120 Agentic AI Use Cases — with BPMN Models & ROI Estimates

An agentic AI use case is a concrete business process, like invoice processing or employee onboarding, where AI agents read documents, extract data and prepare decisions, while a process engine carries the work end-to-end. Each use case in this library describes the trigger, the automated steps, the human checkpoints and the measurable outcome.

Classic process automation follows predefined workflows and rules. Agentic AI adds agents that can understand unstructured input, such as e-mails, PDFs and free text, and prepare judgments within clear guardrails. Scheer PAS combines both: agents do the reading and reasoning, and the process engine keeps every step governed, logged and auditable.

The library contains 120 use cases across 14 industries, including manufacturing, automotive, banking, insurance, retail, healthcare, pharma, chemicals, energy, logistics, telecommunications and the public sector. Each use case is also mapped to one of 9 business functions, such as finance, procurement, human resources, IT operations, customer service and compliance, so you can filter by both.

It depends on volume and process size. Across the 120 use cases in this library, initial estimates range from roughly 80 to over 3,000 hours saved per month, per process. Every one-pager shows the assumptions behind its estimate, and the numbers are replaced with your real volumes before anything is built.

Scheer PAS is a platform for agentic enterprise transformation. It combines AI agents, a BPMN-based process engine, integration and API management in one platform, so companies can automate end-to-end business processes across their existing systems, in the cloud or on premises, with full observability and human control.

Agents handle the unstructured part of the work: they read incoming documents and messages, extract and validate data, and prepare a recommendation. The process engine handles the structured part: it routes each case through the modelled process, calls the right systems, enforces the rules and records every step. The result is automation that can deal with messy input without giving up control.

Most processes in this library pause at defined checkpoints where a person reviews the agent's suggestion and makes the call, for example approving an exception invoice or releasing a payment. Everything before and after that moment runs automatically. The interactive preview in each one-pager shows exactly where that checkpoint sits.

Yes. Every step, whether taken by an agent or a person, is logged in a complete audit trail, so you can always answer who decided what, when and on which basis. Scheer PAS is committed to GDPR compliance and hosts its SaaS instances in European Azure regions, with alternatives such as Germany available.

The models follow standard BPMN notation. The circle with an envelope is the trigger that starts the process, rounded boxes are tasks, and the thick red circle is the outcome. A lightning icon marks steps that run automatically through agents and the process engine, and a person icon marks the checkpoints where your team decides.

The figures shown are initial estimates for orientation, based on typical volumes and effort at each process size, calculated at a blended rate of 60 euros per hour. They exist to make use cases comparable, not to promise results. Before implementation, the assumptions are replaced with your real numbers to produce a validated business case.

Yes, that is the point of the library. Each use case is a proven pattern, not a fixed product. Scheer PAS connects legacy and modern systems alike, so the process is modelled around your system landscape, your rules and your checkpoints rather than the other way around.

Open the one-pager of a use case that matches your situation and check whether the problem description sounds familiar. If it does, request a demo through the form on this page. You will see the use case running on Scheer PAS and get a first estimate based on your own volumes.