What does AI-Supported Decision Management mean?

AI-supported decision management describes the use of models to prepare recurring, similarly structured decisions. The system evaluates the available information, assigns the case to a category, and proposes an outcome along with the reasons behind it. The decision itself is still made by a person, unless an explicit legal basis permits full automation. It differs from a pure rules engine through this model-based component.

Such a system consists of three parts. A rules layer checks the points governed by fixed requirements, such as deadlines, thresholds, and mandatory information. A model assesses the information supplied as free text or as an attachment. An interface brings the two together and shows, for every proposal, a confidence score along with the features that led to it. Cases below a defined threshold go into regular processing without a proposal.

Deploying such a system pays off wherever many similar cases arise, the criteria are fixed, and the relevant information comes together from several systems. Typical cases are reviewing incoming applications, processing claims, and approving invoices. Where every case differs, or the criteria are professionally disputed, the effort of reaching agreement outweighs the benefit.

Its advantage over a purely rules-based check is that unstructured information also feeds into the preparation. Similar cases go through the same review sequence regardless of who happens to be handling them. The time freed up goes to the cases with genuine discretion, where professional judgement is required.

Under German administrative law (§ 35a VwVfG), a fully automated administrative decision is only permitted for bound decisions with no room for discretion or judgement. In every other case, the proposal remains preparatory work, and responsibility rests with the person who ultimately signs off on the decision.