Promoting innovation through cross-departmental collaboration
A successful innovation strategy requires that fresh impetus is not generated in isolation. Many companies report that the biggest challenge they face is transferring ideas and new technologies profitably across different departments. Cross-functional approaches make it possible to utilise synergies and increase the overall effectiveness of innovations. Sharing resources, knowledge transfer and mutual inspiration are key aspects that often lead to sustainable improvements.
For example, a manufacturing company that has developed an automated solution for the production line can scale this technology by integrating other departments such as warehouse logistics or quality control. This not only benefits centralised production, but also gives other areas a boost in efficiency through smart networking. In this way, the marketing department could also utilise data at an early stage in order to plan market launches more precisely. Experience shows that such networking often opens up new fields of action that were not previously recognisable.
Another example can be found in financial services companies. Here, innovative algorithms for risk assessment are not only used in risk evaluation, but also for customer advice and portfolio management. This results in holistic strategies that are better able to meet dynamic market requirements. This illustrates that the targeted further utilisation of ideas in other departments significantly increases innovative strength.
KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract) In this example, a company from the consumer goods industry has extended new AI-based analyses, which were originally developed for the supply chain, to the area of customer service. The data and insights gained helped to optimise service processes and significantly increase customer satisfaction. It became clear that the step across departmental boundaries was the only way to realise the full added value of the innovation.
The role of artificial intelligence in the innovation strategy
Today, artificial intelligence often acts as a catalyst for innovation because it enables data-driven approaches that could not be realised in traditional processes. In addition to automation, AI can help to gain new insights, recognise patterns at an early stage or adapt processes dynamically. However, it is also clear that the main hurdles in companies lie in cultural change and cross-departmental collaboration.
Companies are often approached with questions about how they can efficiently introduce AI into departments that were previously not very well connected or how the new solutions will be received by employees. An accompanying process in which the needs of all areas are taken into account and harmonised provides lasting support here. Clients often report that it is precisely this moderation and a structured but flexible implementation roadmap that is decisive for success. The innovation strategy gains robustness and acceptance as a result.
For example, a media company uses AI to analyse content and better understand user preferences. The insights gained are then incorporated not only into editorial planning, but also into advertising and sales strategies. This cross-departmental collaboration increases the hit rate when addressing target groups and has a direct impact on commercial success.
KIROI BEST PRACTICE at ABC (name changed due to NDA contract) Another use case comes from the mechanical engineering sector, where AI-supported maintenance forecasts were originally intended to improve maintenance. Following its successful introduction, this technology was transferred to the development department. There, data analysis helped to optimise new product designs in a more targeted manner and identify sources of error at an early stage, which accelerated innovation cycles.
Driving force for sustainable change in organisations
Innovation strategies benefit from a holistic view of the company. If you think across departments, you will realise that known challenges in one area can be solved more easily with solutions from another. In addition, employee motivation increases when they are part of a larger innovation process and see concrete progress.
In practice, this means that innovation management utilises teams and departments that otherwise have little exchange and gives them space to develop ideas together. This creates new impetus, which often results from the interaction of different perspectives. For example, the IT department provides valuable insights that are helpful for process optimisation in production - or sales experience helps with the design of service concepts.
KIROI BEST PRACTICE at DEF (name changed due to NDA contract) In a service company, an interdisciplinary team was formed that regularly organised cross-departmental innovation workshops. These workshops resulted in several process innovations that improved service quality and reduced costs at the same time. The involvement of a wide range of specialist departments was crucial for success.
My analysis
In order to realise an effective innovation strategy, it is essential to manage the generation and distribution of ideas within the company in a targeted manner. The challenge lies in not only utilising individual innovations where they arise. Instead, companies should systematically examine the extent to which technologies or findings also bring benefits to other departments. This not only promotes the efficient use of resources, but also increases the acceptance and impact of innovations. The accompanying support provided by structured coaching in the field of artificial intelligence and innovation management can help to break down internal barriers and provide impetus that leads to sustainable change. Clients often report an increased level of innovation and more dynamism through such integrative approaches.
Further links from the text above:
[1] KIROI Step 7: Ideas for other departments - promoting cross-departmental use of AI
[4] Step 7 of the KIROI strategy: practical examples
[7] Introduction to artificial intelligence according to the requirements of the EU AI Act
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