Lawyers and the lack of AI knowledge
As lawyers increasingly recognize the importance of legal technology, and in particular artificial intelligence (AI), for effective and efficient customer service, there remains a significant gap between anticipating impact of technology and an understanding of the technology itself. In the recent Future Ready Lawyer report, 70% of lawyers surveyed in corporate legal departments noted that AI will have an impact on their organization over the next three years. However, only 28% of those surveyed said they understood AI technology very well. A similar knowledge gap was noted among the responding attorneys from law firms.
In this article, we hope to reduce this knowledge gap through a hands-on review of some common legal work that can be supported by AI tools readily available today, much of which can be applied to law. procurement, an essential part of any legal service.
The measure of the success of AI in the legal arena is whether it improves the delivery of customer service by legal experts themselves.
What is AI?
AI can be described as the use of computers and software to replicate human decision making. It can range from automating simpler tasks to exercising human judgment. AI in legal practice today has recently been described as âbetter search and findâ and âControl + Fâ over steroids, âincluding when applied to contract review. This highlights both the power of AI and its current limitations in legal work.
How is AI commonly applied to contracts today?
The most common and effective applications of AI to contracts today are at the beginning and end of the procurement cycle. At first, AI helps in creating early drafts (for example, through tools like Contract Express, HotDocs, GhostDraft (Korbitec), and Leaflet). AI can also be useful in reviewing completed contracts (for example, through tools such as Kira Systems, eBrevia, Diligen, and Luminance).
Document automation tools can be used to assist in the generation of the first draft contracts. Starting with the appropriate document templates, various fields can be “coded” to prepare them for use – these effectively provide placeholders for users to apply common provisions in changing contracts. Once coded, the procurement tool receives values ââfor each field from users, often by filling out a predefined form. These values ââare then compiled into the coded template to complete a draft agreement. While not often included as an example of AI, these tools replicate basic human decision-making and automate associated tasks.
The use of AI to support the human review of completed contracts is now common. The strength of these tools is to find and categorize the types of clauses requested. An expert in the field can then more easily review the clauses identified and exercise his or her judgment. The time, cost, and resources required to âmanuallyâ perform due diligence in transactions have been the primary business drivers for the development of these AI-based contract review tools. There are now many commercially available tools that are pre-trained “out of the box” to identify hundreds of common contract term types, in many cases faster and more accurately than human examination alone. . To varying degrees, these tools can also be trained by users to identify new types of clauses.
The common application of these tools extends beyond transactional due diligence. For example, large sets of documents can be reviewed to gather clauses for drafting future contracts or to suggest clauses for downstream contract management purposes. Data regarding contracts can be collected over time, providing risk profiles and informing future contract negotiations.
Use case development: contract review and negotiation
AI tools to support pre-performance review and contract negotiation are increasingly available. AI tools can perform a variety of basic proofreading activities, examining cross-references, defined terms, and definitional uses. Newer, more complex inquiries examine clauses in a contract under negotiation to show deviations from preferred clause forms. Optional language can be suggested for different clauses of a standard. Automated comments or “red lines” in a document can be generated for user review.
In this way, AI tools move up the value chain to support more judgment-oriented contractual work. From a practical point of view, the tools for this contracting phase initially focused on high volume commercial contracts (such as nondisclosure agreements). Increasingly, this technology can be applied to a variety of other user-selected contracts. The usefulness of AI tools for any type of contract may depend on the number of appropriate examples for the AI ââsoftware to make useful comparisons.
What are the benefits?
Assembling and reviewing contracts supported by AI offers several potential benefits to users and their customers. The time required to draft and review contracts can be significantly reduced. This allows legal experts to focus on delivering more valuable aspects of the practice to their clients. Generating AI contracts can also reduce the overall cost of contract labor. AI tools have been shown to perform tasks with greater precision compared to human examination alone. Incorporating AI into contract review can reduce the risk of error, thereby increasing customer satisfaction. And an added benefit of having the backing of technology to supplement what are often the more mundane aspects of contract work is that it makes practitioners happier.
Humans and AI robots
Hopefully it is now commonplace to wonder if robots will replace lawyers, and just as commonplace to answer that they won’t. Where clear decision-making can be automated, it should be; it is not “lawyer” in itself. However, most legal work in which clients see higher value requires judgment to be exercised, often in contexts not yet easily captured by AI. Common AI contract review tools can spot indemnification provisions in multiple places in a contract, but professional legal judgment is required to assess contextual risk to a client. The value proposition of AI in Law is to enable legal professionals to perform certain complex, high-volume jobs as well as common repetitive tasks faster and more accurately. The measure of the success of AI in the legal arena is whether it improves the delivery of customer service by legal experts themselves.
The rationalization of legal work is indeed a process improvement project. Like all technology, AI is simply a tool to help improve a process. Before committing to technology, users should ensure that they have a clear understanding of existing processes and desired improvements. Automating a faulty process can amplify inefficiencies. If AI technology is the right tool, users can expect to invest a lot of time and expertise upstream in the setup, in addition to the cost of licensing the technology. Even the simplest use case of document automation technology requires considerable work to prepare models for automation. Expected efficiency gains should be measured against the initial investment.
Maximizing the ROI of AI technology can also mean staffing with specialized staff. When AI contract tools are used on a large scale, large legal organizations and alternative legal service providers may have dedicated staff with legal and technical expertise to use or support the use of the technology. Each tool has its own user interface, features and workflows that can generate greater value if the technology is used by an expert. At the same time, it is important that all the specialists dedicated to this work are equally well connected or integrated with the customer service teams that they support; they cannot exist in silos.
For some use cases, users may also need to invest significantly in âtrainingâ or âfeedingâ the AI ââtechnology upstream. Off-the-shelf AI-backed contract review tools are typically ready or “pre-programmed” to identify certain contract terms. Applying these AI tools to new clauses may require extensive training, including loading large sample volumes into the tool. It may also require legal subject matter expertise to validate or correct the findings of the AI ââtool as it is learned.
The number of samples required varies depending on the use case. In the example of automated feedback generation mentioned above, 100 or more suitable contracts might be required to establish a standard or “playbook” from which comparisons or red lines of constant value can be generated. This number can be reduced over time through improvements in technology.
Humans are not perfect, neither is software
Like humans, software systems are imperfect. AI tools considered ‘market ready’ don’t work perfectly. This is acceptable, provided users understand their limitations. When lawyers are reluctant to use AI, sometimes it’s because they think the results should be perfect; but it is not (and cannot be) an achievable goal. Legal professionals benefit from efficiency gains as users of AI review tools, but they also play a quality assurance role.
Users should expect to work closely with legal technology companies to understand and refine AI applications. Lawyers should also be transparent about the use of AI tools with their clients.
While the pool of legal professionals who must be functional AI experts may be small, a critical mass of lawyers familiar with the possibilities and limitations of AI in the legal space is needed for AI be more widely adopted (and for organizations to reap the benefits). While change management is no small feat for organizations in implementing AI technologies, scaling this mountain will now ensure that legal departments and their clients improve their centralized data and be better prepared for other uses of AI that may be to come.