Artificial intelligence is becoming a regular feature of legal drafting workflows in corporate and securities practice. Its use spans everything from generating initial drafts to organizing and summarizing complex information. As with any tool, its effectiveness depends on how it is applied and the context in which it is used. In transactional work, including fundraising and cross border matters, AI can contribute to efficiency while still operating within practical boundaries. This article discusses how AI can be used in legal drafting, as well as the situations in which its use is more limited.
Where AI Performs Well
AI performs most consistently in situations where outputs are structured and repeatable. In legal drafting, this includes standard contract clauses such as confidentiality, indemnification, governing law, and limitation of liability. These provisions tend to follow familiar patterns across agreements, which makes them wel suited to automated generation. AI can also assist in preparing simple agreements, including non-disclosure agreements and basic service contracts, where the structure is stable and does not require extensive customization.
AI is also effective in summarizing documents, extracting key provisions, and reorganizing information into more accessible formats. These functions can reduce the time spent reviewing large volumes of material and support internal workflows by producing initial drafts or reference documents.
Limitations in Transactional Work
Things become less straightforward once drafting moves into live deal dynamics. Transactions are not static exercises. Terms shift as negotiations evolve, positions change, and parties react to each other in real time. A provision that looks standard at the outset can quickly become specific after a few rounds of comments between founders, investors, and counsel.
AI can generate a clean starting point for investment terms or governance provisions, but it does not participate in the negotiation process. It does not pick up on tone, leverage, or the tradeoffs that happen when one side moves on one issue to resolve another. In practice, drafting follows these exchanges, with language adjusted to reflect compromises and timing pressures.
In a financing round, provisions across multiple agreements need to stay aligned as terms evolve. A change in one document can affect others, and those connections are managed dynamically as discussions progress. AI can help produce drafts, but keeping everything consistent while negotiations are ongoing remains a hands-on process.
SEC Filing Requirements
Regulatory workflows highlight a different type of limitation, namely execution rather than drafting. In the context of filings with the U.S. Securities and Exchange Commission, AI can support the preparation of disclosure by organizing information and generating draft language. However, it does not interact with filing systems or complete submissions. Filings through EDGAR require manual uploading, formatting compliance, and verification that all procedural steps have been followed.
Cross Border Structuring
Cross border matters require coordination across multiple legal regimes. AI can assist by summarizing general frameworks or outlining common issues across jurisdictions, which can be useful at an early stage. However, structuring transactions that account for the interaction of these variables remains context driven and often requires tailored solutions.
In a Nutshell
AI is influencing how legal drafting is approached in corporate and securities practice, particularly in areas that benefit from standardization. It performs well in generating routine content and organizing information, while its role is more limited in areas that require customization and coordination.
In practice, AI is most effective when used as part of a broader drafting workflow. It can generate first drafts of standard provisions, create templates, and support document review by identifying common elements or potential gaps. These uses are effective when combined with review and adjustment. Outputs should be treated as preliminary. AI generated language often requires modification to reflect the specifics of a transaction, and reliance without review can introduce inconsistencies.
This distinction is similar to the role that term sheets play in transactions. They provide a high-level summary of key terms but do not replace the detailed agreements that ultimately govern the deal . In a similar way, AI can streamline parts of the drafting process without substituting for the broader analytical work involved in structuring and documenting transactions.
