Process analytics has become predominantly data-driven: deep sequence models such as LSTMs and Transformers now dominate predictive and prescriptive process monitoring and deliver strong average-case accuracy. These models are, however, opaque, require large amounts of data, and do not incorporate the process knowledge that organizations already hold in the form of regulations, declarative constraints, and reference process models. The symbolic side of Business Process Management (BPM) carries exactly this knowledge but is brittle on noisy, real-world event data. Neuro-symbolic AI (NeSy) aims to bridge these two traditions, and a growing body of BPM work has begun to use it. This tutorial gives the BPM community an accessible overview of the relationship between BPM and NeSy. It introduces Kautz’s architectural taxonomy of NeSy and the Learning from Logical Constraints paradigm together with its main implementation, Logic Tensor Networks. It then traces how NeSy attaches to concrete BPM analytics tasks through a sequence of case studies spanning compliance-aware prediction, robust rule injection, healthcare outcome prediction, and prescriptive analytics. The tutorial highlights the failure modes that practitioners must avoid when injecting domain knowledge into deep process models, examines how to evaluate whether injected knowledge actually helps, and closes with open problems and a practical decision aid for when NeSy is worth adopting.
Organizations that run predictive or prescriptive process analytics rely on deep sequence models that forecast next activities, outcomes or remaining times from event logs. These models are opaque, need large amounts of data, and ignore the regulations, declarative constraints and reference process models the organization already holds, so they can predict behavior that violates known rules, most sharply where compliant behavior is rare and the stakes are high. The symbolic side of business process management carries exactly that knowledge but breaks down on the noisy, incomplete logs that real systems produce. Practitioners need to understand when and how to combine the two traditions, and how to avoid injecting knowledge in ways that make a model worse than having no knowledge at all.