In today’s knowledge-driven business environment, organizations aren’t solely defined by structured, repetitive processes. Instead, success depends on their ability to handle the unexpected: The customer complaint that doesn’t fit a standard script; the complex insurance claim; or the legal investigation that requires a context-driven response. It’s in environments like these that adaptive case management (ACM) and dynamic workflows breathe new life into operational excellence, and organizational agility.
Adaptive case management (ACM) is designed for unpredictable, knowledge-intensive scenarios where outcomes are not fully defined in advance. It empowers case workers to shape their workflows in real time, making decisions based on context, available data, and their own judgment. This approach differs to traditional Business Process Management (BPM), which excels at automating rigid, predefined sequences of tasks.
As a high-powered open-source BPM platform, Flowable is uniquely positioned to enable both BPM and ACM through unified models. By supporting open standards like BPMN (Business Process Model and Notation) and CMMN (Case Management Model and Notation), Flowable allows organizations to orchestrate a wide spectrum of work, from the structured to the dynamic, all within a single platform — paving the way for a successful digital transformation.
Conventional BPM is the perfect fit for situations that are predictable, repetitive, and have a clear path to completion. Think of a simple purchase order approval, or an expense report submission.
These types of procedures benefit from the strict controls and efficiency of a predefined workflow. However, the rigidity of such models highlights their limitations when confronted with case complexity and exceptions.
Adaptive case management is designed for unpredictable, knowledge-intensive scenarios where outcomes are not fully defined in advance. It empowers case workers to shape their workflows in real time, making decisions based on context, available data, and their own judgment.
Compare this with the different and often unpredictable needs of unpredictable needs in areas like healthcare, fraud detection, and legal actions. For example, a patient's care plan is rarely a straight line; it evolves with their condition, test results, and the doctor's real-time decisions.
Similarly, a financial fraud investigation is an exploratory matter, rather than a checklist that can be ticked off, step by step. It requires investigators to follow clues, gather evidence, and make impromptu decisions that cannot be modeled ahead of time. Adaptive case management (ACM) is the ideal framework for spontaneous scenarios, as it handles ad hoc, evolving case flows in real-time, whereas BPM requires full definition in advance.
Adaptive workflow automation allows systems to adjust on the fly. They use real-time conditions, AI insights, and user inputs to make intelligent, contextual decisions, going beyond simple flexibility.
The game changer? The emergence of AI agents has taken this adaptability to a whole new level.
AI agents today can learn from historical interactions, predict potential bottlenecks, and autonomously adjust workflows. And with the Flowable 2025.1 release, we’re introducing a new agent engine that elevates AI agents to first-class status, allowing for sophisticated multi-agent collaboration across different systems and gives organizations a holistic approach to operationalizing AI at scale.
For a case worker handling a customer complaint, an AI agent could, for instance, analyze the sentiment of the customer’s communication, suggest a pre-approved compensation offer, or even autonomously trigger an escalation to a manager based on predefined rules. In case management contexts like healthcare, such adjustability enables proactive interventions, like automatically triggering a high-risk patient's care management based on data patterns with no manual action required.
One of Flowable's key strengths lies in its ability to blend structured process management with adaptive case management. The platform’s open-source architecture supports both BPMN and CMMN model engines, combined with Flowable’s AI agent engine. Here’s a more detailed look at how:
Unified modeling: Flowable’s support for CMMN aligns with ACM approaches, enabling model-driven development with ad hoc task orchestration and decision logic. This is complemented by BPMN for structured subprocesses, all connected within the same platform.
Human-in-the-loop: The platform emphasizes a human-centric approach, ensuring that people are familiar with tasks that require both judgment and decision-making. No-code/low-code modeling allows knowledge workers and non-technical stakeholders to define and modify flows on the go, empowering them to respond to new information as it arises.
Advanced capabilities: Flowable offers rule-driven transitions through DMN (Decision Model and Notation), dynamic task assignment, and integration capabilities with other systems. Crucially, the platform provides auditability and a clear view of the entire case lifecycle, from initiation to resolution. This 360-degree case view ensures transparency and compliance, even in the most dynamic scenarios.
Flowable's unique blend of capabilities makes it a top-notch platform for a wide range of use cases, including:
Incident investigation: Whether a security breach or an HR incident, investigations are inherently unpredictable. Flowable allows a case manager to add or remove tasks, involve new parties, and track milestones as new information comes to light.
