This article was written with the assistance of AI and edited by Angela Sabarese.
In a recent CLM webinar, entitled, “Transforming Discovery Through Technology: From Analytics to AI,” a panel of insurance and legal professionals explored the rapidly evolving intersection of artificial intelligence (AI) and electronic discovery. The panelists included Brent Stevens, Concilio; Nathaniel French, Kennedy; Owyn Fisher, Westfield Specialty; and Bernice Ofili, Gallagher Bassett.
Balancing AI’s Challenges and Value
The panel addressed critical challenges facing the industry, including exponential data growth, emerging communication platforms, and the urgent need to balance traditional discovery workflows with AI capabilities. A central theme emerged around transparency and proactive adoption, with the experts emphasizing that law firms must demonstrate AI competency to remain competitive.
Fisher shared a compelling example of AI's practical value: "Defense counsel actually leveraged AI to do kind of a quick review of documents" in a securities case heading to mediation, enabling them to create a timeline that ultimately led to favorable resolution. This illustrated how AI can transform early case assessment from a cost-prohibitive exercise into a strategic advantage.
Cost Allocation
The discussion also tackled thorny questions about cost allocation. "Who bears the cost?" remains unresolved, with French noting that recent surveys show a "50-50 split between law firm and carrier." Ofili offered a solution-oriented perspective: "Show us what you can do...if you're going to resolve a lot of claims and save us money at the end of the day, then yes, we can chip in and help you with the expense for AI."
Ethical Considerations
Ethical considerations featured prominently, with French highlighting ABA Rule 1.1's competency requirements and the growing database of AI hallucination cases—now exceeding 1,600 documented instances. "You can no longer plead ignorance as a defense here," French emphasized.
The panel concluded that partnership, transparency, and demonstrated value will define successful AI adoption in e-discovery, with expectations that the landscape will transform dramatically within the next year.