How NVIDIA scales expertise with ChatGPT Work

At NVIDIA, ChatGPT Work is helping knowledge workers spend less time assembling information and more time acting on it.

For teams like GTM and solutions architecture, ChatGPT has become part of how work gets organized, automated, and scaled. For GTM, it transforms recurring operational processes, while solutions architects are using it to connect fast-moving external developments with NVIDIA’s internal priorities.

Will Daney helps NVIDIA’s global sales, business development, and product leaders execute and measure their strategies. One of his recurring responsibilities is supporting the field organization around GTC, NVIDIA’s global AI conference.

Previously, preparing for GTC required extensive work in spreadsheets: assembling account lists, tracking registrations, and helping teams identify the actions needed to create a productive experience for customers and partners. During the lead-up to the event, Will estimates that manual analysis consumed about 40% of his time. Today, he has turned much of that work into an automated ChatGPT Work process that runs twice a week. Across the 12-week GTC planning cycle, the workflow saves about 16 hours per week.

“I’m able to give time back, work with the actual field team, get to know them better, and help them figure out how to help our customers be more successful,” Will says.

And because he owns the workflow, he can adapt it as the event changes without waiting for a new tool to be purchased, implemented, and maintained. He can also share the underlying process with teams in other regions. Colleagues supporting events in San Jose, Taipei, Europe, and Washington, DC have received his ChatGPT workflows and customized them for their local needs.

Rachita Jain works on the AI operations team within NVIDIA’s marketing organization, where she builds AI workflows and helps teams adopt new tools. Her challenge is keeping pace with an industry where new models, benchmarks, and research appear every day.

The information is readily available. The harder task is determining which developments matter to NVIDIA and connecting them with internal projects, conversations, and priorities. Rachita built a workflow with ChatGPT Work that reviews trusted external sources alongside internal context, identifies meaningful areas of overlap, and surfaces insights that can inform action. Each week, it distills roughly 25–40 external AI updates into 5–8 actionable signals.

“ChatGPT helped me change passive reading into active intelligence,” she says.

The same environment supports the broader building process. Rachita can begin with an idea, explore possible approaches, work through a codebase, debug problems, and refine the result without continually moving between disconnected tools. Initiatives that might once have remained side projects can develop into working products within days. In one case, she moved from idea to working prototype in about 3–5 days, compared with an estimated 2–3 weeks if she had built the components manually across separate tools.

The next opportunity is to scale what’s already working. By turning specialized knowledge into reusable workflows, teams across NVIDIA can adapt proven processes across functions, events, and regions—while keeping the people closest to the work in control of how those processes evolve.

And as the AI landscape continues to change, these shared workflows can help NVIDIA connect external developments with internal priorities more quickly and extend AI-enabled ways of working to more employees. The goal is to give teams more time to interpret findings, collaborate, and focus on work that supports customers.

That potential is already visible in Will’s experience. “ChatGPT has really been a force multiplier for me personally,” he says. “It feels like I have a team working for me. It’s helped me get out of the weeds and focus more on the work that matters.”