Generative AI For Litigation Teams: Navigating The Future.
For the last few years, Generative AI (GenAI) has been the most talked-about topic in the legal industry. Now, as law firms begin to progress from talk to implementation, GenAI is poised to transform the litigation lifecycle. Indeed, during a recent Lexology Masterclass webinar discussing AI for litigation teams, 94 percent of participants agreed that generative AI is here to stay. And 84 percent said that AI is likely a competitive advantage.
While GenAI for litigation can deliver tremendous value, successful deployment requires teams to identify the best approach for integration, select priority use cases, ensure data security, and address common user concerns.
In this article, we’ll explore insights from the AI for litigation teams webinar. We’ll offer an overview of the current state of AI for litigation including the most promising use cases for AI in the litigation lifecycle. And we’ll discuss how to navigate common AI concerns to encourage adoption while reducing risk.
Want to watch the webinar? Download the on-demand Lexology Masterclass now.
The current state of AI in litigation
Similar to previous advancements in legal tech, like eDiscovery, using GenAI in litigation processes will almost certainly become the norm. While we’re not there yet, recent research from Ari Kaplan Advisors reports that half of litigation support directors are beginning to incorporate AI into their workflows. One research participant already using case management software with integrated AI explained their firm’s perspective saying, “… there is an expectation that these products will evolve and be widely available to the point of being standard, similar to a document review platform.”
The momentum around Gen AI in law is clear. From Harvard to Bloomberg Law, legal thought leaders see AI as a transformative force, and Thomson Reuters reports that 70% of legal professionals expect it to significantly impact the profession within five years.
As James Bekier, the director of litigation services at BakerHostetler, noted in the Lexology webinar: “Things keep changing in our world, and we have to adjust to them. This is just a new technology that we can adopt.”
This adaptation is becoming increasingly necessary as firms face mounting pressure to handle larger volumes of data while maintaining efficiency and accuracy. A robust AI tool offers the flexibility to handle the massive influx of data, manage increasingly complex cases and ensure the litigation team can continue to scale.
The competitive imperative
As client expectations evolve, law firms that fail to adapt risk falling behind. Clients are increasingly reluctant to pay for tasks that AI can perform more efficiently, and lawyers must understand the technology to effectively advise clients.
The “democratization of litigation” through AI is particularly noteworthy. Smaller firms can now compete more effectively with larger practices by using AI to handle time-consuming tasks, allowing their lawyers to focus on high-value strategic work.
Litigation teams that successfully implement AI before it becomes standard will gain a distinct advantage. To do this, teams must understand where AI can most effectively be deployed as well as its capabilities and limitations.
Key use cases for AI in litigation
In the course of a case, lawyers often need to analyze complex information to make critical decisions while keeping the big picture in mind often – all with short turnarounds as deadlines loom. Adding to the high-stakes, high-pressure environment is the growing volume, diversity and complexity of discovery data sets.
“There’s not enough lawyers in the world to review all the documents that people are producing these days,” observed Bekier. “My email box used to be, like, a thousand per month in 2010. It’s a thousand a day now.”
AI helps address these market challenges. It lightens the manual workload, giving lawyers more time to focus on what matters most during critical moments: building a winning legal case strategy and delivering client value.
Among leading litigation teams, the most common use cases for AI include:
Discovery:
- Enhanced document review
- Coding for eDiscovery
- Entity extraction
Document management
- Legal topic extraction
- Document review and summarisation
- Contract analysis
Case management
- Legal research
- Deposition preparation
- Transcript management
- Case chronology creation
- Legal case strategy
- Drafting billing narratives
In these use cases, AI is a powerful tool for streamlining processes, but it’s important for users to understand that it’s not a replacement for human insight. Law firms must ensure that AI complements, rather than replaces, the critical analysis and judgments of legal professionals.
Defining AI’s role and addressing concerns
While the potential benefits seem clear, the success of AI within a litigation department will ultimately be determined by users. Before firms can achieve widespread adoption, they must ensure user understanding and client confidence. Clearly defining the role of AI in litigation can help.
AI is a strategic partner, not a replacement
AI is powerful, but it’s not a person, and it certainly doesn’t have a law degree. No matter how advanced, AI isn’t able to replace human logic. AI cannot make strategic decisions or contextual judgment calls, both of which are essential in litigation.
As Raymond Bentinck, Chief Product Officer at Opus 2, noted, “Gen AI automates certain tasks, but doesn’t replace human intelligence. Humans have memory. We have experience. We have imagination.”
