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58 B Y N I L S A M O R E N O AI AND DATA-DRIVEN PROJECT MANAGEMENT Insights for Legal Technologists T he legal field generates vast amounts of data through case files, contracts, client communications, and court records. This wealth of information holds significant potential for improving legal practice. Law firms are increasingly adopting advanced technologies like AI to gain insights. A recent McKinsey report highlights this trend: "65 percent of respondents report that their organizations regularly use Gen AI, nearly double the percentage from our previous survey just ten months ago." While this statistic covers various industries, it underscores the growing importance of AI in data-intensive fields like law. Using these technologies to analyze and manage data allows legal professionals to gain valuable insights, make informed decisions, and achieve better client outcomes. Data- driven project management provides a framework to utilize data and AI in legal practice effectively. 80 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | W I N T E R 2 0 2 4 81 I L T A N E T . O R G Data-driven project management is not just a trendy buzzword but a transformative approach that empowers legal teams to leverage data to plan, execute, and monitor projects more effectively. Legal professionals can optimize resources, mitigate risks, and succeed in their projects by analyzing data, identifying trends, and predicting potential roadblocks. This approach is essential in today 's legal landscape, where firms and legal departments face mounting pressure to increase efficiency, reduce costs, and increase value for their clients. However, the data revolution in law involves more than the expansive influx of artificial intelligence; it is a fundamental shift in how legal work is done and managed. Technology is transforming the legal profession at an unprecedented pace. Cloud computing, mobile technology, and the rise of legal tech startups are changing how lawyers work, collaborate, and serve their clients. Effective data leveraging is essential to maintain a competitive edge in the modern legal field, and robust project management is key to navigating this transformation. Law firms and legal departments can achieve better client outcomes by embracing data-driven approaches. They can use data to understand client needs better, develop more effective legal strategies, and streamline operations. They can also use data to identify new business opportunities and expand into new markets. Legal professionals need a structured approach to project management to leverage data's power effectively. This framework guides them in planning, FEATURES human oversight at critical points. For instance, in AI- powered contract analysis, a legal professional should review the AI's identification of key clauses or risk assessments. CONDUCTING ALGORITHMIC AUDITS Algorithmic audits are systematic evaluations to detect and address bias, and legal technologists should lead these efforts. • How to Conduct an Audit: First, evaluate training data for representativeness (ensuring diverse case types and jurisdictions)—test outputs for disparate impacts across categories like case type or client demographics. Then, use audit tools or partner with vendors offering audit capabilities. Finally, record your findings and implement any corrective actions necessary. • Example: If a contract review tool disproportionately flags clauses from smaller companies, the legal technologist should investigate the training data and retrain the model with a more diverse dataset. CURATING REPRESENTATIVE DATASETS The quality of AI depends on the data it is trained on. Legal tech professionals have a direct responsibility for data curation. • Ensure datasets include various case types, industries, and jurisdictions relevant to your firm's work. Consider actively seeking out data sources MORE ONLINE Read this article from the Winter 2024 issue: epubs.iltanet.org /i/1530716-winter24/79 ó The quality of AI depends on the data it is trained on. Legal tech professionals have a direct responsibility for data curation.

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