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What the term means
The term comes from John Debenham, a researcher at the University of Technology Sydney. His 2002 paper, “Knowledge-Driven Processes Can Be Managed,” defines the idea in its abstract: “A knowledge-driven process is guided by its ‘process knowledge’ and ‘performance knowledge’.” A later 2005 paper extends the account and states that emergent process management needs an agent “driven not by a process goal, but by an in-flow of knowledge, where each chunk of knowledge may be uncertain.”
Two points keep the definition precise. First, the overall goal may be vague at the start, or it may change as participants learn more. Second, the system does not have to understand every piece of context. The process can still be supported and recorded even when its full context is too large to represent.
No regulator or standards body sets a formal definition of this term. It describes one author’s framework for understanding a class of processes, not an industry-wide standard, and it should not be read as a synonym for every workflow tool, knowledge-management program, or AI system.
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How it differs from task-driven and goal-driven processes
The distinction is easiest to see by comparing how each model decides what happens next.
| Model | What directs the work | How stable the goal is | Typical fit |
|---|---|---|---|
| Task-driven | A specified decomposition of activities | Defined by the steps themselves | Routine, repeatable workflows |
| Goal-driven | A stable goal that drives planning and execution | Fixed for the life of the process | Work with a known target but flexible routes |
| Knowledge-driven | Process knowledge and performance knowledge that choose the next goal, task, and participant | May be vague, or may be revised as the work proceeds | Emergent work whose tasks or endpoint become clear only as it develops |
In a knowledge-driven process, the next goal or action cannot be fully specified beforehand. The overall aim may still exist, but it is not a fixed target that planning can lock onto. Examples cited in the literature include exploratory organisational decisions and e-market interactions, where the endpoint is discovered rather than designed.
The two kinds of knowledge that guide the process
Process knowledge
Process knowledge is information relevant to a particular process instance. It is broader than a workflow definition and can include:
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- prior knowledge and background information available at the start
- what participants learn while the instance is running
- information generated by users
- information drawn from the environment while the instance exists
Because this knowledge keeps arriving, the picture of the process at the end can look quite different from the picture at the start.
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Performance knowledge captures how effectively tasks or agents perform. It includes reliability, meaning how consistently a given task or participant has delivered results in the past. It is what allows the process to choose between candidates for the next task, not just decide which task is needed.
How the management cycle works
In plain terms, the process moves through a repeating loop:
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- Review what is currently known about the process and how earlier actions performed.
- Decide which outcome to pursue next.
- Select a task and the person or agent responsible for it, using performance knowledge to compare options.
- Carry out the task.
- Add the resulting process knowledge and performance knowledge to the record, so that the next decision starts from a richer base.
The loop has no fixed end state. It continues for as long as the work needs direction, and each pass can revise the goal that the previous pass set.
Who keeps the judgment role
In the foundational account, the “process patron” (the person responsible for the process) chooses the next goals and tasks, because those choices depend on context that a system cannot fully capture. The system’s job is to record the process, supply knowledge, and support execution.
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Where the model reaches its limits
Debenham notes that process knowledge can include large amounts of general or common-sense knowledge. Representing all of it, and keeping that representation up to date, may be impractical. This is a practical limit rather than a flaw in the model.
When relevant knowledge can be represented and accessed, the process is a more manageable special case, sometimes called a knowledge-base process. When it cannot be represented feasibly, a system may still support execution and record useful artifacts, but it will not fully manage the process. Readers evaluating a tool should ask which of these two situations applies to their work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Related term: knowledge-intensive process management
A separate body of work uses the phrase “knowledge-intensive processes” for non-routine problem solving that needs flexible support. A 2021 article argues that conventional BPM tools tend to focus on predefined processes, while knowledge-management systems can lack task context. It proposes an integrated, adaptable approach that supports dynamic work alongside structured procedures.
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The two phrases overlap in spirit but are not the same term. Use the 2021 work as adjacent context, not as a replacement definition for Debenham’s “knowledge-driven process.”
Further reading
Debenham’s chapter appears in AI 2002: Advances in Artificial Intelligence, published in the Lecture Notes in Computer Science series, pages 191–202. Readers who want the original argument should start with that chapter and the 2005 paper that follows it.
In short, knowledge-driven process management is a way of directing work when the goal is vague or changing and the next step depends on knowledge gathered along the way. It is a precise, somewhat specialised framework, and it is most useful when a team can tell which parts of its work are structured enough to automate and which parts need human judgment.
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