Artificial intelligence: Artificial intelligence is easily the worst offender because of its ability to evoke every science fiction movie you’ve ever seen. Sometimes, “AI” refers to predictive analytics, essentially an algorithm that can make an educated guess based on a lot of pre-existing data, while other times we’re talking about specific AI, essentially a set of algorithms that can look through a lot of data very quickly and pull out the components that fit a set of fairly general criteria. At this point, a more “general AI,” essentially a machine that could perform an intellectual task the way humans do, is still a ways away from where we are technologically. While AI may yet revolutionize the industry, it’s happening much less quickly than the hype might suggest. Thomson Reuters’ “Ready or Not: Artificial Intelligence and Corporate Legal Departments” survey in 2017 found that 50 percent of legal departments were not at all interested in bringing AI on board. Breaking AI out into more specific definitions might give it a little more traction.
Blockchain: Blockchain has a more specific meaning, but its association with bitcoin, the cryptocurrency that first used blockchain technology, can muddle its meaning. Blockchain is often described as a “distributed ledger”: transactions or data get saved to a public “ledger,” and copies of that same ledger are kept in thousands of different places, or nodes, to ensure their validity. Blockchain is the basis for a lot of cryptocurrency (making that “ledger” specific to actual financial transactions), but as people in other industries have begun to see the value in blockchain for things such as medical records and identity verification, the meaning has gotten a little more diffused. What’s more is that many of these “blockchains” are still mostly prototypes and ideas more so than actual applications. Especially as cryptocurrency picks up more steam from the financial sector, technologists may want to find a better way to describe blockchain overall.
Analytics/Big Data: Big Data is probably the piece of legal technology that’s worked its way most permanently into the woodwork of legal services delivery, but that too can scale from basic Excel management to dashboard-based insights to extensive reporting. Billing analytics as an endless ocean of insight drawn from any and all data is a little misleading, given that analytics are still fairly specific to a given task or project. Analytics have become a big part of e-discovery and project management work to keep tabs on timing and budget, but they’re also increasingly used across law firms and legal departments to track employment data, useful documents, diversity metrics and a whole host of other uses. Finding a platform and a program that helps you find what you need tends to vary based on the use case, meaning that developing a language around specific uses and applications for analytics may help practitioners cut through the clutter.
As technology continues to mature into regular use within the legal industry, many of the more popular buzzwords within the legal technology space have been overused almost to the point of nonsense. The next “legal robot” set to revolutionize the legal industry can be anything from a deep-learning algorithm to a Microsoft Word macro. Users are getting more sophisticated, meaning it may be time to move beyond the hype language and form some new language around our legal technology words.