Legal work is outstripping growth in legal headcount
By 2025, 30 per cent of new legal technology automation solutions will combine software with staffing for a “human-in-the-loop” offering, according to a study by US research and advisory firm, Gartner, Inc. Despite growing demand for automation and increasing sophistication of tech innovations, machine learning solutions for corporate legal teams will struggle with the high levels of domain expertise required and high rates of exceptions expected, the study revealed.
“There’s little question that growth in legal work is outstripping growth in legal headcount, and on the surface, this can make advanced automation solutions appear very attractive to legal leaders,” said Zack Hutto, director, advisory in the Gartner Legal & Compliance practice. “The question is whether the legal department has the capabilities required to customize and configure advanced machine learning systems, which will be needed to enable them to handle the unique scenarios and frequent exceptions that come about in this type of work.”
The legal department has lagged many other business units in automation but leaders’ attitudes towards automation in legal have softened since the pandemic brought about twin forces of a sharp increase in workload and a reluctance from CFOs to keep adding headcount.
Solution providers face headwinds in the complexity of legal workflows, differing risk tolerances across organizations, and inconsistent processes that not only weaken returns but also hamper the capture of information needed for training machine learning solutions.
“Legal departments should not avoid automation,” said Hutto. “But the right foundations must be in place. Automation – especially sophisticated AI-driven techniques – should not be seen as a quick fix to old problems.”
Solving automation challenges will require greater process discipline in legal teams so that legal data is consistent and comprehensible to machine learning systems. It also requires a careful blend of technical expertise and legal knowledge that can configure and train machine learning solutions in the specific context of an organization.
Solutions attempting to automate legal work right now tend to demonstrate quite high error and exception rates compared to other business functions. Part of the problem is that data assets between different users are quite distinct. A machine learning solution trained at one company may be useless when applied to another company, Gartner found.
“A hybrid, ‘human-in-the-loop’ model, blending staffing and software, will win out, with the required domain expertise coming from the supply side rather than from within legal departments themselves,” said Hutto. “An inflection point of productivity in legal automation will come when legal departments have machine learning experts who can truly understand the complexity of the legal problems within the context of their organizations.”