As we move into 2026, the healthcare industry faces a somewhat counterintuitive reality: as AI capabilities advance, competitive advantage belongs to organizations recognizing that domain experts – Quality Assurance and Regulatory Affairs (QARA) professionals, clinical experts and safety officers - are uniquely positioned to lead AI transformation, not be replaced by it.

This creates a talent paradox.

While organizations race to hire AI specialists, they are overlooking the rich seam of strategic assets within their walls: professionals combining deep regulatory knowledge, clinical judgement and institutional expertise. For companies navigating the EU AI Act's workforce competency mandates, utilizing the contextual domain knowledge of experts with experience in the regulated environment represents both a compliance accelerator and a strategic, competitive differentiator.

Why Domain Experts Hold the AI Advantage

Conventional wisdom suggests healthcare organizations need AI specialists to lead digital transformation. Reality proves different. While technical expertise matters, healthcare AI success depends on contextual judgement developed through years navigating regulatory frameworks, understanding clinical implications and making decisions where patient safety hangs in the balance.

Consider the fundamental question every AI implementation must answer: when should the system's recommendation be trusted, questioned or overridden? This requires intimate knowledge of regulatory requirements across global markets, clinical context distinguishing cosmetic variations from genuine safety signals, manufacturing realities explaining statistical anomalies and institutional history providing pattern recognition that no dataset captures. This inherent intuition – those tingling “spidey senses” (thanks, Spiderman), that gut feeling that something is “off” – is something that comes with years, often decades of knowledge. It’s acquired rather than taught.

Simply put, a data scientist builds sophisticated models. A quality professional, for example, that understands both AI capabilities and pharmaceutical manufacturing determines whether those models should be deployed, and crucially, when their recommendations should be challenged. And as the EU AI Act transforms workforce competency from competitive advantage to regulatory requirement, this distinction matters profoundly.

Why the EU AI Act Changes Everything

The EU AI Act doesn't merely regulate AI systems; it regulates the people who deploy them. As such, organizations implementing high-risk AI must demonstrate workforce competency in understanding AI limitations, recognizing bias, executing oversight and exercising judgement when systems produce unexpected outputs.

This creates two imperatives for QARA leaders: compliance demands systematic, documentable workforce development, while strategic opportunity exists to transform this mandate into competitive advantage by upskilling domain experts faster than competitors can hire AI specialists lacking healthcare context. Building on that, it’s important to highlight the three competency dimensions that define regulatory compliance.

1. Usability - Ensures professionals interpret AI outputs within regulatory contexts, recognize when results warrant skepticism, execute oversight procedures and escalate concerns appropriately.
2. Trustability - Develops ability to detect bias, understand validation limitations, question anomalous results and assess when AI confidence levels should influence decisions.
3. Governance - Requires understanding of regulatory requirements, documentation expectations, change management procedures and individual accountability.

These competencies build naturally on existing domain expertise and a key takeaway here is that teaching a quality professional to assess AI reliability is often fundamentally easier than teaching an AI engineer two (or more) decades of pharmaceutical manufacturing context.

Reskilling Strategy: From Compliance to Competitive Advantage

Forward-thinking organizations approach AI literacy not as training to complete but as capability to develop systematically and apply strategically. With that in mind, key elements to build into an organizational reskilling pathway should include the following:

• Start with high performers in critical roles - Identify quality, regulatory, clinical and safety professionals combining strong domain expertise with analytical mindset and organizational influence. These become your AI capability champions, augmenting their domain roles with AI literacy enabling better oversight, informed governance decisions and colleague mentorship.
• Design role-based learning pathways - Generic AI training fails because it doesn't connect to daily work. Embed AI concepts within familiar contexts: quality professionals learn through deviation analysis examples, regulatory specialists via submission document generation, clinical safety officers through adverse event signal detection.
• Emphasize hands-on experience over theory - Professionals develop genuine competency through supervised interaction with actual systems - reviewing AI outputs with expert oversight, comparing AI recommendations against manual analysis, participating in validation activities and conducting root cause analysis when AI produces unexpected results.
• Build competency assessment into the process - The EU AI Act requires demonstrable competency, not attendance records. Effective assessment includes scenario-based evaluations in realistic situations; judgement exercises testing escalation decisions, documentation reviews verifying governance understanding and periodic reassessment as systems evolve.

Addressing Talent Shortage Strategically

Healthcare organizations report difficult recruiting AI talent with both technical capability and domain knowledge. This shortage creates opportunities for internal development rather than competing for scarce external hires. Mathematics favors internal development where the known professional context of clinical, quality, regulatory and safety professionals can be augmented with education on AI literacy and data governance to support continuous professional development for the benefit of the company and the professional individual.

