An AI strategy answers three questions: where does AI create measurable value for your business, which rules apply to data and employees, and in what order do you implement? Five steps lead from status quo to a working plan.
Step 1: Assessment
Capture where time is lost today: recurring tasks, media breaks, bottlenecks. This includes a look at your data (what sits where, in what quality) and at the skills in the team. This foundation prevents buying tools nobody needs.
Step 2: Prioritise use cases
Collect possible fields of application and rate each against two criteria: expected value and feasibility. Start with cases promising high value at low risk, typically routine communication, document processing or reporting. Concrete examples are in our article AI in SMEs.
Step 3: Clarify rules and data protection
Before the first tool goes live, you need simple guardrails: which data may go into which services? Who reviews AI output before it is sent? How do you ensure revDSG and GDPR compliance? A one-page AI policy is enough to start and creates confidence in the team.
Step 4: Run a pilot and measure
Implement the prioritised use case as a pilot with a clear timeframe, and define beforehand how you measure success: hours saved, faster response times, fewer errors. A pilot that improves nothing measurable gets stopped, that is part of the strategy.
Step 5: Scale and enable the team
What works in the pilot is rolled out to further areas. Invest in enabling employees in parallel: short trainings, shared prompts, clear responsibilities. AI value is not created by tools but by people using them well.
If you would rather not walk this path alone: our AI consulting accompanies you from assessment to implementation.
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