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Legal professionals are increasingly concerned about how managing administrative tasks is impacting their ability to apply their expertise to more interesting, higher-value work. And according to a Bloomberg Law report, 61% of law firms and 70% of in-house legal teams say improved efficiency is the primary driver of their technology implementations.
Future-thinking organizations are identifying repetitive, manual processes and applying automation to improve their workflows so they can spend more time on core work and strategic value adds.
There are four types of automation teams can implement to achieve greater efficiency: business process automation, intelligent process automation, robotic process automation, and hyperautomation.
This type of automation focuses on systems and processes, applying technology to simplify and automate workflows. For example, a law firm or legal team might apply business process automation to digitize documents and work with them more efficiently in a document management platform. Another type of automation would include legal document automation, which leverages data captured throughout the client relationship that can be used auto-populate templates and create custom legal documents.
Intelligent process takes business process automation to the next level, optimizing automated workflows. It automates even more parts of the process, reducing the number of manual interactions further. To accomplish this, intelligent process automation introduces natural language processing and similar technologies to process data from various sources. For example, a software platform might use intelligent process automation to scan contracts and identify how various clauses match (or don’t) with expected language.
This type of automation is distinguished by its powerful capabilities to handle high volumes. Using robotic process automation, a firm can configure software to capture and interpret applications for manipulating data, triggering responses, and communicating with other digital systems. For example, a firm might use robotic process automation to run trademark searches globally; monitor regulatory issues and receive alerts when an issue requires attention; or conduct due diligence on suppliers and partners.
Hyperautomation is best thought of as a technique that involves various types of automation and uses multiple technologies to accelerate automation across the organization. It typically relies on machine learning and other advanced technologies to identify and implement opportunities to improve efficiency. But hyperautomation is not intended to replace human input. The goal is to fully optimize processes and reduce risk while allowing humans to easily inject their expertise for better performance.
For example, a legal team might use hyperautomation to scan emails and attachments, download data, extract relevant portions of the data, input this data into appropriate locations in a document management system, and then trigger an alert to someone within the organization with important information that needs to be acted upon based on specific expertise.
Gartner, Deloitte, and other technology think tanks have all named hyperautomation on their lists of trending technologies, and for good reason. Investment in hyperautomation has significant payoff because it dramatically reduces errors and improves quality while freeing team members with valuable expertise to focus on more consequential work. Gartner specifically says, “Increased focuses on growth, digitalization and operational excellence have highlighted a need for better, more widespread automation. Hyperautomation is a business-driven approach to identify, vet and automate as many business and IT processes as possible.” At scale, automation has the power to transform workflows and vastly improve the way work is accomplished.
"Hyperautomation lends itself to continual improvement, which builds competitive advantage as the system becomes better at gathering, analyzing, placing, and predicting results. Well-functioning systems have the capacity to streamline workflows initially thought to be too complex for automation — and, at scale, to revolutionize how decision makers use and apply their organization’s data."
— Mike Lucas, Chief Information Officer at Wilson Sonsini
Most organizations have pieced together systems and processes over time, resulting in a mishmash of inefficiency and numerous potential failure points. If this is the case for your organization, consider looking for opportunities for automation and creating standard approaches that encourage consistency. Include systems, processes, applications, workflows, and the people doing the work in your evaluation. For quick wins, look at implementing low/no code tools to improve document automation and approval processes for greater efficiency and higher quality.
"A significant challenge in achieving hyperautomation is connecting systems together so you have all the data points needed, so consider solutions that already work with the tools your people use. As your technologies combine, they become greater than the sum of their parts to bring new capabilities and increased value."
— Mike Ferrara, Senior Manager at Deloitte Legal Business Services
The key here is to strike a balance with best-in-class platforms that integrate seamlessly with one another. Look for technologies that provide an easy-to-use interface with the features you need to perform your desired automations. While finding everything you need in one platform is ideal, for more complex applications you might need to consider multiple integrated solutions. For example, leveraging tools like Microsoft’s Power Automate to extend the functionality of your individual platform’s APIs can net powerful solutions to day-to-day problems. Platforms that openly share their APIs should jump to the top of the list.
Want to see what else thought leaders in legal are talking about? Download 10 Legal Tech Trends for 2022 and Beyond for Law Firms and Corporate Legal Departments to discover more tech trends that have the potential to drive meaningful outcomes.
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