Enterprises built their AI policies for tools that wait for instructions. The systems they are deploying now plan, act, and persist: agentic AI that executes multistep work across business systems with less human involvement at each step. That shift changes the governance question from what a model may say to what a system may do, and it moves accountability questions that once lived in IT onto the agendas of boards and executive committees. Who authorized this action? What contains it when it fails? Who answers for the result?
Akerman’s AI Governance team helps clients answer those questions before regulators, courts, customers, or their own systems answer first. We design enterprise AI governance programs: accountability structures, human oversight calibrated to the stakes of the decision, deployment standards for autonomous systems, and compliance architectures spanning the EU AI Act, U.S. state AI statutes, and sector-specific regimes. Because AI governance is not one discipline, the team draws on lawyers from the firm’s data privacy, technology transactions, labor and employment, healthcare, and litigation practices, assembled around each client’s deployment rather than around a practice-group org chart. The team runs the full arc: we design governance before deployment, and the same team defends clients when AI systems, decisions, and disclosures are challenged, including under the operational AI-washing theories our litigators first mapped.
Regulators are not the only ones asking. Enterprise procurement teams now screen vendors for AI risk before it enters their own ecosystems, and a credible governance program has become a condition of winning and keeping business. We work both sides of that exchange.
We hold ourselves to the same standard. Akerman deploys AI across its own legal work under a governance framework the firm built and enforces: attorney-directed workflows, systems sandboxed within controlled environments, and autonomy expanded only as oversight matures. The lawyers who advise on AI governance operate under it every day. That experience produces counsel with an operator’s bias toward what works: governance proportionate to the irreversibility of what a system can do, designed to enable deployment rather than prevent it.
