Several NVIDIA teams are deploying ChatGPT Work to reduce manual effort, surface fast‑moving external signals, and scale workflows that have proven effective across regions and functions.
Key metrics reported
- During the 12‑week GTC planning cycle, a ChatGPT Work workflow saved about 16 hours per week.
- Some projects moved from an estimated 2–3 week manual build to a 3–5 day working prototype.
- Each week ChatGPT Work distilled roughly 25–40 external AI updates into 5–8 actionable signals.
Who is using it and how
Will Daney, who supports NVIDIA’s global sales, business development, and product leaders in execution and measurement, previously spent significant time preparing for GTC (NVIDIA’s global AI conference) using spreadsheets—assembling account lists, tracking registrations, and identifying actions needed to create a productive experience for customers and partners. He estimates manual analysis consumed about 40% of his time during event preparation. Will automated much of that work into a ChatGPT Work process that runs twice weekly, saving roughly 16 hours per week across the 12‑week planning cycle.
Will says the saved time lets him work more directly with field teams, get to know them better, and help them support customers more effectively. Because he owns the workflow, he can adapt it as the event evolves without waiting on procurement or new tool implementation. He has shared the underlying workflows with teams in other regions; event support teams in San Jose, Taipei, Europe, and Washington, DC have received and customized his ChatGPT workflows for local needs.
Rachita Jain, who works on the AI operations team within NVIDIA’s marketing organization, builds AI workflows and helps teams adopt new tools. Her daily challenge is the pace of the AI industry, where new models, benchmarks, and research appear regularly. The raw information is available; the harder problem is deciding which developments matter to NVIDIA and connecting them to internal projects and priorities.
Rachita created a ChatGPT Work workflow that reviews trusted external sources alongside internal context, identifies meaningful overlaps, and surfaces insights that can lead to action. Each week it condenses roughly 25–40 external AI updates into 5–8 actionable signals. As Rachita puts it, ChatGPT transformed passive reading into active intelligence.
From idea to prototype in one environment
The same ChatGPT Work environment also supports development: Rachita can start with an idea, explore approaches, inspect a codebase, debug, and refine a result without constantly switching between disconnected tools. Initiatives that might previously have remained side projects can become working products in days; in one example she moved from idea to working prototype in about 3–5 days versus an estimated 2–3 weeks if components had been built manually across separate tools.
Scaling proven processes
The next step is to scale solutions that already work. By converting specialized knowledge into reusable workflows, NVIDIA teams can adapt proven processes across functions, events, and regions while keeping people closest to the work in control of how those processes evolve. As the AI landscape changes, shared workflows can help the company connect external developments with internal priorities faster and extend AI‑enabled ways of working to more employees.
Objective: more time for higher‑value work
The stated aim is to give teams more time to interpret findings, collaborate, and focus on customer‑facing tasks. Will describes the tool as a personal force multiplier: “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.”
This internal use case does not promise fully automated decision‑making, but shows how tools like ChatGPT Work can handle repeatable, information‑heavy tasks so people can spend more time on judgment, collaboration, and actions that require human oversight.
