global study of 1,006 IT and business professionals and managers found that organizations cancelled an average of 46% of AI projects between PoC and broad adoption. The share of companies abandoning most of their AI initiatives rose from 17% to 42% in a single year. The methodology differs from Capgemini’s, but the pattern is similar: the closer projects get to real scale, the more of them are dropped (S&P Global Market Intelligence, 2025).
Gartner reported a similar pattern for generative AI in early 2026. At least 50% of GenAI projects had been abandoned after the PoC stage, mainly because of poor data quality, weak risk controls, rising costs, and unclear business value (Gartner, 2026).
The problem predates generative AI
Today, AI is often treated as shorthand for LLMs and agents. Yet the gap between PoC and production existed long before ChatGPT.
Consider a simple predictive model designed to forecast stockouts. The PoC may be technically successful, but it has not prevented a single stockout yet. That requires answers to a different set of questions. Where will scoring take place? How will the result reach the ERP system? Can the decision be automated? How will model quality be monitored, and what happens when it starts to decline?
Document classification works in much the same way. A PoC may achieve 95% classification accuracy, while a production system still needs a confidence threshold, exception handling, human review, and an SLA.
Generative AI changes the shape of the problem, but it does not remove the last mile. The technologies are different. The path from experiment to a working production solution remains much the same.
| Solution type | What makes the PoC easier | What becomes difficult in production |
|---|---|---|
| Predictive ML | Managed ML, AutoML, ready-to-use compute | Data pipelines, scoring, drift, retraining, integration |
| Classification and Document AI | Pretrained models, managed APIs | Confidence thresholds, exceptions, human-in-the-loop review, workflows, SLAs |
| GenAI and RAG | Foundation models, APIs, ready-made components | Grounding, evaluations, hallucinations, security, cost |
| AI agents | Frameworks and managed orchestration | Identity, permissions, tools, autonomy, tracing, oversight |
The bottlenecks have not disappeared. Some have simply changed shape. Modern platforms solve part of the traditional ML engineering problem, but they introduce other challenges. Hallucinations and difficult quality assessments take the place of custom model training, while evaluations and guardrails extend the scope of conventional MLOps.
The technical cost of the first experiment is falling. The total cost of a production system still includes API usage, infrastructure, integration, and human work. A cheaper PoC does not necessarily lead to a cheaper production system.
When an easy PoC leads to bad decisions
Lower-cost experimentation is good news. It allows teams to reject weak ideas sooner and test use cases that would not have been economical even a few years ago. There is a downside: organizations can produce far more PoCs.
Teams often start with the question, “Can we do this with AI?” The answer is frequently yes. The more important question is, “If we can, should we?”
RAND identifies poor problem definition as one of the most common causes of AI project failure. Teams can spend months improving a solution that ultimately has little impact on the organization. When an experiment is easy to launch, it is also easy to skip the question of whether the project is worth pursuing in the first place (RAND Corporation, 2024).
The conversation therefore needs to move from “Which model should we use?” to “What problem are we solving, who are we solving it for, and what will change if we succeed?”
A low barrier to entry also encourages demo-driven AI. An impressive demo comes first. Only later does the organization ask who owns the business outcome, what the baseline is, and who will support the solution once it is live.
A clear boundary matters here. A PoC is meant to test hypotheses quickly. Technical success alone does not justify a production deployment.
O’Reilly documented the scale of this problem at the end of 2023. Although 67% of respondents said their companies were using generative AI, only 18% reported having applications in production. Selecting the right use cases was the most commonly cited barrier. Generative AI did not create this problem. It increased the number of ideas that organizations can test quickly (O’Reilly, 2023).
A PoC should answer more than one question
A traditional PoC asks whether something works technically. An AI PoC needs to answer two additional questions. Is the result good enough to create measurable value? Is there a realistic route to secure deployment and ongoing support?
A model tested only on manually prepared data tells us little about how it will behave in a real operating process. If we do not know what will happen to the model’s output inside the business process, we do not have a solution yet. We have a prediction.
Production design should begin during the PoC. This does not mean implementing every production component from the start. It means knowing whether the project has access to real-world data and whether it accounts for edge cases, security, integration, economics, and human oversight. These questions should not appear two days before go-live.
Gartner’s research on AI maturity illustrates the difference. In highly mature organizations, 45% of respondents said they had kept AI initiatives in production for at least three years. The equivalent figure for organizations with low AI maturity was 20%. Project outcomes depend on the quality of the model and on the decisions made around it (Gartner, 2025).
