Enterprise AI has moved from an era of speed to an era of control, says Fulcrum Digital founder Rajesh Sinha. He lays out a seven-layer sovereign AI architecture, why many projects fail at the inference layer and agent use cases in insurance, finance and manufacturing.
The 200-year-old insurer got 91% of its employees using AI, cut claims review from two hours to 15 minutes and built governance that speeds projects up instead of slowing them down. Two Crum & Forster leaders explain how.
The fastest path to production AI is running it where sensitive data already lives, argues Everpure VP of AI Kaycee Lai. He lays out what AI-ready data requires, why unstructured data is the biggest blocker and who he thinks will win the AI race.
Trust in agentic AI trails generative AI by 10 points, and 97% of people override AI recommendations, according to a 2,700-person survey cited by Reggie Townsend of SAS. He argues governance is how enterprises scale human judgment.
Leaders from New Balance, Mastercard, Omnicom, Earnest and Crum & Forster are measuring AI value as token-based costs climb. Their advice: tie AI to business metrics, track cost per outcome, right-size models and build shared platforms.
Leaders from Tokio Marine, Morgan Stanley and The Fedcap Group describe what happens after the first agent ships: a validation tax on human reviewers, workflows that fail more often than models, and proprietary context as the place where advantage builds.
Data lakes and lakehouses were built for human users, not agents that run around the clock and scale to thousands, says Teradata's Ben Sanders. He explains why pilots break in production, how missing context drives up cost and latency, and the six components of enterprise context.
AI agents will overreach, and traditional security controls weren't built for probabilistic software, says Rubrik's Cal Al-Dhubaib. He offers real examples of agent overreach, a three-part model of context-based control and three questions every team should ask before AI fails.
Anyone can build an AI agent now, says IBM's Sabtain Khan, but few enterprises get agents deployed and used. He argues agent sprawl is an operating model problem and that regular testing, feedback loops and a control plane help agents reach production.
Banks are using AI to rerun fixed chatbot workflows, not to act autonomously, according to Midships CEO Ajit Gupta. Checking each agent's purpose, objective, plan and API call, with signed human approvals, can keep autonomous agents on track, he says.