Start here — how we engineered an enterprise AI development model that replaced ad-hoc tool adoption with an orchestrated pipeline delivering measured velocity gains.
Written for you — the architecture and engineering behind an enterprise AI operating model, the foundations that had to exist before agents could operate, and what we got wrong along the way.
How we turned 25 years of specialist knowledge into AI systems — the compound advantage of encoding domain expertise into agents, and why the evaluation history behind it is what a competitor can't shortcut.
Complex revenue intelligence for the hardest claims - from automated workflows to predicting outcomes before they happen, validated by measurable client results and covering the compliance architecture underneath it.
Start here — how AI-driven complex revenue intelligence predicts revenue at risk before it's lost, and the measured financial outcomes it delivers for our clients.
How 25+ years of specialist expertise was encoded into AI agents — the institutional knowledge that makes the financial results possible.
The methodology behind the platform — the engineering rigor that makes the results sustainable and the advantage compounding.
Start here — the measured client outcomes and the value creation story, and the complex revenue intelligence platform generating them. This paper answers "what does this investment actually produce, and why can't it be copied?"
The enterprise AI operating model that creates compound returns accelerating every quarter — and why first-mover advantage matters.
The compounding mechanism — how 25 years of specialist knowledge became an evaluation-driven moat that competitors can't shortcut.
For your technical diligence - the architecture and engineering rigor behind the platform.
Start here — complex revenue intelligence for the hardest claims (VA, workers' comp, MVA, out-of-state Medicaid), and the measured results it's producing: recovered revenue, reduced denials, and operational improvements.
How SOPs and specialist workflows built over 25 years were turned into AI systems that reason about complex claims like your best people.
The development methodology — understand how these systems are engineered, tested, and governed with human oversight at every step.
Brian Kenah is CTO at EnableComp and the driving force behind the enterprise AI operating model documented in this series. He has spent 25+ years building and scaling healthcare technology organizations, including CTO at Azalea Health, senior leadership at Sharecare, and 13 years leading product and engineering at NextGen Healthcare.
David Urbina is Head of AI at EnableComp, where he leads the architecture and development of the agentic AI platform behind the company's complex claims automation. Previously, he helped enterprise clients adopt cloud-native AI at AWS. He specializes in building production-ready AI systems that solve real-world problems at scale.
Both authors are available for conference keynotes, breakout sessions, panels, and media interviews on enterprise AI transformation in healthcare.
For inquiries, contact marketing@enablecomp.com