AI-Augmented Legal Workflows: A Map to Success

The program opens with its three-part framework: Define, Design, Deploy. This session covers the Define stage: identifying low-risk, high-yield entry points in your current practice, learning what is actually done in a workflow, and knowing every stakeholder to talk to. It then addresses where exceptions fit into the proposal, getting the project plan down on paper, moving the budget past roadblocks, and planning measurement so it is part of the process from the start.

You’ve chosen a workflow that you want to automate. Here’s a roadmap on how to map the parts of your workflow to technologies — both AI based and not — that will let you make it into something you can get up and try out. The session covers the lingo for talking to engineers, selecting the right AI tools for each workflow step, and starting small and iterating until each step is solved. It identifies where people come in to involve, evaluate, and approve, then turns to assembling and testing the workflow. It closes with building in ROI and quality metrics beyond time saved, including accuracy rates, client delivery speed, and error reduction, and preparing for rollout.

The final session takes the workflow to production. It addresses security concerns, working with users to test the automation and incorporate their feedback, and getting signoffs from IT and management, followed by education and rollout. It then covers keeping the roadmap open to growth, using what you learned to prepare for the next workflow, and applying principles of reuse. It closes with client conversations and billing realities: managing client expectations, transparency regarding AI usage, and the impact of AI efficiency on hourly billing models.