Why AI Governance Can’t Wait
Healthcare leaders face an unprecedented challenge: how do you govern a technology that’s already embedded in clinical workflows before consensus frameworks are fully developed? Artificial intelligence in medicine has moved from theoretical promise to operational reality faster than governance structures could adapt. This acceleration creates a paradox for healthcare executives—AI is simultaneously an emerging technology requiring careful oversight and an established tool already influencing patient care decisions. The tension between these realities is reshaping how health systems approach implementation strategy, stakeholder engagement, and risk management.
The American Medical Association’s declaration that AI is “happening now” rather than representing medicine’s future captures this urgency. Meanwhile, parallel conversations about who should control AI deployment and how quickly systems should scale reflect a healthcare industry grappling with governance in real-time. These aren’t abstract policy debates—they’re immediate operational questions with direct implications for patient safety, workforce adaptation, and competitive positioning.
From Future-Tense to Present-Tense AI
The shift in language around healthcare AI is instructive. When the AMA’s leadership emphasizes that AI is a current reality rather than a future prospect, they’re acknowledging a fundamental change in how the medical profession must engage with the technology. This isn’t about preparing for eventual disruption—it’s about managing ongoing transformation while patients are already being served by AI-augmented systems.
This temporal shift carries significant implications for medical education, credentialing, and professional standards. Physicians entering practice today need immediate competency with AI-enabled diagnostic tools, not theoretical knowledge about what might eventually arrive. Health systems can no longer defer AI strategy to long-range planning cycles when algorithms are already reading radiology images, predicting sepsis risk, and optimizing surgical schedules. The infrastructure, training, and governance frameworks that organizations assumed they’d have years to develop must now be built while the technology is actively deployed.
Callout: Healthcare organizations face a unique governance challenge: building regulatory frameworks, training programs, and oversight mechanisms for AI tools that are already influencing clinical decisions. The luxury of preparation time has evaporated, requiring leaders to govern and implement simultaneously.
This present-tense reality also exposes gaps in existing regulatory and reimbursement structures designed for slower innovation cycles. When AI tools evolve through continuous learning rather than discrete version updates, traditional approval pathways struggle to keep pace. Healthcare leaders must navigate these regulatory ambiguities while ensuring patient safety and maintaining compliance—a balancing act that requires new approaches to risk assessment and quality assurance.
The Stakeholder Question: Who Decides?
The governance challenge extends beyond pace to participation. The fundamental question of who should shape AI’s role in medicine reveals competing interests and expertise domains. Technologists understand algorithmic capabilities and limitations. Administrators focus on operational efficiency and financial sustainability. Clinicians bring patient care expertise and workflow knowledge. Patients hold stakes in safety, privacy, and care quality. Each perspective is necessary but insufficient alone.
The argument for inclusive, multi-stakeholder governance isn’t merely democratic principle—it’s practical necessity. AI systems that technologists design without clinical input often fail in real-world workflows. Administrative decisions made without patient perspectives risk overlooking privacy concerns or accessibility barriers. Clinical adoption suffers when physicians feel excluded from implementation decisions. The complexity of healthcare AI demands governance structures that can integrate these diverse viewpoints into coherent strategy.
Yet inclusive governance creates its own tensions. Broader stakeholder involvement slows decision-making when speed appears competitively advantageous. Consensus-building across groups with different priorities and vocabularies is resource-intensive. Healthcare organizations already stretched thin may view extensive stakeholder engagement as a luxury they can’t afford. This tension between thorough governance and rapid deployment sits at the heart of current healthcare AI strategy debates.
The Fast-Slow Framework: A Middle Path
Catholic Health’s approach offers one model for navigating these competing pressures. Their “fast-slow” strategy acknowledges both AI’s immediate relevance and the need for careful evaluation before scaling. Quick pilots test promising applications in controlled environments, generating real-world data about performance and integration challenges. Slow, deliberate assessment of these results informs decisions about broader deployment, allowing organizations to be simultaneously agile and cautious.
This framework addresses several governance challenges at once. Rapid piloting satisfies the urgency to engage with current AI capabilities while demonstrating responsiveness to competitive pressures and innovation opportunities. The measured evaluation phase creates space for stakeholder input, safety assessment, and workflow refinement. By separating experimentation from full deployment, organizations can learn from controlled failures without exposing entire patient populations to untested tools.
The fast-slow approach also acknowledges different risk tolerances across AI applications. Administrative uses like scheduling optimization may warrant faster scaling than clinical decision support tools directly affecting diagnoses. Revenue cycle applications might move quickly while patient-facing chatbots require more extensive testing. This nuanced, application-specific governance reflects growing sophistication about AI risk stratification in healthcare settings.
Callout: The fast-slow implementation model represents more than cautious innovation—it’s a governance framework that creates structured space for stakeholder input and risk assessment while maintaining competitive responsiveness. This approach may define successful AI adoption across healthcare.
Implications for Healthcare Organizations and Workforce Strategy
These leadership perspectives on AI governance carry direct implications for how healthcare organizations structure themselves and develop talent. If AI is already embedded rather than approaching, workforce planning must shift from “preparing for AI” to “operating with AI.” This affects recruitment priorities, training investments, and role definitions across clinical and administrative functions.
Healthcare organizations seeking talent need professionals who can work effectively in AI-augmented environments—clinicians comfortable with algorithmic decision support, administrators who understand AI capabilities and limitations, and technical staff who grasp healthcare’s regulatory and ethical complexity. Platforms like PhysEmp that connect healthcare employers with AI-literate candidates become increasingly strategic as this talent demand intensifies. The ability to quickly identify professionals with both domain expertise and technological fluency may determine which organizations successfully navigate AI integration.
Governance structures themselves require new roles and competencies. Chief AI officers or equivalent positions must bridge technical, clinical, and administrative domains. Ethics committees need members who understand algorithmic bias and data privacy alongside traditional bioethics. Quality assurance teams require skills in validating AI system performance. These aren’t future job descriptions—they’re current needs for organizations already operating AI-enabled systems.
The stakeholder inclusion principle also suggests that successful healthcare organizations will develop mechanisms for ongoing input from diverse voices. This might manifest as AI advisory councils with patient representatives, regular clinician feedback loops on AI tool performance, or partnership structures that give frontline staff meaningful input into technology decisions. Organizations that treat governance as participatory process rather than top-down decree may achieve smoother adoption and better-designed implementations.
Conclusion: Governing in Real-Time
The healthcare AI governance challenge is ultimately about making thoughtful decisions at unprecedented speed. Leaders can’t wait for perfect frameworks or complete stakeholder consensus when the technology is already operational. Yet rushing deployment without adequate safeguards, inclusive input, or careful evaluation risks patient safety and workforce disruption. The organizations navigating this paradox most successfully appear to be those embracing structured experimentation, multi-stakeholder engagement, and application-specific risk assessment.
As AI’s presence in medicine deepens, the governance approaches healthcare leaders adopt now will shape the field for decades. The question isn’t whether AI will transform healthcare—it already is. The question is whether that transformation will be guided by inclusive, thoughtful governance or driven by technological and commercial momentum with inadequate input from those most affected. Healthcare leadership’s ability to balance urgency with deliberation may determine not just competitive success but the trustworthiness of AI-augmented medicine itself.
Sources
AMA CEO: AI is not medicine’s future—this is happening now – American Medical Association
Who should decide the role of AI in the future of medicine? – Aeon
Catholic Health CEO’s fast-slow approach to AI – Becker’s Hospital Review
