Law firms are racing to adopt AI but clients across Asia's legal market are still waiting to see tangible value delivered
A new analysis from Law.com finds that two years into the legal industry's generative AI (artificial intelligence tools that draft, analyse, and summarise text) adoption race, a significant gap has opened between the pace at which law firms are deploying AI tools and the value that clients say they are actually receiving. The piece focuses on Asia's legal market as a case study but identifies a dynamic with global resonance: firms are investing heavily in AI platforms, often at significant cost, while clients remain unconvinced that the efficiency gains are being passed through in the form of lower fees or meaningfully faster, higher-quality work product. Separately, a piece in the New York Law Journal examines how generative AI and machine learning AI have transformed trade secret law, both in terms of the risk that confidential training data constitutes a misappropriation and in terms of the difficulty of protecting proprietary legal analysis when it is processed through third-party AI systems. In the same corpus, a US federal judge, Judge Jesse Furman, discusses the applications of AI for judicial work and reflects on one of the first sanctions cases involving AI-generated court submissions. Together, these threads point to a profession at an inflection point: the tools are proliferating, but the legal frameworks governing their use, and the client relationships that justify their cost, are still being worked out.
Why this matters
The 'AI value gap' framing is commercially important because it signals the next pressure point in law firm AI adoption: client scrutiny. Firms that have made significant technology investments will face increasing pressure to demonstrate that those investments translate into client value rather than being absorbed as margin improvement. For City firms advising large corporates, this creates a competitive dynamic where the ability to articulate and evidence AI-driven efficiency gains becomes part of the pitch. The trade secrets angle adds a distinct legal risk dimension: firms processing client confidential information through AI tools that use public or shared model infrastructure may inadvertently create disclosure risks, which is a live concern for clients in M&A, litigation, and regulatory matters.
On the Ground
A trainee working on an AI governance or technology mandate would assist with reviewing and marking up data processing agreements to ensure client confidential information is not used to train third-party AI models, help draft AI governance policy documents for internal or client use, and prepare vendor due diligence questionnaires for AI tool providers.
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“How should a Magic Circle or elite US firm demonstrate genuine client value from its AI investments, and what legal risks does AI tool adoption create for the firm itself?”
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