AI Agents for In-House Lawyers: Practical Lessons

The rise of artificial intelligence has reshaped industries—but are in-house legal teams truly making the most of it? AI agents offer powerful ways to automate tasks, boost productivity, and save time. AI agents for in-house lawyers could revolutionize their work, yet many lawyers hesitate, unsure how to start or whether the risks outweigh the benefits.

In this practical and forward-thinking conversation, Olga Mack and Kassi Burns—two respected legal technology leaders—share how AI agents for in-house lawyers can used safely and effectively. Olga brings a strategic vision for AI in legal leadership, while Kassi, a senior attorney specializing in eDiscovery, offers firsthand experience building and deploying GPT-powered agents in day-to-day legal work.

Watch the full conversation with Olga Mack & Kassi Burns here:

Kassi brings a grounded perspective rooted in the everyday realities of legal work. Her experience in eDiscovery has given her a sharp understanding of data management and tech’s role in legal operations. She sees AI agents for in-house lawyers not as futuristic experiments but as tools to automate predictable tasks—like drafting routine documents or summarizing bulk information. Her use of templatized GPT prompts shows how repeatable instructions can generate consistent and reliable results with minimal input.

What sets Kassi apart is her focus on low-risk applications. She recommends lawyers begin with creative or internal-use cases, where mistakes have little to no legal consequence. Her examples—like Jane Austen–inspired letters or playful skincare advice—illustrate how AI agents can be used for experimentation before being applied to client-facing work. For in-house lawyers, this means using AI to draft FAQs, internal memos, or preliminary reports, reserving human review for anything high-stakes.

Creating an AI agent doesn’t require deep technical knowledge. Kassi emphasizes that tools like GPT Builder make it accessible for non-engineers. Her advice? Start with a clear goal: what kind of output do you want, in what tone, and with what context? Training data quality matters, even if it’s simple. She used a mix of public domain content and straightforward prompts, proving that perfection isn’t necessary to get value. The key is iterative refinement—adjust, test, repeat.

For in-house lawyers working with sensitive information, Kassi urges caution. AI agents should never be fed confidential client data unless tools meet the highest standards for privacy and compliance. She also notes that training data can introduce bias if not thoughtfully selected. The message is clear: treat AI output as a starting point, not an unquestioned final draft. Review, revise, and disclose AI use when appropriate.

Kassi takeaway is empowering: AI agents for in-house lawyers offer a path to increased productivity, but success requires a balance of curiosity and responsibility. By starting small, testing thoroughly, and using sound judgment, lawyers can embrace AI without sacrificing quality or trust. It’s not about replacing legal expertise—it’s about scaling it. For in-house teams stretched thin, this could be a game-changer.

Watch the full conversation here:  Notes to My (Legal) Self: Season 6, Episode 13 (ft.Olga Mack & Kassi Burns)

At Notes to My (Legal) Self®, we’re dedicated to helping in-house legal professionals develop the skills, insights, and strategies needed to thrive in today’s evolving legal landscape. From leadership development to legal operations optimization and emerging technology, we provide the tools to help you stay ahead.

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