May 24-25, 2027, the San Jose McEnery Convention Center will host the AI & Big Data Expo North America. According to the official event site, the show centers on moving artificial intelligence from pilot projects to production across large enterprises, with expanded tracks on strategy, governance, and developer execution (AI & Big Data Expo).
What the AI Big Data Expo is signaling for 2027 buyers
The program reads like a referendum on AI theater. Track titles such as “Enterprise AI Implementation, ROI & Adoption” and “Future of AI – Reliable, Transparent & Innovative” say plainly where buyers are in 2027: done with demos, focused on outcomes. That emphasis—spelled out on the event site—suggests the show will push practical templates for deployment and the controls needed to keep systems in bounds (event site).
That matters because most large organizations now face the same bottleneck: proving value at scale while meeting internal policy and emerging external rules. A visible tilt toward explainability and oversight lines up with the direction of frameworks like NIST’s AI Risk Management Framework, which many compliance teams already reference in playbooks (NIST AI RMF).
Inside the agenda: governance, ROI, and “Physical AI”
The 2026 agenda preview—used as a guide for the upcoming edition—lays out a two-day split. Day one packages AI strategy and autonomous intelligence with data platforms and pipelines. Day two turns to enterprise rollouts, ROI, and trust. Two free tracks stand out for 2027 planning: an AI Developer Day focused on the path “from prototype to production,” and a “Physical AI” stream covering systems that blend models with hardware in the real world (agenda preview).
Expect the AI Developer Day to be busy. Engineering leaders keep saying the same thing: their blockers are less about model choice and more about shipping reliable services—monitoring drift, managing data contracts, and building safe interfaces. A hands-on track can close that gap, especially paired with meetups and a learning hub the organizers highlight on the site.
The “Physical AI” focus is timely. Companies are piloting AI on factory lines, in logistics, and in field service. Bringing those use cases onto the floor hints the expo will lean into integration topics—sensors, edge compute, and safety baselines that span IT and OT.
Why San Jose and the venue choice matter
Hosting the show at the San Jose McEnery Convention Center places the program inside Silicon Valley’s orbit. That increases the odds of deeper participation from platform teams and infrastructure partners who drive day-two decisions—networking, data platforms, and GPUs—rather than only front-end demos.
The organizers also pair the event with co-located shows across cloud security, IoT, edge, intelligent automation, and data centers. That cross-traffic is by design: AI rollouts live or die on data access, controls, and runtime capacity. A pass that lets buyers compare options across those stacks can shorten decisions. For context on the broader pairing, the IoT Tech series shows how adjacent domains feed enterprise AI programs (IoT Tech Expo).
Who’s on stage and what that implies
Headliners on the site include Lutz Beck, CIO of Daimler Truck North America, and Chad Smykay, AI CTO and Distinguished Technologist at HPE. Those roles point to a program shaped less by model labs and more by operators who own uptime, budget, and compliance. When CIOs and platform architects lead, sessions tend to dig into change management, integration cost, and how to make pilots clear audit lines.
That approach fits the tone across the AI Big Data Expo materials: move from experimentation to operating models. Expect panels that trade novelty for runbooks—procurement filters for model services, vendor contract terms around IP and indemnity, and migration paths from shadow projects into managed environments.
Tickets, timing, and how to get value from the floor
The event pages show early deals expiring and encourage attendees to book time in the Learning Hub and at meetups. If your goal is to move one production workload forward in 2027, plan around those. Use governance sessions to lock requirements, then take them directly to vendors on the floor. The co-located security and edge shows can help you compare policy tooling, network designs, and deployment patterns in one pass.
For builders, the hackathon and developer day are the fastest routes to pressure-test an idea against real constraints—data quality, SDK fit, and deployment hooks. Treat the floor like a test plan: collect latency data, integration steps, and cost curves, not just demos.
The AI Big Data Expo leans into what enterprise teams need next: repeatable implementation, safer operations, and proof that models pay for themselves. In San Jose on May 24-25, 2027, that mix—business tracks, developer work, and cross-stack partners—should give buyers and builders the same room to make decisions they can stand behind (full event details).
Related reading: Meta AI • NVIDIA • AI & Big Tech