Wexler's Head of Commercial Strategy, Cail Wyn Evans, joined Philip Young of Garfield AI, Philip Roberts KC of One Essex Court, Warren Little of BT Group, and Oran Gelb and Joanna Munro of BCLP to discuss 'AI in Litigation, beyond the hype: What works, what doesn't, and where are we heading?' Here are seven of the most interesting ideas that came up.
Will AI reduce litigation?
Some platforms are seeing users issue ten or twenty times more claims than they used to, because it is now economic to do so. The mechanism is Jevons’ paradox: when steam engines became more efficient in the nineteenth century, coal consumption increased rather than decreased. As the cost of bringing a claim falls, the number worth bringing rises; the same dynamic is playing out in litigation volumes more broadly.
Which AI tools are working?
The reliable ones stay close to the evidence. Walled-garden tools (confined to uploaded documents, or to court decisions and legislation) came in for consistent endorsement. The value of blocking internet access is that every assertion traces to a document in the matter, which separates fabrication (an assertion with no basis in evidence) from the different problem of applying accurate information incorrectly. At the other end of the workflow, some platforms are already taking small debt claims from chasing letters through to default judgments and trial bundles.
Who's pushing AI adoption?
Increasingly, the client. BT Group were early adopters of Microsoft Copilot, now use Wordsmith configured to take BT's view of its market, and are looking at specialist litigation tools rather than general business AI, so the team can do more of the work itself. Clients like BT now expect their external counsel to use AI, because they use it internally; from the firm side, the list of clients refusing AI is shrinking.
Where does it go wrong?
In predictable places. Sycophancy built into general models is a known problem: they will tell you your question was the most insightful anyone has ever asked. It was noted that consumer-grade models used by defendants in debt claims can miss their best arguments, weakening their own position. And the downstream harm can be severe: a claimant often discovers the case is weak only at trial, after the time and money are gone.
What still needs a lawyer?
The elements that were never only about documents. The client relationship, and translating an AI output into a client's commercial reality, depends on knowing the business and its strategy—something no tool currently replicates. There was scepticism that the late nights juniors once spent paginating built the skills that matter; the real lesson may have been watching senior lawyers read clients and commercial situations. Verification still sits with the lawyer too, though there was a caution against treating the human alternative as flawless: "Have you appeared in front of a human judge?"
Does AI cut costs?
The room split on it. Some argued that new tools tend to add process rather than remove it, and that litigation costs more than it ever has. The counterview focused on certainty rather than headline price: when you can constrain the cost variables in a matter, you can quote fixed fees for defined stages, which moves a client from an open-ended estimate to a defined range. That changes which cases get run; meritorious claims worth several million pounds that previously couldn't justify their own costs now can, and litigation funders, whose central problem is uncertainty over budgets, can back cases they'd otherwise decline.
What's holding legal AI back, and where will it go next?
The courts, more than the technology. Firms still serve documents to each other by email in 2026, while Singapore and the United States already run shared digital service. One proposal was putting an experienced software engineer on the committees that revise the Civil Procedure Rules, which were never written to be captured in code. The county court bulk issuing centre in Northampton already operates on its own rules inconsistent with the CPR. Looking further out, the profession may split, with high-volume work moving to technology and complex work staying with lawyers who sit on top of it , and agentic tools and open APIs are the direction of travel. On disclosure, current Bar Standards Board guidance asks for transparency where AI materially affects the scope of your services, stopping short of a duty to declare every use.