The Wall Street Journal’s recent article titled How Judges Are Using AI to Help Decide Your Legal Dispute captures a shift that has been underway for some time. Courts are not experimenting with AI because it is fashionable. They are doing so because the workload has outpaced their existing processes.

One example in the article that is especially instructive:

Federal Judge Xavier Rodriguez ran an experiment after completing a seven-week trial involving Texas election law. His chambers had spent months reviewing hundreds of thousands of exhibits, testimony from more than seventy witnesses, and extensive case law before issuing a 140-page opinion. Out of curiosity, Rodriguez ran the same evidentiary record through an AI tool. Within minutes, it produced a draft that included findings of fact that had taken weeks to assemble. The output was not perfect and required verification, but it was good enough to change how he approached the work going forward.

That experiment illustrates what AI is actually doing well in legal settings.

It is compressing time spent on synthesis and organizing. It is not replacing judgment. In his example, Rodriguez remained responsible for the decision. The AI helped create structure in an overwhelming body of material that his clerks spent 10 months with, but I did not make any decisions.

Judge Rodriguez’s experiment points to a broader set of uses that are easy to imagine inside a modern courtroom.

A system like TimePilot could ingest the full evidentiary record of a case and generate competing timelines drawn from different parties’ submissions, flag inconsistencies between testimony and exhibits, or surface where factual claims rest on thin or circular support. It could map which filings rely on the same underlying evidence, highlight where a single document is carrying disproportionate weight, or show how factual assertions evolve across briefs, motions, and hearings.

None of that resolves legal questions. But it does change how quickly judges and their clerks can orient themselves to a complex record.

It also changes what clerks spend their time doing. Instead of manually assembling chronologies, cross-referencing citations, or re-reading the same material to answer narrow factual questions, clerks can focus on what courts actually need from them: testing arguments, checking assumptions, identifying gaps, and stress-testing draft reasoning against the record.

In that sense, AI does not remove clerks from the process. It removes some of the least judicially valuable work from their desks.

The work that remains is judgment-heavy, adversarial, and interpretive. That work does not disappear. If anything, it becomes more central.

This pattern is already familiar in other parts of the justice system. Tools like TimePilot are used hundreds of times a day by law enforcement agencies and district attorney offices to ingest large volumes of reports, transcripts, video, and other primary materials, and to generate timelines, cross-references, and issue maps that would otherwise take analysts or prosecutors days or weeks to assemble. As with Judge Rodriguez’s experiment, the value comes from speed and organization, not from substituting human decision making.

The backlash against AI in law has largely stemmed from failures to respect that boundary. Lawyers submitting filings with fictional citations. Draft opinions containing factual errors that went uncaught. In each case, the problem was not that AI was used. It was that verification, traceability, and responsibility were treated as optional.

What the WSJ reporting makes clear is that judges are not asking AI to decide cases for them.

They are asking it to help manage scale. Most judicial work in the United States occurs in high-volume courts under significant resource constraints, which makes tools that compress preparation time especially useful. Reducing time spent on clerical synthesis allows more time for legal reasoning and deliberation.

This is where the conversation needs to be anchored in 2026. Not on whether AI belongs in the courtroom, but on whether its use strengthens or weakens the integrity of the process.

Systems used in judicial and investigative settings must allow decision makers to see how conclusions were reached, trace claims back to source material, and intervene when outputs are wrong. Speed without auditability creates risk. Transparency without human control creates a different kind of risk.

Across courts, law enforcement, and education, the same principle holds.

AI is most valuable when it helps professionals engage more directly with primary sources and reduces mechanical overhead, while leaving responsibility clearly with the human actor.

Judge Rodriguez’s experiment works for the same reason that operational tools work every day in investigative settings: they accelerate preparation, not judgment.

The future of AI in law will not be determined by whether judges use it. It will be determined by whether its use can be examined, explained, and defended in the open.