AI Center of Excellence
You have access to AI. Now make it work across your organization.
Healthcare organizations are investing in enterprise AI, but access to models is only the starting point. We help you identify the right use cases, establish governance, build the operating model, and move AI into production, with measurable business outcomes and controls appropriate for healthcare.
Access to a model is not the same as the ability to use it.
An enterprise agreement with an AI provider gives your organization capable models. It does not tell you which use cases are worth pursuing, what they are worth, how to keep them safe near protected health information, who owns the decisions, or how to move one from a pilot to something running in production. Healthcare AI pilots stall for many reasons: weak or unclear return, data that is not ready, integration complexity, no clear owner, or low adoption once they ship.
The questions a healthcare organization has to answer are specific: which use cases are worth the effort and what each is worth, what a model is allowed to do near PHI and where a person has to stay in the loop, how a clinical or operational team evaluates an AI output before acting on it, and how any of it is audited. Answering those questions well is the difference between AI that produces measurable results and AI that stays stuck in pilots.
From use-case assessment to running in production.
We help you stand up the capability end to end, and we can take priority use cases all the way into production rather than handing you a framework and leaving.
Readiness and use-case assessment
Inventory the candidate use cases across your operation and score each one for value, feasibility, data readiness, and risk.
You receive: an AI readiness assessment, a use-case inventory, a prioritized opportunity list, and preliminary business cases.
AI governance framework
Define the policies, risk tiers, and human-in-the-loop rules for AI in your environment, built around HIPAA, PHI handling, and clinical safety, and covering vendor and model selection, security review, data retention, and ongoing model evaluation.
You receive: AI policies, risk tiers, evaluation standards, approval requirements, and monitoring and audit controls.
The operating model
Stand up the Center of Excellence itself: the roles and decision rights, the intake and review process, and the way use cases move from idea to approved to live. This is what keeps AI managed as it scales rather than sprawling across teams.
You receive: a CoE charter, defined roles and decision rights, a use-case intake process, and deployment standards.
Enablement and delivery
Get your teams productive with the right guardrails, and take the priority use cases into production with the logging, validation, and human approval each one needs. We build and run, not just advise.
You receive: workforce training, an implementation roadmap, a first production use case, and adoption and value tracking.
Governance that holds near a patient record.
Generic AI governance is not enough when a model sits near PHI and clinical decisions. The framework we build is specific to how healthcare is regulated and how care is delivered.
What a model may read, what it may not, and how protected health information is handled, with safeguards set to the risk of each use case.
For consequential clinical or financial decisions, a person stays in the loop. AI informs the decision rather than making it, and the boundary is set per use case.
We define the logging, validation, escalation, and approval each use case needs, so AI-assisted work can be reviewed and stood behind.
The same principle runs through all of it: we define how AI outputs are evaluated, what can be checked automatically, and where human review is required. The controls depend on the use case and its risk. It is the approach behind our deployed work, applied to your AI program.
A healthcare operations partner, not a generalist AI advisory.
We know healthcare operations
We work inside patient access, provider operations, scheduling, revenue cycle, and the payer operation every day. We assess use cases against how the work actually runs, not a generic framework.
We have deployed governed AI
For one radiology practice, reporting questions moved from 3 to 5 days to under five minutes, with early validation accuracy above 98%. See the case study →
We build and run, not just advise
We do not stop at recommendations. We can establish the governance, take priority use cases into production, and remain accountable for how they operate.
We work on the systems you have
Your AI program sits on top of the systems you already run, with integration into your data and your EHR, not a new stack to adopt.
Where should your organization put AI to work first?
Whether you are evaluating enterprise AI, establishing governance, or trying to move pilots into production, we will help you identify the highest-value opportunities and build a practical path forward.