David Wakeling incubated a software product to help banks restructure contracts after the financial crisis, realizing the scale of work made it infeasible to do manually with lawyers. 10 years later, Allen & Overy has dozens of different software products managed by an interdisciplinary team of lawyers, software engineers, and data scientists. They're also one of the most advanced adopters of LLMs, where they're finding significant business value.

Key Takeaways

• Background on Allen & Overy (A&O) & David [00:21]
• How did you arrive at this model for the Markets Innovation Group? [01:51]
• What about your background led you to do this? [04:08]
• How did you think about the revenue model for these products? [05:43]
• How does this interact with the by-hour billing model? [09:03]
• Does this enable you to sell other labor hours, or is it really just the software license? [11:00]
• How many products do you now have live and in market? [11:58]
• Is there a pattern to which of these solutions has been most successful, vs the others? [13:25]
• What cautions would you give to other firms / companies trying to build a similar model? [14:58]
• What was your first exposure to the "AI" space? [16:32]
• How exactly do you find using the platform? What use cases are most helpful? [20:40]
• How would you quantify the labor hours you've saved with this, across each category? [22:31]
• How do you think about this issue of governance? How do you advise clients on this? [26:53]
• Which of these problems do you feel are less well solved in the market? [29:09]
• How do you think about the buy vs build decision? [32:18]
• We believe we save a couple hours per week; across 3.7k lawyers, that's a lot of hours of lawyers' time [33:56]
• Where do you think value occurs then? [35:38]
• How do you think about "future-proofing"? [41:35]
• How do you operationalize this? [43:35]
• Does the product have to change between models? [44:22]