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Technology In Disputes

Date: 17/06/2024 Type: Articles Topic: Private Client | Trusts | Wills and Estates | Inheritance | Next Generation Wealth | Investment and HNWI’s | Tax |

The disruptive potential of technology, in particular Generative Artificial Intelligence (Gen AI), in the delivery of legal services and resolution of disputes is fast becoming a key topic in conversations amongst legal professionals and advisors, with the tone a mix of excitement, skepticism and trepidation.

The Master of the Rolls, Sir Geoffrey Vos, has championed the digitalisation of the justice system (LINK) with the adoption of online dispute resolution platforms, smart contracts and blockchain and has touted the revolutionary effect that Gen AI and other advanced machine learning tools will likely have on the legal ecosystem. In many areas we are already observing a shift towards technology adoption with online case management systems, virtual courts and digitalised bundles as commonplace.

Gen AI's Growing Influence in Legal Disputes

Gen AI has been the focus of much of the recent hype with many legal and professional services firms rushing to understand how they leverage and coexist alongside this potentially game-changing technology.

Technology experts are anticipating the improvement in capabilities that next generation (ie. GPT-5 and tailored models (trained for specific tasks)) will deliver whilst the vast majority of professionals are still getting to grips with the current capabilities, and it's easy to start feeling overwhelmed. Predicting the impact of technology on legal disputes, even over the next 12 months, is very difficult (but exciting, we might add).

Our client conversations indicate the majority of businesses are experimenting with Gen AI and are not yet geared up to absorb this change, though the legal and professional services sector is expected to be one of the most impacted in how it delivers its core services. And it's the high-volume, repeatable and labour-intensive tasks often handled by junior staff that are likely to be impacted first. These include:

  • Review of large volumes of unstructured data - Organising and analysing large and unstructured data sets is time consuming and prone to human error (such as emails, contracts, invoices, financial reports, transcripts or statements). Each document requires careful review, categorisation and identification of relevant information for the case.
  • Information accuracy and quality - Verifying that data (which is being relied upon in cases) is accurate, complete and triangulated to supporting evidence from multiple sources within the data set, is labour intensive but crucial in building and sustaining a legal case.
  • Research - Undertaking extensive research into a subject matter, whether past judgements or market evidence and benchmarks to independently corroborate a narrative.
  • Preliminary drafting and reporting - Whether this includes preliminary drafting of court submissions, instruction letters and questionnaires.

If harnessed to full effect, Gen AI could significantly contribute to each of these areas, providing advantages through a greater speed and breadth of information retrieval from multiple sources, cross-referencing, translating documents or even “net-new” creation of content.

Given the ever improving capabilities and clear benefits, legal practitioners must honestly assess their organisation's readiness, prioritise use cases, and start embedding the technology into daily workflows. However it is crucial to remain alive to the risks and limitations of Gen AI, principally around confidentiality of information, hallucinations and model bias. Beyond testing and selecting suitable Gen AI tools, organisations must also focus on adoption and enablement. Upskilling and fostering a culture of innovation is crucial for driving adoption.

Current Legal Technology Innovations

Given the weight of the narrative around Gen AI, it can be easy to overlook existing technology and solutions that are already transforming legal dispute delivery. While these tools are well-established and reliable, Gen AI may be able to dramatically increase their accessibility and speed of delivery, enabling more legal professionals to leverage their capabilities. Examples include:

  • Data Consolidation Platforms - Use of advanced analytics and coding tools (such as Python) have become indispensable in this regard, with their versatility being demonstrated across various sectors. Similarly, cloud platforms such as Microsoft Fabric or Palantir are powerful for managing and analysing complex data sets in a more streamlined and efficient manner reducing error.
  • Enhanced insights - Dashboarding and visualisation tools, such as Power BI, create interactive and dynamic reports, that facilitate more effective data interrogation and communication, highlighting trends, patterns, anomalies and insights that would be challenging to identify manually.
  • Machine learning and Deep retrieval - Machine learning and natural language processing (NLP) tools, such as Kira and Luminance, that help automate and enhance document review and analysis, using algorithms to identify, extract, classify and compare relevant information, such as clauses, entities, dates and terms. This can rapidly triage and flag potential issues, risks and discrepancies, as well as recognise patterns and predict outcomes. For example, predictive analytics are being used to forecast judicial behaviour and trial outcome. By analysing historical data, we can gain insights into how courts might rule on certain issues in a given case, which is powerful for strategising and preparing for litigation.
  • Strategic planning - Genetic algorithms and optimisation techniques help solve complex and multi-faceted problems, such as resource allocation, task prioritisation, and strategies selection, providing comprehensive evaluations of outcomes and trade-offs.

The Future of Legal Disputes

Gen AI is not a standalone solution, but rather an enabler and enhancer of many of the existing tools and techniques already used in the legal dispute context. Gen AI will no doubt help improve the accuracy, speed and scalability of high volume tasks and analysis, by learning from the data and the feedback, and by adapting and refining the algorithms and models. Gen AI can also help generate new insights and recommendations, by applying advanced analytics, such as sentiment analysis, network analysis, predictive modelling and gap analysis, to the data. However, Gen AI also requires a robust and reliable data infrastructure, a clear and ethical framework, and crucially human oversight and validation, to ensure its quality, reliability and accountability.

Research shows current Gen AI capabilities can already mirror those of a trainee/junior, delivering tasks in a fraction of the time and cost, but still requiring significant senior oversight. Gen AI should be viewed as an enabler, creating greater capacity for higher value tasks to be performed. As part of the transformation, dispute practitioners will need to rethink their economic model to be more value-based vs charging on time and materials - indeed this could be one of the greatest challenges to the longstanding institutional approach of the timesheet based charging model.

For legal professionals, now is the time to start upskilling in data analytics, understanding Gen AI's capabilities and limits, and rethinking entrenched processes. Firms should develop clear Gen AI adoption roadmaps, invest in the right tools, and start piloting them on real cases.

This isn't about replacing humans, but empowering them to deliver better, faster, more cost-effective services in a world of expanding legal dispute complexity. We therefore expect the emphasis to remain on a human-led, tech-enabled disputes ecosystem, with individuals and relationships remaining central to the effective delivery and resolution of disputes. The future of legal services is here - and it's an exciting new frontier for those willing to boldly go forward.

If you would like to read more about PwC’s 8 stage Gen AI value-realisation flywheel approach that practitioners can leverage to help guide their Gen AI strategy then please refer to the following LINK.



Jonny Rodwell & Nazia Khalfey - PwC
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