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AI Governance

Know which AI to pursue. Know how it performs.

We have designed, implemented, and run an enterprise AI governance program inside a health system. We help teams evaluate proposals, make decisions, and track what happens after approval.

Published research

Nature npj Digital Medicine

An early pipeline framework for assessing vendor AI solutions to support return on investment.

Binkley CE, Bouslov D, Zaidi A, Kaye L, Whalen R, Sethi S, Azar J. 2025;8:368.

doi:10.1038/s41746-025-01767-z

Read the paper

Our co-authored Perspective in Nature Portfolio's npj Digital Medicine proposes a framework for evaluating vendor AI through strategic alignment, executive sponsorship, impact and value, and risk. It gives leaders a disciplined starting point before a purchase decision.

What it means inside a health system

A decision needs a path beyond the meeting.

Clear
Requests arrive from different departments with different definitions of value. A preferred platform can move ahead while a higher-value problem waits. Approval can be mistaken for evidence that a tool works.
Concise
We set common priorities and decision rights, compare use cases, route risk review, and require local evidence before deployment. The work connects IT, clinical, operational, finance, and risk leaders.
Complete
A portfolio that records why each use moved forward, who owns it, what conditions apply, and whether the expected value appears after launch.

What we build

What a working program includes.

  • Priorities and valueShared criteria for strategic fit, expected benefit, evidence, cost, and readiness. A business case that lets leaders compare requests across departments and platforms.
  • Intake and inventoryA route into governance from clinical teams, IT, procurement, and research. A register for proposed and deployed uses, including AI embedded in vendor products.
  • Proportionate reviewClinical influence, patient harm, data sensitivity, autonomy, evidence, and oversight determine which experts review a use and what must be resolved.
  • Decision rightsNamed owners, approval authority, conditions, exceptions, and a written rationale. Every requester learns the decision and the next action.
  • Evidence before releaseLocal validation, a defined workflow, training, safety limits, and a measurement plan agreed before a pilot expands or a tool goes live.
  • Operation after launchCompare adoption, value, performance, and safety against the baseline. Set a route for incidents and changes to models or workflows, with authority to pause or retire a tool.

Vantage

Every AI decision. A clear path forward.

Vantage connects the work behind AI governance: the request, the risk review, the committee decision, and the follow-through. See what needs attention, who owns the next step, and why a decision was made.

The governance practice also works with a client's existing tools. We recommend Vantage when it helps the program run, not as a condition of working with us.

Build a governance program your teams can use.

Bring your current process and where it falls short: requests waiting for review, unclear ownership, or tools already in use without follow-up.

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