A detailed inventory and process analysis is crucial for success on the way to a holistic AI strategy. Artificial intelligence works best where processes are clearly structured, and this is precisely where many companies still have room for improvement. Many AI initiatives fail less because of the technology than because of historically grown data structures. Distributed silos, a lack of concepts and inconsistent storage make the use of generative AI in particular considerably more difficult. High-quality data structures are the key to holistic AI readiness. This is why our AI architects start by collecting the data basis, analyzing process flows and infrastructure and clarifying fundamental questions regarding compliance and data protection. In the same step, an AI readiness assessment is carried out, in which systematic benchmarking classifies the current situation and thus forms the basis for every further decision on the path to productive AI use.

Once a clear picture of the current situation and pain points as well as a classification of the optimization options has been obtained, the second step is to clarify fundamental questions of data management and its governance. Companies need to gain a clear picture of whether it is better to store their data centrally via a data lake or decentrally via a data mesh. Once the decision has been made, a basic data inventory and cleansing can take place, including risk assessment and classification, which can also clarify ownership, access rights and auditing. This results in the optimization of the IT target image: This includes consolidated data provision, scalability of possible AI workloads and simple data integration into the appropriate AI models. This is where the advantage of a holistic AI service provider comes into play: our AI architects do not design the data architecture in isolation, but always in the context of your existing IT landscape. It is crucial for the acceptance of such an AI agenda to familiarize the workforce with the new technology stack in initial awareness training sessions. Our AI experts have developed workshops for this purpose in which points of contact and a basic understanding are created that enable the workforce to identify with implementation ideas from new or already known use cases.

After a comprehensive analysis of the current situation, an in-depth process analysis, subsequent structuring and cleansing of the database, quick-win use cases can be quickly validated and implemented with added value by seamlessly integrating AI solutions into the existing core processes. Very classic use cases can be developed, for example, in the interaction of sales and CRM processes. Here, AI agents support lead scoring, the prediction of cross-selling and upselling potential in sales, pattern recognition in marketing or the proactive design and avatarization of customer service. AI agents also prepare information, support meetings, create summaries and suggest next steps. This frees up more time for value-adding activities, while humans retain the decisive role. As an AI service provider with 4813 realized IT projects, our AI experts ensure that these use cases do not remain in pilot mode, but are sustainably transferred to regular operations.