Compliance workflows: In legal or regulatory environments, a systematic approach can handle routine filings, while CMMN models can manage the exceptions and inquiries that deviate from the norm, ensuring every step is documented and auditable.
Insurance claims and complaint handling: These are both strong examples of hybrid scenarios where a structured sub-process, like verifying a policy, can be embedded within a broader, case-based orchestration that adapts to each unique claim or complaint, powering insurance claims automation.
See adaptive case management software in action.
Implementing adaptive case management with Flowable is best approached with a clear, focused strategy. Start with a pilot use case, perhaps an HR incident response, or a customer complaint process.
Design the CMMN model: This is all about goal-oriented processes. Begin by defining the milestones, stages, and tasks within the model. This is where you establish the context and goals of the case, rather than a rigid sequence of actions.
Embed rules and decisions: Utilize DMN to embed clear rules and decision tasks. For example, a decision table could automatically route a customer complaint to a specific team based on the product and customer tier.
Enable adaptability: Design the model to allow for human-in-the-loop adaptability. Case workers should have the power to add ad hoc tasks, re-route a case, or trigger new processes as needed.
Monitor and refine: Use Flowable's dashboards and reporting tools to monitor the evolution of cases, identify common patterns, and refine your models over time. Each case provides valuable data that can be used to improve the next.
The next step for adaptive case management is putting AI agents to work on the predictable parts of an unpredictable case. Agents take on predictive routing, anomaly detection, and SLA risk, flagging a case that is likely to miss its deadline and escalating it before it does, while the case worker keeps the decisions that need judgment.
What keeps that safe is the orchestration layer. Process orchestration governs how agents, rules, and people interact across the life of a case, with audit trails and human checkpoints built in, so AI augments the work under human control rather than acting on its own.
To see how this works in practice, explore Flowable's case management solutions or read our companion piece on orchestration versus choreography for a closer look at that control layer.
Every organization's case management challenges are different. Speak with our specialists to explore practical ways to streamline workflows, reduce administrative burden, and support better outcomes. Contact us about a demo, and see how we can improve your case management workflows.
Adaptive case management (ACM) is an approach to knowledge-intensive work where the outcome is not fixed in advance and the path to it is decided in real time by the person handling the case. Each case is a living record that gathers data, documents, tasks, and decisions as the situation develops, so a case worker can respond to what is actually in front of them rather than to a predefined script. Where business process management (BPM) is built for predictable, repeatable work that follows the same steps every time, adaptive case management is built for the work that resists that structure.
The difference is where the decisions live. BPM defines the full path in advance: the sequence of steps is modeled before the work begins, which is why it pairs with BPMN (Business Process Model and Notation), the standard for structured, repeatable processes. Adaptive case management defines the goal and the constraints instead, then leaves the path to the case worker, which is what CMMN (Case Management Model and Notation) was built specifically to support. Most regulated operations need both, which is why Flowable's Agentic Case Platform runs structured process automation and adaptive case work in a single model rather than asking teams to stitch two products together.
Adaptive case management fits work that cannot be reduced to a fixed sequence: claims investigation, compliance and regulatory work, complex onboarding, fraud and incident investigation, and healthcare care plans. What these have in common is that each one is high-stakes work where the next step depends on judgment about the specific case, not on a flowchart drawn in advance.
CMMN stands for Case Management Model and Notation, the open standard created specifically for case work. Rather than locking in a fixed sequence of steps, it models milestones, stages, and tasks that can activate as a case develops, which is what gives a case worker room to adapt. Because open standards protect against lock-in and keep work portable, enterprises evaluating any platform should ask whether their orchestration layer supports open standards like CMMN.
Inside an unpredictable case there are still predictable parts, and that is where AI agents do the work: sentiment analysis, document classification, anomaly detection, and similar routine tasks. The case worker keeps the judgment calls, the decisions where accountability and context matter most. Audit trails and human checkpoints are how that combination stays compliant and accountable, so AI augments the work under human oversight rather than running unsupervised. Process orchestration is the layer that makes it hold together, governing how agents, rules, and people interact across the life of the case.
Adaptive case management software is used by operations, compliance, and customer service teams in regulated industries such as banking, insurance, healthcare, and legal as it facilitates auditability. The primary users are knowledge workers and case managers who own decisions and outcomes, not RPA developers building automation scripts. In practice it suits anyone whose work is exception-driven rather than process-driven.
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