Moving from Skepticism to Necessity: The Parallels in AI Adoption Across the Legal Industry

It’s still relatively early days for AI in the legal sphere, which means that the industry currently stands at a technological crossroads. Many forward-thinking legal organizations are actively embracing, or at least testing, different AI platforms – but more broadly, from an end-user perspective, there is a touch of skepticism around AI’s value and whether adoption is necessary or not.

This cautious approach mirrors the early days of e-discovery welcoming advanced document review technology in the early 2000’s but then warily embracing tools like Analytics and Technology Assisted Review (TAR) in the 2008-2012 time period – solutions that have continued to evolve and are now viewed as indispensable. A closer look at the historical trajectory of e-discovery (a niche industry also going all in on AI these days) reveals some striking parallels with this AI era generally and helps illustrate why AI adoption might be poised to follow a similar path.

Don’t rock the boat

When e-discovery was first introduced, it offered a way to more effectively process the enormous volume of documents and emails that might need to be produced for a legal request for production, helping to identify responsive documents, filter out non-responsive and privileged information, and categorize responsive documents to develop case strategy.

A few years later, TAR came on the scene. Some early adopters in the space started taking advantage of these tools as soon as they were available. Others, however, took a more skeptical view, regarding it as a “black box” whose inner workings and decision-making were too opaque.

The most risk-averse path for this latter group was to stick with the status quo rather than embrace new tech.

A similar hesitancy currently hangs over AI, with legal professionals wondering whether they should use it or not. Given the high stakes of legal work – there are serious repercussions for lawyers and their clients alike if things are done incorrectly – it’s understandable that many legal professionals would rather not rock the boat.

A shift in perception

In 2012, TAR was endorsed by Judge Andrew Peck in Da Silva Moore v. Publicis Groupe (S.D.N.Y., 2012), and that helped pave the way for adoption. The skepticism around advanced e-discovery solutions started to melt once legal professionals realized how well it could assist them in “finding the needle in the haystack” during discovery reviews that involved huge volumes of documents. It also helped that clients wanted to work with firms leveraging solutions that could streamline their case, enhance their legal strategy, and, of course, save them lots of money in e-discovery costs.

AI is currently experiencing a similar shift in perception among legal professionals as they identify the use cases where AI can best assist them and streamline their workflows.

Making short work of MSA Summaries

To take just one example of AI in action: imagine a corporate legal department. A lawyer might be tasked with creating summaries of all the Master Service Agreements (MSAs) within the organization so that individuals don’t have to flip through a 30-page document just to find an answer to a specific question regarding the company’s rights and obligations under that contract.

AI can quickly and efficiently review MSAs and extract important data points, which can then be used to create a comprehensive summary of that MSA: who are the parties, what are the terms, what is the termination policy, and so on.

Additionally, AI can lend an analytical eye to any incoming MSA to make sure the contract is structured properly. The lawyer could interface with the AI and tell it: “Our protocol at this organization is to ensure that every MSA includes a specific limitation of liability provision. Does this MSA comply?” If AI flags that the agreement doesn’t comply, this could indicate that the limitation of liability provision got altered, deleted, or significantly watered down during the negotiation process and needs to be revisited.

More lightbulb moments

Picture a lawyer at a law firm that needs to build Commercial Real Estate Lease Agreement views for their client and extract out key data points: How much is the rent? When is the rent due? What are the renewal options? Early termination rights? Is the tenant able to reassign the lease to another party, and if so, do they have to provide notice? How much advance notice is required? Again, AI ensures that the lawyers can quickly perform this lease abstraction exercise.

It’s important to emphasize that in any of these scenarios, AI is complementing the legal professionals, not replacing them. There’s still going to be an actual human who analyzes the output – including any evidential trail of how it came up with the answers it did – to refine it using domain expertise and contextual judgment. But increasingly, these specific use cases are lightbulb moments for legal professionals who are starting to see the case for AI come into focus.

From skepticism to necessity

When massive efficiencies were introduced into discovery reviews, it didn’t just give legal professionals a new way to work – it gave them a new way to do right by their clients. AI, when applied to the right use cases, is now delivering the same benefits – cutting costs, saving time, elevating the quality of legal output, and allowing lawyers to focus on high-value legal work. Much like that era, the days when AI is viewed with hesitation and uncertainty, today are poised to give way to something else: a new chapter where it’s viewed as nothing less than indispensable.