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1. Elite litigation is increasingly becoming a data game, with attorneys using analytics to study judges, opponents, motions and historical outcomes.
2. Algorithms can identify patterns buried inside thousands—or millions—of court records that no litigation team could realistically analyze manually.
3. But the competitive advantage comes with a warning: courts are making clear that lawyers remain personally responsible for what technology produces.

By Samuel López | USA Herald

For generations, great trial lawyers built their reputations around instincts that were difficult to teach: knowing which argument would move a particular judge, anticipating an opponent's next move, recognizing when to settle and understanding which facts were likely to resonate with a jury.

In 2026, some of those instincts can increasingly be supplemented by something else: data.

Behind the scenes of modern litigation, sophisticated attorneys and law firms have access to increasingly powerful litigation-analytics systems capable of examining enormous collections of judicial decisions, docket activity, motion practice, attorneys, law firms and litigation outcomes.

The result is quietly changing what it means to be exceptionally prepared for a lawsuit.

The attorney of the future may not simply know the law better than opposing counsel. That lawyer may know the numbers behind the law.

The Rise of the Algorithmic Litigator

Litigation analytics is fundamentally different from asking a chatbot to write a legal brief.

These systems can mine historical litigation data and expose patterns that would be extraordinarily difficult for a lawyer—or even an entire team of lawyers—to identify manually.

Bloomberg Law, for example, describes litigation analytics as technology capable of searching millions of data points involving judges, attorneys, law firms and companies to identify trends and help lawyers develop litigation strategy. Earlier Bloomberg Law research explained that analytics can reveal such information as how frequently a judge has granted particular types of motions and the experience of attorneys and firms in particular categories of litigation.

That creates an intriguing possibility.

Before filing a critical motion, counsel may be able to examine how the assigned judge historically handled similar motions.

Before confronting opposing counsel, attorneys can investigate that lawyer's litigation history.

Before recommending settlement, counsel can examine comparable litigation, timing and historical outcomes.

Before constructing an argument, lawyers can use technology to rapidly locate relevant precedent within an ocean of judicial opinions.

This does not mean an algorithm knows how a judge will rule.

It means attorneys increasingly have another source of intelligence from which to make decisions.

And in high-stakes litigation, even a modest informational advantage can matter.

The “Overachiever” Strategy

The most consequential use of algorithms may therefore be neither replacing lawyers nor allowing them to do less work.

It may allow extremely ambitious lawyers to do more.

Call it the algorithmic overachiever strategy.

A conventional attorney might research the controlling statute, Shepardize the leading cases, examine the relevant procedural rules and prepare the strongest argument possible.

The algorithmically equipped overachiever can potentially do all of that—and then add another layer.

What percentage of comparable motions has this judge granted?

Does the judge repeatedly rely upon particular authorities?

How long does the judge typically take to rule?

Which arguments succeeded in comparable cases?

Has opposing counsel litigated this issue before?

How did those cases end?

Which attorneys have repeatedly appeared before this judge?

Are there patterns in case duration, disposition or motion practice that could affect litigation strategy?

None of those questions necessarily determines what happens in the next case. Every lawsuit has different facts, evidence and law.

But collectively, the answers can provide something litigators have always coveted:

a more detailed map of the battlefield.

From Legal Research to Litigation Intelligence

This represents an important evolution in legal technology.

Traditional computerized legal research largely helped attorneys answer a question: What is the law?

Modern litigation analytics increasingly addresses a different question:

What has actually happened when people litigated under these circumstances?

That distinction could prove enormous.

Lawyers have always informally collected intelligence about judges and adversaries. Veteran attorneys remember prior rulings. Firms maintain institutional knowledge. Lawyers ask colleagues about experiences appearing before particular judges.

Algorithms can potentially industrialize that process.

Instead of asking five attorneys what they remember about a judge, a litigation team can examine a large body of that judge's actual record.

The technology does not eliminate human judgment.

It gives human judgment more information.

The Lawyer Still Has to Think

There is an equally important dividing line, however, between using algorithms as intelligence tools and blindly allowing artificial intelligence to practice law.

Courts are drawing that distinction forcefully.

On June 3, the Ninth U.S. Circuit Court of Appeals sanctioned two attorneys after briefs contained nonexistent authorities, misattributed quotations and serious misrepresentations of actual cases that resulted from generative-AI hallucinations.

The Ninth Circuit made an important point: the problem was not simply that artificial intelligence had been used.

The court explained that professional rules are not violated merely because AI assists with research or drafting. Responsibility arises when attorneys sign and file documents containing fabricated or materially inaccurate authorities.

That distinction may ultimately define the successful algorithmic lawyer.

AI can be the analyst. The attorney must remain the lawyer.

Algorithms can search.

They can compare.

They can rank.

They can identify correlations and patterns.

They can help attorneys test arguments and discover information that might otherwise escape notice.

But counsel still must determine whether a correlation actually means anything, whether precedent controls, whether evidence is admissible, whether an argument is ethically permissible and whether the machine's output is even correct.

A New Form of Litigation Inequality?

There is another issue courts and policymakers may eventually have to confront.

Advanced litigation intelligence costs money.

Large firms and well-funded litigants can purchase sophisticated research platforms, maintain internal databases, employ legal-technology specialists and devote substantial resources to analyzing litigation.

Smaller firms and self-represented litigants may have considerably fewer resources.

The concern is not necessarily that algorithms make litigation unfair. Wealth disparities have always affected access to investigators, experts, discovery resources and large litigation teams.

But algorithmic litigation could introduce a new dimension to that longstanding imbalance: informational asymmetry.

One side may someday enter a courtroom knowing not only the applicable law, but having computationally examined years of relevant decisions, attorneys, motions and litigation behavior.

The other side may simply have Westlaw, a yellow pad and experience.

The Next Great Lawyers May Be Human-Machine Teams

The popular debate surrounding artificial intelligence and law frequently asks whether AI will replace attorneys.

That may be the wrong question.

The more immediate question is whether attorneys using algorithms will outperform attorneys who refuse to use them.

The best litigators have always searched for informational advantages. They hire investigators, consult experts, study judges, conduct mock trials, examine jury demographics and scrutinize opposing counsel.

Algorithms simply expand the arsenal.

And that could produce a fascinating paradox.

Technology advertised as a way for lawyers to work less may become most powerful in the hands of attorneys determined to work harder—lawyers who use machines not as substitutes for preparation, but as tools for taking preparation beyond what was previously humanly possible.

The courtroom will remain human.

Judges will still judge. Jurors will still evaluate credibility. Lawyers will still have to stand up and persuade.

But increasingly, the attorney standing at counsel table may have something previous generations never possessed:

an algorithmic view of the battlefield before the fight begins.

ABOUT THE AUTHOR

Samuel López is a Senior Legal Analyst, investigative journalist and legal researcher with more than two decades of experience analyzing litigation and complex legal controversies. His reporting for USA Herald focuses on emerging legal trends, artificial intelligence, insurance, technology, appellate litigation and cases shaping the future of American law.