The right data strategy can help your AI plan succeed:

Before exploring its role in powering the effective use of gen AI tools, it is critical to understand what a “data strategy” entails in a corporate legal setting. In general, a data strategy is a comprehensive plan that outlines how an organization collects, stores, manages, shares, and uses data.

Generative AI (gen AI) has ushered in a wave of significant transformations across a range of business functions, including corporate legal functions. This innovative technology has the potential to transform legal departments and enhance efficiency across a spectrum of tasks, such as analyzing data, researching legal issues, summarizing documents, and comparing information. Legal organizations are attempting to embrace these new tools. However, they frequently fail to consider a key component needed for success: a robust data strategy.

Before exploring its role in powering the effective use of gen AI tools, it is critical to understand what a “data strategy” entails in a corporate legal setting. In general, a data strategy is a comprehensive plan that outlines how an organization collects, stores, manages, shares, and uses data. It is a roadmap that aligns the organization’s data initiatives with its strategic business goals. A robust data strategy addresses issues including data governance, data quality, data architecture, and data literacy, helping to ensure that enterprise data are treated as assets that can drive decision-making and innovation. Finally, a foundational component of a data strategy is a blueprint that provides tactical insights into the systems where data resides, the interconnectivity of those systems, and the data stored there, as well as the business questions/considerations that each system (and its associated data) is intended to address. In the context of a corporate legal function, this will typically influence how department resources are capturing and storing information related to legal matters, contracting, and law department knowledge.

Generative AI, a subset of artificial intelligence (AI), uses machine learning models to create, review, and analyze content including text, images, and even software code based on user input and the data it can access. As an example, in the context of corporate legal functions, gen AI can be leveraged or trained to support the following use cases:

• Review contract templates to help identify deviations in third-party paper and generate responsive positions that the legal team has pre-approved.
• Organize and manage the vast amount of knowledge within a legal department. It can help in categorizing, searching, and retrieving information quickly and efficiently.
• Analyze historical case history to generate strategic insights, aiding in the creation of tailored legal content to enhance a company’s negotiations and litigation strategy and results.
• Streamline the process of reviewing legal invoices. It can analyze line items, compare them against agreed-upon rates and rules, identify billing errors or discrepancies, and help ensure compliance with the company’s billing guidelines.

However, the quality and accuracy of the output heavily relies on the quality and depth of the underlying data available to the AI tool.

Without a well-defined data strategy, in-house legal teams may face numerous challenges in implementing gen AI, which can limit the effectiveness of the tool. These include:

• Inaccurate outputs: If the data used to train the gen AI models are not accurate or comprehensive, the large language models underlying gen AI may generate faulty, incomplete, or misleading outputs. Unless caught and corrected, such outputs may lead to suboptimal decisions and increased risk for the organization. In addition to significant reputational and  other implications for the legal department and individual attorneys, low-quality gen AI outputs can also create other major risks—from financial, regulatory, litigation, public relations, and other perspectives—for the business as a whole.
• Inefficient or slow models: Without a well-organized data architecture, it may be difficult for gen AI models to access and use data effectively. This could lead to inefficiencies in the use of gen AI and a decrease in the overall productivity of the legal function.
• Compliance Issues: If the data used to train the gen AI models are not compliant with relevant data protection regulations (including but not limited to HIPPA, GDPR COPPA), the organization could face legal and reputational risks. Similarly, using improperly obtained and unlicensed data to train large language models (LLMs) can create serious intellectual property implications.

A robust data strategy can serve as the foundation for successful Gen AI integration. This strategy, tailored to harmonize with the company’s overarching business objectives and goals of the enterprise and its corporate legal function, helps serves as the North Star guiding all initiatives. At its core, the development of a data strategy for Gen AI implementation, typically a collaborative effort between legal operations and technology resources, should empower corporate legal departments to evolve into more effective business partners. Developing a data strategy usually involves these key steps: