Legal AI Explained: A No-Nonsense Explanation of AI in Law Firms

Amid the ever-growing hype surrounding legal AI, it’s difficult to distinguish fact from fiction. To understand the importance of AI in law firms, it’s important to grasp how AI complements the nature of the professional services industry. And, pragmatically, it’s critical to understand how firms can use AI to augment the business of law.

In Part 1 of our Legal AI Explained series, we’ll take you on a journey to:

  1. Get a no-nonsense understanding of the reality and hype of AI in law firms
  2. Explore the intersection of legal AI with traditional professional services business models and offer strategies to navigate the connection
  3. Show real-life examples of AI in law firms across the client engagement lifecycle, and illustrate the steps your firm can take towards legal AI enablement

This multipart series — consisting of interviews, short videos, and quick-reads — will explore many unanswered questions relating to AI in law firms. The series is split up into the following chapters:

  • Chapter 1 — Why AI in Law Firms Matters (this chapter)
  • Chapter 2 — Why AI Matters for the Law Firm CXO; the Intersection with Data-ism; and Discerning the Credibility of Different Legal AI Models
  • Chapter 3 — Moving from Legal AI Ambitions to Reality
  • Chapter 4 — Machine Learning and AI in Law Firms: Augmenting the Business of Law

Suffice to say, the business landscape for almost every law firm has fundamentally shifted, due to many factors, including three big drivers:

  1. Challenging market conditions — The global pandemic has driven dynamic changes in the way firms operate, from remote working and virtual relationship building to the reallocation of resources to countercyclical practice areas.
  2. Growing competition —Beyond traditional ranked competitors, alternate legal services providers, including the Big Four accounting firms, now account for more than 4% of client budgets and are growing at double-digit rates.
  3. Evolving client demands and expectations The 2020 Wolters Kluwer Future Ready Lawyer survey found that when evaluating law firms, corporate legal departments rate the ability to use technology to improve productivity, efficiency, collaboration, and work processes as the top criteria. Meanwhile, law firms believe legal departments rate price as the most important criteria, highlighting a gap in expectations.

Unsurprisingly, firms are looking beyond their traditions to seek novel ways to differentiate and to grow. AI is one of the mechanisms firmly in their sights.

Starting at Square One: What is AI?

The proliferation of buzzwords in the world of AI is exhausting. According to “The State of AI 2019,” a MMC Ventures study, close to 40% of 2,830 AI startups across 13 EU countries show little evidence that AI is material to their overall value proposition.

AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving. Previously, AI has been commingled with automation[1], yet there are some crucial distinctions between the two. Although AI mimics human intelligence decisions and actions — facilitating both efficiency and effectiveness enhancements — automation focuses on streamlining repetitive, instructive tasks. Recent advancements in the field of AI have surfaced many approaches to enabling this intelligence, and with it a plethora of new terms that are destined to become next year’s buzzwords — including neural networks, machine learning, and deep learning, to name a few.

All these novel AI approaches help facilitate the key goal of the average person: the ability to problem solve by translating input data into outputs with greater speed, insight, or accuracy. Although this may sound a lot like a software algorithm, the innovations in AI relate to the role that data plays in the effectiveness of problem solving. In short, the field of problems being solved by AI today are not linear (e.g., 2+2 = 4) but rather contain such complexity that they can only be approximated, not solved, and by association often require more than one algorithm. It’s these algorithms that make up the layers within a neural network — a diagrammatic representation of the layers of cognition that the human mind goes through when solving problems.

Regardless of the latest AI approach, the fact remains that AI is not a silver bullet to drive business success for your firm. It can be an augmentative tool to better serve clients, but it is still just a tool. To reinforce this point, a glance at Gartner’s Hype Cycle for AI (September 2020) acknowledges that many of the AI approaches most relevant to the legal sector (e.g., natural language processing, deep learning, chatbots) will only reach their “plateau of productivity” in 2 to 10 years.

So Should We Back Off on AI Enablement?

The advancements of AI remain exciting and impressive, as seen in other fields. Consider the role that certain AI technologies have played in helping identify people who are likely to experience the onset of Alzheimer’s disease or detecting “COVID-19 coughs” inaudible to the human ear. Although many of these algorithms don’t translate directly into legal-industry use, they showcase the truly differential capabilities that AI can provide.

Looking back within the legal sector, what can firm leadership tackle today to lay the groundwork for AI enablement in the future? We’ll explore two intersecting aspects: first, navigating the traditional billable hour model with measured enablement of AI, and then understanding the role AI plays in a law firm’s innovation agenda.

Where Does AI Fit Within the Constraints of a Traditional Law Firm Business Model?

The billable-hour fee arrangement still drives a significant portion of the average law firm’s revenue, with Bloomberg Law’s 2019 Legal Operations & Technology Survey suggesting 75% of fee arrangements still fall within this category. If it were ever in doubt that many firms continue to make money by keeping junior lawyers busy, correlation analysis of 3 years of Am Law 200 data shows the profit per equity partner of a firm can be predicted with 85% accuracy based on its revenue per lawyer. This business model, and individual billable-hour targets, often influence fee earner behaviors away from the efficient allocation of work to the most appropriate resource or approach. This is most apparent in firms suffering from an excess of fee earners with a limited volume of work.

