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Artificial intelligence is becoming part of everyday health care operations, bringing new opportunities alongside important governance questions that deserve cross‑functional collaboration. Discussions across the sector increasingly suggest that successful AI adoption extends beyond technical performance and includes the people, processes and standards that support responsible decision-making.
At the HIMSS AI in Healthcare Forum, panelists emphasized that roadblocks to managing AI effectively require mature data standards, stronger governance foundations, and early engagement among clinical, operational, technology, legal and compliance stakeholders. Participants also highlighted the importance of connecting governance with real clinical workflows and organizational culture, so AI initiatives can support patient care in practical settings. Those conversations suggest that governance is becoming an ongoing organizational responsibility that develops through collaboration across disciplines.
Dan Eduardo Gonzalez, Pharm.D., founder of ClarityRx Advisory, a health care strategy and AI governance advisory firm, believes health care already possesses valuable governance expertise within its existing clinical workforce. Drawing on his experience as a bilingual Spanish- and English-speaking pharmacist, he argues that pharmacists have spent decades overseeing medication safety, regulatory compliance, patient outcomes and high-consequence clinical decisions. “Health care doesn’t need to invent AI governance from scratch,” Gonzalez says. “It already has professionals who have been practicing disciplined decision-making under complex clinical conditions for years.”
That perspective reflects how ClarityRx Advisory works with health care and technology leaders seeking practical guidance on AI governance, cybersecurity and data strategy. Gonzalez observes that conversations surrounding AI often place significant attention on technology, while governance discussions receive less visibility. In his view, health care organizations benefit when accountability, clinical judgment and operational realities develop alongside technological innovation, allowing governance frameworks to reflect how care is actually delivered across different environments.
He notes that pharmacists contribute a distinctive perspective because their daily responsibilities already involve balancing multiple priorities that influence patient care. Every medication review requires careful consideration of clinical evidence, safety, ethical responsibilities, regulatory expectations, operational demands and individual patient circumstances.
Those decisions often involve collaboration among physicians, nurses, insurers, regulators and patients, placing pharmacists at an important point where clinical knowledge and governance naturally intersect. Gonzalez says, “Those experiences cultivate practical judgment that can contribute meaningfully to AI oversight as health care organizations evaluate new technologies entering clinical practice.”
Current AI adoption trends further highlight why those perspectives deserve consideration. According to a 2026 global tech report, 86 percent of surveyed health care organizations are embedding AI into workflows, services and value streams, while many continue making substantial investments in digital transformation. The report also notes that regulatory complexity, evolving AI oversight and data privacy considerations remain important factors influencing technology strategies. These findings suggest that technological advancement and governance maturity may need to progress together so organizations can support sustainable implementation over time.
For Gonzalez, governance discussions become more effective when clinical expertise participates from the beginning of the conversation. That conviction has been shaped not only by his years in health care but also by his own experience engaging with organizations developing AI solutions.
He shares, “During interviews with an AI health care company, I recall asking what I considered a fundamental governance question: ‘Where does the clinician sit relative to the engineers – before the build, during, or after?’” Rather than prompting a discussion about collaboration, the question was interpreted as a lack of interest in serving as an individual contributor clinician. For Gonzalez, the exchange highlighted that organizations often recognize the value of clinical expertise but have not always established clear expectations for when clinicians should contribute during AI development.
Timing matters because it influences the role clinicians are able to play. He notes that when clinicians participate before development begins, they help shape the architecture alongside engineers. When they join during development, they can still influence key decisions and workflows. When they are brought in only after a platform has been built, their role often becomes validating decisions they had little opportunity to influence. Gonzalez sees this as an opportunity for organizations to strengthen governance by defining clinical involvement earlier in the development process rather than treating it as a final review step.
He also believes this approach can improve organizational performance by reducing unnecessary rework across teams. It’s estimated that for every hour of “unproductive labor” per employee per week, the average annual cost to a company is approximately $15,000. This illustrates how inefficient collaboration and delayed decision-making can carry meaningful operational costs. Bringing clinicians, engineers, compliance professionals, and business leaders together earlier may help organizations address questions before they become larger implementation challenges.
AI can influence decisions that affect patient care, making frontline clinical insight an important part of governance alongside technology, compliance, cybersecurity and executive leadership. According to Gonzalez, pharmacists understand how decisions unfold inside real health care environments because they routinely evaluate competing priorities while maintaining patient safety as an important consideration.
He believes clinicians who seek opportunities to collaborate with engineering teams are often motivated by the same objective that guides their clinical practice: identifying risks early, supporting adoption, and helping ensure technologies reflect the realities of patient care before they reach clinical environments. Their experience may help governance discussions account for operational realities before challenges emerge during implementation.
His perspective reflects lessons developed across more than 14 years working in hospitals, directing clinics during the 2020 COVID pandemic, pharmacy benefit management organizations, health insurance and population health. The challenge he tackles does not have a language. Therefore, Gonzalez’s background as a bilingual practitioner helps bridge the gap between access and impact, ensuring that practical solutions are there for all who need them.
During these 14 years, Gonzalez observed how operational pressures, regulatory expectations, financial considerations and patient needs frequently converge within the same decision. Those experiences reinforced his belief that successful governance depends on communication across departments, shared accountability, and practical decision-making that reflects everyday clinical operations.
Ultimately, instead of asking how AI governance should be created, health care leaders may benefit from asking which professionals have long contributed to managing complex clinical decisions where safety, compliance, ethics and operational judgment come together every day. They may also benefit from asking where those professionals are invited into the AI development process, and whether they are helping to shape decisions from the outset or evaluating them after key decisions have already been made. Gonzalez believes pharmacists represent one important part of that answer.
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