Why do AI projects fail?

In this video, our CEO Patrick explains why AI projects fail in practice and what you can learn from this for your own projects.

Many companies embark on AI projects full of expectations. However, the hoped-for success often fails to materialize. This is not because of the technology, but because important fundamentals are missing.

A lack of data strategy, unclear responsibilities, isolated departments, or unrealistic expectations of the «magic of AI» lead to promising initiatives failing before they can have an impact.

These findings are based on real experiences, studies, and numerous discussions with companies that genuinely wanted to use AI.

Common obstacles in AI projects

No understanding of AI in a business context

If specialist departments and decision-makers are not on board, the project will remain isolated.
Lack of technical integration

AI without connection to existing processes remains a gimmick.
Lack of responsibility and ownership

Who is responsible? If this remains unclear, the project will fail.
Too little cooperation between IT and specialist departments

This leaves a lot of potential untapped. Only by working together can real added value be created.
Technology is prioritized over processes

Many start with the tool and not with the actual problem.
No change management

Without communication and training, the project will fail due to resistance.