Most AI consultants have never carried responsibility for operational outcomes. Most construction operators have never built AI systems. I've spent time on both sides — as a licensed construction supervisor in Massachusetts and as an enterprise product leader who has worked at the intersection of AI and operations.
I spent twelve years leading enterprise software and product initiatives at companies including Citizens Bank, Fidelity Investments, Optimizely, and Jabra. Over the last four to five years that work shifted increasingly toward AI — contact center intelligence, personalization engines, predictive models. I know how AI projects get scoped, how they get sold internally, and how often they fail to deliver what was promised.
My background in enterprise product management also taught me how to translate complex operational problems into systems people actually use. That turned out to be exactly the skill the next chapter required.
A few years ago I stepped out of software and into the field.
I joined a high-end residential general contractor in Boston and spent the next several years working as a Site Supervisor and Construction Project Manager, helping deliver more than $30 million of luxury residential construction projects. I hold a construction supervisor license in Massachusetts.
I wasn't brought in as an AI consultant. I was there to run projects, coordinate trades, solve field problems, process change orders, build estimates, manage client relationships, and carry responsibility for project outcomes.
What I found inside the business surprised me.
The company had software, processes, and documents. What it didn't have was a structured way to capture and transfer the operational judgment that experienced people used every day. Pricing logic lived in the owner's head. Every document started from scratch. New project managers needed months before they could work independently — not because they weren't capable, but because there was no way to transfer what the experienced people knew.
The owner was in every conversation because no one else had the context to answer client questions.
I'd been around enough AI projects to recognize what was actually happening. The problem wasn't that they needed better AI. The problem was that the business wasn't legible — not to AI, not to new hires, not to the owner when he wanted to step back.
You can't automate what hasn't been documented. You can't delegate what hasn't been described. The gap wasn't technological. It was structural.
That realization led to Construction OS — an AI operating system that combines documented SOPs, pricing intelligence built from real project history, and document automation into a single operational foundation. Contract packages that took four to eight hours now take thirty minutes. Preliminary estimates that took half a day now take an hour. New project managers ramp significantly faster because the institutional knowledge is in the system, not locked inside experienced employees.
The estimating, change ordering, documentation, and communication challenges that Construction OS addresses aren't theoretical. They're the same problems I dealt with personally on active projects.
I now work with service businesses — primarily in construction and related trades — to document how they actually operate, structure that knowledge into the right containers, and build AI systems on top of something solid.
The result isn't just automation. It's a business that becomes easier to scale, easier to train, and less dependent on any single individual. AI that actually works because the operational knowledge is there for it to work from.
I lead every engagement directly and bring in trusted specialists when implementation requires additional engineering, design, or integration expertise.
If what you're reading sounds like a problem you're living, a 30-minute conversation is the right first step.