While some legal professionals recognize knowledge management as a shield against the perils of artificial intelligence, it’s important to note that the two can actually complement each other. AI can bolster your legal department’s knowledge management by locating relevant sources and turning them into effective work products.
Below, we dive into what generative AI is, explore valuable use cases for GCs, and discuss key success factors when using it as a knowledge management tool.
- Generative AI is the next step in data usage by mimicking human abilities to observe, perceive, and create.
- The benefit of AI in knowledge management is its ability to improve the speed and accuracy of your work product.
- When adopting AI in your legal department, consider important guardrails such as access to performance reports of the model and verification processes.
What Is Generative Artificial Intelligence?
Generative AI, a type of machine learning, uses algorithms to mimic human reasoning through the creation of text, images, sounds, and more. You can distinguish generative AI from other types of machine learning where predictive models merely observe and classify materials. To illustrate, consider the difference between a model that can identify an indemnity clause in a purchase agreement and a model that can draft an indemnity clause for a purchase agreement.
While both types of machine learning have their place in a corporate legal department, generative AI has the promise to greatly improve the output and speed of your operations. This potential stems largely from the different ways AI can improve knowledge management for your team.
How AI Can Further Your Knowledge Management Goals
Corporate legal departments house large amounts of valuable information, encompassing documents, communications, and the collective wisdom of attorneys and staff. The primary goal of knowledge management is finding ways to gather, distill, and organize that information to enhance current processes and protect the health of the department.
Generative AI fits seamlessly into this practice because it can exponentially increase the efficiency of knowledge work through automation. The potential applications of AI in GCs and legal department’s acknowledgment management and related workflows are boundless. For example:
- Preparing layman’s responses to questions from stakeholders in other business units.
- Creating checklists for legal projects.
- Serving as an initial reference when researching an unknown subject.
- Creating clause banks for contracts and updating those terms when the law or market practice changes.
- Developing core documents for knowledge management, such as contract playbooks and process maps.
- Drafting first versions of legal documents, including memos, motions, briefs, contracts, or standard letters.
5 Keys to Success When Using AI as a Knowledge Management Tool
AI holds the promise of improving the speed and accuracy of your legal team’s knowledge. However, recent incidents show the technology is not without fault and room for growth still exists. GCs and legal departments can still enjoy the benefits of generative AI but should do so with caution. Consider the following keys to success when working with an AI model in legal operations.
1. Give Your AI System Useful Inputs and Datapoints
Essential to an AI model providing quality responses and work product is the quality of the information and data it retrieves. This requires practice and training with your team to learn how to effectively prompt the model to deliver the knowledge you need.
2. Get Data on the Performance of the AI or LLM Software
Before committing to an AI program, gather information on its current performance. Get data on confidence scores and overall accuracy to hedge against the risk of acquiring an AI model rife with flaws that could end up creating more work than saving time.
Performing this due diligence is especially critical for GCs of large corporations, where the consequences of error multiply from the size of the institution. For example, you’d want to avoid situations where a single drafting error multiplies because of its use in thousands of contracts.
3. Trust, But Verify the Work Product
Even with a high degree of confidence in the AI system, you still need to review the work product it provides on a case-by-case basis. While this may require extra time and effort from your legal team, it pales in comparison to the time it would take to create a document from scratch. If your department’s resources are stretched thin, consider implementing regular quality assurance checks to catch mistakes before they snowball.
4. Start Small with Low-Risk Projects and Use Cases
When starting with AI, it’s better to walk before you run. Limit your initial use to high-volume, low-risk projects, either because of their overall value or the other guardrails in place to prevent errors.
For example, you might use AI with basic contract templates that require little deviation and customization, such as an employee NDA. Alternatively, you might deploy the program for simple tasks like preparing communication to gather information from a stakeholder in another business unit for an upcoming project.
5. Regularly Refine Systems and Update AI Models as Legal Information Changes
The practices of your legal department will change over time either because of institutional preferences or mandates from outside the company. For example, shifts in the law, new regulatory hurdles, or market changes. Ensure you have methods and plans to update AI models with new information relevant to their work in your legal department.
For More on Leveraging New Technology in Your Legal Department
Exigent is an ALSP that prioritizes the use of data and technology to improve the workflows of corporate legal departments around the world. By pairing technology with the diverse experiences and skills of our team, we help legal departments grow in a measured, cost-sensitive manner, with a focus on productivity.
We offer a variety of organization, automation, and workflow management solutions for legal departments looking to benefit from new technologies like artificial intelligence and language learning models.