Why AI Projects fail
Although companies around the world invest massively in artificial intelligence, the hoped-for success often fails: The majority of AI projects fail because they are not accepted by users.
The reasons are manifold: There is a lack of clearly defined use cases, trust in the technology is low or the specific benefit In everyday working life, remains unclear. Many projects are implemented as a pure technology push, without taking into account the actual needs of users. As a result, AI solutions are not used or even rejected in the company.
- Lack of user focus causes AI projects to fail
Role and Impact of Designers
Designers play a central role as mediators between artificial intelligence and users.
They analyse specific user requirements, structure and moderate interdisciplinary workshops and transfer complex, technical and business requirements into clearly understandable, user-centered design solutions.
This structured approach ensures that AI projects are consistently tailored to the needs of users and generate real added value.
Designers thus prevent AI initiatives from losing relevance and transform abstract technologies into practical, user-oriented solutions.
- Designers ensure comprehensibility, trust and inclusion
Human-Centered Design
Studies and best practices show how important a human-centered approach is for the success of AI projects.
Methods such as explainability, bias audits and rapid prototyping ensure that prototypes can be quickly validated and continuously improved. Comprehensible interfaces and transparent use of data are crucial for gaining user trust and increasing acceptance.
Companies that rely on human-centered design achieve higher adoption rates and a better return on investment.
- Human-based design increases acceptance
