How to evaluate AI for investigations

Estimated reading time: 5 minutes · Last updated: 14 October 2026

Law enforcement must treat AI outputs like any other source: know who produced them, when, and how they were verified. Poll results from a Police1 webcast in partnership with Thomson Reuters — as first reported by Thomson Reuters Legal Solutions — showed 72% of attendees said their agency is already using or evaluating AI tools and 87% said AI is helping public safety. The panel of Edgar Gallardo, Michael Murphy and Andrew Pellington recommended a four-part vetting framework: confirm data provenance, prefer purpose-built systems with citation trails, assess courtroom defensibility, and verify measurable time savings before adoption.

Where the data actually comes from — and why provenance matters

Every investigator knows an anonymous tip needs verification; an AI output is no different. Edgar Gallardo argued investigators should press vendors to say exactly where each data point originated and how recently it was verified, because an unverifiable answer is functionally the same as an uncorroborated informant.

The practical risks are concrete: Murphy warned of what he called “garbage in, garbage out.” If a consumer-grade model pulls from unnamed forums or ephemeral web caches, a later query can return a different account. That instability undermines repeatability and cross-examination.

Ask vendors for a citation trail that links every output back to a named source and a date-stamp, and test whether the same query returns consistent, sourced results over time before you rely on a tool for investigatory steps that feed warrants or charging decisions.

How consumer AI differs from purpose-built investigative platforms

Consumer-grade AI is optimised for general-purpose answers, not evidentiary standards. The panel noted these tools frequently lack audit logs, named citations, and data controls; any personal data entered may reside on third-party servers outside an agency’s control.

Purpose-built investigative platforms, by contrast, pull from curated, verified sources, date-stamp results and produce an auditable chain from query to output. That traceability is what lets an investigator explain a step on the stand rather than simply asserting a result.

When evaluating vendors, compare source traceability, retention policies for query logs, and whether outputs include links to the underlying public record or dataset; those items are the practical difference between a usable investigatory product and one that creates evidentiary risk.

Can the output hold up in court? Documentation and defensibility

A superficially plausible AI answer and a properly sourced output can look identical to a non-expert, but only the latter survives adversarial scrutiny. Gallardo tied defensibility to lifecycle documentation: search warrants, arrest paperwork and threat assessments now face closer review and defence strategies increasingly target the tools used to produce investigative leads.

What courts and opposing counsel will test is provenance and process: show the query, the data pulled in, the dates, and the tool’s reasoning or linkages. An audit trail that records queries, sources and date-stamps gives prosecutors something to explain and a defense less room to challenge.

Demand vendor documentation that ties outputs to named sources and preserves the original query and results; if a platform cannot produce a dated citation trail, an agency should treat that limitation as disqualifying for any step intended for judicial presentation.

ROI and operational impact: what agencies should expect and verify

Time savings are the most immediate ROI metric for investigative teams. Panel attendees reported spending five to ten hours weekly on research tasks, and Thomson Reuters cited an internal 2025 study showing CLEAR Investigate customers reported research time reductions of up to 47%. Those figures set a baseline for vendor claims.

Agencies should ask vendors for real-world usage data that demonstrates measurable time savings, not vendor marketing. Request customer-case metrics, sample workflows and an explanation of how the vendor calculates percent reductions so you can match claims to your unit’s baseline hours.

For smaller agencies, also evaluate cost per hour saved and whether the vendor supports documentation workflows that reduce report-writing time as well as search time; a credible ROI case links saved investigator hours to specific tasks (reporting, backlog review) and to verifiable before-and-after metrics.

How adoption can play out

The bottom line

Bringing AI into investigative workflows is already happening; the practical question is whether an agency does so deliberately. Insist on source provenance, persistent citation trails, demonstrable time-savings, and documentation that preserves queries and outputs. Where vendors cannot show dated links to named sources or cannot demonstrate consistent results, treat those shortcomings as disqualifying for investigatory use that may feed warrants or court filings.

What to watch

• watch for vendor-supplied, agency-specific usage studies showing before-and-after investigator hours; no date has been set.
• watch for formal agency AI-use policies or procurement guidelines that specify citation-trail and retention requirements; no date has been set.
• watch for court decisions that examine AI-generated evidence and audit logs; no date has been set.