Strategic clarity in a single day
How a decision-makers' workshop at executive level took a mid-sized company's AI strategy from vague interest to a concrete roadmap.
Starting point
The executive management of a mid-sized company was watching the rapid development of AI with growing pressure to act. The questions were fundamental: which developments matter in the short term, and which in the medium term? How are prices and access changing? What does model sovereignty mean for us? And above all: what does all of this mean, in concrete terms, for our business model? What was missing was not interest, but a structured assessment — beyond hype and headlines.
High information pressure: new models, providers, and pricing models all the time — hard to place
Unclear strategic implications: what do current AI developments mean for the company's own business model?
Open questions on data sovereignty and dependence on individual providers
No reliable basis for investment decisions in AI
Solution
In a full-day workshop, executive management was guided through the relevant dimensions of the current AI landscape — not as a technology lecture, but as a strategic working session. Each block of topics was applied directly to the company itself: what does this mean for us? Where are our opportunities, and where are the risks?
Market overview: short- and medium-term AI developments, price trends, and performance forecasts — ordered by relevance to the company's own industry
Data and model sovereignty: open-source versus proprietary models, European alternatives, vendor lock-in risks, and mitigation strategies
Competitive analysis: how are direct competitors and adjacent industries positioning themselves in their use of AI?
Business model analysis: a systematic assessment of which parts of the company's own value creation are strengthened, changed, or threatened by AI
Investment and gap analysis: where there is a concrete need to act, where quick wins are possible, and where strategic investment is required
Results
By the end of the day, executive management had not only a better understanding of the AI landscape, but a concrete frame for action for the next 6–18 months. The discussion moved from "Should we do something with AI?" to "In what order do we implement these three measures?"
Clear investment priorities
Executive management identified three concrete AI initiatives with different time horizons — including a realistic estimate of effort and of the expected effect.
A well-founded position on model sovereignty
In place of blanket concerns, the company now has a differentiated strategy: which data stays internal, where cloud models are acceptable, and which dependencies are accepted deliberately.
Productivity potential quantified
For two core processes, concrete productivity gains were calculated — the basis for sound business cases rather than vague promises.
Executive management as the driver of AI
The workshop created a shared understanding at leadership level. Since then, AI initiatives are no longer delegated to IT; they are steered strategically.
Services used
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