Checking In on Legal’s AI Transformation: A Matter of Perspective

Findley Penn-Hughes, a Senior Associate at Mayer Brown, comes on to help provide a practical check-in on where AI actually stands in legal practice.

Findley and I compare our experiences adopting AI over the past few years—from early hallucinations that undermined trust to the point where tools like Harvey, Copilot, Claude, and ChatGPT have become part of everyday work. We also dig into why the next step, agentic AI, is proving much harder than the demos suggest. Findley shares his experiments building AI agents to monitor regulatory developments and draft client alerts, along with the technical and reliability problems he encountered.

A major part of the discussion is cybersecurity. For law firms and legal departments, the issue is not just whether an AI system produces accurate work, but what happens when it is given access to sensitive client data, internal systems, and the ability to take actions autonomously. We talk about the risks of data leakage, third-party model providers, increasingly capable cyberattackers, and the possibility that more autonomous agents introduce entirely new attack surfaces. Even where those risks can ultimately be managed, the uncertainty itself helps explain why legal organizations are moving more cautiously than the AI hype cycle might suggest.

We also spend time on incentives. Lawyers are still largely rewarded for producing billable work, not for spending time building systems that make that work faster or eliminate it altogether. That creates an obvious tension for associates and partners asked to invest non-billable time in AI workflows. Firms may need to rethink billable-hour credit, compensation, staffing, and the role of dedicated AI specialists. And as clients increasingly expect AI-driven efficiencies to translate into lower fees, firms will also have to work out how they price legal work and capture value from the technology they build.

From there, we explore other barriers slowing AI adoption across legal: messy and unstructured data, questions about how much proprietary “secret sauce” law firms really have, uncertain economics, and a technology market with no clear winners yet.

We close by discussing what AI could mean for law firm business models, competition between firms, junior lawyer training, and the future shape of the profession. Our conclusion is cautiously optimistic: AI is already changing legal practice, but the industry is still much earlier in the transformation than the hype sometimes suggests.

https://www.linkedin.com/in/findley-penn-hughes

ALL VIEWS EXPRESSED OUR OWN

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