Imagine an AI agent tasked with reviewing a supplier agreement. It cross-references the limitation of liability with the company’s standards, identifies a minor deviation, and accepts the agreement. The analysis is spotless, yet the decision could remain unauthorized.

For years, the focus in legal AI discussions has been on accuracy: understanding questions, identifying the correct laws, and avoiding making up cases or misinterpreting contracts. These concerns are crucial, but as AI begins not just to answer questions but to execute transactions, a new problem arises: authorization.

AI systems are evolving from passive analyzers of information to active participants in business operations such as making purchases, negotiating terms, and approving changes. Soon, these agents could autonomously conduct actions based on contract stipulations.

This shift introduces a fundamentally different kind of risk. A system might correctly interpret a standard indemnity provision or a permissible price increase, but does it have the authority to accept these terms on behalf of the company? Different transactions—such as those involving regulated data or critical suppliers—might require specific approvals that AI systems are not equipped to seek out.

Humans manage these complexities daily through policies, approval hierarchies, and institutional knowledge. They understand when to escalate issues or seek approvals, navigating the nuances of corporate governance. AI agents, however, require explicit boundaries and clear directives.

Legal teams must now consider not only an AI’s ability to understand contracts but also what actions it is permitted to take based on that understanding. Questions of independent decision-making, acceptable deviations, and required approvals need to be addressed comprehensively. Governance becomes not just about capability but about authority.

The stakes are high. Unauthorized actions by AI could lead companies to inadvertently enter into agreements or make commitments that were never sanctioned by the appropriate human authorities. These aren’t just hypothetical issues; they represent real legal risks that could have significant consequences.

To prevent such scenarios, legal teams need to design an "authority layer" for AI systems before they are widely deployed. This includes setting clear guidelines on decision rights, approved actions, prohibited commitments, and escalation processes. It's about ensuring that even if an AI system can act, it only acts within the confines of what has been explicitly authorized.

As AI continues to integrate into the legal field, the question moves beyond "Did the AI get it right?" to "Who gave it the right to make that decision?" This transition challenges current legal frameworks and requires a new focus on the governance of AI actions in business contexts.

The evolution of AI in legal systems isn't just about technological advancement—it's about adapting our legal infrastructures to manage and govern these new tools effectively. As we move forward, it’s clear that the next major challenge in legal AI isn’t just understanding the law—it’s about implementing it responsibly.