Rising data volumes, growing data diversity, and mounting cost pressures are the reality in disclosure exercises of every size, not just the largest matters. AI, both generative and agentic, is emerging as a genuine release valve, absorbing much of the first-pass work and giving lawyers faster access to the evidence they need to build their case. This session explores how that shift moves legal value from identifying information to interpreting it and shaping strategy, what "trust but verify" looks like when AI is, in large part, determining what documents are disclosed, and how firms and in-house teams should rethink training, workflows, and risk appetite as the division of labour between lawyer and machine evolve

The relationship between corporate legal departments and their outside firms has always run on trust, expertise, and value — but AI is changing how each of those is defined and demonstrated. In-house counsel increasingly want visibility into how firms use AI on their matters, what it means for cost predictability, and whether it changes the calculus of which work stays in-house versus goes outside. Meanwhile, firms are under pressure to show that AI adoption translates into real value rather than just faster billing cycles. This session brings in-house and outside counsel together to discuss how these conversations are actually playing out — the new questions being asked, the new expectations being set, and what a productive AI-era partnership looks like from both sides of the table.