How Legal Tech AI Companies are Driving Customer Adoption
When Judge Andrew Peck published the legal opinion that rocked the eDiscovery-verse, Da Silva Moore, over a decade ago I was convinced that the future for the practice of law would be inextricably intertwined with AI and machine learning. And yet, here we sit today, still having to dispel the false narrative of human review as the gold standard in eDiscovery. What went so wrong? And how do we begin tipping the scales in favor of AI adoption in eDiscovery?
A few critical missteps in the launch of some AI legal tech startups are to blame for the lack of adoption by lawyers and organizations alike. Savvy organizations are flipping the script on these missteps and seeing AI software in eDiscovery embraced at faster rates as a result.
The most well-known early iteration of machine learning or AI in eDiscovery, Predictive Coding, hit the stage in a big way. Even going so far as patenting the term and leaning into being the first AI solution for legal. In an industry predicated on risk mitigation and not often welcoming to change (heck much of legal precedent dates back to before there were typewriters let alone AI), being the newest cutting-edge technology is not necessarily a selling point.
In reality, the technology powering TAR, active learning, and yes, predictive coding, was far from being novel outside of legal. Machine learning and artificial intelligence were first conceived in the 1950s with many flavors widely in use by industries including financial services, retail, manufacturing, healthcare, and more. While AI, machine learning, and natural language processing (NLP) may have been novel in the legal industry, they were tested and trusted by a wide array of industries at large.
A second critical misstep with early applications of machine learning and artificial intelligence in legal technology companies was a lack of transparency in terms of how the technology actually worked. By hiding the secret sauce, and in many cases pretending it was much more advanced and sophisticated than it really was, early eDiscovery AI became off-putting to law firms and legal practitioners. No lawyer worth their salt wanted to have to explain to a judge how this black box worked. It was hard to establish trust in a solution or process that was unnecessarily complicated.
Newer iterations of AI-powered legal technology and especially eDiscovery tools have embraced a more transparent approach. Rather than shrouding machine learning in lambda calculus and advanced statistics (hello F score, precision, and recall), the next-gen of automation solutions use simpler language and seamless integration to appeal to a wider user base. This approach, combined with the proliferation of AI-powered technology in people’s personal lives (Google, Uber, GPS, and even spam filters) has served to greatly demystify legal AI.
Another thing that discouraged law firms and legal service practitioners from embracing legal AI was the need for both a unique “AI Workflow” and an over reliance on statisticians, AI experts, and linguists to execute. In making eDiscovery AI appear sophisticated and complex, early tools actually discouraged many legal practices from adopting the technology. They were worried they lacked the skills to effectively leverage the tech and frankly were afraid of making an error that could dramatically impact their case.
Now, much in the way that Google utilizes arguably the most sophisticated search algorithms while it is so simple to use that my 7-year-old nephew can type in a query, newer Legal AI solutions have begun leaning into iPhone-easy user interfaces and seamless AI integration. Coupled with the thousands of cases that have benefited from greater accuracy and speed, not to mention reduced cost, of uncovering evidence or insights using AI, the shift away from over-complicated solutions requiring special skills, workflows and expertise have tipped the scale in favor of greater AI adoption.
