Part 4 - AI for legal professionals: Litigation strategy
While many in the legal profession are now familiar with its use in tasks such as legal research and document review, AI is beginning to make inroads into more complex and sensitive domains, including litigation strategy. As machine learning models become more sophisticated, lawyers and litigation funders are starting to explore how predictive analytics tools might shape decisions both within cases and the merits of pursuing litigation at all. Likewise, mediators are beginning to consider how AI might assist them in the mediation context. The fourth instalment in our series on AI for legal professionals[1], this article focuses on how AI could be used by lawyers, litigation funders and mediators to inform their strategies.
Lawyers
Clients naturally want to know the merits of their case. For generations, a lawyer’s analysis of a case’s merits has been shaped by a combination of legal analysis, professional judgment and experience drawn from similar disputes. Litigators rely not just on precedent but also on their accumulated understanding of how courts, judges and opposing parties tend to behave.
For lawyers, the strategic potential of AI lies chiefly in its ability to process and identify patterns within vast quantities of litigation data. If AI can analyse large volumes of historic litigation data and outcomes, it could begin to replicate, at least in part, the instincts and expertise of seasoned litigators. Emerging AI tools claim to predict the likely outcome of a dispute, analyse a judge’s prior rulings, model litigation risk or suggest optimal timing for settlement. For solicitors and barristers alike, this information could inform a host of strategic decisions, from the structuring of pleadings to forum selection and the tone of advocacy.
Systems of this kind promise to reduce risk in litigation strategy by offering data-driven insights into the likelihood of success at trial. In turn, such analysis could help clients make more informed decisions about whether to bring or defend proceedings and assist in settlement strategy.
However, there are important limitations in the current generation of AI models. Predictive AI works by applying statistical methods to past data, meaning it can only forecast outcomes based on what has previously occurred. It is rare that any two cases are identical and even subtle changes in a fact pattern can lead to different outcomes. This poses an even greater challenge when a dispute involves a novel legal issue or an unusual factual situation and where precedent is scarce.
The quality of the underlying datasets also matters. Publicly available court records do not capture disputes resolved before trial, nor do they reflect commentary from the legal community that highlights areas of controversy or decisions under Parliamentary scrutiny. An algorithm cannot easily replicate the nuanced, experience-based insights of advocates who have appeared before particular judges and learned how best to present arguments to them.
In short, AI can process patterns in the record, but it cannot yet capture the breadth of knowledge that litigators accumulate across years of practice. These shortcomings, however, are not insurmountable. As models are trained on broader datasets (including, potentially, law firms’ internal knowledge systems) they may become more sophisticated and useful in predicting case outcomes. As with conversation-based generative AI systems, some firms may, in time, develop proprietary systems which combine public data with their own experiences of litigation. It is even conceivable that the judiciary itself could one day use predictive tools to inform aspects of case management or decision-making.
Third party litigation funders
While TAR tools generally rely on traditional machine learning, generative AI systems, trained on vast datasets, can go further. Whatever functions AI tools can perform now, they will be able to deliver them more effectively as the technology improves.
AI promises to supplement this process by providing funders with predictive tools that can analyse historical data across jurisdictions, assess the likely success rate of similar claims, forecast the duration of proceedings and estimate judicial behaviour or settlement timing. In a competitive market, such analytics may offer a commercial edge, allowing funders to screen cases more efficiently and manage their portfolios with greater accuracy.
One of the most immediate impacts could be an increase in the speed and efficiency of the funding process. With the use of machine learning tools, it might be that funders are able to evaluate cases more quickly, while also improving the accuracy of their assessments. This acceleration should make the funding application process more client-friendly and, by further refining the ability of funders to back claims which are likely to succeed, may ultimately lower the overall cost of funding.
However, the limits of AI are just as pertinent for funders as for lawyers. An AI model trained on incomplete or biased data may give misleading results, particularly where historical data is sparse or skewed.
Mediators
For mediators, the promise of AI lies with the development of tools that make the mediation process more efficient, insightful and responsive. One of AI’s most valuable applications is in pre-mediation preparation. By drawing on large datasets of previous disputes, AI could help mediators anticipate areas of contention, assess the likely sticking points and enter the mediation with a clearer understanding of where difficulties may arise. A study by researchers at the University of Cambridge in 2022 found that machine learning models could predict risk attitudes with an accuracy of up to 72%. Mediators of the future may therefore choose to use AI to help them analyse the risk tolerances of the parties and tailor their approach accordingly.
During the mediation itself, AI tools are increasingly capable of providing real-time support. While experienced mediators are already highly attuned to parties’ emotions, real-time sentiment analysis tools, for example, can offer mediators an augmented ability to identify changes in tone, body language or word choice that may not be immediately apparent, particularly in virtual or hybrid settings. By highlighting when a party appears frustrated, disengaged or conciliatory, these tools will give mediators an additional layer of awareness that can inform when to intervene, pause or reframe the conversation.
Machine learning systems trained on historic case outcomes can suggest possible resolution options based on how similar disputes have been settled. Such tools can broaden the menu of creative options that mediators place before the parties. They may also provide a useful ‘reality check’ where parties’ expectations are significantly out of line with what has been achieved in comparable cases.
While AI is unlikely to change the essence of mediation, which rests on human connection and dialogue, as the technology advances, it is likely to become a valuable ally for mediators, enhancing their ability to manage the mediation process and broker resolutions effectively.
Comment
AI is unlikely to displace the skills of experienced litigators, the judgment of funders or the human connection at the heart of mediation. However, various tools are emerging which can support and enhance those roles.
Used responsibly, predictive analytics and other AI applications may bring greater clarity to case assessment, efficiency to funding decisions and insight to the dynamics of mediation. The challenge for the legal profession will be to harness these opportunities while recognising that creativity, discretion and empathy are, and will remain, central to resolving disputes.
In the next edition of our series on AI for legal professionals, we will look at the ways in which AI is being deployed in arbitration.
Footnotes
[1] See our previous instalments
25 September 2025 Part 1 - AI for legal professionals: Where to start? |
09 October 2025 Part 2 - AI for legal professionals: Hallucinations |
23 October 2025 Part 3 - AI for legal professionals: Document review and disclosure |
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