AI-readiness assessment
Find the healthcare AI work worth funding.
Before a team buys a tool or builds an agent, rank the opportunities by business value, data fitness, workflow fit, privacy risk, and implementation cost. Leave with a decision-ready map and a practical next project.
Turn AI enthusiasm into a funding decision.
A readiness assessment creates a shared view across business, data, technical, risk, and operating teams. It answers what can move now, what needs groundwork, and what should not be built yet.
- Use-case valueWhat decision, revenue, cost, quality, risk, or adoption problem would improve?
- Data fitnessIs the relevant data available, owned, documented, current, and safe to use?
- Workflow fitWhere does the work live today, and where can a model or agent help without creating more friction?
- Risk and controlsWhat privacy, security, review, escalation, and change-management conditions must be in place?
- Implementation costWhat people, systems, model strategy, integration, and operating cadence will the work require?
A map the team can act on.
The assessment is designed to make the next decision easier to explain to executives, technical teams, and risk owners.
- Ranked opportunity mapUse cases grouped by value, readiness, risk, cost, and time to learn.
- Data and workflow findingsWhat is available, what is missing, and which operating constraints matter.
- Governance gap viewReview points, escalation paths, documentation, model choices, and boundaries to resolve.
- Next-project recommendationA focused build, landscape assessment, or operating-system workstream that can earn the next investment.
Start where the pressure is real.
Readiness with a decision at the end.
How long does the assessment take?
The standard offer is a focused two-to-four-week engagement. The exact scope depends on the number of use cases, systems, stakeholders, and decisions in play.
Do we need to choose an AI provider first?
No. The assessment can recommend a multi-provider model strategy or a deep single-provider path based on the work, policy, integration, delivery speed, and current commitments.
What happens after the assessment?
You get a clear next decision. That may be a build sprint, a healthcare data landscape assessment, a dbt accelerator, a Data AI Operating System build, or a decision to wait until the groundwork is ready.
Have more AI ideas than funding decisions?
Share the use cases, data systems, deadline, and decision owner. The next step is a paid assessment that leaves the team with a map it can use.