Canada: Seven Legal Technologies Every Lawyer Should Know About

Seven hot legal work product technologies, what they do, why you should care and examples of each.

  • Legal technology is hot. So hot that it is easy to be overwhelmed by the choices available. So hot that it is difficult to understand what the tools are actually capable of.
  • In this first post on LOx, we take a look at seven technologies with the potential to do legal tasks or create legal work product. These technologies are at various stages of maturity for use within law departments and law firms, but all of us should be aware of what they do or have the potential to do.
  • The tools listed are examples only as there are many other applications out there. So, do your diligence to determine what might be right in your circumstances. After all, looking at them is part of the fun!

1. Document Proofing

What they do: Document proofing tools analyze documents during the drafting process. They will check a wide range of details such as defined terms, cross-references, citations, quotation marks, numbering, phrases, grammar and even drafting.

Why they matter: These tools improve the quality of legal documents in both corporate and litigation practices and can correct hard to see errors. They provide efficiency and reduce risk.

Examples: Contract Companion (Litera Microsystems), Drafting Assistant (WestlawNext), Word Rake

2. Document Automation

What they do: Document automation tools code repeated elements of a document or suite of documents. When a new document needs to be created, the lawyer, clerk or other user completes a questionnaire and the document is generated based on those answers. The questionnaire will collect data for the document such as the names of the parties as well as information that will control which clauses are included such as whether an NDA is one-way or two-way.

Why they matter: Document automation tools have been around for a long time. As clients and law firms alike are looking for more process efficiencies, interest in document automation has increased and there has been an uptake in use of these tools in the past couple of years.

Examples: Contract Express (Thomson Reuters), HotDocs

3. Decision Trees and Expert Systems

What they do: Decision trees and expert systems combine software with subject matter expertise and knowledge to automate the processes, expertise, reasoning and decision-making typically done by a human subject matter expert. The software can range from simple decision trees, to complex rules-based algorithms, to A.I. based tools.

Why they matter: In rules-based areas of the law where there are repetitive patterns, there is potential for legal advice in that area to be automated. Law firms are taking advantage of these tools to provide advice to clients through technology applications and subscription services. Legal departments are using the tools to gain efficiencies over repeated questions from their business stakeholders.

Examples: Neota Logic, Rainbird, VisiRule

4. Technology-Assisted Due Diligence

What they do: These tools use A.I. to rapidly classify documents and identify common due diligence issues such as change of control, assignment, termination, etc. They extract applicable clauses so that review by lawyers across a large number of documents is easy. Some of the tools also support the due diligence process and workflow, and can build the due diligence report as the work progresses.

Why they matter: While not as ubiquitous as e-discovery, technology assisted due diligence is on the rise. The experiences of vendors and their customers are showing that technology assisted review with a combination of lawyers and technology is more efficient and more reliable than using humans alone. In an age where diligence is often limited to a portion of the documents and only to red flag issues, these tools may allow clients to expand the breadth and depth of diligence done in certain circumstances. They have also been a catalyst for law firms and clients to think of other ways to leverage this technology (see Contract Analysis below).

Examples: eBrevia, Kira Systems, Luminance

5. Contract Analysis

What they do: The use cases for A.I. contract analysis are numerous which, when combined with an equally wide range of tools purporting to perform such analysis makes this a difficult technology category to navigate. Three example use cases are (i) clause identification and extraction where the software is trained to identify and extract specific types of clauses beyond those addressed in a standard due diligence exercise; (ii) contract review where the software is used to review and analyze a contract against a standard; and (iii) contract management.

Why they matter: Of the example use cases, the potential impact on contract management is the most significant for companies and in-house legal departments by "changing the tools [companies] use to contract, influencing the content of contracts, and affecting the processes by which [companies] contract."1    For lawyers, a recent study 2  which pitted 20 corporate lawyers against an AI platform to analyze the risks in non-disclosure agreements shows the potential for significant efficiency in relation to these types of tools.

Examples: KM Standards, LawGeex, RAVN (iManage), the diligence tools listed above depending on use case

6. Transaction Management

What they do: These tools are designed to make the transaction process run smoothly. Each of the applications is different but some of the functionality they address relates to document organization based on a closing agenda, task management, circulating and commenting on documents, facilitating the signing of signature pages and the creation of closing books.

Why they matter: Obvious benefits for transactions aside, we see these tools as examples of practice-specific applications that are designed to address narrow issues in particular areas. The proliferation of tools that will meet the needs of smaller numbers of lawyers within law firms and law departments will have an impact on technology purchasing decisions, applications management, training requirements and the users themselves.

Examples: Closing Folders, TheDocYard, Workshare Transact

7. Legal Research Tools

What they do: These are technologies that use artificial intelligence and data driven analysis to support legal research. These tools typically use a combination of natural language processing and machine learning to answer questions and provide recommendations. The data driven tools are providing information and insights that have not been available to researchers previously. Newer tools are emerging to tackle old problems differently such as tracking legislative developments and identifying relevant changes in the law.

Why they matter: A.I.-driven legal research has the potential to reduce the time needed to prepare quality research. It may also allow in-depth research to be done in circumstances where it otherwise would be cost prohibitive. Predictive analytics from data driven research have the potential to change the way dispute and litigation strategies are developed. Finally, start-ups with new ideas are shaking up the legal publishing market. However, it will still take some time before many of these tools become mainstream and are the go-to for legal researchers.

Examples: BlueJLegal (Canada), Codify (Canada), Knomos (Canada), Loom Analytics (Canada), CARA (US), RAVEL (US), ROSS Intelligence (US)

Footnotes

1 Beverly Rich, "How AI is Changing Contracts". February 12, 2018. https://hbr.org/2018/02/how-ai-is-changing-contracts

2 https://www.lawgeex.com/AIvsLawyer/

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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