Firms are, however, becoming increasingly compelled to change by external factors, and these factors weigh more heavily on firms where a greater proportion of revenues relate to more commoditized legal services. These factors include:

  • Clients enhancing their own capabilities through hiring larger in-house legal teams or initiating operational improvements (e.g., process redesign or technology enablement)
  • New market entrants (e.g., alternate legal service providers) offering significant price competition through a laser focus on efficiency
  • Other incumbent service providers leveraging their scale or service delivery investments (e.g., flexible lawyers, AI, etc.) to be able to tackle this type of work at lower cost to clients

AI’s nascency in replacing human intelligence means that it primarily addresses the more commoditized end of legal services work, or other law firm activities that are unable to generate direct revenues (e.g., non-fee-earning work). Here are examples of AI in these spaces today:

  1. Time narrative normalization —Rewriting a narrative to correct errors and comply with standardization rules imposed by either the client or the firm
  2. Matter pricing —Analyzing previous matters, including timecard data, engagement milestones, and client requirements, to help set and track pricing for client engagements from quote to bill
  3. eDiscovery —Enabling a vast number of documents to be surveyed, and using those relevant to search criteria to be identified more quickly, accurately, and inexpensively
  4. Contract generation — Helping lawyers draft consistent, appropriate, and up-to-date documents both in the transactional and litigation spheres, by reference to databases of precedents

So where does that leave us? Some questions may remain in your mind:

  • Is AI enablement relegated to firms that purely focus on commoditized legal services?
  • Should my firm wait and see how things mature, then participate in a couple of years?

We would argue the answer to both questions is no, given the currency afforded to many firms’ innovation credentials offered by AI.

AI Is at the Leading Edge of the Innovation Agenda, but Has Your Firm Addressed the Foundations?

The digital revolution continues to disrupt legal services, forcing these traditionally conversative organizations to evolve. Many firms have leveraged innovation as a significant brand differentiator and are investing in tools that augment their core capabilities. These investments not only enable innovative service delivery, but also tend to provide access to other, more strategic client opportunities.

It’s important to emphasize the various approaches to innovation available to firms outside of AI, including leveraging design thinking approaches to solving legal problems, using technology to automate processes, and connecting ever-greater datasets to better understand trends (i.e., data analytics).

The Intapp Law Firm Innovation landscape shows the stages firms have to progress through.

  • Core capabilities – Core Capabilities are the knowledge and expertise underpinning a firm’s services portfolio
  • Connected technology – Connected Technology enables technology as a foundation for more effective service delivery (both client facing and internal) which drives benefits for client experience and operational effectiveness. Examples include:
    • Allen & Overy: aosphere
    • Clifford Chance: Automation Academy
    • Dentons: Nextlaw Labs
    • BLM: Innovations
  • Connected data – Connected Data augment connected technologies by leveraging data as an asset to facilitate client insights previously unknown. Examples include:
    • Addleshaw Goddard: Intelligent Delivery
    • Ashurst: ESG Ready
    • Clyde & Co: Data Analytics Lab
    • Hogan Lovells: Engage: LIBOR
    • Simmons & Simmons: Wavelength
  • Applied AI – Applied artificial intelligence transforms connected data and lessons on past business patterns into accurate, predictive business capabilities. This further augments the firm’s pace and effectiveness of delivery. Few firms have yet to reach this wave

Innovation initiatives suffer the same never-ending treadmill of progress required of legal services to avoid the pitfalls of commoditization. With that in mind, it’s clear that law firm leaders need to consider AI’s business implications in the next wave of strategic differentiation.

What’s on the Horizon for AI in Law Firms?

Changes legal services delivery have been on the horizon for some time, and the stark market shifts of 2020 only accelerated the pace of these shifts. Clients are exerting greater pressure on law firms, which is only adding to the drive to innovate.

Clients are looking for legal services that are faster, cheaper, and more efficient, and law firms must respond by finding new and innovative solutions to deliver what clients seek. Developing a greater understanding of use cases where your firm can apply AI is one way you can meet and exceed client expectations, and help augment and future-proof your business models. Given the plethora of examples where we are already seeing AI applied across the legal industry, it’s clear that this isn’t a multiyear transformation journey, but rather something that can — and should — start today.

In the next chapter of the Legal AI Explained series, we’ll dive deeper into the specifics of effective AI enablement, using our Intapp AI applicability framework. This will help you and your fellow leaders determine how you can apply AI today across your firm’s client engagement lifecycle. We’ll also address the considerations that will help you make better decisions about AI in your businesses.


To learn more about how Intapp Strategic Consulting works with clients to put legal AI to work across the client matter lifecycle, please get in touch.

[1] Note: As of 2020, Robotic process automation software has been removed from the Gartner Hype Cycle for AI:

Written by:

Jack Hackney

Senior Consultant, Strategic Consulting

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Kian Poon

Director, Strategic Consulting

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