Legal teams are rapidly adopting artificial intelligence to handle document review, drawn by the technology’s ability to process enormous volumes of information at speeds traditional methods cannot match.
The efficiency gains are real, but speed alone does not make a review process defensible in court or before regulators.
As AI becomes more deeply embedded in eDiscovery workflows, legal professionals must look beyond traditional performance metrics such as recall and precision to ensure sound outcomes.
Recall measures how effectively a process identifies relevant documents, while precision considers how many of the documents flagged are actually relevant to the matter.
Both measurements offer useful insight, but strong metrics do not automatically translate into a strong legal strategy for the case at hand.
Legal matters are rarely static, as new facts emerge, issues evolve, and the relative importance of custodians and communications can shift significantly over time.
An AI model can identify patterns within large datasets, but it does not independently understand how those patterns fit into the broader legal strategy of a matter.
Legal teams should therefore be asking not only how well the model is performing, but whether they are finding the information that truly matters and whether they are confident in the process driving those decisions.
One of the more persistent risks surrounding AI-assisted review is the assumption that automation reduces the need for human involvement, when in practice it demands a different and more strategic form of it.
Rather than reviewing every document manually, experienced legal professionals must oversee the AI process strategically, establishing frameworks, evaluating model results, and monitoring reviewer decisions throughout the lifecycle of the matter.
Continuous Active Learning, commonly referred to as CAL, illustrates how this human-technology relationship can work particularly well in complex matters.
Instead of training a model once against a fixed understanding of relevance, CAL continuously incorporates reviewer decisions into the prioritisation process, adjusting which documents should be reviewed next as attorneys make calls.
Early in a case, the legal team may have only a partial understanding of the facts, but after reviewing key communications, timelines, or relationships, that understanding can change significantly.
A static workflow may not respond effectively to those discoveries, whereas a continuously managed workflow can adapt as new information surfaces and legal priorities shift.
Strategic oversight also addresses one of the most pressing questions surrounding AI adoption in legal workflows, which is whether the process can withstand challenge from opposing counsel, regulators, or courts.
Simply stating that an AI system produced a particular result is unlikely to provide the level of confidence that clients and courts may expect when scrutinising discovery decisions.
A defensible process requires documented reasoning, consistent quality controls, and experienced attorneys who can investigate anomalies and explain decisions made throughout the review.
That human layer provides something the technology itself cannot deliver, which is accountability for the judgments that shape the outcome of a matter.
Strategic oversight can also help legal teams use AI for more than document classification, surfacing key communications, unusual patterns, and important custodians far earlier in the discovery lifecycle.
Those early insights can influence broader litigation strategy, prompting counsel to investigate issues more deeply, adjust discovery priorities, reassess settlement considerations, or modify the review protocol entirely.
The most effective model for document review combines the processing power of AI with the context, judgment, and legal accountability that only experienced attorneys can provide.
Moving attorneys away from repetitive review tasks and toward higher-level responsibilities, including evaluating results, refining strategy, and managing risk, is where the greatest value lies.
Recall and precision still matter as measurements, but they are not a complete strategy, and the organisations that recognise this distinction will achieve the greatest long-term advantage from AI adoption.