AI is making lawyers faster, but Biglaw firms are now confronting a difficult question about whether that speed is destroying their own revenue model.
The core tension is straightforward: if artificial intelligence compresses the hours required to complete legal work, firms that bill by the hour stand to earn less, not more.
ILTACON 2026, scheduled for late August, is expected to push this conversation forward in significant ways across the legal technology industry.
Experts from Above the Law and Litera will attend the conference and bring their findings back to a wider audience through a dedicated post-event session.
That session is set for September 9th at 1 p.m. ET, offering attendees one hour of CLE credit alongside substantive discussion on the future of AI in law firms.
Among the topics on the agenda is the concept of tracking “RoAI,” a framework for measuring the return on artificial intelligence investments made by law firms.
The session will also examine whether law firms are genuinely prepared to rethink the billable hour model this time around, rather than simply absorbing AI as another back-office tool.
Grant Hewlett, a legal strategy and operations executive, works with global law firms and corporate legal teams to improve financial performance, operational execution, and long-term organisational health.
Bob Ambrogi, a lawyer and journalist who has written and spoken about legal technology for more than two decades, hosts the podcast LawNext and the weekly roundtable Legaltech Week.
Stephen Embry, a national litigator and advisor in mass tort, class action, and privacy arenas, also writes for TechLaw Crossroads and Above the Law, rounding out the speaker panel.
The central argument running through the event is that efficiency gains from AI must be approached with commercial discipline, not just technical enthusiasm, if firms are to remain profitable.
Law firms that fail to develop a commercially minded strategy around AI risk automating away the very billing structures that have sustained Biglaw for decades.
The discussion promises to move beyond abstract predictions and into practical frameworks that firms can apply to align their AI investments with genuine financial outcomes.