Start with the problem, not the tool
First ask which recurring work eats time: drafting texts, sorting requests, searching documents, summarising reports. Only then choose the suitable model. Starting with the tool means looking for a problem afterwards.
A sensible roadmap
- Collect use cases and rank them by value, effort and data sensitivity.
- Set the framework: which data may go into which services? Who decides?
- Start a pilot with one clearly defined case and a measurable goal.
- Evaluate: does it save time? Is the quality right? What does operation cost?
- Roll out or discard, with training and a policy for everyone involved.
Local or cloud?
Local models, for example with Ollama, give more control over data. Cloud services such as OpenAI, Anthropic Claude or Google Gemini often offer more powerful models without your own hardware. Which route fits depends on data, budget and expectations. A mix is often sensible. See also the comparison of the options.
Typical mistakes
- Starting without rules and leaving staff alone.
- Adopting results without checking them.
- Entering confidential data into unchecked services.
- Starting too big instead of with a small pilot.