In an era where artificial intelligence is revolutionizing business models, companies have a lot at stake. The difference between digital pioneers and laggards is increasingly decided by the question: Do you have a well-thought-out AI strategy that is implemented in relevant areas? According to Goldman Sachs, the use of AI enables productivity increases of 25 percent on average. But how can companies systematically unlock this potential?

The answer lies in an AI strategy that is systematically derived from the business strategy and defines concrete framework parameters for all affected business areas.

The Four Dimensions of a Successful AI Strategy

A successful AI strategy requires consideration of four central dimensions:

The development of a successful AI strategy requires methodical derivation from the overarching business strategy. This forms the foundation for all subsequent strategic decisions. The desired positioning in the AI field can be derived from the business strategy. Companies with a pronounced digitalization agenda often strive for a pioneering role, while others consciously choose a more conservative approach and rely on proven industry practices. Clear AI goals are also derived from the business strategy:

For example, is the focus on increasing efficiency, improving service quality, or accelerating revenue growth?

In addition to the business strategy, the company's status quo regarding AI must be determined. A systematic evaluation of the four dimensions creates transparency about the current AI maturity level and enables realistic alignment of the AI strategy.

With our Intero Consulting AI Readiness Analysis, we support you in systematically evaluating your company's current maturity level across all four dimensions.

This structured approach of top-down (business strategy) and bottom-up (AI readiness analysis) ensures that the AI strategy is not only economically sensible and oriented toward actual business goals, but also technically feasible.

A proven approach is the gradual introduction and implementation of AI use cases according to depth of impact and complexity. It is generally recommended to start with benefit-oriented implementations in familiar territory before tackling more complex use cases.

The AI strategy must also include potential risks and mitigation measures of AI technology, as well as describe a governance structure with clearly defined responsibilities and processes.

Finally, a successful AI strategy requires the definition of clear success metrics and a continuous monitoring and adjustment process.

After developing the AI strategy follows the crucial phase of implementation. An agile project setup has proven successful, in which the necessary organizational structures are first built up. These include four central areas:

Building technical infrastructure, developing processes for identifying, implementing, and operating AI potential, building expertise, and establishing a governance framework.

 How you can successfully include the human factor in the strategic anchoring of AI is shown by our Intero Consulting Model of Change.

To sustainably anchor AI in the company and achieve strategic goals, AI activities should subsequently be transferred to a Target Operating Model. The most important success factors for an AI-specific Target Operating Model and how it can contribute to continuously optimizing established structures will be covered in our next blog post.