@mardehaym: Talked AI architecture with the head of product and engineering at a PE-backed SaaS company doing ~$15M in annual reven…

Talked AI architecture with the head of product and engineering at a PE-backed SaaS company doing ~$15M in annual revenue. What I heard could be ripped from any mid-market SaaS trying to bolt AI onto a mature product. One person owns both the roadmap and architecture. He can't hit pause to figure out AI. Nobody owns AI. No strategy doc. No infrastructure. Just solo experiments. They tried Claude. His words: "It did a bunch of silly crap." Whole org still glued together with manual Excel. Nobody's connecting the dots between those workflows and what AI could automate. That's the brownfield problem. The market's obsessed with building new products from scratch with AI. But can you get faster at doing difficult, nuanced things on a mature codebase that predates LLMs by a decade? Adding AI here means re-architecting, not just bolting on. He's got 5 developers, a QA engineer who's already outpaced, and one PM. The domain is so specialized that replacing a single senior dev takes 6+ months. You can't lose people when the replacement cycle is longer than most PE reporting windows. The engineers don't believe AI can handle their product, and I respect that more than blind enthusiasm. The engineering lead said it directly: anyone who can't be evidence-based about AI's role won't be involved in the rollout. PE partner running AI transformation across the portfolio was on every call. His involvement cut the vetting cycle by roughly 60%. The CEO joined the third call in approval mode, not evaluation mode. PE doesn't fund 90-day pilots. They fund 30-day sprints. What closed it wasn't a slide deck. We showed real code reviews and a live automation demo, and his questions shifted from "can you do this?" to "when do we start?" within that session. The job they hired us for: accelerate feature development on a mature, complex product without losing institutional knowledge. He needed his skeptical engineers to see AI work on hard problems before competitors in his vertical got there first. If you're running a mid-market SaaS product that predates LLMs, this is probably your reality too.