Most companies do not fail at AI technology — they fail at missing strategy. This guide shows how to develop an AI strategy that works in practice.
Why Most AI Projects Fail
Over 60% of AI projects fail to deliver expected ROI. The most common reasons: missing clear objectives, wrong use case, poor data quality, lack of change management, and unrealistic expectations.
Step 1: AI Readiness Assessment
Evaluate four dimensions: data availability, IT infrastructure, process maturity, and organisational readiness. These factors determine whether your company is ready for AI and which use cases are realistic.
Step 2: Use Case Identification and Prioritisation
Prioritise by two dimensions: business value (how large is the savings potential?) and feasibility (how available is the data? how complex is the integration?). The ideal first AI use case combines high business value with high feasibility.
Step 3: The Pilot Approach
Never start large. A clearly defined pilot proves the value of technology for your specific use case, creates internal acceptance through visible results, and minimises financial risk. The ideal pilot takes 5 working days, costs under €5,000 and shows measurable results within 2 weeks of go-live.
Conclusion
A successful AI strategy requires discipline: clear objectives, the right first use case, a proven pilot and consistent change management. Companies that systematically follow these steps achieve measurable results.