This article discusses the potential negative impact of AI on the skill development of junior configuration managers in the realm of configuration management. It highlights the risks of reliance on automation, including skill degradation and reduced critical thinking. Proposed solutions include manual practice, graduated automation, and competency gates to preserve human expertise alongside AI adoption.
This article discusses the integration of AI in configuration management, highlighting the challenges posed by probabilistic decision-making compared to deterministic systems. It emphasizes the need for governance frameworks to validate AI outputs against compliance requirements and establish confidence thresholds for manual review in decision-making processes.
This article discusses challenges of AI-assisted product changes, particularly context degradation in large language models as conversations progress. It introduces scaffolding, a structured approach to maintain contextual integrity in change management. By emphasizing task decomposition and context engineering, organizations can improve AI performance and enhance governance in engineering workflows.
This article discusses the integration of AI in configuration management, highlighting the governance gaps between probabilistic AI outputs and established deterministic standards. While AI-driven tools improve efficiency and data accuracy, concerns arise about the erosion of human expertise and the need for frameworks to validate AI-generated analyses, especially in regulated industries.
In case you missed them, you can find the following How do YOU CM2? posts in the 50th edition🥳 of the Future of CM newsletter:
🧑🚒 When ‘Saving the Day’ Becomes the Problem.
🐺 Why does Configuration Management have such a bad reputation?
😵💫 Are you planning on hallucinating your next product?
What is the quality level of the product data in your organization?
🤥100%? — You’re lying to yourself.
✅99%? — Hard to believe.
🆗95%? — Impressive, but not enough.
❌Less than 95%? — That’s the reality for most organizations.
Rob Ferrone’s and Oleg Shilovitsky’s LinkedIn bromance 😉 has resulted in an interesting post by Oleg: How to Build PLM Applications We Love Using AI. His premise (my interpretation) is to build Minimum Loveable PLM applications as small applications supported by AI that fulfill a specific purpose using data from a shared data platform. No MVPs, but MLPs! Around the same time, Michael Finocchario published The Agentic AI Revolution: Reimagining PLM as… Read More »How the CM Baseline and Agentic AI enables the future of Enterprise applications.
The article critiques traditional change management methods, emphasizing the advantages of collaborative workspaces over classic file check-in/check-out systems. It discusses challenges such as data consistency, real-time collaboration, and workflow traceability while advocating for flexible data models and robust integration. Successful implementation requires a supportive mindset and proper tools.
On May 15, I presented about the future of configuration management enhanced by AI at the IpX International CM2 Congress. The presentation highlighted a fictitious day with an AI assistant, Chrissy, illustrating its role in tasks like meeting organization, impact analysis, and change planning, while addressing data quality and bias concerns in AI systems.
This article discusses enhancing the CM Baseline’s knowledge graph by integrating product and event data, creating a comprehensive ‘product 360.’ It emphasizes the significance of Event Knowledge Graphs to uncover insights overlooked by traditional analysis. By modeling relationships between events and states, it proposes improved predictive capabilities and behavior understanding in product development.