Technology
The impact of AI on the disputes and investigations landscape
Published by Jessica Weisman-Pitts
Posted on September 26, 2022
6 min readLast updated: February 4, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on September 26, 2022
6 min readLast updated: February 4, 2026
By Phil Beckett, Managing Director and Head of Disputes and Investigations at Alvarez & Marsal in Europe and The Middle East
To understand how Artificial intelligence (AI) has impacted the disputes and investigations landscape we must first define what exactly AI is, not least because there have been many varying definitions over the years.
AI covers a wide range of applications. In this context what I am really describing is machine learning which refers to the simulation of human intelligence in machines that are programmed to behave like humans and mimic their actions. It’s a term that can also be applied to any machine that exhibits traits associated with a human mind such as learning, rationalisation and problem-solving to achieve a specific goal.
AI and the legal industry; unlikely bedfellows?
When put like this, the modern and fast-moving pace of AI may seem (at least perhaps to some anyway) somewhat at odds with the perception of diligence and the measured, considered approach that typically define the legal industry. But this is far from true. Because, in actual fact, lawyers and indeed their clients, have become far more comfortable with the concept of technology being used in legal proceedings over recent years. And Covid has, of course, escalated this too.
That being said, people often debate what is driving increased adoption of technology like AI in the legal sector. When it comes to exploring cause-and-effect, it can be difficult to ascertain whether technology like AI is driving change in the legal landscape, or rather whether pressure from clients to be more efficient in dispute resolution is driving this technology innovation. The truth is that both forces are playing a role. With incremental change, the benefits and efficiencies filter through to clients, who in turn become accustomed to (and comfortable with) a new level of service provision and demand ‘more of the same’. In this way, adoption snowballs.
How is the industry utilising AI right now?
AI, comprising a wide variety of technologies, adds to the toolbox that a digital forensic professional uses. And not only this, it tends to build on and enhance existing tools and introduce new techniques and approaches, too.
Artificial intelligence is a great tool to identify relevant evidence more efficiently. In litigation, for example, the use of algorithms and AI, including continuous active learning (CAL), is now widely accepted by the courts as a tool to reduce the amount of manual document review time and to prioritise data sets for review. CAL can predict the relevance of a document, based on those manual review decisions previously applied in the case. Likely relevant documents are prioritised to the investigations team, reducing the time and cost in uncovering the relevant information. Aligning the right technology with the data source allows teams to better detect patterns in linked documents. Furthermore, adopting some of the processes and technology used in litigation can improve investigations in the future, too.
AI also allows experts to cull irrelevant documents from their review and can help them to leave no stone unturned during the investigation. Experts can then spend more time on valuable analysis and reflection, rather than wasting precious time on wading through irrelevant data and documents. This subsequently enables the team to provide the best possible advice, based on robust data and via an AI-powered review.
What does the future hold for AI?
It is clear that AI will open many possibilities, some that can be foreseen and others that cannot, but they need to be appropriately scrutinised as part of any forensic process.
Without a shadow of a doubt, AI will enhance a lot of what we do and will likely unlock new investigative techniques, too. For instance, AI will do a better job when it comes to making routine, repeatable decisions, thereby freeing up lawyers to do what they do best; the law. It will be more and more useful from a quality control perspective and it will also help bring relevant documents to the surface quicker. That being said, it will not replace the human totally, just make the human more efficient. But more on this later.
With the courts now more accepting of AI, there will be greater emphasis on professionals to show they understand and are using the technology correctly. We can expect unfavourable rulings for parties who are basing decisions, particularly around the culling or dismissing of documents, based on the incorrect application of AI.
AI will become a fundamental process across the lifecycle of disputes and investigations. Post-processing insights such as date range summaries and search term analysis will be complemented by highlighting and grouping conceptually similar documents and visualisations showing the interactions and relationships between people in the case. Improvements in accuracy and efficiency will be found in the quality control process, with AI used to help identify potentially relevant documents that may not have been considered relevant by the review team. Most importantly, quality control will no longer be left to the end of the project but will become a continuous, evolving process used throughout the project lifecycle.
Through the continued use of AI, we will see increased comfort and trust from lawyers and their clients. With the ability to identify relevant material quicker and visualise patterns in communications, lawyers can spend less time on the routine, repeatable review processes and more time on strategy.
AI should make investigators more effective.
Technology has come a long way, and there are so many great options available. However, complex cases will always require experienced disclosure and tech professionals to get the most out of the technology. This is because dispute resolution involves an inherent human element and will need that human touch to truly be able to achieve positive outcomes. As such, it is critical that an expert understands how the AI works, what the technology is doing and what that means. Experts cannot simply press a button and blindly accept whatever follows; this much is clear.
Ultimately, technology such as AI can be a fantastic tool for uncovering and gathering information quickly and efficiently, especially since so much intelligence and evidence is stored electronically these days. But at the end of the day, the key is empowering the human investigator to be more effective through use of this technology. It is this augmented approach, marrying the ‘best’ traits of human and technology, that will breed the most optimal outcomes.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans.
Machine Learning is a subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on tasks through experience.
Digital forensics is the process of uncovering and interpreting electronic data for use in legal investigations and proceedings.
Continuous Active Learning (CAL) is an AI technique that predicts the relevance of documents based on previous manual review decisions, streamlining the document review process.
Quality control in investigations refers to the processes used to ensure that the data and findings are accurate and reliable throughout the investigation lifecycle.
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