Where AI in IT Truly Helps
The most promising use cases in IT organisations are not always the most spectacular. Often it is the labour-intensive, repetitive tasks where AI delivers the greatest immediate value:
- IT Support and Service Desk: Automatic ticket categorisation, solution suggestions, incident history summarisation
- Documentation: Automatic creation and updating of technical documentation
- Code review and generation: Supporting developers with routine tasks
- Reporting and analysis: Faster evaluation of large datasets, natural language queries on BI systems
- Knowledge management: Intelligent search across internal documents and knowledge bases
What is Often Underestimated: Governance
The technical implementation of AI tools is often simpler than expected today. The organisational questions are what slow down or derail many projects:
- Which data may be transmitted to external AI services?
- How do we ensure AI-generated outputs are verified?
- Who is responsible when AI assistance leads to incorrect results?
- How do we document AI usage for compliance purposes?
A well-conceived AI usage policy is not a brake — it is the foundation for scalable, trustworthy AI deployment.
The Right Entry Point: Start Small, Learn Structurally
- Choose a concrete, well-defined use case — not the biggest, but one with a clear success measure
- Establish governance foundations in parallel (policy, responsibilities, data protection assessment)
- Run a pilot with real users and measure results
- Capture lessons learned systematically and use them for scaling
Conclusion
AI in the IT organisation is neither a silver bullet nor hype without substance. There are real, implementable use cases with measurable value today. The key is to start pragmatically, think about governance from the start — and have the courage to say: this use case is not worth it.