The most common AI-project mistake is starting with a model instead of a process. For businesses, the key question is not which model currently wins benchmarks, but which repetitive work can be supported or automated reliably.
1. Find processes with high repetition
Good candidates are tasks that occur frequently and follow similar patterns: classifying enquiries, extracting information from documents, summarizing content, preparing responses or moving data between systems.
2. Clarify data quality and access
An assistant can only work as well as the information it receives. Company knowledge should be structured, current and access-controlled. In RAG systems, sources, permissions and update processes are at least as important as the language model.
3. Risk determines the level of automation
Low-risk tasks can be fully automated. Financial, legal or customer-critical decisions often benefit from a human-in-the-loop approach: AI prepares the work and a person approves it.
4. Make success measurable
Define criteria before implementation: processing time, error rate, number of manual steps or response time. This makes it possible to judge whether automation actually creates value.
5. Start small and integrate properly
A focused workflow using real data is usually more valuable than a broad “AI transformation” plan without concrete integration. If logging, roles and interfaces are designed correctly from the start, successful components can be expanded later.