HHS Launches Artificial Intelligence Strategy
The Department of Health and Human Services has released a department-wide AI Strategy to improve healthcare delivery, public health, human services, biomedical research, and agency operations. The plan sets clear direction for scaling AI use where it can deliver measurable outcomes and reduce administrative friction.
HHS said the strategy delivers on the White House's AI Action Plan, executive orders, and Office of Management and Budget guidance. Acting Chief AI Officer Clark Minor said the goal is to use AI to empower the workforce and drive innovation across the department. Related policies include the Executive Order on AI from the White House and OMB's AI guidance for federal agencies.
The five pillars
- Ensure governance and risk management for public trust
- Design infrastructure and platforms for user needs
- Promote workforce development and burden reduction for efficiency
- Foster health research and reproducibility through gold standard science
- Enable care and public health delivery modernization for better outcomes
Each pillar includes a vision, strategic goals, example metrics, and ongoing activities to move from pilots to production.
Near-term moves called out by HHS
- Establish standardized minimum risk practices for high-impact AI.
- Develop and maintain an inventory of AI use cases.
- Advance a OneHHS AI-integrated Commons that offers compute, shared data resources, testbed environments, and reusable models to speed AI development.
Why this matters for executives
This strategy gives leaders a path to scale AI with consistent guardrails and shared capabilities. The priority now is to fund, govern, and staff the work so divisions can plug into common services and meet risk standards without slowing delivery.
- Map current AI pilots and systems to the five pillars; name accountable owners and define success metrics.
- Stand up an enterprise use-case inventory and intake process tied to privacy, security, and ethics reviews.
- Budget for shared compute, data pipelines, and testbeds; prefer reuse over custom one-offs.
- Launch focused upskilling and human-in-the-loop practices to reduce staff burden and improve quality.
- Build reproducibility into research with documented datasets, model reporting, and audit trails.
- Pilot AI in care delivery and public health where outcomes can be measured and bias can be monitored.
What to watch
Expect more guidance on risk practices, inventories, and the rollout of the OneHHS AI-integrated Commons. The departments that move first on shared services and clear metrics will capture the quickest wins while staying within policy.
If workforce development is a near-term gap, consider curated resources by role to accelerate adoption: AI courses by job.
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