• Process mining is maturing as vendors move into generative and agentic AI.
  • Germany-based Celonis is cited as an example of vendors expanding into these capabilities.
  • Generative and agentic AI can speed insights and automate decisions — but bring governance and accuracy risks.
  • Companies must balance adoption with controls to avoid costly automation mistakes.

What’s changing in process mining

Process mining — the practice of extracting insights from event logs to map and improve business processes — has long been focused on visualization, bottleneck detection and rules-based recommendations. Now, vendors are adding generative and agentic AI capabilities that can create narrative explanations, propose remediation steps in plain language, and in some cases suggest or trigger automated actions.

Germany-based Celonis is one of the vendors publicly linked with this shift. The move toward generative models and agentic workflows signals that process mining is moving from descriptive analytics toward more proactive, automated decision support.

Why this matters for businesses

Generative and agentic AI can change how organizations use process mining in three ways: faster insight-to-action, improved accessibility, and deeper root-cause discovery. Natural language summaries make complex process data easier for managers to understand. Agentic capabilities — autonomous software agents that can carry out tasks based on AI recommendations — can shorten the time between identifying an issue and resolving it.

For organizations already investing in process optimization, these advances offer real FOMO: peers may gain efficiency and cost savings if they adopt the new capabilities quickly. That creates pressure on others to evaluate and potentially accelerate their own implementations.

Risks and governance concerns

The same powers that promise speed also introduce new risks. Generative models can hallucinate or oversimplify causal relationships in process data, and agentic systems can make or recommend operational changes that have unintended consequences if not carefully supervised. Governance, transparency and human-in-the-loop safeguards are essential to prevent automation mistakes that could disrupt supply chains, finance close processes or regulatory compliance.

Security and data privacy are also critical: process mining relies on event logs that can contain sensitive information. Adding AI layers increases attack surface and complexity, so security controls and data minimization practices must keep pace.

How companies should respond

Start by treating generative and agentic features as accelerants, not replacements, for existing process governance. Run pilots focused on high-value, low-risk processes so teams can evaluate model accuracy and the real-world impact of agentic interventions. Establish clear approval paths for any automated actions, and require explainability from vendors so recommendations can be audited.

Finally, involve stakeholders across IT, security, compliance and process owners early. That cross-functional oversight reduces the chance of over-automation and ensures improvements are both effective and safe.

The rise of generative and agentic AI in process mining marks an important maturity milestone. It offers clear upside — faster insights and more action — but success will depend on disciplined adoption and strong governance rather than blind acceleration.

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