AI Governance: What Businesses Need to Get Right About Responsible AI

As AI shifts from isolated pilots to embedded decision infrastructure, weak governance turns small model issues into systemic business risk—especially as oversight expectations rise (e.g., the EU AI Act and the NIST AI RMF). Codora’s post breaks AI governance into practical operating components: clear ownership, development/deployment standards, structured risk assessment, documentation/auditability, and continuous monitoring for bias and performance drift. It also highlights common traps (treating governance as a checkbox, assuming vendor compliance, neglecting data governance, and deploying without monitoring). Tech leaders can use this as a checklist to define cross-functional roles, controls, and review cycles before AI becomes “invisible” infrastructure.