Strategically achieving AI maturity – clear priorities for each maturity stage
Understanding, planning, and implementing AI maturity: The key to transformation
The path to AI maturity is not a sprint, but a learning-oriented process. Crucial is not only the right timing, but also the appropriate priority depending on the development phase. The AI maturity level shows where a company stands – and which strategic priorities it should set to achieve targeted progress. The five maturity levels – from exploring Realizing – do not follow a rigid sequence, but rather a growth-oriented learning path.
Exploring – Building AI Maturity through Processes and Perspectives
The most important thing here is learning. Mature companies begin with standardized processes for developing and deploying AI even before implementing large use cases. Also important: Diversity in the team – Different roles and perspectives foster creativity and an early understanding of limitations and potential.
Top Drivers:
- Repeatable processes
- Diverse roles & perspectives
- Suitable models for the right use cases
Planning – Prioritize use cases and select models
This phase focuses on model fitness and operationalization. Companies should specifically select those models that fit their prioritized use cases – and address the scalability of the processes early on.
Top Drivers:
- Model-Use-Case-Fit
- Repeatable processes
- Data quality & infrastructure
Scaling – Repeatability as a growth driver
Scaling requires more than technology – it needs standardization. Templates, toolkits, and governance frameworks help to efficiently roll out new solutions. At the same time, the demands on ethical guidelines and accountability are increasing.
Top Drivers:
- Organization & Culture
- Technology & Data Strategy
- Governance & Standards
Realizing – Measuring and scaling impact
Those who make it this far are thinking about AI strategically. Now the focus is on concrete business impact: revenue, innovation, new business models. Transparency and fairness are also gaining relevance – not least because AI is now in widespread use.
Top Drivers:
- Business Strategy
- Governance & Compliance
- Organizational anchoring
Conclusion: Trust is the new competitive advantage
Companies that strategically increase their AI maturity lay the foundation for real value creation with artificial intelligence. The key is to know the current stage of development – and to derive the appropriate priorities from it.
A well-thought-out AI strategy, combined with structured implementation, cultural integration, and technical scalability, enables AI solutions within a company that not only work but also deliver measurable business impact. In this way, AI becomes not just a vision for the company – but a driver of growth.
Source: Microsoft. (2024). AI Strategy Roadmap. https://info.microsoft.com/ww-landing-ai-strategy-roadmap-navigating-the-stages-of-ai-value-creation.html
