The Intersection of AI and High-Stakes Investigations
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April 08, 2026
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In environments in which sifting through huge data volumes is routine and the stakes are high, generative artificial intelligence (“AI”) has become a critical tool to help investigators accelerate key investigative steps. When deployed as a layer of quality control to mitigate the risk of something being missed or as a tool to test an argument or theory, generative AI can help legal and investigatory teams find and understand information in ways that were not previously possible.
For example, in a recent internal investigation, the lead investigator included specific questions he knew to have been critical in previous, similar fact-finding efforts by the organisation, helping to progress lines of inquiry. In another case, generative AI was used to provide detailed descriptions of photographs, analysing 50,000 images over a weekend, where there simply wasn’t the time or the resources to do it manually. These examples just scratch the surface of how domain experts can apply AI-augmented workflows and analytic solutions in creative and effective ways to meet the unique needs of an investigation. For success, expertise remains key, so that workflows can be tailored to fit the scenario in question, taking all necessary steps to ensure accuracy and defensibility, including using the most appropriate measure of effectiveness for AI tasks.
Every investigation will have its own challenges and involve nuances that require critical problem solving. There are countless examples of cases in which a single piece of obscure information — such as difficult-to-decipher handwritten notes that explain fraud methodologies that might not be immediately clear or nefarious activity captured on mobile devices that wouldn’t necessarily be picked up by search terms — impacts the entire case.
However, there isn’t always a smoking gun. In those instances, expert-led AI workflows can rapidly surface insights to find materials of interest by interrogating data with natural language queries. For example, in an intellectual property theft or trade secrets case, it may be sufficient to find just a sampling of relevant documents to justify taking protective measures and consider whether a wider investigation is warranted.
Additional generative AI use cases include detecting anomalies and identifying supplementary information against a version of events upon receipt of new material in a case, comparing witness statements for anomalies, and analysing expert testimony to quickly make sense of incoming materials. Due to large language models’ understanding of language, they are also effective at identifying unusual activity and coded conversations in a variety of communications channels, including chat.
Beyond these applications, there will be growing interest and sophistication in agentic workflows, with investigators increasingly enlisting the help of agents to kickstart an inquiry by quickly surfacing the relationships between key players and related insights. Progress on this front is ongoing, with agents that carry out advanced tasks with a suitable level of supervision and report back for further instructions.
Generative AI is exciting and disruptive. The pace of change is phenomenal, with new developments constantly emerging. When layered with humans in the loop who have expertise in investigations and related fields, this technology will be pivotal in making investigations more efficient and enabling workflows and results that may otherwise be impossible.
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Published
April 08, 2026
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