P2P

26 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | S P R I N G 2 0 2 3 they were deploying, and how it would transform the due diligence process. Over time, some of these organizations have changed their strategy. Many now talk about these tools as providing an easy way to capture and consolidate findings from reviewers in a due diligence process. They also talk about some of the findings being auto-populated, reducing the amount you have to CTRL+F a phrase like "governing law" in a document. Note: there's no mention of AI at all here. This presents a shift towards elucidating the specific use cases and value from a user's perspective. This must also be done from a business perspective: how will spending money on a new tool bring value or more money back into the business? Law firms have long dealt with a tension between efficiency and revenue models based on billing time. For example, will cost reductions be passed on to clients? It is not as simple as saying that the improvement in technology itself will make these issues go away. Tackling the right workflows Coincidentally, the quality of an AI solution and how to ensure adoption of it both rely on largely the same thing: understanding and articulating the value they bring to users. For example, it is common to hear statements (especially around AI tooling) that a given solution "will draft documents for you", or "help you review documents". But these statements are too high level, and not detailed enough to make a decent product or convince people to change how they work. For example, drafting a document is usually comprised of a few different processes. Here are the processes commonly involved in producing a first draft. 1. Finding a starting point such as a template or a prior example 2. Amending the starting point at a basic level (often a simple exercise of replacing names and dates) 3. Tailoring the starting point to take account of nuances in the current situation (e.g., to reflect bargaining position) 4. Reviewing other actual examples of other contracts to see whether any further useful nuances can be added 5. Preparing a comparison against your starting point 6. Collecting comments from others (e.g., a partner) and reflecting these in the document To take this example further, the "finding a starting point" workflow has been subject to a huge amount of discussion recently with generative AI tools such as ChatGPT. Indeed, many believe that generating a starting point for a contract is one of the key use cases of these tools in the legal industry. But we should not just stop there. AI can generate legal contracts, but is this necessarily better than using a template? What are the things people are looking for from a starting point, and can AI provide these to people (e.g., context, provenance, explanations of drafting, etc)? Can AI be used to improve findability of content, rather than generating it? Or perhaps it can draft a contract, not to be used as a starting point, but to be used by the lawyer in step 4 to see if they have missed anything? The other possibility is that emerging AI technologies might cause you to be more willing to reconsider the process as a whole. For example, if the whole process is simply too long, can a generative AI model be fed a term sheet that parties have agreed on to produce the "legal drafting"? The F E A T U R E S

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