This article from the How Do YOU CM2? series emphasizes the importance of capturing design rationale in decision-making for effective knowledge management. It highlights how failing to document the reasoning behind design choices leads to inefficiencies, particularly for new hires. AI can assist in documenting these rationales seamlessly during the engineering process, improving future decision-making.
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.
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.
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.
Recent incidents involving autonomous vehicles highlight safety concerns and the complexities of AI development. As AVs face challenges in predictable behavior, the need for Explainable AI becomes crucial. Ensuring accountability and transparency in AI decision-making can build public trust while addressing the risks associated with testing on public roads.
OpenAI’s release of GPT-4 enhances Large Language Models (LLMs) capabilities for impact analysis in configuration management. By leveraging LLMs, knowledge graphs can be utilized to reveal implicit relationships and support decision-making during changes. Upcoming tools, like NeoChat, hint at significant advancements in data exploration for engineers and managers.