Why is an AI strategy important?

What an AI strategy looks like heavily depends on a company's size and goals. Large companies usually benefit from planning AI broadly and across all areas from the start. Smaller companies often fare better by starting with individual projects. This allows them to gain initial experience without immediately tying up a lot of resources and creates a foundation for later investments.

Regardless of size, an AI strategy is always an ongoing process. The path to a data-driven company is never fully completed. Therefore, the strategy should be closely linked to concrete use cases and regularly reviewed.

An AI strategy is especially beneficial when a company:

• plans initial AI applications,

• wants to make processes more efficient,

• aims to automate recurring tasks,

• wants to counteract or address a skills shortage, or

• is already experimenting with AI.

The earlier goals and priorities are set, the easier it is to make decisions later on.

AI strategy in Germany and Bavaria

Both the federal and state governments are pursuing their own AI strategies – with clear priorities and billion-dollar investments. They show: Good framework conditions also determine how successful AI becomes in practice.

Germany: The national AI strategy (2018, updated 2020) was supplemented by the AI Action Plan of the BMBF. By 2025, the federal government is providing around 5 billion euros, including over 1.6 billion euros through the Action Plan. Focuses: more AI professorships, stronger practical transfer in companies, and "AI made in Germany".

Source: AI strategy of the German government

Bavaria: With the Hightech Agenda, the Free State has been investing around 5.5 billion euros in total since 2019, including 360 million euros specifically in AI – such as in research, AI professorships, and the transfer to medium-sized businesses. Four priorities: Intelligent robotics, data science, healthcare, and mobility.

Source: Hightech Agenda Bavaria

Developing an AI strategy in 5 steps:

• Step 1 – Identify problem

Sit down with your team and gather specific everyday problems: Which task is repeated constantly and wastes time? Where do errors often occur, for example, in data entry, scheduling, or quote creation? Note for each task how many hours per week it takes – this way, you'll quickly see where the biggest leverage is.

• Step 2 – Set goal

Formulate a specific, measurable goal for your problem instead of a general intention. Instead of "We want to become more efficient," say: "We want to reduce response time for customer inquiries from two days to two hours" or "We want to cut the error rate in invoice checking by half." This way, you can clearly say after the project whether it worked.

• Step 3 – Choose use case

Select a task that occurs daily or weekly, is clearly defined, and can be implemented within a few weeks – like summarizing emails, pre-sorting applications, or automatic creation of quote templates. Avoid cross-departmental large projects for the start; they take a lot of time and carry a high risk of failure.

• Step 4 – Define responsibilities

Appoint a fixed contact person who will accompany the project from start to finish – for example, a manager from the affected department or a particularly tech-savvy person on the team. This person plans the timeline, gathers feedback from employees, and reports progress to management.

• Step 5 – Evaluate experiences

Agree on a specific date at the start, after 4 to 8 weeks, to take stock: Was the goal set in Step 2 achieved? How did the team embrace the new solution? Record the insights in writing and decide based on them whether to expand, adjust, or discontinue the use case.

These and many other pieces of information on the AI Compass topic "Strategy" can be found in our download portal: