When Should You Rely on Robots for Document Management?
Understanding when it's best to use AI is pivotal in demonstrating the value it has for certain workflows. But for the technology to really make a difference, certain variables have to align up just right.
May 10, 2019 at 09:00 AM
3 minute read
Using artificial intelligence in document management can conjure various emotions, from confusion over what the process entails to fear of the mistakes it may cause or apprehension that the technology could eventually replace someone's job. While the negative thoughts on AI may differ, the end result is usually few actually using the AI-powered tool.
But legal tech providers believe such reluctance toward AI can be solved by fully understanding how the technology works and knowing when are the best scenarios to use it—like during high-volume document scenarios.
Earlier during day two of iManage's ConnectLive conference in New York, a panel of legal department directors offered tips regarding tech adoption and noted engagement and encouragement are some of the key ways to ensure consistent software usage. The conference's “How to Be an AI Champion Within Your Organization” session provided further tips on ensuring tech adoption by offering a crash course from iManage staff technical specialist Dorian Levy on what artificial intelligence entails and how to best leverage it to encourage usage.
For starters, situations where you have a high volume of documents, understand what you need from the documents and have a previously trained AI program are some of the best use cases for leveraging AI software.
AI can't just be an empty phrase for lawyers and staff to use it, Levy said. They must see it in action, providing beneficial assistance that allows lawyers and staff to work on more than a single project at a time.
“You can have a very accurate product but if no one is using it … it doesn't add business value,” Levy said.
While an individual's need for AI may vary, not all AI document management programs are similar and their results are impacted by the data used, Levy noted.
While out-of-the-box AI programs are sometimes trained on public data that could work well for lawyers, it may not be familiar with the content of all submitted documents, Levy said. Likewise, “quick machine learning” also has limitations because that algorithm struggles with documents saved in various program languages, which makes collaborating with various files difficult. Thus, legal should adopt software that leverages advanced machine learning because it requires input from multiple documents to enhance its artificial intelligence and learns from corrections, Levy argued.
Seeing the results created by artificial intelligence in its workflow can motivate a law firm or corporate legal department to use the software, he added.
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