BLOG

AI is pushing humans out of the loop. The response many are taking is to figure out how to put humans back in. That is the wrong response. The answer is not human-in-the-loop. The answer is agent-in-the-loop. Train AI agents to participate in the governance loops that already exist. AI agents are replacing human workers who operated within those loops every day — workers who followed SOPs, escalated exceptions, maintained standards, and kept promises. When you remove those hu

That's the answer I hear when I ask organizations what work they're delegating to AI agents. Don't worry about defining the work. Don't worry about characterizing its complexity. The AI will sort it out. The end by any means. This sounds like progress. It is the abdication of governance. And no amount of forensic auditing will put back accountability for what was not there to begin with. Start with the work This is why I've been drawing on Elliott Jaques' work on Requisite Or

Operational Compliance Landscape When viewed through an operational lens, governance is not just oversight, accountability structure, or decision authority. Governance is the act of regulating organizational effort towards organizational values. This differentiates traditional approaches — Compliance 1 — focused on procedural compliance. It defines Compliance 2 : Operational Compliance. When it comes to regulatory design, there are four primary types, each requiring its own

Why Governance Starts with Obligations, Not Decisions "Requisite Authority — the decision-making capacity necessary for an obligation owner to fulfil their obligation." Scroll through any governance-focused discussion on LinkedIn right now and you'll find a recurring theme: organizations need decision authority at the point of execution. The argument is intuitive. Operations move fast. People closest to the action can't wait for three levels of sign-off. Therefore, push decis

Up until now, we created, stored, and moved data to where it was needed to drive our businesses. This was the world of Information Technology (IT) — and the foundation of Enterprise Architecture. That era is ending. AI has already absorbed virtually all the unstructured data available in the world. Large language models didn't just process that data — they internalized it. Now we need to build AI for the business — harnessing operational data, engaging the system of record, a

Canada's sovereign AI infrastructure is being built right now. Federal investment is flowing into domestic compute capacity. New privacy legislation is imminent. Environmental scrutiny of AI energy consumption is intensifying. AI governance frameworks are formalizing. And the compliance obligations facing data centre operators span seven distinct domains — each evolving independently, many of them overlapping in what they demand from the same operational activities. The organ

For decades, compliance has been one of the most reactive functions in the enterprise—more reactive than finance, operations, or even IT. While there are reasons why this is the case, this excessive reactivity has created a mission-critical gap: a dangerous vacuum where managerial accountability should exist but has been replaced with busywork. The Abdication Problem Managers, for the most part, have quietly abdicated their compliance responsibilities. They've handed them off

I just attended a webinar from a leading GRC vendor promoting continuous risk assessment for AI. The topic seemed timely and the solution promising, so I gave it my full attention. What I heard : AI introduces significant risk across organizations and within every functional silo. Fair enough. ⚡ The pitch: With all this risk, you need a system to manage it comprehensively. OK. What they demonstrated was little more than a risk register combined with task management—where task