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Our approach

The operating model for enterprise AI.

Every enterprise technology earned an operating model before it could scale. Software got the development lifecycle, IT got service management, and AI has arrived at scale with none of it. Everyone is talking about the AI. Almost no one is ready to run it safely and at scale. This is how the best health systems will run it, and how we run it with them.

Manage it. Deploy it.

The point of view

Five principles we believe in.

  1. 01

    One source of truth.

    If you cannot name every AI across your enterprise, each with a defined owner and a clear purpose, you do not have governance, you have sprawl. Every agent on one record, with an owner, the data it touches, its risk tier, and what it returned.

  2. 02

    A real lifecycle, not ad hoc.

    Treat every agent like code in production. Nothing goes live without passing evaluation, policy, and review, and everything can be rolled back.

  3. 03

    A human structure on the hook.

    Every agent has a named owner, and a standing body with the authority to say no. Tools do not create accountability. People operating a clear structure do.

  4. 04

    Run it like a portfolio.

    Measure what each agent returns, and retire what does not earn its place. Most health systems have never retired an AI tool.

  5. 05

    Keep the referee separate from the players.

    The party that grades the AI should not be the one selling it. Overture sells none of the models it governs.

How it works

What working with Overture looks like.

  1. Assess

    The AI Operating Assessment maps your whole AI picture: every AI already running, including the shadow tools no one owns, where new AI pays off, the overlap to cut against your vendor roadmaps, and where you land on the maturity model. You leave on the Score, not holding a report.

  2. Stand up

    We stand up the operating model on your platforms: the Score, the lifecycle, the policies, and the governance structure, wired through the controls you already have.

  3. Run it with you

    A forward-deployed pod runs it with you as a continuous cycle: assess, set policy, build, deploy, prove, enable. You reach the outcomes, not just the software.

    1. Assess
    2. Set policy
    3. Build
    4. Deploy
    5. Prove
    6. Enable
    Continuous

  4. Carry more over time

    As your team levels up, they carry more and we focus where we add the most. The best outcome is you needing us less.

You get the platform, the pod, and the outcomes, on the systems you already run.

The maturity model

The AI Operational Maturity Model

Five levels, from ad hoc to compounding. Most health systems sit at Level 1 or 2. Buying a tool does not raise your level. Operating the discipline does.

Where does your system stand?

Five dimensions, one question each. The profile fills in as you go, and your level is written out in full when you finish.

  1. L1 Ad hoc and unseen
  2. L2 Aware and reactive
  3. L3 Managed and governed
  4. L4 Optimized and value-driven
  5. L5 Compounding and self-improving

Your profile

  • Visibility and inventory

    L?

  • Lifecycle

    L?

  • Oversight proportional to risk

    L?

  • Accountability and ownership

    L?

  • Value and retirement

    L?
01 / VISIBILITY AND INVENTORYCan you produce a current list of every AI running across your system?
02 / LIFECYCLEHow does new AI get into production?
03 / OVERSIGHT PROPORTIONAL TO RISKHow do you decide how much scrutiny an AI gets before it goes live?
04 / ACCOUNTABILITY AND OWNERSHIPWho is accountable for an AI once it is live?
05 / VALUE AND RETIREMENTDo you measure what each AI returns, and retire the ones that do not earn it?

0 of 5 answered. Answer the five questions to see where you land.

Book the AI Operating Assessment