Q: What does agentic AI do and how does it change legal work? A: Agentic AI can plan and execute multi-step tasks, drafting, summarizing and analyzing documents at scale before handing results back for lawyer review. Lawyers retain judgment and final sign-off, so firms must decide what the agent can do safely and what requires human checks. Q: Where should a firm begin when learning how to adopt agentic AI? A: Start with structured, repeatable tasks such as document review checklists, closing set assembly, standard clause analysis and simple diligence summaries, ideally within groups like emerging technology and venture capital. Prove value in one lane with a measured pilot and resist chasing every use case at once. Q: How should firms choose a platform when evaluating agentic AI solutions? A: Run a requirements-based assessment followed by a pilot and prioritize bilingual support, data security and audit trails, agentic workflows, integrations with DMS and email, and a clear vendor roadmap. Because applications evolve quickly, set decision checkpoints and reassess candidates before scaling firmwide. Q: What adoption programs help lawyers learn to use agentic AI effectively? A: Implement structural programs such as secondments that place lawyers into the innovation team at 50 percent time for a year and a cohort of AI engagement lawyers who provide hands-on support across the firm. Leaders should commit personal training time, run office visits and set weekly goals while sharing wins to build comfort and usage. Q: What guardrails should be in place before agents act on client matters? A: Decide which outputs agents can draft internally and require lawyer approval for external communications, enforce strict filters and logs for privilege, confidentiality and PII, and require sources or citations to firm documents or trusted law. Provide lawyers with review checklists and clear stop points where the agent must be escalated to a human. Q: Which use cases deliver the biggest impact when firms adopt agentic AI? A: High-volume tasks like due diligence review and examination for discovery transcripts move the needle because agents can ingest large contract sets, flag key clauses and dates, summarize testimony, extract admissions with citations and draft follow-up questions. Lawyers then validate top issues and refine requests, shifting hours from sifting to arguing and reporting faster turnarounds and clearer risk memos. Q: What metrics should firms track to measure success when they adopt agentic AI? A: Track cycle time for tasks, quality measures such as fewer rework loops and stronger citations, adoption rates for target tasks, client feedback and repeat work, and changes in revenue mix like more fixed-fee wins enabled by better margins. Share dashboards with practice leaders and celebrate concrete wins weekly to sustain momentum. Q: What common pitfalls should firms avoid when adopting agentic AI? A: Avoid buying a platform before picking use cases, launching firmwide without guardrails or training, assuming AI simply replaces hours instead of shifting them to higher-value work, ignoring bilingual needs or citation standards, and failing to plan for vendor roadmap changes. Addressing these pitfalls early helps reduce risk and improve adoption outcomes.