Make AI governance real.
Most health systems have formed an AI committee. The work that makes governance real, a funded mandate, an intake gate with authority, a living inventory, and continuous monitoring, is still ahead. We help you build it.
Where forming a committee meets operating a program.
0%
of healthcare organizations have an AI governance committee
0%
have a mature AI governance structure and a fully formed AI strategy
0%
of healthcare professionals admit to using unauthorized AI tools at work
The gap between forming a committee and operating a governance program is where risk lives: shadow AI, ungoverned models, diffuse accountability, and policies that exist on paper only. Closing that gap is organizational work, and it is exactly the work we do.
What good AI governance looks like.
Six traits separate a governance program that works from a committee that stalls. None of them are about the technology.
One accountable executive
A named owner with real decision rights and an escalation path, positioned one level below the CEO. The title matters less than the authority. Committees with no single owner stall. Only 28% of organizations say their CEO oversees AI governance.
A funded mandate
Credible benchmarks put sustaining governance at 10–15% of total AI spend (npj Digital Medicine). Governance that competes for leftover budget loses to the next tool purchase.
An intake gate with teeth
A mandatory front door for every AI use case, with the authority to say no. If any AI can go live without passing intake, governance is still on paper.
A living inventory
A complete, owned register of every AI system: vendor tools, embedded EHR models, and homegrown applications. A disciplined spreadsheet beats an unused platform.
Lifecycle stage-gates
Risk-tiered checkpoints from intake through validation, pilot, production, and retirement, with go/no-go thresholds at each gate.
Monitoring and enforcement
Local validation, drift detection, incident response, and rollback paths, paired with sanctioned alternatives so clinicians have a safe way to say yes.
Tool sophistication does not correlate with governance maturity. The binding constraint is organizational, not technical.
AI governance builds on data governance.
Your data governance program is the substrate. AI governance is the control layer on top of it, governing how intelligence is produced, deployed, and changed. Each discipline you built for data extends to AI; the scope and tempo change.
Health systems with mature data governance have a head start. The committee charter, stewardship habits, and audit discipline transfer directly. What changes is that the governed asset now makes recommendations, drafts notes, and acts.
A structure that works.
Three tiers, clear authority, and a control plane the enterprise owns. Keep it simple enough to read at a glance and strong enough to enforce.
Tier 1
Executive Steering Group
Sets risk appetite, funds the mandate, resolves escalations. Owns the program's authority.
Tier 2
AI Governance Council
The multidisciplinary decision body. Owns the intake gate, risk-tiering decisions, policy, and the AI inventory. Decisions are recorded and enforceable.
Clinical AI review
Validation, workflow fit, human-in-the-loop design.
Privacy & security
Access control, data minimization, vendor security review.
Vendor & procurement
Evidence requirements, contract terms, change notification.
Operations & monitoring
Drift detection, incident response, rollback readiness.
Operating model: Most medium and large systems do best with a hybrid model. The enterprise owns standards, intake, inventory, and monitoring (the control plane), while service lines own workflow fit and local outcomes. Fully centralized models bottleneck; fully federated models sprawl. Service lines plug in at Tier 3 for local validation and adoption.
Who needs a seat at the table.
Who leads it?
The Chief AI Officer role is emerging, and most health systems have assigned the mandate to an existing leader instead: in a national survey of health AI leaders, only 13% held a Chief AI or Chief Analytics Officer title, while half were CMIO-type informatics executives (Poon et al., JAMIA 2025). Either path works. What the program needs is one named executive with decision rights, budget authority, and a direct line to the CEO. We help you decide whether to create the role, expand an existing one, or sequence from one to the other as the program matures.
CIO / IT
Approval workflow, integration standards, logging, inventory, cost governance.
CMIO / Clinical Informatics
Intended use, local validation, adoption thresholds, human-in-the-loop design.
CISO / Privacy
Access control, data minimization, incident handling, vendor security review.
CDO / Analytics
Model registry, evaluation datasets, performance dashboards, lineage.
Legal / Procurement
BAAs, transparency terms, change-notification clauses, evidence rights.
Compliance / Quality
Policy alignment, audit readiness, safety event review.
Clinical & Operational Service Lines
Outcome ownership, override tracking, escalation of safety and adoption issues.
HR / Workforce Education
AI literacy, role-based training, acceptable-use awareness.
This is where Govern meets EducateDiffuse accountability is a common failure mode.
When multiple entities are named as collectively accountable for an AI system, no individual is answerable for any given one. The fix is simple to state: every production AI system gets one named owner, accountable for the system's performance, safety, and compliance, and drawing on technical, security, and clinical expertise as needed.
One system, one name.
What the program must address.
Eight components, from charter to culture. Click any one to expand.
From baseline to a program that runs.
Six engagements across three phases. Most systems start with a baseline and sequence from there. Process before platforms.
Governance Maturity Assessment
A structured review of your committees, policies, inventory, intake, and monitoring against published maturity models (HAIRA, NIST AI RMF). You get a scored baseline, gap analysis, and a sequenced roadmap. Built for community and regional systems as well as large IDNs.
AI Inventory and Risk-Tiering Sprint
We build your first complete AI inventory, including embedded EHR models and shadow AI discovery, and stand up a risk-tiering rubric your council can apply consistently.
Committee Design and Chartering
Structure, membership, decision rights, escalation paths, meeting cadence, and a charter with real authority. We design the council and working groups to fit your operating model and culture.
Intake Gate and Policy Buildout
A mandatory intake process with risk-scaled review, plus the core policy suite: systemwide AI policy, generative AI use, clinical validation and change control, and vendor transparency requirements.
Vendor Assessment Framework
An evidence-forcing evaluation checklist and process aligned to CHAI model cards, Joint Commission guidance, and ONC transparency attributes, embedded into your procurement workflow.
Governance Advisory Retainer
Ongoing support as the program matures: KPI reviews, monitoring program design, incident response tabletop exercises, regulatory tracking, and council facilitation.
Sequencing principle
Process before platforms. We help you avoid shelfware by proving governance adoption first, and buying tooling only when the process is ready for it.
Standards we build on.
We meet you where the field is converging, mapping your program to the frameworks regulators and accreditors already recognize. In 2025 alone, 33 health-AI laws were enacted across 21 states, and the Joint Commission and CHAI issued the first national guidance on responsible AI use.
NIST AI RMF 1.0
Govern, Map, Measure, Manage. The de facto US reference framework.
CHAI
Coalition for Health AI: assurance standards and model cards for healthcare AI.
Joint Commission + CHAI
RUAIH: the first national guidance on responsible AI use in healthcare (Sept 2025).
ONC HTI-1
Transparency and risk-management requirements for predictive decision support.
Duke ABCDS
A proven lifecycle stage-gate model from a leading academic health system.
HAIRA Maturity Model
A five-level, resource-tiered maturity model published in npj Digital Medicine.
Governance is the foundation. Let's build yours.
G4E starts with Govern because everything else, exploration, education, enablement, and empowerment, depends on a trustworthy foundation. Whether you are chartering your first committee or maturing an established program, we meet you where you are.
