From Pilot to Production: Why Dutch SMEs Get Stuck After the AI Proof of Concept
From Pilot to Production: Why Dutch SMEs Get Stuck After the AI Proof of Concept
TL;DR: A successful AI demo is not a successful AI deployment. Most Dutch SMEs stall between proof of concept and production. Here are the root causes and concre…
There is a pattern that repeats with striking consistency across North Holland SMEs. A team runs an AI proof of concept. The demo looks compelling. The stakeholders nod. And then nothing happens.
The proof of concept enters what practitioners now call "PoC purgatory" — a state where the pilot was technically successful but never transitions into a production system that delivers ongoing business value. According to research from RAND Corporation, approximately 80% of AI projects fail to reach production deployment. Among sub-50 employee companies in the Netherlands — where 95% report experimenting with AI tools but fewer than 5% report extracting measurable operational value — the gap between demonstration and deployment is one of the defining problems of the current AI adoption cycle.
This is not a technology problem. It is a structural one. And it has identifiable root causes that can be addressed before your next PoC becomes another archived slide deck.
What PoC Purgatory Actually Looks Like
PoC purgatory does not present as failure. That is what makes it dangerous. The proof of concept produced results. The model worked on the test data. The demo generated genuine enthusiasm.
But weeks pass and the PoC is not in production. What happens instead is a series of reasonable-sounding delays: "We need to clean the full dataset first." "IT needs to scope the integration." "We are waiting for the vendor to confirm pricing for the production tier." "The person who ran the pilot is now on another project."
Each delay is individually defensible. Collectively, they are symptoms of a PoC that was designed to demonstrate feasibility — not to transition into an operating system.
The cost is not just the sunk investment in the pilot. It is the organisational credibility loss. After one or two PoCs stall, teams become sceptical of the next AI initiative. Leadership loses patience. The window of organisational willingness to experiment narrows.
Root Cause 1: No Production Data Pipeline
The most common structural failure. The PoC ran on a curated dataset — manually prepared, cleaned, and formatted for the demonstration. Production requires a pipeline: automated data ingestion, transformation, validation, and delivery to the model on a schedule.
Most sub-50 employee companies do not have a data engineering function. The person who ran the PoC probably assembled the dataset manually. When the question becomes "how do we feed this model fresh data every day," there is no one to answer it and no infrastructure to support it.
Exit strategy: Before any PoC begins, define the production data source and confirm that data can flow to the model without manual intervention. If it cannot, the first deliverable is not a model — it is a data pipeline. Budget and scope accordingly.
Root Cause 2: No Integration Plan
A PoC that runs in a notebook or a standalone application is not a production system. Production means the AI output reaches the people who need it, in the system they already use, at the moment they need it.
For a 20-person logistics company, that might mean the AI-generated delivery route recommendation appears inside the dispatch tool — not in a separate dashboard no one checks. For a 35-person professional services firm, it means the AI-drafted client summary is available in the CRM before the account manager's Monday call — not as a CSV export.
Most PoCs skip integration entirely because integration is expensive, slow, and requires knowledge of the existing tech stack that the AI consultant or vendor does not have.
Exit strategy: Require an integration specification as a PoC deliverable. Not a working integration — but a documented plan covering: which system receives the output, what format, what trigger, and what fallback when the model is unavailable. If the consultant cannot produce this, they have demonstrated the AI but not the deployment.
Root Cause 3: The Consultant Left After the Sprint
This is structurally predictable. Many AI consulting engagements are scoped as time-boxed sprints: four weeks, six weeks, eight weeks. The sprint produces a PoC. The consultant delivers a final presentation. The engagement ends.
What remains is a working prototype that nobody inside the organisation fully understands, maintained by nobody, with documentation that describes the model but not the operational context around it.
The gap is not malicious. It is a scoping failure. If the engagement is designed to end at the PoC, the engagement is designed to end before the hard work begins.
Exit strategy: When scoping an AI consulting engagement, explicitly define what happens after the PoC. Who owns the transition to production? What is the handover protocol? Is there a support window? If the engagement contract ends at the demo, you are paying for a demonstration, not a deployment. Price and expect accordingly.
