Shared agentsAgentic AI
Enterprise AI agents are outcome driven: from task automation to proactive decisions and goal pursuit, acting independently within clearly defined boundaries.
- AI & Intelligent Platforms
//Artificial Intelligence
AI business processautomation.
AI runs inside business processes governed by an orchestration layer, which decides when an AI task runs, what data it sees, and where a person reviews the result.
Control
Process control, even with AI in the loop
AI tasks run as steps inside a governed process, not a black box bolted on the side.
Traceability
Workflows your auditors can trace
Every AI task carries a full lineage: what it saw, what it decided, and who reviewed it.
Speed
Faster decisions, humans where needed
AI clears the repeatable work; human review sits only where it's actually needed.
//Overview
What is AI business process automation?
AI business process automation runs AI tasks, such as reading documents, classifying requests or drafting decisions, inside governed business processes, where an orchestration layer decides when the AI runs, what data it sees and where a person reviews the result.
DataObserve Flow orchestrates AI inside the processes you already run, such as claims, onboarding, procurement and exception handling, on BPMN, CMMN and DMN open standards, with human judgement designed in and every step traceable for auditors.
//Where AI runs inside processes
AI inside the processesyou already run.
Claims processing
Claims processing
AI handles intake, classification, and triage. Simple, low-risk claims move straight through; high-risk ones go to a person.
//AI with DataObserve Flow
Full flexibility in when and where to use AI
Orchestrate AI inside processes
Orchestration makes AI safe at scale: AI tasks are components of the process, not its centre.
Human judgement by design
Case management for unpredictable work, with AI supporting each decision.
Open standards: BPMN, CMMN, DMN
Processes, cases and decisions stay portable and inspectable.
Unify technologies
RPA, AI, chat, and enterprise systems through APIs and connectors.
AI that learns from process data
Faster modeling, and process data to train ML models.
//Explore more withDataObserve
More from Flow · AI & Intelligent Platforms
Agentic Case Platform
The case is the harness: it decides what an agent may do.
- AI & Intelligent Platforms
Case Management
Automation, orchestration, and human decision-making combined, to adapt to unpredictable tasks and decisions over the life of a case.
- Case & Process Management
Process Orchestration
Encode and enforce the end-to-end process, so the business can evolve fast at minimal cost, with long-running automations for routine and exception-driven work.
- Case & Process Management
//Use cases
Artificial Intelligence: use cases
Claims processing
Let AI read claim documents and draft assessments for adjusters.
Customer onboarding
Extract and check application data, with people approving exceptions.
Procurement
Route requests and approvals with AI pre-checks against policy.
Exception handling
Triage high volumes of exceptions and escalate the ones that need judgement.
//FAQs
Artificial Intelligence: questions teams ask
How is AI process automation different from RPA?
RPA repeats fixed screen steps. AI process automation handles unstructured inputs and judgement-like tasks, inside a process that controls when AI acts and when a person decides.
Can AI decisions be audited?
Yes. Every AI task runs as a step in the process, so its inputs, outputs and the person who reviewed it are recorded.
Which AI models can DataObserve Flow use?
Flow is model-neutral: processes call the models you choose, and you can swap a model without changing the process logic.
Where should people stay in the loop?
Wherever the decision is high-stakes, regulated or uncertain. The process model makes those review points explicit.
