In an October 22, 2026 post, OpenAI's Head of Forward Deployed Engineering, Colin Jarvis, described the Forward Deployed Engineering (FDE) team he says he founded in early 2025 to close what he calls the 'deployment gap'—the distance between what models appear capable of and what people can reliably use to do their work. According to the post, FDEs embed with customers to work on their hardest problems, build repeatable solutions where models can do the job, and define gaps for OpenAI's Research teams where they fall short. The post cites examples. John Deere deployed OpenAI's APIs to provide farmers personalized setup guidance and recommendations for technology including See & Spray, which the company says can reduce chemical use by up to 70%. In mid-2025, OpenAI says roughly 15 FDEs worked with a large semiconductor customer on more than 10 use cases to make its verification process—which it states can account for about 70% of a project's effort—'AI-native.' The post reports results including approximately 48% time saved through AI-based directed test generation and a 22% median performance improvement through kernel optimization. The figures and account come from OpenAI and are not independently verified.
- OpenAI says it founded its FDE team in early 2025 to close the 'deployment gap'
- John Deere used OpenAI APIs to scale personalized farmer guidance
- OpenAI reports ~48% time saved and 22% median performance gains with a semiconductor customer
What it means for you
This is OpenAI explaining its own consulting model and showing off customer results, so read it as vendor positioning rather than news that changes your plans. The useful lesson buried in it is the one you can apply without OpenAI: their stated rule is 'start with the outcome, do what it takes to succeed, then follow with scale'—and they admit a slick demo often hides whether something actually works in production. That is the same gap most organizations hit when they bolt AI onto a process nobody has mapped. The reported numbers come from OpenAI and haven't been checked by anyone independent.
Try this
Pick one process where you're tempted to add AI, and before touching any tool, write down the specific outcome you want and how you'd measure 'good enough in real life.' If you can't define that, you're not ready to automate it yet.
Who should care
Owners and operations leads weighing a custom AI build, and anyone who's been pitched a tool on the strength of a demo.
Skip this if
You're not actively considering a custom AI integration or vendor engagement—this is a case study for buyers, not a development you need to act on.
Sources: OpenAI — read the original