Root Cause 4: No Change Management
Even when the data pipeline exists, the integration works, and the system is technically ready — production AI changes how people work. And people do not change how they work because a model is accurate.
The warehouse team that has manually categorised incoming stock for eight years will not adopt an AI classifier because the PoC showed 94% accuracy. They will adopt it when someone they trust shows them how it fits into their morning routine, what happens when the classifier is wrong, and that their expertise still matters.
Change management in a 10-to-50 person company does not require a formal programme. It requires one person — ideally someone already respected by the team — to own the rollout, handle objections, and iterate on the workflow until it works for the people using it.
Exit strategy: Name the internal change owner before the PoC starts. If no one has the bandwidth or mandate for this, factor it into the engagement scope. A deployed AI system without adoption is operationally identical to a failed PoC.
Root Cause 5: Success Criteria Were Never Defined
A PoC that aims to "explore what AI can do" will always succeed on its own terms — and never produce a clear go/no-go decision for production.
Production-oriented PoCs start with a measurable question: "Can this model reduce invoice processing time from 12 minutes to under 4 minutes, with fewer than 2% errors, using data from our actual ERP?" That question produces a binary answer. Binary answers produce decisions. Decisions produce production deployments.
Exit strategy: Before the PoC begins, agree on the specific metric, the threshold, and the data source. Write it down. At the end of the PoC, compare the result to the threshold. If the threshold is met, the next step is production planning — not another round of exploration.
How to Design a PoC That Leads to Production
If you are planning an AI PoC and want to avoid the purgatory pattern, build these five elements into the design from the start:
- Production data source confirmed — not a curated sample, but the actual data the model will consume in production
- Integration specification included — a documented plan for how the output reaches users in their existing tools
- Post-PoC ownership defined — a named person or team responsible for the transition, with budget allocated for the production phase
- Change owner identified — someone inside the organisation who will manage adoption with the affected team
- Binary success criteria agreed — a specific metric and threshold that produces a go/no-go decision
None of these add significant cost to the PoC. They add significant probability of the PoC producing a production system.
Frequently Asked Questions
What is PoC purgatory in AI projects?
PoC purgatory describes the state where an AI proof of concept was technically successful — the model worked, the demo was compelling — but the project never transitions to a production deployment. It is characterised by a series of reasonable-sounding delays that collectively prevent the AI system from delivering ongoing operational value. Research suggests approximately 80% of AI projects experience this pattern.
Why do AI proofs of concept fail to reach production at Dutch SMEs?
The most common root causes are: no production data pipeline (the PoC ran on manually curated data), no integration plan for connecting the AI output to existing business tools, the consulting engagement ending at the demo without a handover plan, no internal change management, and success criteria that were never defined in measurable terms.
How do I move an AI pilot from proof of concept to production?
Design the PoC for production from the start. Confirm the production data source before beginning, include an integration specification as a deliverable, define who owns the transition after the pilot ends, name an internal change owner, and agree on a specific metric and threshold that will trigger the go/no-go decision.
How much does it cost to move from AI PoC to production?
The production phase typically costs two to five times what the PoC cost — primarily for data pipeline engineering, system integration, testing, and change management. The mistake most SMEs make is budgeting only for the PoC and treating production as a follow-up discussion. Budget for both phases before starting, or accept that you are funding a demonstration rather than a deployment.
Further Reading
- What an AI Readiness Assessment Should Cover
- When Not to Buy AI Consulting Yet
- How to Run an Internal AI Pilot Without Creating Governance Debt
- What a First 90 Days of AI Adoption Should Look Like for a 10 to 50 Person Company
Get Out of PoC Purgatory
If your team has a proof of concept that stalled — or you are about to start one and want to design it for production from day one — our AI Consulting engagement is specifically designed to bridge the gap between demonstration and deployment.
If you are not yet sure whether your organisation is ready for a PoC at all, start with an AI Readiness Assessment to confirm the conditions are in place before you invest.
