AI-Assisted Investigations
AI can make investigative workflows faster by helping researchers search, classify, summarise, compare and organise large amounts of information. The safest and most useful model is AI-assisted investigation: the system accelerates the work while evidence, uncertainty and human accountability remain visible.
High-value uses
AI is well suited to repetitive tasks such as extracting fields from documents, grouping similar records, summarising long material, proposing search strategies, identifying possible relationships and preparing structured research notes.
The hallucination problem
A fluent answer is not evidence. AI systems can produce plausible but unsupported statements. Investigative workflows should therefore require source references for material claims and make it easy to distinguish extracted evidence from model-generated analysis.
Human-in-the-loop controls
Researchers should review consequential matches, reputational findings, financial conclusions and ambiguous identity resolutions. Approval gates are especially important before public or external actions are taken.
Evaluation and telemetry
An AI investigation system should measure more than speed. Useful measures include evidence coverage, correction rates, false matches, source freshness, task completion, review time and cost. These measures allow workflows to improve without rewarding unsupported certainty.
The operating principle
Use AI to increase the amount of structured work a researcher can handle, not to remove the obligation to verify important conclusions. The goal is faster, more explainable research.