Episode 187: Experimentation – is it the next collaborative legal practice?
Oct 30, 2023
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The podcast discusses the integration of experimentation into the legal industry, including approaches to experimentation, funding challenges, experiments with document review and drafting, techniques for capturing experiment learnings, creating the right balance in experimentation groups, the use of Co-Pilot for drafting, AI's impact on the legal industry and upskilling lawyers, and the review and extraction process.
Experimentation drives legal innovation and staying relevant in the industry by encouraging grassroots experimentation and open thinking.
Different organizations have varying approaches to experimentation, either prioritizing formalized processes or allowing experimentation to emerge organically, with collaborative efforts and dedicated support to ensure efficient implementation.
AI, including generative AI and predictive analytics, holds great potential in enhancing decision-making in the legal sector, and law firms are actively experimenting with AI tools for various purposes such as drafting documents and automating processes.
Deep dives
The Importance of Experimentation in Legal Innovation
Experimentation is a crucial part of driving legal innovation and staying relevant in the industry. By encouraging grassroots experimentation, organizations can tap into the curiosity and creativity of their employees, allowing them to explore various tools and technologies. Open thinking and knowledge sharing are emphasized, ensuring that employees learn from their experiments and share their insights with others. Additionally, larger experiments with low entry barriers enable innovative ideas to be tested and evaluated through lean canvases and simple business cases. The rapidly evolving nature of legal technology necessitates constant experimentation, as failing to adapt to changes could render organizations irrelevant in the long run.
The Approach to Experimentation
Different organizations have different approaches to experimentation. Some prioritize formalized processes with clearly defined hypotheses and quantitative measurements of success. Others take a more flexible and unstructured approach, allowing experimentation to emerge organically. The focus is on creating space and time for individuals to explore and tinker with new technologies. Collaborative efforts are encouraged, with non-technical experts partnering with technical experts to ensure a holistic understanding and efficient implementation of experiments. The use of prompts, reverse mentoring, and dedicated digital academies help onboard and support individuals who may initially be hesitant or skeptical about experimentation.
The Role of AI and Predictive Analytics in Decision Making
AI, including generative AI and predictive analytics, holds great potential for enhancing decision-making in the legal sector. Experimentation with AI models, such as GPT 3.5 Turbo and CLAU2, has been conducted in areas such as document review and drafting. The building of a model-agnostic approach has been crucial to ensure the right fit for specific use cases. Offering prompt engineering sessions and demonstrating the benefits of AI in delivering proactive assistance have helped overcome initial skepticism and facilitate wider adoption. Cost optimization and considerations of data integration have also been key factors in decision-making regarding the use of AI models.
The Benefits and Challenges of Using AI in Law Firms
Law firms are experimenting with AI software for various purposes, including review, extraction, drafting, and bulk review. The main objective is to understand the specific use cases where these tools are effective and to gather evidence on their applicability in different practice areas. The engagement and support of champions who are knowledgeable about the technology is seen as crucial for successful adoption. Training and upskilling of lawyers are important to maximize the potential of AI tools, and collaboration between technical and legal experts in fusion teams is beneficial. Trust in large AI providers, such as Microsoft and OpenAI, is generally high, although it is important to ensure data privacy and security when working with AI systems.
Applying AI to Document Automation and Drafting
Law firms are exploring the use of AI, including the Co-Pilot by Microsoft, for drafting and document automation. Co-Pilot has proven to be excellent for drafting, particularly in the initial stages where starting is often the most challenging part. Synthesized data and safe data sets are being used for experimenting, and there is a focus on understanding the potential impact of AI on document automation processes. Some firms are also considering how AI can change search processes and searching guidance within their organizations. While AI providers assure data privacy, firms remain cautious about utilizing sensitive or confidential data and are waiting for models to be localized to their jurisdictions.
ChatGPT4 came after ChatGPT1 – getting from 1 to 4 and beyond took time, money, failure, frustration, resilience, agility, collaboration and multidisciplinary teamwork. These factors are all part of experimentation but they’re not typically hallmarks of the legal industry. So, how do we get from here to the new digital legal world?
In this podcast, Michelle Mahoney, Executive Director Innovation at King & Wood Mallesons got into the nitty gritty of what experimentation means in legal and how GenAI is encouraging its integration into legal BAU in discussion with this incredible panel of legal experimenters: