Workflow automation and AI development

Usually automation first. Most of the time a team loses goes to repeated manual steps, which automation removes more cheaply and more predictably than a model. Softwise recommends AI only where it moves a metric the business already tracks.

By keeping a person in the loop and validating before go-live. For a Paris insurance firm, Softwise ran document extraction in shadow mode against real claims first, comparing model output to what staff actually recorded, and only went live once that comparison held. In production every extraction is reviewed or spot-checked by staff before it touches a claim.

No, and that is usually the point. The Paris claims work added AI extraction to an existing process without replacing any system the staff worked in: documents are classified, verified in a review screen, and pushed into the same back office as before. Automation that forces a team onto new software tends to be abandoned regardless of how well the model performs.

By agreeing which existing number it should move before building it. Softwise adds AI only where it changes a metric the business already measures, which means the result is checkable against the figure the team was already watching rather than against a claim invented afterwards.