Successfully implementing AI in companies – What is important?
It is currently impossible to provide a general answer on how to successfully implement AI in companies due to the technology's rapid development. AI is a dynamic field that cannot be studied under stable conditions. Instead, knowledge about the successful use of AI emerges directly in practice – through active learning, experimentation, and reflection on experiences.
The research project ai:conomics provided an ideal foundation for this. In close collaboration between academia and practice, the impacts of AI on the working world were examined. This not only generated insights into the effects of AI but also provided a deeper understanding of how different stakeholders approach AI implementations in businesses. A distinctive feature of the project was its co-creative approach: an interdisciplinary team worked alongside partner companies and external experts, enabling valuable process learning about co-creative collaboration.
This lessons-learned document summarizes the key insights that are particularly relevant for companies seeking guidance on AI implementation.
It is intended as a stimulus for companies implementing AI, to encourage and to give orientation to:
- aligning the companies innovation culture and processes with the continuous AI learning process.
- understanding AI innovation as a cross-company design field and setting the exchange with experts from other companies as a standard practice.
- seeing practice and implementation research as complementary to each other and being able to better overcome uncertainties and challenges together.
The spotlight is on three key aspects for successful implementation:
Obeservation 1: AI implementation as a co-creative learning process
Insights from the field showed that successful initiatives involved numerous internal stakeholders in the development of content and decision-making, from brainstorming to the development of innovation projects ready for implementation and the rollout to the wider business.
- 1. Recommendation: Targeted, needs-oriented capacity building for key stakeholders.
- 2. Recommendation: Agile corporate culture - try things out, make mistakes, reflect.
- 3. Recommendation: Process design and moderation as key success factors
Observation 2: Cross-company exchange on process design and transformation
It became apparent that practice-oriented exchange about challenges and good practice in the introduction of operational AI can reduce barriers to innovation and minimize costly planning errors and process delays. However, such an exchange of experiences between companies regarding the design of implementation processes rarely takes place to date.
- 1. Recommendation: Find suitable spaces for exchanging experiences - and gradually expand them
- 2. Recommendation: Clarify the communication framework - and gradually expand it.
Observation 3: Collaboration with independent research
The scientific evidence on the opportunities and risks of AI is still limited, which poses challenges for companies in their decision-making processes. Collaborations with independent research offer the opportunity to incorporate expert and contextual knowledge, neutral external perspectives, and productive irritation. At the same time, they enable a deeper understanding of the impact of AI on organizations, employees, and productivity. This presents an opportunity to amplify the positive effects of AI.
- 1. Recommendation: Identifying good use cases for innovation-related research together
- 2. Recommendation: Recognize different approaches in research and operational innovation and shape the path together
- 3. Recommendation: Understand collaboration as a shared mission.
