AI Unlocks Dormant Process Management Paradigms
summary
José San Román Álvarez de Lara explains that traditional process-centric management (fixed diagrams, stages, role-based flows) fails under real-world variability, and revisits the artifact-centric approach from research that models business artifacts and their states instead of rigid process flows. He argues that generative AI and autonomous agents (able to interpret states, validate conditions, and trigger transitions) now make artifact-centric systems viable, turning static process diagrams into adaptive, context-driven systems.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 8:12Artifact paradigm failure
- 15:17Complex process decomposition
- 18:52AI proof of concept
- 27:11Emergent system properties
- 31:51Production deployment
- 40:08Future of AI processes
- 42:00Q&A
related talks
14mThe 4 Patterns of AI Native Development
24mHow Agentic AI Empowers Creative Thinking
17mAutonomous AI Agents in ERP – When Corporate Control Anticipates Rather Than Reacts
19mContext-Aware AI Dev : Next Wave of Engineering Productivity
44mSpec-driven Development: How AI Changed Everything (And Nothing)
22mAgentic AI for Engineering Leaders | Strategy & Execution Guide
27mMulti-Agent AI: Transforming Business Operations and Value Creation
27mAgentic Engineering: Working With AI, Not Just Using It
