What is an AI strategy?
Short answer: Großartiger advises on "What is an AI strategy?" as a Berlin-based SEO and AI agency, remote across DACH.
An AI strategy is a prioritized plan for which specific business processes get automated, sped up or improved with AI, based on effort, impact and data readiness, instead of trying AI tools without a goal.
Reviewed by Manuel Maliszewski
Managing Director, SEO, AI and Web and Product Development
Last updated:
Short answer
An AI strategy is a prioritized plan for which specific business processes get automated, sped up or improved with AI, based on effort, impact and data readiness, instead of trying AI tools without a goal.
What an AI strategy actually delivers
An AI strategy doesn't start with a tool, it starts with a business problem. Großartiger works with the company to identify which processes cost the most time, where data is already structured, and where AI support can make a measurable difference, in customer service replies, content production or internal research, for example.
That analysis produces a prioritized roadmap, usually 3 to 6 use cases, ranked by implementation effort and expected impact. A typical project costs between €3,000 and €15,000, depending on how many processes are involved and how deep the technical integration into existing systems needs to go.
The difference from just trying tools is prioritization: instead of testing ten AI tools in parallel without measuring impact, a strategy defines clear success criteria per use case and a rollout timeline. The output is a concrete implementation plan, not a position paper.
Example: a mid-sized service company
A service company with 40 employees spends a lot of time manually answering recurring customer questions. The AI strategy flags this as top priority, defines an AI agent for first-response drafting with human approval for complex cases, and plans rollout over 6 to 8 weeks with a clear target metric: cutting first-response time by at least 50 percent.
What an AI strategy at Großartiger includes
- Process analysis: which recurring tasks currently cost the most time.
- Data readiness check: is the needed data already structured, or does it need prep work.
- Use case prioritization: 3 to 6 use cases, ranked by effort and impact.
- Tool and model selection: the right AI model and tool per use case, no one-size-fits-all.
- Integration plan: connection to existing systems (CRM, website, internal tools).
- Success criteria: measurable metrics per use case, defined before the start.
- Rollout timeline: concrete milestones instead of a vague position paper.
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Frequently asked questions
Do we need AI experience on the team before building a strategy?
No. The strategy is built together with Großartiger, regardless of current AI knowledge in the company. AI training can be part of the rollout.
How long does developing an AI strategy take?
The analysis and strategy phase usually takes 2 to 4 weeks, followed by prioritized rollout of the individual use cases.
Does an AI strategy replace existing staff?
The goal is usually relief from repetitive tasks, not headcount reduction. Most use cases shift time toward higher-value work.