Understanding how systems behave—whether traditional software or advanced AI—has become essential not For AI systems, monitoring extends to model performance metrics, prediction latency, and data drift detection In AI contexts, observability encompasses the full model lifecycle—from data ingestion through training In AI systems—where complex models often operate as black boxes—explainability techniques like SHAP, Imperative As regulatory pressures intensify across industries—from GDPR's right to explanation to emerging AI

Nowhere is this clearer than in AI adoption. We are not governing AI. We are handing the work to AI agents that have no sense of where the organization is going — agents that

This incident has highlighted the limitations and risks associated with relying solely on AI-generated While ChatGPT and other similar AI tools provide utility across various industries, including the legal This incident not only raised questions about the accuracy of AI-generated content but also emphasized The use of AI tools should never substitute proper legal research and verification. legislation to regulate the use of AI where public safety may be at risk.

This is never more important now when it comes the use of Artificial Intelligence (AI). If organizations want to steer aware from harms associated from the use of AI in their value chain, they must explicitly state their objectives for the responsible use of AI.

Much of the current discussion on AI governance focuses on evaluating decisions at the point of execution Commitments as the Backbone For AI agents, Promise Theory offers a way to carry these priors forward. He chairs the Digital Engineering Committee for Engineers for the Profession (E4P), serves on OSPE’s AI

processing (removal of waste), data lakes, machine learning, and other forms of artificial intelligence (AI We don’t think we our heads, we think with AI. And for that we need algorithms and AI where the rules are transparent and explainable for people to Don’t only think with your AI, think with your head.

The rise of AI has both amplified this challenge and brought it into sharp focus. As organizations face new obligations for transparency and explainability in their AI systems, they're deploying large language models for simple text tasks that simpler algorithms could handle, running complex AI The massive compute requirements of modern AI often exemplify this waste. 4. automated reports no one reads, robotic processes that move data unnecessarily between systems, or AI

Decades of governance literature — COBIT, RACI, corporate governance theory, and more recently the AI What This Means for AI When an AI agent operates autonomously, it exercises decision authority without The governance design question for AI isn't "where should decision authority live."

feel compliance is approaching a turning point, where we’ve always been heading but now faster due to AI the object under regulation: safety, security, sustainability, quality, legal, and now responsible AI This challenge becomes particularly evident when considering emerging obligations from AI regulations Compliance will require demonstrating actual performance and outcomes—how AI systems behave in practice And will this now be shortened due to AI?  

Organizations relying on AI as their main intelligence source should recognize this constraint. While "keeping humans in the loop" with AI is commonly advocated, even this approach falls short.

Today, as AI reshapes our field, there's a new reality we must confront: compliance is probabilistic. In this article I challenge conventional compliance wisdom by asking: What will you do when AI predicts So what will you do when AI predicts that the confidence level (assurance) in meeting your obligations

The reason that matters more now is AI. It does not change what compliance is.