Introducing AI, but how?

1. Ensure transparent and early communication 

Reservations about AI usually arise from uncertainty about how it works, its limits and the quality of results, not from rejection. Transparent communication about goals, areas of application, limits and pilot projects as well as the early involvement of specialist departments promotes trust and prevents unrealistic expectations.

2. Clearly position AI as a supporting tool

The results at Implenia Civil Engineering GmbH show that AI is accepted as a supporting tool to human expertise; however, there is reluctance to transfer more decision-making to AI. There are doubts about the quality of results and trustworthiness. AI should therefore not be positioned as an autonomous decision-making authority, but rather as data-based support whose results require human review. Clear responsibilities and transparent test steps strengthen trust in the long term.

3. Realistic expectations

In addition to positioning AI as a supportive decision-making tool, it is crucial to make its performance limits transparent and promote realistic expectations. As AI works on the basis of data and probability, results may be incomplete or incorrect. If typical application scenarios and typical sources of error are presented transparently, the understanding of AI can be strengthened and both inflated expectations and unfounded fears can be reduced.

4. Promote systematic skills development

The level of knowledge of employees in dealing with AI is predominantly neutral to slightly positive: there is an awareness of AI, but there is often a lack of confidence to act. It is therefore crucial to provide practical training that teaches not only technical knowledge but also how to deal with AI results, uncertainties and prompting skills. Through application examples and accompanied test phases, knowledge can be translated into confidence and trust in AI can be strengthened.

5. Design organizational and technical framework conditions in a targeted manner

In addition to individual factors, the acceptance of AI is strongly influenced by organizational framework conditions. A lack of strategies, unclear responsibilities and inadequate digital structures act as key obstacles. A clear AI strategy with clear goals, responsibilities and integration steps, reliable data structures and stable technical integration are therefore crucial.

6. Actively involve employees in the implementation process

Actively involving employees, for example through participation formats, feedback opportunities or pilot applications, can help to strengthen acceptance in the long term and identify practical use cases. Feedback formats and adaptations based on practical experience make it possible to make the introduction process learning-oriented and participatory.