You're evaluating how to move beyond AI copilots into a governed, production-grade AI operating model — and what the technical architecture looks like at enterprise scale.
Start here — the enterprise methodology for orchestrating AI agents across your full development lifecycle, with measured velocity gains.
Written for you — the architecture, governance, and technical decision framework for building and scaling an enterprise AI operating model.
How the recursive framework compounds advantage — AI systems building AI systems with governed handoffs.
The production results — how specialized agents automate complex claims and predictive models shift revenue cycle from reactive to proactive.
The client outcomes — measurable results that validate the platform and the methodology behind it.
You care about the financial impact — how AI translates into recovered revenue, reduced denials, predictive intelligence, and a stronger bottom line on the hardest claims in healthcare.
Start here — the measured financial outcomes for our clients. This is the paper that answers 'what does this produce?'
The production capabilities — how AI automates complex claims workflows and how predictive models identify revenue at risk before it's lost.
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.
You're evaluating AI as a value creation lever — compound returns, defensible moats, and measurable production outcomes that distinguish real capability from marketing claims.
Start here — the measured client outcomes and the value creation story. This paper answers 'what does this investment actually produce?'
The enterprise AI operating model that creates compound returns accelerating every quarter — and why first-mover advantage matters.
Production results and the competitive moat — domain-specific AI that encodes decades of institutional knowledge no competitor can replicate.
The compounding mechanism — how each agent built makes the next one faster and better, creating an advantage that widens over time.
For your technical diligence — the architecture, governance model, and compliance framework under the platform.
You manage the day-to-day complexity of claims processing and want to understand how AI handles the workflows your team knows are hardest — and the results it's producing.
Start here — how specialized AI agents automate document intake, classification, letter generation, appeals, and routing for complex claim types including VA, workers' comp, MVA, and out-of-state Medicaid.
The measured client outcomes — how this translates into recovered revenue, reduced denials, and operational improvements for the organizations we serve.
How SOPs and specialist workflows built over 25 years were encoded into AI agents that reason about complex claims like your best people.
The development methodology — understand how these systems are built, 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