This suggests a clear strategy: hire domain experts and develop their AI literacy for AI applicability within their domain. Organizations pursuing the opposite - hiring AI specialists and developing domain expertise - face longer timelines, higher costs and retention challenges as technology professionals seek cutting-edge development roles rather than regulated healthcare governance. These approaches address talent constraints while building capability:

• Structured knowledge transfer before automation captures judgement embedded in processes through documented decision trees, case libraries, recorded expert explanations and formal mentorship.
• Redesigned development paths deliberately expose junior professionals to complex scenarios through rotational assignments, edge-case investigations, cross-functional problem-solving and graduated responsibility with oversight.
• Evolved veteran roles shift experienced professionals toward exception handling, coaching, validation activities, complex problem-solving and cross-functional leadership.

Operationalizing AI Readiness: Practical Implementation

Moving from strategy to execution requires addressing three practical challenges:

1. Governance structures that are enablers - Effective AI governance requires multidisciplinary teams with genuine authority combining quality/regulatory professionals, clinical/scientific experts, data scientists and IT professionals. The mandate extends beyond initial approval to continuous monitoring, periodic revalidation, model drift management and escalation authority for safety concerns.
2. Validation approaches adapted for AI - Traditional validation assumes stable, deterministic systems. AI requires adapted approaches: initial validation across representative scenarios, ongoing monitoring detecting degradation, periodic revalidation as contexts evolve and controlled update procedures. A pragmatic solution many organizations adopt: disable self-learning in production. This sacrifices adaptability but gains regulatory compliance through traditional validation, audit trail integrity, performance predictability, and manageable risk through controlled updates - strategic decision-making prioritizing deployment viability over theoretical optimization.
3. Change management that builds trust - AI disrupts workflows and professional identities. Effective change management requires transparent communication about AI's amplifying role, frontline involvement in design and validation, confidence-building through hands-on training, demonstrated leadership commitment and celebration of early wins.

The Competency Cliff: Why This Matters Now

Organizations face strategic urgency: AI automates tasks that traditionally provide learning opportunities while experienced professionals with critical institutional knowledge approach retirement. This "competency cliff" creates sudden expertise loss as automation eliminates developmental pathways and veterans exit simultaneously.

The risk is concrete. Junior quality professionals who have never manually reviewed deviations may lack judgement recognizing when AI flags require escalation. Regulatory specialists who never drafted submissions from scratch may miss nuances in AI-generated materials. Clinical reviewers who never personally evaluated adverse events may trust AI assessments without skepticism. There are immediate, common-sense actions that organizations can take to address this:

• Capture institutional knowledge before it departs through documented judgement frameworks, codified expert reasoning, case libraries and veteran-junior mentorship.
• Preserve development opportunities by deliberately assigning complex cases manually with oversight, maintaining manual process capacity, creating judgement-building simulations and measuring capability development alongside efficiency.
• Position AI literacy as career accelerator - domain experts combining regulatory expertise with AI literacy become more valuable than either pure specialists or AI engineers lacking healthcare context.

From Framework to Reality: Next Steps

For QARA leaders navigating AI transformation while meeting EU AI Act requirements, there are four clear actions that provide immediate direction:

1. Assess current AI literacy - Identify who possesses foundational understanding versus who requires development. Map this against roles most likely to interact with AI systems. This baseline enables targeted capability development.
2. Identify high-value, low-risk pilots - Begin with limited-scope implementations providing clear value without excessive risk: document management, surveillance signal detection, quality control applications. These build capability while demonstrating tangible value.
3. Establish governance before deployment - Create multidisciplinary teams with authority to approve implementations, monitor performance, and mandate corrections. Document decision frameworks, validation approaches and oversight procedures.
4. Invest in workforce development systematically - Implement role-based training connecting AI concepts to daily work, provide hands-on experience with actual systems, develop competency assessment demonstrating understanding and integrate AI literacy within existing quality programs.

Strategic Reality

Healthcare's AI transformation is neither optional nor reversible. Organizations recognizing domain experts as strategic assets rather than obstacles position themselves for sustained competitive advantage. Those investing in systematic workforce development rather than recruiting scarce AI talent build capabilities competitors cannot easily replicate.

The EU AI Act's workforce competency mandates transform this from strategic choice to regulatory requirement. The professionals already within your organization – quality specialists, regulatory experts, clinical leaders, safety officers – possess judgement, context and expertise that algorithms cannot replicate and no external hire can instantly acquire.

The competitive advantage belongs to organizations building AI literacy into domain expertise, creating hybrid professionals who understand both what technology can do and when it should be questioned. This is the talent paradox resolved: in an age of AI, human expertise becomes more valuable, not less, provided that expertise evolves to include AI literacy.

For QARA leaders, the path forward is clear: upskill your domain experts, establish robust governance and build capability infrastructure, transforming regulatory compliance into competitive advantage. Organizations executing this strategy don't just deploy AI safely - they create sustainable differentiation grounded in trust, expertise and, ultimately demonstrated patient benefit.