Overview
ODSC AI NYC 2026 is a two-day, in-person AI for Work Summit held in Midtown Manhattan on December 2 and 3, 2026. It is built around the idea that AI tools are widely available, but the way work gets done often has not changed to match them. The summit focuses on turning AI into useful, repeatable workflows inside real teams and organizations, covering agents, retrieval, evaluation, governance, and enterprise deployment. Sessions are organized by job function and responsibility rather than by vendor or model.
Dates and Venue
The summit runs December 2 to 3, 2026, with doors opening at 8:00 AM each day. It takes place at Jay Conference Center Bryant Park, 109 West 39th Street, New York City. The venue sits in Midtown Manhattan near Bryant Park, Times Square, Penn Station, and Grand Central, with major subway lines and hotels nearby.
Who Should Attend
The summit is aimed at AI engineers, data scientists, ML engineers, and architects; sales, marketing, finance, operations, and HR professionals; executives, team leaders, founders, and people leading AI adoption; and newer AI builders who want hands-on technical foundations.
Tracks
- Build: Agents, RAG, evals, observability, and production AI engineering. Advanced sessions cover multi-agent orchestration, context engineering, eval suites in CI, and cost and latency routing.
- Business Workshops: No-code working sessions for sales, marketing, finance, operations, and HR, focused on documents, reporting, and recurring tasks.
- Lead: Governance, ROI measurement, rollout planning, and operating models, including a panel on proving ROI when no baseline was recorded.
- AI Engineering Fundamentals: A five-session beginner sequence running from a first API call to a running agent, covering LLM and agent basics, context engineering and RAG, simple agents, evals and observability, and production basics.
Agenda
Day 1 opens with a shared keynote, then moves into track workshops, followed by an evening networking reception. A VIP dinner with speakers and instructors is available to Leadership Plus pass holders. Day 2 includes function labs for Sales and Marketing, Finance and Operations, and HR and People, advanced Build sessions, governance and measurement working sessions, and a afternoon session on quality control and drafting a team AI operating agreement. The day closes with a keynote and summit wrap-up. Highlighted sessions include a keynote by Dr. Jon Krohn on the growing task horizons of AI agents, a four-hour hands-on LLM evaluation harness workshop led by Bruno Gonçalves, an observability workshop led by Michael Levan, and a four-part full-day AI for Work training led by Sheamus McGovern. Session times will be published once the schedule is final.
Speakers
Announced instructors include Jon Krohn, Paige Bailey, Sid Dhulipalla, Michael Schrage, Chris Hadges, Michael Levan, Christine Long, Thomas J. Fan, Bruno Gonçalves, Pablo Vega-Behar, Tom Nassr, Gerry Wolfe, Mark Kashef, Sheamus McGovern, Greg Rosner, Mike Taylor, and Ben Morss. Additional speakers and session titles will be added as they are confirmed.
Format
The summit is hands-on and workshop-driven, combining labs, working sessions, panels, and open discussion rather than one-way keynotes. Some business workshops ask attendees to bring a real task, its files, or a laptop with an AI tool installed.
Passes
Training passes are offered at an early rate of $449, and Leadership Plus passes at $649, until midnight ET on Friday, October 30. Full pricing of $800 and $1,200 applies afterward. A free Open Pass includes keynotes and the open talk track but excludes workshops, function labs, and Leadership networking. Team passes are available for groups of three or more.
FAQ
Which track should I choose? Choose Build for experienced technical practitioners, Business Workshops for non-coding business functions, Lead for those responsible for AI adoption and governance, and AI Engineering Fundamentals for regular AI users who want to understand how systems are built.
Is this a technical or business event? Both. It connects technical implementation with business adoption and measurable value.
