The Pragmatic Court, Part 2

In Part 1, I argued that courts should stop treating AI as an abstract debate and start treating it as an operational reality. The daily posts that followed were snapshots of practical entry points. This second post puts the full sequence in one place, from the lowest risk uses to the more powerful workflows, along with the boundaries that keep human judgment where it belongs.

The easiest place to start is writing support that does not touch the record. Think administrative communications, policy drafts, training materials, and public explanations. Used that way, AI is not doing judicial work. It is simply reducing friction in daily operations while courts build competence in low stakes settings.

From there, move into organization work where the source stays visible and the output is easy to verify. That might mean transcribing en banc minutes, creating a meeting agenda, compressing a long email chain into a short internal decision memo, or pulling key dates into a clean timeline. In chambers, it can be as simple as tightening an outline or converting rough notes into questions for oral arguments or a pre-trial conference. In every example, the value is not that the tool is brilliant. The value is that it helps you get oriented faster, while the underlying material remains the reference point.

This is also where the most important value shows up for chambers without enough support staff. Not every judge has a law clerk, and not every court has central staff. That reality matters. Used responsibly, AI can help close that gap, not by deciding cases, but by reducing the time spent organizing what is already in the case file. This is about relieving pressure on chambers without outsourcing judgment. Use the tool for organization and support, and keep the decisional work human.

As these products mature, more of this will live inside the case management systems courts already use. Not as custom builds or futuristic automation projects, but as features layered into existing tools. One of the simplest CMS tools will be one that is used for mechanical compliance and completeness checking, meaning a tool that flags missing required components, miscalculated deadlines, or caption mismatches before they cause delay. The output is a checklist and a set of flags that a human reviews. It is not a ruling.

Once a court is comfortable with that kind of bounded support, a next step is using GenAI to draft a neutral bench memo that organizes what is already in the briefs and record into a standard format. It is not an opinion, a recommendation, or a disposition. It is a preparation tool that helps a judge get oriented quickly and consistently. Done correctly, the memo focuses on procedural posture, key dates, issues as framed by the parties, the standard of review, and an argument map grounded in what was filed.

None of this works unless courts maintain discipline. The lane is augmentation and support, not replacement. And that lane needs guardrails that are practical, not performative.

The guardrails do not need to be complicated. They just need to be clear enough that people can follow them when the docket is heavy. If AI is being used for summaries, you must still read the source document and treat the summary as a convenience, not authority. If AI is being used for drafting, the human must remain in control and direct the writing, because the human’s name is on it. And if the work touches anything that could affect the outcome of the case, there should always be a human approval step before anything moves forward.

For most court work, the materials are already in the public record, and that matters because it lowers the temperature on the privacy and security debate. But courts also handle juvenile matters and other cases that contain sealed filings. So the conversation should be practical. Use mainstream tools with the proper settings where the risk is low, and have a more controlled lane for the categories of cases where heightened care is part of the job.

And this is not a decision a judge should make alone. Courts should have this discussion with their technologists and security teams. Not to turn it into a months long project, but to get clarity on what tools are safe to use, what data policies apply, and what guardrails make sense for each type of case.

That is why some courts will decide that even responsible use of external services creates a governance problem they do not want to own, at least for certain case types. Others will want tighter control over data flow and auditability, even if it takes more effort. For those courts, an on-prem solution layered on top of the CMS maybe a legitimate option. When the model runs inside the court network and is limited to retrieval from the case file and the court’s rules library, the court can modernize while keeping sensitive materials inside the walls and keeping the audit trail in its own control. Courts should be able to align deployment choices with the risk of the work rather than be forced into a one size fits all posture.

The sensible way to approach all of this is to start small. Begin with writing and administrative support where review is easy and stakes are low. Move into organization tasks where sources remain visible and verification is straightforward. Explore built in CMS mechanical checks where the rules are objective. Then, when you get comfortable with the tools and learn their strengths and weaknesses, consider whether drafting and neutral preparation memos are appropriate in your environment, under your rules, with your guardrails.

Courts will use AI. The question is whether that use will be disciplined from the start or retrofitted with justifications later. If courts start with low barrier workflows, keep the source material visible, require human review, and draw a bright line around decisional work, the judiciary can capture real operational benefits without trading away legitimacy. That is the lane worth building.