Build, Apply, Lead, or Fundamentals? How to Choose Your ODSC AI NYC Track

Which ODSC AI NYC track should you choose? ODSC AI is bringing its workshop-driven AI for Work Summit to Jay Conference Center Bryant Park on December 2 and 3, organized around four tracks: Build, Apply, Lead, and AI Engineering Fundamentals.

Each approaches AI from a different role, from engineering production systems to redesigning everyday workflows and leading organization-wide adoption. The tracks are sorted by what you’re responsible for at work, not by seniority or how much AI experience you already have—which is exactly where most people get stuck picking one.

Here’s how to tell which track matches your day-to-day role.

1. Build: for people shipping the system

If you are already building agents, copilots, RAG applications, evaluation systems, or other production AI workflows, start with Build.

Made for AI engineers, ML engineers, architects, and other technical practitioners already building agents, copilots, RAG systems, or evals, the sessions center on making agent workflows reliable enough for production. You’ll build eval suites a team will actually run, instrument agents for observability, and improve retrieval over messy, permission-sensitive enterprise data.

You leave with patterns for observability, evaluation, routing, retrieval, and system design you can bring back to your own stack. If your day-to-day already involves choosing models by cost, latency, and risk, this is the track.

2. Apply: AI in Your Workflow

Apply is built for business practitioners in sales, marketing, finance, operations, and HR who want to put AI to work in their own function without writing code. Sessions cover delegating real tasks to AI without redoing the work afterward, turning documents and source material into finished deliverables, and turning one-off prompts into repeatable workflows.

You leave with a workflow map for your function, a delegation checklist, and a clearer sense of what to automate versus keep human. This is the track for someone already using AI tools who wants a repeatable system, not ad hoc prompting they redo every time.

3. Lead: for people funding and governing AI

Lead is built for executives, AI leaders, data leaders, founders, and anyone else who owns adoption decisions: what gets funded, governed, and scaled. The sessions work through measuring AI value when no baseline was ever captured, building governance that doesn’t block every team, and deciding which AI efforts to scale, change, or stop.

You leave with a 90-day AI adoption roadmap and a measurement approach for value and ROI. If your job is deciding what gets funded and who’s accountable for it, Lead is the track.

4. AI Engineering Fundamentals: for people who use AI but haven’t built with it

AI Engineering Fundamentals is the on-ramp for people who use AI daily and want structured, hands-on exposure to how it’s built: LLMs, APIs, agents, RAG, evals, observability, cost, routing, and guardrails. The sequence moves from simple prompting to structured applications, through context engineering and a first agent, and into production constraints like cost and guardrails.

You leave with direct, hands-on experience with the building blocks behind modern AI systems, beyond the vocabulary you already have. This is the track for people who have used AI tools but haven’t built a system from scratch.

If your role spans more than one track

Plenty of people don’t fit neatly into one bucket: a technical manager who still writes code, or a business analyst evaluating vendor tools. In that case, choose based on the outcome you want to leave with. A working system points to Build. A workflow you’ll run yourself points to Apply.

A roadmap for leadership points to Lead. Hands-on exposure to the fundamentals points to Fundamentals. Attending with a team makes this easier, since each person can train in the track that fits their role, then regroup around the same question the summit closes on: what should your team build, test, change, or stop doing next?