ChatGPT, Claude, Gemini, or a locally hosted model: the options shift faster than business processes change. Tying a workflow firmly to one provider creates a dependency that becomes expensive within twelve months.
The Model Is the Interchangeable Component
In an automated workflow, the language model handles a clearly defined part: it reads a document, classifies a text, or drafts a response. The rest of the workflow consists of rules, checks, and system access that work independently of the model.
We build in exactly this separation on purpose. The model sits behind a defined interface and can be swapped without rebuilding the process.
What Breaks When You Switch Otherwise
Without this separation, three dependencies arise that block a switch.
- Prompts tuned to one provider’s quirks that produce different results with another model.
- Output formats that are not validated and therefore pass straight into the next steps.
- Business logic that lives in the prompt instead of the workflow and is therefore not traceable.
Data Protection Belongs in the Architecture
Which data a model may see at all is a question of architecture, not of provider choice. Personal and business-critical data can be masked before hand-off, or the step runs on a locally hosted model.
In both cases the workflow stays the same. That is the practical advantage of a model-independent setup: the data protection decision does not change the process.
Start Small, Stay Measurable
Good first use cases are narrowly defined and have a clear result: reading incoming invoices and checking them against the purchase order, categorizing and assigning enquiries, preparing reports from existing data.
Each of these cases can be measured in hours and error rates. That is the basis for deciding where automation pays off next.