AI can enhance law firms' processes

As artificial intelligence evolves from a supportive tool to an essential participant in legal processes, the imperative for robust governance of AI-generated outputs is growing. Traditionally, legal compliance relied on comprehensive human records such as emails and formal case files to track decisions made. Now, however, law firms are increasingly adopting AI for routine tasks, like summarising cases and conducting initial research, which poses new challenges for accountability and oversight.

“Artificial intelligence is taking over tasks once handled exclusively by junior associates,” remarked Jon Bance from Leading Resolutions. This integration of AI into standard workflows is evident, with tools like Anthropic's Claude becoming embedded in platforms such as Thomson Reuters and DocuSign. However, one major concern is that AI often fails to leave a permanent record for its conclusions, which can significantly influence legal strategy. To address this, firms must modernise their systems to effectively audit AI-generated outputs.

Matter intake is typically the initial bottleneck in legal operations, but AI presents additional difficulties regarding data capture and accountability. “AI complicates matter intake, where client requests arrive through various digital channels,” Bance continued. Legal practices need to ensure that AI tools are subjected to the same record-keeping standards applied to human work. This calls for matter management systems to document prompt-response chains and interaction logs alongside traditional drafts, thereby expanding evidence-gathering methodologies to encompass automated processes.

The crux of achieving a proper balance between efficiency and oversight lies in ensuring human judgment remains at the forefront. “While powerful, AI still generates errors or misunderstands context,” Bance warned. He emphasised that AI should serve as an assistant rather than replace the nuanced decision-making tasks carried out by legal professionals. Regular monitoring of AI outputs for accuracy is critical, alongside ensuring the integrity of the data context used—“garbage in, garbage out” is a key consideration to ensure quality results.

Bance also believes in the importance of clear guidelines regarding AI's limitations within legal analysis. Establishing governance boundaries is essential for the responsible and transparent deployment of AI models and can protect firms against potential liabilities. “Strong internal frameworks prevent fines and reputational damage while building client trust,” he explained. To achieve this, firms require comprehensive audit logs that trace an AI’s final output back to its initial prompt, providing clarity on how conclusions are reached and thereby mitigating the risks of bias and privacy issues.

Bance summarised the challenge by stating, “The question is whether AI’s role in legal work can be held to the same transparent standards as a human colleague." Under regulations such as the EU AI Act, firms must be prepared to defend AI-assisted work against scrutiny from clients or regulators. Ultimately, those teams that succeed will be the ones where humans maintain ultimate responsibility for the final output.