What Changed: A Six-Week Launch
Stampli, an intelligent procure-to-pay platform, recently launched Deep Finance™, a product that turns procurement and accounts payable data into executive spend intelligence for CFOs and business leaders. The launch was a test of speed: from prototype demo to public go-to-market (GTM) launch and first shipped product, the team completed the entire process in about six weeks. That’s a dramatic shift from the months or even quarters such a launch typically takes.
The key was using OpenAI’s ChatGPT Work and Codex to parallelize product development, positioning, design, communications, enablement, and operations. With design resources and outside contractors already committed elsewhere, the marketing team turned to Codex to transform evolving product decisions into review-ready assets. These included a seven-part blog series, launch emails, a webinar and its supporting deck, social and paid creative, a PR Newswire release, the Deep Finance web page, and sales enablement materials. Codex even helped create the launch’s hero animation, handling roughly 90% of the polished animation work before a contractor finished the opening scene and final format.
The results were measurable. Stampli estimates the launch would have taken about 243 modeled active role-hours without Codex. With Codex, it took approximately 77 hours—saving roughly 166 hours, or 3.16x faster production. That’s a 68% reduction in production time, all while keeping full human review and final approval on everything customer-facing.
How It Works: A Daily System, Not a One-Off
The infrastructure behind the Deep Finance launch isn’t a special project setup—it’s the same system Stampli’s product marketing team uses every day. Previously, keeping product materials current required interviewing product managers, reading Jira tickets, reviewing GitHub, and working through meeting notes. The team then had to translate that information into help center articles, presentations, one-pagers, and other assets.
Stampli automated much of that process with a GPT-powered system. It gathers information from product systems and meeting notes, then helps keep those materials up to date. This means the team can continuously refresh product knowledge without manual digging.
The same automations enable content creation at scale. Agents connected to the company’s source of truth can produce material for the website and social channels. According to Melad Zahedi, Director of Product Marketing, “it’s multiplied the output of a small team by 10x, putting out hundreds of pieces of content on a weekly basis, where it was limited to just a couple before.”
This system is built for daily use, not just launches. It’s a shift from ad-hoc AI assistance to a structured workflow where AI agents are embedded in the team’s operations.
Practical Use Cases: From Meetings to Metrics
ChatGPT Work also helps employees bring relevant business context into important decisions. Zahedi uses GPT-powered automations as a “second brain” to organize information across a schedule filled with back-to-back meetings. “Being able to go to every meeting prepared with context, understanding what is needed from me in that meeting and how to stay efficient with my time, has been an amazing benefit,” he says.
A concrete example: In one executive meeting, a question arose about metrics stored across HubSpot and other systems. An employee was able to quickly ask Codex to retrieve and analyze the relevant data during the call. Zahedi notes, “This is something that would’ve taken our FP&A team half a day to put a report together, give us a model, and give us an answer that we felt confident in. Someone was able to do it with 20 seconds of keystrokes.”
This isn’t just about saving time on reports—it’s about making insights available at the moment they’re needed, without waiting for a separate analysis cycle.
Limitations and Trade-offs
While the results are impressive, the case study also highlights important caveats. First, the time savings are estimates based on modeled active role-hours, not a controlled experiment. The 243-hour baseline is an approximation of what the work would have taken without Codex, not a measured comparison.
Second, human review and approval remained essential. All customer-facing content went through full human review, meaning the AI didn’t replace judgment—it accelerated the drafting and iteration process. The hero animation required a contractor to finish the opening scene and final format, showing that AI still has limits in highly polished creative work.
Third, the system depends on a well-maintained “source of truth”—the agents connected to company systems need accurate, up-to-date data to produce useful content. If the underlying systems are messy, the AI output will reflect that.
Finally, the case study is from a single company, so the results may not generalize. The success depended on Stampli’s specific workflow, team skills, and willingness to adopt AI tools.
The Takeaway: Rethink Workflows, Not Just Tasks
For product builders and AI tool learners, the Stampli case offers a clear lesson: AI tools aren’t just for speeding up individual tasks—they can reshape entire workflows. By connecting product context, meeting notes, decisions, and messaging guidelines into a shared system, Stampli turned a complex, cross-functional launch into a repeatable, scalable process.
The key isn’t the tools themselves but the willingness to rethink which steps can be automated and which decisions still need human judgment. Stampli’s team used ChatGPT Work and Codex to keep product knowledge current, surface business insights, and move ideas to market faster. The result: a six-week launch that would have taken months or quarters.
As Zahedi advises, “Being curious and just asking, ‘What can I do?’ and trying everything first through ChatGPT will unlock a lot of latent capacity that you didn’t realize was there in your organization—and in yourself.” For teams looking to adopt AI, starting with a curious mindset and experimenting with tools on real problems is the first step toward unlocking that latent capacity.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.