Enterprises deploying artificial intelligence in high-stakes legal environments must prioritize access to trustworthy, relevant information alongside model quality to ensure reliable outcomes, according to Max Christoff, chief technology officer of Everlaw. Christoff emphasized that for AI to deliver dependable results in regulated industries such as law, it must be grounded in verified sources, including statutory law, case law, court rulings, regulations, and supporting evidence such as documents or records.
Christoff highlighted the critical need for AI tools to link each response directly to its source material, with transparency about information gaps. This approach supports legal professionals’ responsibility to verify the origins of AI-generated conclusions before relying on them.
Legal cases often involve enormous volumes of diverse information that exceed typical AI models’ analytical capabilities. According to Christoff, Everlaw’s platform has managed data sets as large as 239 million documents, spanning emails, messages, contracts, security footage, scanned handwritten records, and multilingual content. Their AI research tool, Deep Dive, allows lawyers to query entire case databases using natural language questions and receive answers substantiated by citations to relevant evidence.
Christoff also underscored the importance of interoperability among AI tools in legal practice. Effective workflows require seamless information exchange between legal professionals, evidentiary records, legal research, and prior work product. Closed systems risk missing vital context, introducing inefficiencies, and creating governance vulnerabilities such as data duplication, loss of control, and reduced security. To address this, Everlaw integrates with platforms like Google Gemini, Microsoft Copilot, Anthropic Claude, Thomson Reuters CoCounsel, Harvey, and Legora, ensuring that legal teams can access reliable, governed evidence regardless of their chosen work environment while maintaining security and permissions intact.
Drawing broader lessons for enterprise use of AI, Christoff emphasized that responsible adoption depends on understanding the specific context of information within an industry. For legal teams, this means maintaining connection to governed evidence with secure permissions and audit trails as data moves between tools and users. Other sectors can apply similar principles by anchoring AI workflows to managed business data, balancing enhanced capabilities with information governance.
Regarding generative AI, Christoff noted its value in enabling legal professionals to analyze more information than is humanly possible, thereby extending their capacity and allowing them to focus on judgment-intensive tasks. Nonetheless, he acknowledged ongoing hesitancy among lawyers due to potential risks, underscoring the need for a balanced evaluation of benefits, costs, and risks. In regulated fields, practical utility must be weighed against security, safety, and permission requirements, with context serving as the key determinant. Christoff concluded that generative AI’s greatest value lies in its ability to augment legal efforts without compromising professional standards.