SAP and Fresenius are building a sovereign AI backbone for healthcare
SAP and Fresenius are partnering to deliver a sovereign AI platform that keeps data processing inside clinical environments and under strict control. For hospitals and health systems, that means governance-first AI without sending sensitive data into public clouds that can't meet local rules.
The goal: move AI from pilots to production with a controlled environment where models can run securely, at scale, and with clear accountability.
From pilots to production
The companies plan to build an open, integrated ecosystem that hospitals can actually use-not another isolated proof of concept. They describe it as a digital backbone for a sovereign, AI-supported healthcare system across Germany and Europe.
Fresenius leadership frames the collaboration as a practical way to speed up digital transformation and give clinical teams tools that are secure, simple, and scalable-so clinicians can spend more time with patients, not wrestling with systems.
What the stack looks like
The platform is grounded in SAP Business AI and the SAP Business Data Cloud, creating a compliant foundation for operating AI in care settings. The focus is responsible handling of health data-a prerequisite for automating workflows that touch patients and clinicians.
For hospital leaders, this points to an AI operating model that prioritizes data sovereignty, auditability, and interoperability from day one. It's the infrastructure piece most organizations are missing when trying to scale beyond pilots.
Fixing fragmentation with "AnyEMR"
The partnership tackles one of healthcare's biggest blockers: fragmented data. SAP's "AnyEMR" strategy supports integration across diverse hospital information systems using open standards like HL7 FHIR.
By connecting HIS, EMRs, and clinical apps, Fresenius can roll out AI-supported solutions that improve throughput, handoffs, and documentation across the care chain. The aim is an individual, scalable platform that enables connected, data-driven processes without ripping and replacing core systems.
Investment and ecosystem
Both companies plan to invest a mid three-digit million euro amount over the medium term. Funding will support AI-enabled transformation in German and European healthcare through a mix of internal development and joint investments in startups and scaleups.
Expect a growing library of tools that plug into the sovereign platform-accelerating adoption while keeping data under strict governance.
What leaders at SAP and Fresenius are signaling
Fresenius underscores the need for a sovereign European solution that accelerates digital change and supports clinical work without adding friction. SAP emphasizes setting new standards for data sovereignty, security, and practical AI-so providers can use digital workflows to improve patient care at scale.
Together, they're positioning a model where healthcare AI advances without compromising compliance or control.
What this means for healthcare leaders
- Make sovereignty and governance the base layer, not an afterthought.
- Plan integration around open standards (e.g., FHIR) and an "AnyEMR" approach.
- Prioritize data quality, consent, provenance, and traceability before model rollout.
- Stand up clear model lifecycle management: validation, monitoring, drift, and rollback.
- Define security boundaries for on-prem, edge, and controlled cloud environments.
- Build a vendor and startup ecosystem that can plug into your data backbone.
- Upskill clinical and data teams to operate AI safely and effectively.
Why this matters now
Europe's next phase of healthcare AI will be built on sovereign infrastructure. Without a controlled environment and clean integration across systems, AI programs stall on compliance, trust, and operational reality.
This move by SAP and Fresenius points to a practical path: secure data, interoperable systems, and AI that serves clinicians and patients-not the other way around.
Explore SAP's approach to enterprise AI here: SAP Business AI.
If your teams need focused upskilling on AI for clinical operations and data governance, see our AI courses by job.
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