New study reveals five success factors that determine AI transformation in companies and institutions

Companies across virtually all sectors are facing the challenge of integrating artificial intelligence (AI) approaches into their business models in order to operate more efficiently or successfully in the market. Despite the undisputed need for AI, it is often unclear which levers within an organisation actually need to be adjusted to achieve this.

These uncertainties exist both in the private sector and among research institutions, which also have to undergo AI transformation. A joint study by the German Aerospace Center (DLR), Saarland University and Frankfurt School of Finance & Management set out to provide clarity on the actual drivers of success in AI transformation. To this end, 300 private-sector companies and 30 research institutions were surveyed to examine which aspects are particularly conducive to successful AI transformation.

The greatest contribution to the success of AI transformation comes from topics grouped in the study under the success driver “Processes & Implementation”. These include aspects such as conducting a proof-of-concept approach or adopting an agile methodology when introducing AI technologies within an organisation.

“At DLR, we consider a proof-of-concept approach to be indispensable when introducing AI, as it enables us to set up a limited, experimental project designed to demonstrate whether a specific AI idea is technically feasible, viable from a data perspective and makes business sense for us — before investing significant time and money in full implementation,” says Klaus Hamacher, Deputy Chair of the DLR Executive Board.

Setting the right course in terms of “Strategy & Leadership” also supports successful AI implementation. Professor Sven Heidenreich of Saarland University sees a clear AI objective, a long-term AI strategy and the targeted prioritisation of resources as key sub-aspects. However, he warns against AI budgets that are too limited: “Without substantial additional investment in AI or a radical reallocation of resources within the organisation, AI transformation is unlikely to succeed.”

For AI projects, the right technological decisions and the creation of suitable infrastructure are similarly important to strategic alignment and successful organisational implementation. “A company’s IT infrastructure must be scalable and capable of adapting flexibly to changing AI requirements,” says Professor Ronald Gleich of Frankfurt School of Finance & Management, drawing on the findings of the survey. Since data form the foundation of AI solutions, accessible, high-quality data platforms are also essential. Particularly for small and medium-sized enterprises, the experts additionally recommend technology partnerships in order to manage the wide-ranging and investment-intensive implementation of AI.

Finally, successful AI application also depends on taking governance principles into account as well as cultural and employee-related factors.

As the study results show, among so-called AI transformation leaders — that is, companies that are particularly successful in piloting and implementing AI — all five of the aspects mentioned, namely “Strategy”, “Organisation”, “Technology”, “Governance” and “Culture”, are strongly developed in relation to AI. Conversely, based on the study results, it can be stated that none of these aspects can be neglected without jeopardising the success of AI transformation.