We help healthcare providers, payers, and life sciences build the literacy, governance, and operating discipline to adopt AI where patients and clinicians are best served.
Healthcare is the sector where AI's potential is most transformative — and where the gap between potential and realised value is widest.
McKinsey estimates (2023) that AI could unlock between USD 200 billion and USD 360 billion annually across health systems, split roughly between clinical productivity, administrative efficiency, and improved outcomes. Applications span diagnostic imaging, clinical documentation, triage, operational planning, claims processing, and increasingly direct patient communication.
Yet a 2024 Bain & Company survey found that only around 15% of healthcare providers had moved beyond AI pilots into scaled deployment. The reasons are predictable: clinicians who were not involved in system design, governance that stops at compliance, and workforce training that treats AI as IT rather than as a new way of working.
Realising healthcare's AI opportunity requires the same four fronts as financial services — literacy, governance, adoption, trust — but with stakes measured in patient outcomes.
Healthcare Boards and executives face AI decisions that are simultaneously clinical, operational, regulatory, and ethical. Most leadership teams lack a shared framework for deciding which applications to pursue, how to govern them, and how to explain them to patients and staff.
AI in healthcare touches the EU AI Act, the Medical Device Regulation (where AI qualifies as SaMD), Swissmedic oversight, data protection law for special-category health data, and professional conduct standards — often simultaneously. Designing governance that satisfies all of them without blocking innovation requires structural thinking.
The most consistent finding in healthcare AI research is that clinician involvement in system design is the single biggest predictor of adoption. A 2023 JAMA study found adoption rates up to three times higher in systems designed with clinical input versus those deployed top-down. Treating adoption as a change-management afterthought produces expensive shelfware.
AI that interacts with patients — through communication, triage, or decision support — is a trust decision. Explainability, consent, and clinician-in-the-loop design are not compliance checkboxes. They are the conditions under which patients will accept AI in their care.
We build shared vocabulary across directors, executives, and clinical leaders — so AI decisions can be debated substantively rather than deferred to IT or a transformation office.
We distinguish between the literacy a chief medical officer needs and the literacy a COO needs. Different roles, different decisions, different training pathways.
The oversight a hospital needs for diagnostic support AI is different from what it needs for rostering AI. We help design governance that fits both.
Where AI qualifies as SaMD, we help executives understand the overlap with the EU AI Act and structure compliance so both regimes are satisfied with one coherent system.
Training and deployment of clinical AI is fundamentally a data governance problem. We work on consent, de-identification, audit trails, and the sharing arrangements that make AI possible while protecting patients.
We work with executive teams to redesign the processes AI touches — clinical documentation, triage, imaging review, claims adjudication — so intelligence extends clinician capacity rather than replacing clinical judgment.
We embed clinical judgment in AI system design, not as a checkbox but as a source of input. We build role-specific training for clinicians, nurses, and administrative staff, so AI becomes a tool they shape rather than one that is imposed.
Where AI touches patients, we work with executives on explainability, consent flows, and the points in care where human contact must be preserved.
We help leadership identify the AI applications most likely to improve clinical quality, operational efficiency, and patient experience — and sequence them against what the organisation can actually absorb.
high-risk classifications for safety components and medical applications
where AI qualifies as a medical device
national-level oversight for medical devices and therapeutic products
with specific focus on special-category health data
emerging framework for secondary use of health data
cantonal health authorities, professional colleges, hospital accreditation
AI-by-default is a forced adoption model that introduces ethical, financial, and governance risk faster than organizations can manage it.
Retour sur la journée nationale suisse consacrée à la discrimination algorithmique et à la responsabilité politique.
Three hyperscaler outages disrupted millions of users in 2025. AI is amplifying both the potential — and the fragility — of the cloud.
AI adoption in healthcare succeeds when clinicians help design it, Boards can govern it, and patients can trust it.
StratEdge helps you build all three.
