Pick a workflow before you pick a model
“We need AI” is not a requirement. “We need to summarise support conversations with a human approval step” is. Start with a narrowly defined workflow, the input data it uses, the expected output, and the person accountable for reviewing it.
This makes evaluation concrete. You can test whether a tool is accurate enough for the use case, whether it supports the required languages, and whether the proposed controls work in practice.
Four areas to compare
Capability
Use representative, non-sensitive test cases. Look for output quality, consistency, latency, language support, and how the system handles uncertain requests. A strong general benchmark does not guarantee a good result for your documents or customers.
Data handling
Ask whether prompts and outputs are retained, used for training, or available to support teams. Review regional processing options, DPA terms, subprocessors, account controls, and API logging. Do not assume a provider's headquarters tells the whole story.
Governance
Decide which actions require a human review, how users can report errors, and what records you need for audits or customer questions. The European AI Act may create obligations depending on your role and use case; seek appropriate legal advice when the risk is material.
Cost and resilience
Model pricing varies with volume, context size, hosting mode, and supporting services. Test cost with realistic prompts, set usage limits, and design a fallback for temporary provider failures or low-confidence outputs.
A sensible first rollout
Launch one workflow with clear success criteria. Measure the time saved, error rate, reviewer feedback, and unexpected data flows. Then decide whether to expand, change the model, or stop.
European AI providers can be attractive for local language support, regional operations, or deployment flexibility. The durable advantage, however, comes from pairing the right tool with a well-designed workflow and accountable human oversight.