AI is a rapidly advancing field. While its earliest iterations have been around for decades, more recent developments have introduced a raft of sophisticated terminology which can be daunting.
However, for the purposes of in-house legal work, grasping the basics is simpler than it might seem. Once you understand these key concepts, you’ll be better equipped to navigate the market, bypass the jargon, and focus on extracting actionable value from AI tools.
When you go to market for AI-powered legal tech, you’ll notice these two terms pop up a lot. Both have an important part to play in any sustainable AI strategy – and understanding the differences between them is critical to informed adoption.
GenAI is currently dominating the technology spotlight across the board – and the legal sector is no exception. A key industry report last year found that three in four lawyers expect to incorporate GenAI into their work in some form in 2024.
Built on LLM models, what sets GenAI apart from its predecessors is its ability to generate new outputs beyond the limits of the data it is trained on.
Rather than simply extracting existing information to help answer questions, GenAI creates “human” responses by using that information to predict the most likely answer. GenAI is probably best known for spawning programmes like ChatGPT.
The open-ended potential of GenAI to generate novel solutions to complex problems is compelling. However, carefully chosen use cases and guardrails for output validation are critical for these tools to be safely relied on for legal work.
Extractive AI is GenAI’s less glamorous (but no less useful) predecessor. Designed to identify concepts, extractive AI draws from human-supervised examples within closed, usually small and curated, datasets, using pattern recognition to provide specific responses to questions.
While it won’t generate original content, extractive AI returns precise and consistent results within clear source parameters.
Because extractive AI works with tightly curated, human-reviewed datasets and real-world examples, it is well suited for the high-volume datasets used in legal work. It can also be safely relied on to return clear results which are easy to validate.
A great example of extractive AI at work is invoice data extraction, which automatically identifies and populates data points from PDF invoices into your e-billing system so that you don’t have to do manual data entry.
AI tools are a critical enabler for businesses who want to optimize efficiency and stay competitive – and for in-house legal teams, the question of integrating them into everyday workflows is no longer one of “if” but when.
There are now a myriad of proven advantages to be gained from leveraging AI for legal work, and it’s helpful to have them front of mind as you begin your adoption journey.
Here are just a few of the benefits that AI can deliver for legal teams:
What’s most valuable to you will depend on the specific goals of your in-house legal team. For example, your top priorities might be to increase efficiency and strengthen compliance.
However, an adoption strategy which includes AI as an integrated part of your wider technology roadmap is ultimately likely to deliver most, if not all of these advantages – not just because many will arise as a natural consequence of others, but because a staged approach will allow you to expand your focus over time.
Now that you have a clear understanding of the fundamental principles and benefits of AI in relation to in-house legal work, it's time for the next step in your adoption journey – getting prepared.
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