Which model suits your business is decided by data protection and contract, integrations, cost model, administration and fit for your tasks, not by a ranking. Claude, ChatGPT and Gemini are capable offerings that keep changing. So here you get criteria and a test method instead of a recommendation that would be outdated within a few months.
Which criteria are decisive?
The table shows eight criteria with the question to put to each provider. Weight each row yourself from one to three.
| Criterion | Question for the provider | Weight (1 to 3) |
|---|---|---|
| Data use | Are my inputs stored or used for training, and can I control that? | |
| Contract and data protection | Is there a data processing agreement, and what about transfers to third countries and sub-processors? | |
| Storage location | Can I limit processing to the EU? | |
| Integrations | Does the tool fit my systems, such as office suite, calendar, CRM, interface? | |
| Cost model | Do I pay per user or by usage, and are there usage limits? | |
| Task fit | Does it solve my typical tasks well enough? | |
| Administration | Is there central user management, sign-in through the company account, logs? | |
| Exit | Can I export data and change provider? |
What must you check especially for data protection?
As soon as personal data reaches a model, the GDPR applies. Clarify whether a data processing agreement exists (Art. 28 GDPR), where the data is processed and whether inputs are used for training. Business plans often come with different terms from private accounts. Providers' privacy notices change; read the current version before you decide: Anthropic, OpenAI and Google Gemini. More on this topic in Using ChatGPT in your business, GDPR-compliant. This is not legal advice.
Also look at administration: sign-in through the company account and the ability to block access centrally when someone leaves. Without such features, staff often drift to private accounts ("shadow AI") that you cannot control.
How do you test task fit?
Choose three to five typical tasks, such as drafting a customer reply, summarising a document and extracting details from a text. Anonymise the examples. Give each candidate the same instruction, rate the answers without knowing which model wrote them and note errors, speed and cost. The exact procedure is described in How to evaluate a new AI model.
Record the result and your reasons. If two candidates are level, the area where you will not compromise decides: usually data protection or integration.
Does it have to be one provider?
No. Many businesses use the right model for each task, for example one model for long documents and another for integration into an office suite. That does raise the administrative effort, though. Build processes so that the model can be swapped. Open models on your own hardware are also an option, as Open-source vs proprietary AI models shows.
The EU AI Act also requires deployers to ensure sufficient AI literacy among their staff. So set a short usage rule: which data is allowed, who checks results and how errors are reported.
- Tasks named for which the model will be used
- Data protection, contract and storage location checked for each provider
- Criteria weighted and candidates rated
- Two or three candidates compared with anonymised test tasks
- Cost model calculated with actual usage
- Usage rule for staff set
- Date set for the next review
Conclusion: criteria, test, review
The choice between Claude, ChatGPT and Gemini is not a matter of faith but a weighing against your rules and tasks. Weight the criteria, test with your own examples and review regularly. If you would like support with selection and introduction, see our AI automation service or describe your tasks.
