Three steps for a successful data and AI strategy

We believe that every company can realize value potentials from AI, because the most important prerequisite for this is data - already with the companies. On the way to a successful data and AI strategy there are typically the following three steps:

• Defining a roadmap: determining data potential (identification of "valuable data"), generating ideas for data and AI use cases, evaluating and prioritizing use cases, and drawing up a roadmap; This step ensures that the use cases are prioritized with the highest value potential and released for implementation
• Development of prototypes: development of prototypes for prioritized use cases and preparation of connection to the business processes; This step ensures that early movement into the data and AI strategy occurs and value potentials are realized.
• Long-term transformation: Implementation of further use cases, definition of relevant role profiles for data and AI tasks, adaptation of relevant business processes, training / empowerment of employees, etc., in this step, the prerequisites are built up, thus the data and AI strategy is implemented sustainably.

The second step (development of prototypes) is crucial, and companies can barely get started early enough. The danger is otherwise great that the data and AI strategy lands as nice paper in the filing cabinet. As an ambition level, companies should sit down for three months until they have a first AI prototype. No need to panic, but no reason to wait.