Celonis Process Intelligence Day

Identify AI opportunities through a structured, evidence-based approach grounded in process intelligence. By analysing operational data – such as activity volumes, automation gaps, process deviations, and data quality – organisations can systematically uncover areas where AI can deliver meaningful impact. Each opportunity is evaluated against defined criteria, including potential value and implementation effort, to support prioritisation.

This results in a clearly defined portfolio of AI use cases, aligned to business objectives and feasibility. It enables organisations to move from insight generation to execution planning in a disciplined and informed manner.

Embed relevant business context into process data to support more accurate and reliable AI outcomes. While standard data models capture process execution, they often lack the semantic depth needed for informed decision-making. Business Context Enrichment addresses this by integrating business rules, relationships, and domain-specific attributes into the data model.

This includes elements such as approval hierarchies, supplier criticality, customer segmentation, regulatory requirements, and SLA commitments. With this enrichment, AI solutions operate with greater alignment to organisational policies and real-world constraints, enabling more context-aware and business-relevant decisions.

Establish a transparent and controlled framework for managing AI-driven operations. This approach provides visibility into how AI agents perform across systems, including their actions, decision patterns, and impact on business processes. It supports consistent monitoring across both internal platforms and external environments.

By combining performance monitoring with governance controls and human oversight, organisations can manage risk proactively, maintain compliance, and refine outcomes over time. This lays the foundation for scaling AI in a controlled, accountable, and sustainable manner.

Strengthen the underlying technology and data environment required to support scalable AI adoption. This approach focuses on key engineering dimensions, including data architecture, model quality, performance optimisation, and code standardisation. It helps identify and address inefficiencies that may impact system reliability and scalability.

Through structured governance and ongoing monitoring, organisations can improve platform stability, optimise  manage costs, and ensure consistency across implementations. This creates a robust foundation for delivering and scaling AI and analytics initiatives.