For many organizations, the question is no longer whether to adopt an AI governance program. Most organizations already have one (or at least the beginnings of one) in the form of AI use policies, intake processes, vendor diligence questionnaires, data-use restrictions, employee training and legal review procedures. But the AI landscape is moving faster than many of those programs were designed to handle.
In 2026, AI governance is becoming less about whether and how employees may use generative AI tools, and more about how organizations manage AI that is embedded across the enterprise: in software as a service (SaaS) platforms, customer-facing products, developer tools, HR systems, cybersecurity workflows, marketing stacks, productivity suites and increasingly autonomous AI agents. The result is that many organizations’ original AI policies and procedures, which are often focused on employee prompting, confidential information and public chatbot use, need to be updated for a more complex environment.
This update highlights some key issues that general counsels and legal departments should be revisiting now as part of their AI governance. These include the rise of agentic AI, the expansion of third-party and SaaS vendor AI risk, topical updates regarding AI and IP (including open source and licensing of training data), AI litigation risk, and a rapidly developing patchwork of AI-specific laws and regulations in the United States and abroad.
The core takeaway is simple: AI governance should not be treated as a one-time policy project.
It should be a living legal, compliance, privacy, security and product governance framework that evolves with the technology. But AI governance can build on existing policies, rather than requiring an entire “net new” set of policies.
For GCs, 2026 is the year to pressure-test whether existing AI governance fits the ways in which AI is procured, deployed, embedded and used across the organization.
