AI readiness check: Assess data, processes, people and risks
By Kevin Kröger, Geschäftsführer, Software und Plattformbetrieb
AI readiness does not mean testing as many models as possible. A company is ready when it can identify a clear use case, appropriate data, a responsible person, measurable quality limits and a safe operating path.
Is the process even suitable?
A good starting case occurs frequently, has a clear beginning and a verifiable result. Unclear decisions with high consequences for errors are less suitable as a first pilot. Record the time required today, typical errors and exceptions so that the benefits later can be measured.
Which data may be used?
Clarify origin, quality, permissions and retention of data before model testing. Reduce personal or confidential content to what is necessary. Document what data reaches an external environment and what protocols are created.
How does the test become a business?
Define quality criteria, cost limits, manual control and an abandonment path. Log model and prompt changes. The NIST AI Risk Management Framework offers a voluntary structure for controlling, classifying, measuring and treating risks.
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Frequently asked questions
- How many use cases should start at the same time?
- One or two clearly defined cases are enough to start with. In this way, measurement and learning remain manageable.
- Do we need perfect data?
- No. However, data must be sufficient for the chosen purpose, legally usable and understood in terms of quality.