Blog Post

What Will a Tech-Enabled Legal Industry Look Like?

June 19, 2020

It’s no longer a question of whether tech will change the legal industry — most firms have already pivoted to incorporate the benefits of tech like AI, digitization, cloud storage and more. Now, it’s a question of what the industry will look like after all this technology has become fully enmeshed in the daily reality of legal work.

More than that, it’s time to define what the ideal state of that future should look like: one where technology enables and enhances through positive disruption that changes the way we work. rather than disrupts or destroys.

We’ve already made a few predictions about how AI will impact the legal profession, including:

Fewer manual and routine tasks
Fewer — yet more broadly knowledgeable — lawyers
The ubiquity, even necessity, of legal tech

But what will the continuing effects of these changes be? What about the implications of AI in more specific use cases, such as contract discovery or litigation?

What will change

Research & data discovery

Perhaps the most glaring transformation (and one that’s already well underway) is in research practices. A combination of tools, such as optical character recognition, natural language processing and machine learning enables AI programs to scan through case files for keywords, phrases, paragraphs or even case structures that match the search target.

What would take days for a team of paralegals to discover takes minutes for an AI program. This means far less manual work for legal teams, and much quicker turnaround times.


A natural consequence of this redistribution of tedious research work is that the responsibilities of junior associates or those in training will be completely reimagined. Rather than gather the materials needed to make a decision or build a strategy, junior associates and legal assistants will be more likely to monitor and optimize AI-led research.

Under the traditional model, junior associates became more knowledgeable about the legal history of their field through this research. With AI to conduct this work for them, instead, they will likely become experts in how to use AI to support their legal projects. At the same time, the results of AI-led research will still surface relevant cases and precedents for junior associates to assimilate.

Competency requirements

Because AI can pull an enormous range of strategic insights incredibly quickly, lawyers will no longer have to hyper-specialize; instead, they will possess greater depth and breadth of expertise through AI’s superior research abilities.

AI may make legal professionals more knowledgeable in more domains, but this change will also require them to adopt new competencies. Specifically, legal professionals will need to know how to use technology and to leverage the benefits of AI. To best serve clients and to compete in the world of business, becoming technologically competent will not be optional.

Client expectations

All of these shifts have major implications for client expectations.

Why should a client pay for hours upon hours of billable research time when the same could be accomplished in minutes by software programs?

Do clients need to find lawyers with granular knowledge of a particular set of laws within a particular county? Or do they simply need to find one that can use AI to hone in on the laws most pertinent to a particular use case?

Legal strategies

Perhaps the most intangible technology-driven transformation is in legal strategy. A recent Forbes article postulates that machine-learning models will shift the practice of law itself, including:

  • Predicting the outcomes of pending cases using relevant precedent and fact patterns
  • Proactively planning litigation or negotiation strategies based on those insights, even working to minimize the number of cases that go to trial
  • Deciding which cases are worth taking based on the probability of desired outcomes

The full extent of how machine learning and other technologies will transform the fundamental work of legal professionals is impossible to predict with 100 percent certainty. We can say, however, that advanced technology will become enmeshed in every facet of legal work. With that, best practices, common approaches and legal strategies will be fundamentally transformed.

A powerful use case: Contracting

This tech-enabled future is already making itself known in certain fields, particularly contract discovery, review and management.

Here’s how just a few of these predicted changes result in a new way to manage your contracts:

  • Data discovery — With contract data stored in a central and AI-enabled contract management platform, data discovery is exponentially quicker. Intelligent search capabilities return the information you need, from late payments to clauses of interest.
  • Competency — Using AI to manage the nitty gritty of contract management, legal professionals can focus less on the organizational knowledge required to manage contracts and more on deriving business value from their portfolio.
  • Expectations — As tech enables faster turnaround, risk management via automated triggers, easier compliance and more, client businesses will see legal contract teams as a value center instead of a cost center. Smart content management software, for instance, can offer a comprehensive overview of the business’s contract health, note potential threats and even predict business outcomes based on past performance.
  • Strategies — The fundamentals of contracting will change with AI. Agile contract management, for instance, uses the insights of software programs to reassess risk distribution and build contracts better suited for modern business.

What won’t change

Legal methodology

There’s some debate among industry thought leaders over how AI will influence our approach to law.

Some believe that AI tools will fit smoothly into that approach. That same article in Forbes, for instance, notes that “the law is in many ways particularly conducive to the application of AI and machine learning. Machine learning and law operate according to strikingly similar principles: they both look to historical examples in order to infer rules to apply to new situations.”

Others have pushed back, arguing that tools like machine learning are predicated on probability rather than causality — making the presence of a legal professional that can make inferences essential.

To use the classic example of AI classifying a picture of a cat, AI (as it currently stands) is built to tell us the probability that a picture contains a cat — not why it’s a cat, whether it matters that it’s a cat or if there should be a cat in the picture.

In the legal field, AI can analyze precedent and fact patterns to determine whether a given case fits a certain model, but it can’t address the lingering questions or build a strategy that’s ideal for each individual, unique case. Those tasks remain firmly in the domain of legal professionals.