McKinsey Says AI Governance Needs to Be Built Into the Workflow — Here's What That Looks Like
McKinsey argues organisations will embed control agents directly into AI workflows — comparable to DevSecOps. Every agent action should be logged and explainable in real time.
The Agentic Organisation: McKinsey's Governance Model
McKinsey's September 2025 paper on the 'agentic organisation' describes a new operating model for enterprises that have moved beyond AI as a productivity tool into AI as a decision-making participant. In an agentic organisation, AI agents take actions — they don't just generate suggestions. They procure, communicate, route, and decide. The governance challenge this creates is fundamentally different from governing an AI model that generates a recommendation a human then acts on. It requires governance that is embedded in the workflow, not applied to the output.
The DevSecOps Analogy: Why It Matters
McKinsey argues organisations will need to embed control agents directly into AI workflows in the same way DevSecOps embedded automated security checks into software delivery pipelines. In a DevSecOps model, security is not reviewed after code is written — it is enforced at every commit, every build, and every deployment. The equivalent for AI governance is controls that activate at intake, at deployment, at model update, and at decommission — not governance that reviews AI systems annually after the risk has accumulated. Every agent action should be logged and explainable in real time, covering data privacy, financial thresholds, and policy compliance.
The Five Pillars of the Agentic Organisation
McKinsey frames governance as one of five structural pillars of a next-generation 'agentic organisation' — alongside business model, operating model, workforce, and technology. The inclusion of governance as a structural pillar (not an enabling function) reflects a shift in how enterprises with mature AI deployments think about accountability. Governance is not the compliance function's job. It is a designed property of the operating model itself: built into how decisions are made, how actions are logged, and how exceptions are escalated.
What This Means for AI Governance Buyers
McKinsey's picture of embedded, real-time-logged control agents has practical implications for any organisation evaluating AI governance platforms. The governance platform should not sit alongside the AI workflow — it should be the record of the AI workflow. That means automated evidence linkage: when an AI system takes an action, the governance record updates. Traceable history: every decision, rating change, and exception is timestamped and attributed. Continuous re-validation: when a model updates, the risk assessment reopens. When a control degrades, the acceptance voids. These are not aspirational features. They are the minimum viable properties of a governance platform designed for the era McKinsey describes.
McKinsey's picture of embedded, real-time-logged control agents mirrors Wahid AI's own evidence model: automated evidence linkage, traceable history, and continuous re-validation are a practical, already-shipping implementation of the governance pillar McKinsey describes as still emerging for most enterprises. Wahid AI ships with all five characteristics: embedded intake classification, real-time risk updates, traceable audit history, continuous obligation evidence, and automatic re-opening of acceptances when linked controls degrade.
RELATED TOPICS
Ready to operationalise these governance frameworks?
Wahid AI integrates ISO 42001, EU AI Act, NIST AI RMF, and APRA CPS 230 into one governed workflow. See it in a 30-minute demo.
EXPLORE FURTHER