The operating model behind scalable AI
An organization may be good at producing demonstrators yet struggle to decide which ones to develop. In that case, it lacks a mechanism for turning an experiment into a lasting business capability. That mechanism can be described in five stages.
VALUE
Start with the problem. Who owns the outcome? What is the baseline? What level of improvement would justify the investment? This is where the organization needs to address the return on its AI investment. Technology comes after those questions.
BUILD
Choose the right approach. It may be a conventional model, a classifier, RAG, or an agent. In some cases, AI will not be the best tool at all.
CONTROL
Define the conditions under which the AI system can operate. What data can it process? Where is human involvement required? How will escalation work? Governance belongs in the design process, not in a final review before go-live.
RUN
Go-live does not end the project. A model can lose accuracy because of drift. An agent may select the wrong tool. Production systems need ongoing monitoring of AI quality, cost, and behaviour.
SCALE
This is where actual scale begins. Can the solution be extended to another team? Does the organization have reusable components and the skills needed to support them?
VALUE → BUILD → CONTROL → RUN → SCALE.
The model is one part of this process, not the entire process.
At MINDBOX, this is what a mature approach to AI means. Accountability covers the entire solution lifecycle, from selecting the problem and designing the architecture to implementation, support, and further development.
Europe shows how wide the industrialization gap can be
Research cited by IDC found that only 13% of the European AI PoCs assessed and built in 2023 and 2024 reached production. This result applies to a specific group of projects. It should not be presented as a universal “87% failure rate for AI in Europe.”
IDC’s proposed remedy is more useful than the headline figure. Organizations need an operating plan that connects strategy with governance, as well as a technology operating model covering applications, data, and infrastructure (IDC, 2025).
Every PoC should end with a decision
Foundation models have accelerated experimentation, but the path to production still depends on decisions made by the organization. A good PoC ends with one of two outcomes: scale the project or stop it.
The expensive third option is a technically successful experiment that spends months in limbo.
Your PoC works. It still has not reached production
If your PoC is stuck between a demonstrator and production, we can help identify the constraint. It may be business value, architecture, integration, governance, or ongoing support. MINDBOX can help bring clarity to the decisions and prepare the solution for secure deployment and scale.
Autor:
Daniel Chrapczyński
Head of Intelligent Automation & AI
Sources
- Capgemini Research Institute, The AI-powered enterprise: Unlocking the potential of AI at scale, 2020. The report found that 53% of organizations had moved beyond pilot or PoC, compared with 36% in 2017. Only 13% had reached “AI at scale.” The study covered 954 large enterprises.
Capgemini report: The AI-powered enterprise - S&P Global Market Intelligence, Voice of the Enterprise: AI & Machine Learning, Use Cases 2025. The study covered 1,006 IT and business professionals and managers in North America and Europe. On average, 46% of projects were cancelled between PoC and broad adoption. The share of companies abandoning most AI initiatives increased from 17% to 42%.
S&P Global: AI experiences rapid adoption, but with mixed outcomes - Gartner, Why 50% of GenAI Projects Fail: And How to Beat the Odds, 2026. At least half of GenAI projects were abandoned after the PoC stage. The barriers included data quality, risk controls, cost, and unclear business value.
Gartner: Why 50% of GenAI Projects Fail - O’Reilly, Generative AI in the Enterprise, 2023. This global study of more than 2,800 technology professionals found that 67% of respondents were using GenAI, while only 18% reported production applications. The main barriers included use-case selection, legal issues, risk, and compliance.
O’Reilly: Generative AI in the Enterprise 2023 - IDC, European Artificial Intelligence Services 2025 Vendor Assessment. According to the research, 13% of the European AI PoCs assessed and built in 2023 and 2024 reached production. IDC points to the need for both an operating plan and a technology operating model.
IDC: European Artificial Intelligence Services 2025 Vendor Assessment - RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, 2024. Based on interviews with experienced AI and ML practitioners, the report identifies poorly defined business problems as one of the main patterns behind failed projects.
RAND: The Root Causes of Failure for Artificial Intelligence Projects - Gartner, AI Maturity Survey, 2025. The survey included 432 respondents and was conducted in the fourth quarter of 2024. Among respondents from highly mature AI organizations, 45% said they had kept AI initiatives in production for at least three years, compared with 20% in organizations with low AI maturity.
Gartner: 45% of High-Maturity Organizations Keep AI Projects Operational for Three Years
