The legal sector’s understanding of generative artificial intelligence and legal analytics has significantly matured since the initial surge of interest sparked by ChatGPT. While widespread adoption is still nascent, its transformative potential for legal workflows—from litigation and contract insights to legal spend analysis and even cybersecurity incident response–is evident.
Today’s tools, accessed through ALSPs by 66% of law firms and 64% of in-house corporate law professions, according to a UnitedLex survey–transcend simple large language model integrations, offering sophisticated workflow management. Consequently, corporate legal departments and law firms are strategically evaluating the balance of rushing to keep up artificial intelligence and legal analytics utilization with careful planning.
How artificial intelligence and legal analytics work together
The convergence of artificial intelligence and legal analytics is transforming legal workflows by providing deeper insights and automating complex tasks. AI, particularly machine learning, enables the analysis of vast datasets of legal documents, case law, and other relevant information. Legal analytics tools use these AI capabilities to identify patterns, trends, and correlations that would be extremely difficult, if not impossible, for humans to detect manually. Further, AI automates repetitive and time-consuming tasks, such as eDiscovery document review, drafting discovery response documents, and contract analysis. Together, by providing data-driven insights, AI and legal analytics enhance the quality of legal decision-making.
Harnessing artificial intelligence and legal analytics for legal workflows
The path to AI implementation is complex, yet navigable. It’s vital to maintain realistic expectations, recognizing that generative AI is a tool, not a cure-all, and requires careful attention to risks, skill development, and integration challenges. Key considerations include the following:
1. Identify use cases
Determine specific areas where AI can add immediate value to legal workflows, augmenting human effort to minimize time and effort while accelerating results. For example, for litigation teams are seeing significant efficiencies by applying generative AI to eDiscovery document review, mentioned above, privilege logging, and other repetitive, high-volume tasks like contract intelligence and M&A due diligence review.
2. Evaluate solutions
When legal teams evaluate AI solutions, their focus extends beyond mere technological novelty. They meticulously assess functionality to ensure the solution aligns with their specific needs, demand robust accuracy in AI-powered insights, and prioritize usability for seamless integration into existing workflows. Security is paramount, requiring strict adherence to data privacy and security regulations.
Procurement strategies must be carefully considered, weighing the merits of in-house development against purchasing from software vendors or engaging legal service providers with expertise in AI, like UnitedLex, from developing and managing to integrating AI into legal workflows, to determine the most efficient and effective access to the AI technology.
3. Testing and piloting
To effectively implement generative AI, legal teams should conduct pilot projects to assess its accuracy, speed, and performance. This testing is happening, to large extent, through their ALSPs who have invested in innovation centers to pilot generative AI products. A phased approach, starting with limited deployments, allows for gradual integration and process optimization. This may involve reevaluating existing workflows to align with AI capabilities.
4. Rollout and training
Implementing new technologies can be challenging. It is important to have a clear change management plan in place to address potential resistance and ensure a smooth transition, and address data AI readiness.
The accuracy and effectiveness of artificial intelligence and legal analytics solutions depend on the quality of the underlying data. It is important to ensure data AI-readiness: that data is clean, accurate, and up to date. For generative AI, in particular, to be effective, training data must be suitable. Certain types of data, like images, audio/video, and poorly formatted documents, can present compatibility issues with AI models. Biased or erroneous training data, such as miscoded eDiscovery documents, can lead to amplified biased outputs. Responsible data sourcing, including provenance, ownership, and for what purposes it may be used, is critical.
Finally, like any new skillset, to empower legal professionals with artificial intelligence and legal analytics capabilities, thorough training and knowledge transfer are essential. Well-designed educational programs will streamline the implementation process and make adoption more successful.
5. Continuous improvement
In the context of AI-driven legal solutions, continuous improvement transcends mere software updates; it’s a dynamic, iterative process embedded within the service delivery model.
Providers like UnitedLex emphasizes ongoing performance monitoring, leveraging its own data analytics capabilities to track key metrics related to efficiency, accuracy, and cost savings. This involves not only scrutinizing the AI models themselves but also actively soliciting user feedback from both internal teams and clients. This feedback loop is crucial for identifying potential bottlenecks, refining workflows, and adapting the solutions to evolving legal landscapes.
UnitedLex’s approach goes beyond simply “fixing” issues; it focuses on proactive optimization, regularly retraining AI models with new data, and incorporating advancements in machine learning to enhance predictive accuracy and automation capabilities. Furthermore, by refining its processes and methodologies, it ensures that the integration of legal analytics and AI remains aligned with best practices and the specific needs of its clients. This commitment to continuous improvement ensures that that AI solutions consistently deliver optimal value, driving tangible improvements in legal operations and fostering a culture of innovation.
Maximize the value of AI and legal analytics with UnitedLex
The increasing sophistication of generative artificial intelligence and legal analytics, and newer forms including agentic AI, signals its inevitable integration into the legal sector. While promising innovation and efficiency, a cautious approach is crucial. Legal teams can achieve positive outcomes by focusing on practical AI applications, like routine task automation, and proactively mitigating potential risks.
Ultimately, successful AI adoption hinges on practical experimentation to understand its functionality, a thorough assessment of its benefits and potential risks, and optimization through relatively low-risk, high return entry points, all facilitated by the guidance and support of experienced service providers
UnitedLex offers a comprehensive suite of legal analytics and AI-powered solutions to help law firms and corporate legal departments transform their workflows, including litigation and investigations, legal operations, incident response, and intellectual property. To learn more, let’s chat.
