ChatGPT in the Enterprise: What Factory Automation Taught Me About Scaling AI Tools
CyberAgent's rollout of ChatGPT Enterprise reminds me of watching PLCs spread through manufacturing in the 90s, for better and worse.
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You know what ChatGPT Enterprise reminds me of? The first time I saw a Siemens S7 PLC get networked across an entire production line back in '96. Everyone thought it would change everything overnight. It did change things, just not overnight, and not always how we expected.
I've been reading about CyberAgent's deployment of ChatGPT Enterprise, and look, here's the thing: this is the same pattern I watched play out with every major automation technology for three decades. Japanese company, known for being methodical, rolls out a new tool across advertising, media, and gaming divisions. They're using it for quality improvements and faster decision-making. Sounds familiar.
The Scaling Problem Nobody Talks About
When I was at Kuka, we had this running joke about "pilot project purgatory." A customer would trial one of our robots, get fantastic results, then spend two years trying to figure out how to roll it out to the rest of their operation. The robot wasn't the problem. The problem was everything around it: training, integration, maintenance schedules, the guy in Plant 3 who refused to learn the new interface.
CyberAgent's approach, using enterprise-grade security and Codex for their developers, suggests they've thought about this. But the OpenAI blog post is light on specifics. We don't know how many employees are actually using it daily, what the failure modes look like, or whether the productivity gains hold up after the novelty wears off. I called my old colleague at Fanuc last week (he's consulting now), and he made a good point: the companies that succeed with these tools aren't the ones who adopt fastest. They're the ones who figure out where the tool actually fits versus where people just think it should fit.
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