From PoC to production: Why many AI projects get stuck in medium-sized companies
The euphoria is often great: the first AI prototype works. The feasibility is proven. But then happens – nothing. Many AI initiatives in medium-sized companies die a quiet death between the laboratory and real value creation. We analyze the “PoC trap” and show how you can bridge the gap to operational operations.
Artificial intelligence has arrived in German medium-sized businesses – at least in theory. The innovation departments (or dedicated lone wolves) experimented. There are successful proofs of concept (PoC): the image recognition that identifies cracks in the workpiece, or the forecast model that predicts raw material requirements. The presentation to the management is going excellently, all the traffic lights are green.
Six months later you ask about the project, and the answer is sobering: “It’s not running quite smoothly yet,” “We’re waiting for IT resources,” or “The business department isn’t using it.”
This phenomenon is so common that it has a name: Pilot Purgatory. Gartner once estimated that up to 85% of all AI projects never reach production maturity. In pragmatically oriented medium-sized companies, where every investment must show a clear ROI, this ratio is fatal. If the first two projects fizzle out, the topic of AI is often burned out for years.
Why is the step from “playground mode” to “production mode” so difficult? It is rarely due to the AI technology itself, but mostly to the organizational and technical framework conditions.
The 5 most common pitfalls on the way to production
A PoC takes place under laboratory conditions. Production is the harsh reality. This difference is often underestimated.
1. The “laptop illusion” (integration hurdles)
Many PoCs are developed in isolation - often on a data scientist's powerful laptop or in an isolated cloud environment. Everything works perfectly there. But productive operation means: The AI model has to talk to the 20-year-old ERP system, process real-time data from machine controls (PLC) and feed the results directly back into the employee user interface. The pitfall: The integration costs into the historically grown IT landscape of medium-sized businesses are often five times higher than the development of the actual AI model. If this is not planned for early on, the project will die due to interface complexity.
2. Data quality: laboratory vs. reality
Manually cleaned “golden datasets” are often used for PoC. A CSV export that someone has manually cleaned of errors. However, in live operation, the AI has to deal with “dirty” data: sensors fail, employees type errors into the system, data formats change unannounced. A model that had 99% accuracy in the lab crashes to 60% in reality and becomes useless. The pitfall: The assumption that the data quality in everyday life is just as high as in the test data set.
3. Missing operating concept (MLOps)
Software that runs once usually continues to run. AI models do not. They “age”. If customers' purchasing behavior changes or a new machine is in production, the old training data no longer fits the new reality - the prediction quality decreases (data drift). Who is monitoring this? Who retrains the model? In medium-sized companies, there are rarely dedicated MLOps (Machine Learning Operations) teams that take care of the care and maintenance of AI models. The pitfall: AI is treated like classic software (“fire and forget”) instead of like a living system that needs ongoing support.
4. The human factor: lack of trust
No matter how precise an AI system is, if the experienced production master doesn't trust it, he will ignore it. PoCs are often “black boxes”: They spit out a result but do not explain why. Medium-sized experts who have known their machines for 30 years do not like to be patronized by opaque software. The pitfall: The end users are integrated too late. The solution is built “for” them, not “with” them.
5. No clear business case beyond innovation
Some PoCs are started because the technology is fascinating (“We have to do something with GenAI”). A PoC is allowed to play. A production system must make or save money. It often turns out after the PoC that scaling would be so expensive (e.g. cloud costs, licenses) that the business benefit does not cover the ongoing costs. The pitfall: Infatuation with technology instead of a cool cost-benefit calculation.
4 strategies to bridge the gap
How can medium-sized companies overcome these hurdles and successfully bring AI onto the streets?
A. Production First Mindset
Think of the ending from the beginning. Ask the unpleasant questions as early as the PoC phase:
- If this works, who will run it on a daily basis?
- What infrastructure should it run on? (Do we have the necessary cloud environment?)
- What happens if the model makes wrong decisions? (Liability question) A successful PoC should not only prove that it can be done technically, but also how it will work operationally.
B. Invest in a solid data platform instead of standalone solutions
Instead of building a new technical infrastructure for every AI project, it is worth building a central data platform (e.g. a data lakehouse). This standardizes access to data and greatly simplifies the path from development to operation. Once the basis is in place, the second, third and fourth AI projects will be significantly faster and cheaper.
C. The human-in-the-loop approach
Especially in medium-sized companies, where quality is the top priority, AI should initially rarely make fully automated decisions. Start with assistance systems: The AI makes a suggestion, the human decides. This builds trust. If the system works reliably, the level of automation can be increased gradually. This takes away the fear of employees being replaced.
D. Establish MLOps pragmatically
You don’t need a 20-person AI team. But you need clear responsibilities. Define early on who is responsible for retraining the models. Often this can be largely automated through modern platforms, but someone needs to receive the alerts when the model “drifts”.
Conclusion: Courage for industrialization
The proof of concept is the easy part. The industrialization of AI – i.e. its integration into boring, daily processes – is the real work.
This work is enormously worthwhile for medium-sized companies because this is where the real efficiency gains lie. Successful companies don't celebrate the successful PoC, they celebrate the first day on which the system is in regular operation and delivers real added value. Don't let your pilot starve in the air - give him a runway.