AI in litigation is completely transforming how legal professionals approach the review process. However, for all its capabilities, misconceptions and false expectations of the technology are still widespread.
Like many other industries, legal professionals fear that AI is a potential threat to their job role. The truth, however, is quite different. AI is a powerful solution, but only when given the proper foundation and support from a qualified team.
In this blog post, we'll explore AI in litigation and four essential tips to ensure your firm uses AI safely and effectively.
1. Know when to use the right tool
Getting the most out of an AI solution means knowing it inside out. For example, you might have a tool that can identify the relationships between words and phrases but can't decipher information from images or media-based formats.
Understanding that using this tool wouldn't be wise for a batch of images or scanned documents is how to make your eDiscovery processes more efficient.
One of the most simple but crucial ways to get the most out of your AI in litigation is to know what the different tools in your inventory do and prepare your datasets accordingly.
Doing so maximises the speed and accuracy of AI-driven reviews, as you'll be playing to the strengths of your available tools. Plus, you'll minimise the chance of sticking points or errors when using your tools.
2. Cut your time, not your team
AI innovations can enable businesses to be more efficient than ever before. Many paralegals and attorneys still worry about AI's progression in eDiscovery as technology advances and becomes more prevalent in the industry. AI allows firms to carry out dozens of complex, logic-based calculations at great speed.
With this in mind, however, we're still far from AI replacing humans altogether. Looking at AI in litigation, it can be easy to overestimate its capabilities, with immediate thoughts of slashing budgets or reducing your internal headcount.
Despite its capabilities, AI still requires a strong, capable, and well-sized team for its implementation and ongoing management—both for quality control purposes and to assess how to get more out of the available tools.
If you want to maximise your strategic investment in AI, aim to do more in less time rather than do more with fewer people.
