The article discusses the challenges of dataset validation within the CM2 framework, emphasizing the distinction between creator and user perspectives. It highlights how AI can enhance validation by checking technical requirements and simulating user reviews to identify ambiguities. The co-ownership model ensures accountability and clarity in dataset usage for all stakeholders.
This article emphasizes the importance of structured data in engineering change management, highlighting that 30-50% of engineering capacity is wasted on rework. It advocates for AI-assisted impact analysis within a defined framework, such as CM2, which fosters collaboration between engineers and AI. This approach enhances understanding and accountability in decision-making.
This article discusses the importance of human accountability in AI-assisted configuration management, emphasizing that governance failures, rather than technical issues, led to AI’s biggest failures in 2025. It highlights the necessity for clear ownership in processes and warns against over-relying on AI, which cannot replace human judgment, responsibility, and engagement in organizational culture.
This article compares change control processes across aerospace, automotive, and medical device industries, highlighting their distinct approaches shaped by different failure modes. It emphasizes the need for cross-industry learning to adapt traditional frameworks to modern challenges, particularly in handling continuous software updates. A unified CM2 framework is proposed for enhanced governance.
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 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.
This article discusses the importance of effectively communicating the value of Configuration Management (CM) systems to executives, particularly in the context of preventing costly product recalls. It highlights the financial implications of recalls in the automotive and medical device industries, advocating for CM2 principles as a strategy for risk mitigation and protecting brand value.
The article discusses the challenges of presenting Configuration Management (CM) to executive boards, emphasizing the need to shift the narrative from technical details to business outcomes such as predictable revenue, risk mitigation, and product confidence. It highlights the high costs of bad data and offers strategies to reframe CM as a strategic asset that supports leadership objectives, ultimately enabling business growth and trust.
This article discusses the role of maturity models in organizational improvement, particularly in configuration management. While they can provide structure, they often become distractions if misused. Organizations should focus on addressing specific problems instead of chasing higher maturity levels. Effective governance and senior leadership are essential for achieving genuine improvements.