Only in the interplay of these four dimensions does a complete picture of one's starting position emerge. This can serve as the basis for a robust AI strategy that goes beyond individual tool decisions.



People

AI changes work methods, which is why acceptance within the team determines the success or failure of the entire transformation. Who already possesses knowledge and methodological competence, who proactively tries out new tools, and who can act as a multiplier in their department? Equally important is the question of what training and formats the team actually needs to use AI safely and effectively. After all, it is precisely the employees who know their processes best who should be able to translate them into concrete AI use cases themselves, rather than leaving this translation work solely to IT or external consultants. This dimension ensures that employees support the change rather than hindering it.

Use Cases

AI unfolds its value through concrete use cases, per department and across departments. Where can time be saved today, where do recurring, manual, or text-heavy tasks accumulate, and where can the quality of work be noticeably improved? The key is not to get stuck with individual isolated solutions, but to collect, evaluate, and prioritize use cases company-wide based on effort and impact.

Technology & Data

Without a common platform with access to relevant company data, AI usage remains piecemeal and difficult to control. This dimension clarifies which models and integrations a company actually needs, how systems such as ERP or other central data sources can be connected, and where the most valuable but also most sensitive data resides. This also includes the question of which AI providers to rely on: The race between the large language models is far from over; anyone who exclusively commits to a single model like ChatGPT or Claude today makes themselves unnecessarily dependent. Access to multiple models via a common platform keeps companies flexible, both in terms of performance and cost.

Governance

Legally compliant and secure AI usage requires clear rules. Who decides which tools may be used, and who is responsible for this? How are use cases classified, for example with regard to the EU AI Act, and what industry-specific requirements are added? A dedicated steering committee ensures that GDPR, order processing, and internal AI guidelines are not left to chance but are considered from the outset.

Anyone who honestly considers these four dimensions will ultimately arrive at a clear target image: What does the company want to achieve qualitatively and quantitatively with AI, and how can this be measured in twelve months? This target image is not an additional work step, but the logical consequence of people, use cases, technology, and governance. It is also the benchmark against which budget, responsibilities, and the progress of the AI transformation can later be measured, and thus the basis for all subsequent decisions. Without this target image, AI usage remains a loose collection of individual initiatives; with it, it becomes a roadmap that both management and the team can orient themselves by.



An AI strategy does not begin with the selection of a tool, but with the right questions about people, use cases, technology, and governance. Companies that take this time make their next decisions based on a clear target image rather than gut feeling, thereby creating the foundation for AI usage that truly has an impact beyond individual tools. In the end, this preparatory work pays off: through faster decisions, higher acceptance within the team, and AI usage that measurably impacts the business.