To effectively and safely use AI, users must understand these limitations and view AI as an assistant or partner. It can help with tasks, but it still requires input and review from legal practitioners.
For example, AI can help structure data and generate reports, but it lacks the deep understanding of context that lawyers bring to cases. While AI can extract relevant facts from documents, it may not understand their significance within a specific legal framework. Lawyers still need to review AI-generated summaries to align them with case strategy and ensure they capture the critical elements. Keeping this in mind, litigation teams can better define the role AI should play in their processes.
Selecting a solution that addresses common AI concerns
Adopting AI isn’t merely about acquiring advanced tech; it’s about finding solutions designed to address common AI concerns including security, accuracy, and adoption. AI for litigation teams should protect confidentiality, mitigate risks, and assist lawyers in managing data-intensive litigation. “You need to walk in with your eyes wide open,” Bekier advised.
Data security
Handling confidential client information is central to the legal profession, making data security a primary concern. A secure legal AI tool must safeguard sensitive information at every stage, from input to analysis to output. Law firms must ensure that any GenAI tool has robust data protection policies, such as clear data ownership clauses, defined storage protocols, and strict retention or deletion timelines. Without these safeguards, sensitive data could be exposed, inadvertently shared, or retained by third-party platforms.
Firms should select solutions that adhere to strict privacy standards, employ robust encryption methods, and comply with data protection regulations. These principles should be embedded throughout the development of any AI solution and clearly communicated to users and clients to offer reassurance.
Accuracy and hallucinations
One of the most common questions that comes up when discussing GenAI in litigation is “How can users be sure the information AI provides is factual and correct?” It’s an essential question because without proper guidance and careful review, GenAI models can produce responses that are plausible but factually incorrect, known as “hallucinations.” Given the importance of accuracy in litigation, selecting an AI solution designed to safeguard users is critical.
Thoughtful design and development can help by ensuring that AI only references data provided within specific cases. This prevents the model from introducing external, unverified knowledge into its responses.
To further support accuracy, the AI provides source citations within documents, allowing lawyers to verify the information and track its origins. This dual approach—restricting AI knowledge to case-specific data and providing verifiable sources—reduces the risk of inaccuracies and ensures transparency, building trust among users. Lawyers need to remain in control of their work product, guiding the AI to support decisions without overwhelming them with irrelevant information.
Change management and adoption
Lawyers tend to be cautious adopters, especially in the legal sector where risk mitigation is the norm. The skepticism may stem from a lack of visibility, making it harder for them understand the tool’s value or relevance. Seeking feedback from key stakeholders during the selection, planning, and rollout steps of the process can help secure buy-in. In addition, consider using a pilot program with tech-savvy teams before moving forward with department-wide deployment. This smaller group can offer feedback, identify optimization opportunities, and highlight initial successes.
Still, incorporating AI into the litigation lifecycle may require shifts in both workflows and mindsets. If using a standalone solution to assist with case management, users need to incorporate additional steps into their process to import documents to the AI solution, create prompts, review outputs, and export the results.
This challenge can be alleviated by using a case management solution with integrated AI. This ensures AI results can be traced back to case documents, enhances accuracy, and reduces disruption to current processes.
Whether using a standalone or integrated AI tool, a thoughtful training approach, ongoing enablement, clear governance, and realistic expectations will be essential.
Building value through strategic implementation
Success with AI implementation requires a measured, strategic approach which involves:
- Establishing clear governance policies and guidelines around using GenAI for litigation to protect client data
- Conducting thorough cost-benefit analyses for each implementation, prioritising high-value, high-impact use cases
- Involving all stakeholders—clients, lawyers, and AI experts—to smooth implementation and build confidence
- Selecting a scalable, tool designed for litigation and case management processes
- Ensuring proper training, ongoing enablement, and quick support for legal teams
- Incorporating feedback from lawyers to fine-tune the use of AI, ensuring it supports rather than disrupts existing workflows
- Maintaining rigorous validation processes for AI-generated content
Looking ahead
Selecting the right AI for litigation isn’t just about today’s needs; it’s about future-proofing your practice with secure, reliable, and adaptive technology. By automating tasks like document review, research, and summarization, AI allows lawyers to dedicate more time to strategic, high-value work. By embedding AI thoughtfully, firms can set themselves apart as forward-thinking, client-focused partners in the evolving legal landscape.
To explore this topic in more detail and hear insights from experts on how leading litigation teams are using AI, watch the webinar now.
Or, if you want to see AI for litigation teams in action, request an Opus 2 demo.
