Choosing local AI or cloud AI starts with permitted data transfers, offline requirements and responsibility for operating the system. Different workflows inside the same company may need different answers. Map the work before choosing one technology for everything.

Separate data from actions

A public product description, internal cost calculation and customer conversation have different handling requirements. For each workflow, identify sources, recipients and external services. Even with local generation, an application may contact email systems, a CRM or telemetry services; account for that traffic separately.

In a proposed hybrid setup, internal document retrieval could stay on the station while approved public copy goes to an external model. Make that transition visible. Automatically uploading the whole document when a local model fails is a poor way to conceal a performance problem.

Compare the same accepted result

Consider two ways to prepare a weekly review. One employee manually assembles files in a cloud chat. Another uses a local assistant that reads an approved folder and saves a sourced draft. Comparing only request charges misses differences in preparation, review, maintenance and repeatability.

Run identical assignments through both arrangements and measure the complete path to an accepted result. Record corrections and review time separately. A familiar interface should not substitute for a quality assessment.

Three practical arrangements

- Local: your company controls the computing node and organises its operation. - Cloud: the selected service processes agreed data under its service terms. - Hybrid: documented routing rules allocate work, with explicit external processing.

Revisit the choice when conditions change

Changes in workload, data handling or availability requirements justify another evaluation. A small, occasional task may not warrant a dedicated local station. Repeated internal operations may place greater value on consistency and operational control. AI Office can be designed around these conditions while keeping the model and runtime replaceable.