Why So Many AI Pilot Projects Fall Through

According to IBM, Only 16 percent of AI initiatives have been scaled company-wide. By the end of 2025, at least every Second GenAI project abandoned after the proof of concept. «Many companies have now proven that AI works from a technical standpoint. But the real challenge begins after that,» explains Artur Heidt, managing director of KAMIE Solution Factory Deutschland GmbH. «A proof of concept shows what is possible. But it doesn’t yet answer the question of whether a solution can be operated cost-effectively, integrated into existing processes, and used in day-to-day operations.»

The pilot is just the beginning

A proof of concept typically involves a small number of users, selected data, and clearly defined processes. In production, the requirements increase significantly: interfaces must function reliably, access rights must be properly managed, data must be continuously updated, and results must be reliably reproducible. «In a pilot project, many weaknesses can still be addressed manually. That doesn’t work in production,» says Heidt. «If employees have to constantly check results, edit data, or correct errors, a demo can still be convincing. In daily use, however, this quickly becomes a cost factor.» That’s why the eventual go-live should be considered right from the start. What matters is not only whether an application works technically, but whether it can be operated sustainably and with reasonable effort.”.

Without clear benefits, AI remains an experiment

Another reason for failing AI projects is a lack of a business case. In addition to insufficient data quality and inadequate risk controls, Gartner cites rising costs and unclear business value as key causes of failed GenAI projects. «After the proof of concept, the question shouldn’t just be: Does the AI work? The crucial question is: What improves economically?» emphasizes Heidt. «Without clear metrics, it’s nearly impossible to assess later on whether an application is truly successful.» The benefits can include shorter processing times, fewer errors, higher productivity, more readily available information, or additional revenue. At the same time, companies must take total costs into account. In addition to licenses, these include integration, data preparation, operation, monitoring, support, and training. Current figures show that AI can pay off financially: 52 percent of companies already using AI report a measurable contribution to business success. 77 percent see their competitive position as having improved.

Data as a Bottleneck in Scaling

Many pilot projects rely on a carefully prepared dataset. In regular operation, however, AI systems must work with actual company data. In small and medium-sized businesses in particular, this data is often scattered across ERP systems, CRM systems, documents, emails, Excel files, or specialized applications. «AI can deliver excellent results in tests using selected data. The key question is whether it can also work reliably every day with the company’s real data,» explains Heidt. According to IBM 72 percent of the CEOs surveyed consider their own company data to be crucial for unlocking the value of generative AI. Companies should therefore determine early on what data is needed, where it is located, how up-to-date it is, and who is responsible for its quality. This does not require an immediate modernization of the entire data landscape. For an initial use case, a clearly defined data set—specifically made available for productive use—is often sufficient.

Going live requires clear accountability

Furthermore, AI projects should not be the sole responsibility of IT. Senior management defines goals and priorities; the business unit understands the processes and requirements; and IT is responsible for integration, security, and technical stability. «A pilot project can work with a small team. A production system requires clear lines of responsibility,» says Heidt. «Who monitors quality? Who decides on changes? Who is responsible for data, operations, and results? If such questions aren’t addressed until after the pilot, delays will result.» Especially in upper-mid-market companies, short decision-making paths often clash with complex IT landscapes. New AI applications must therefore not only be able to be rolled out quickly but also fit seamlessly with existing systems and processes over the long term.

Start small, think productively

For medium-sized companies, a clearly defined pilot project is often the right way to get started. However, it should not be viewed as a technical playground. «Companies don’t have to launch a major transformation program right away,» Heidt summarizes. «But they should already be thinking about cost-effectiveness, data, integration, and responsibilities during the proof of concept. Only then will a good idea become a productive application.» The true success of an AI project, therefore, is not evident in a successful demo. It only becomes apparent when the application is used on a long-term basis and makes a measurable contribution to the business.

Source: KAMIE Solution Factory Deutschland, LLC 

This article originally appeared on m-q.ch - https://www.m-q.ch/de/warum-so-viele-ki-piloten-versanden/