Answer first
A good first local-AI workflow is repeated, bounded, measurable, and sensitive enough that local control has real value. The scorecard below helps identify whether the next step is a local specialist, a hybrid route, or more scoping.
Local AI readiness scorecard
Score a workflow for local specialist adoption. The worksheet stays in this browser.
What the score teaches
Local AI readiness is not only hardware readiness. A workflow needs a useful niche, clear inputs, a measurable output, available local context, and a feedback path. The best candidates become model-breeding parents.
Recommended evidence to collect
| Evidence | Why it matters |
|---|---|
| Data-boundary note | Explains why local execution is valuable. |
| Example set | Gives the specialist a concrete task. |
| Baseline timing | Shows whether latency or productivity improved. |
| Edit distance | Measures how much human correction remains. |
| Local hardware profile | Shows whether the model fits the deployment target. |
| User acceptance notes | Turns human review into the next breeding signal. |
Next step
When the score is high, open Sovereign Local Model Patterns or the Local AI Adoption Roadmap.
Source reports used for this guide
These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.