AI: It’s Not About Chips, It’s About People, Systems, and Culture

Everything today is AI. There is scarcely a corner of our lives where it is not discussed, considered, or contemplated. AI conversations often center on its enabling technology and hardware. These things are truly impressive, monumental even, and without precedent. They are also in themselves only part of the fuller picture of what AI is.

Description automatically generatedIn René Magritte’s famous painting “Ceci n’est pas une pipe” (This is not a pipe), he challenges us to look beyond the surface. Magritte reminds us that things are often quickly understood at a surface level, but deeper meaning lies beyond the obvious. In the same way, when we talk about artificial intelligence, we often reduce it to hardware. But just as Magritte’s painting is not truly a pipe, a GPU is not AI. AI is far more than its infrastructure; the surface elements. AI must also be understood in how it is woven into systems, shaped by people, supported by human capital, training, trust, and culture—all of which must adapt and learn from it.

The real challenge is not in the tools themselves, but in the human and organizational systems that either unlock or constrain AI’s potential. You can have the most sophisticated infrastructure in the world, but without the right mindset, culture, and processes, AI will not deliver on its promise. Success in AI also demands change management strategies that ensure people are ready to embrace new ways of working. Human capital development and targeted training are key to enabling AI success. AI thrives in environments where trust is built—trust in the technology, trust among people, and trust in leadership.

Learning From History: Overcoming Resistance to Technological Change

AI is at a pivotal moment, much like past technological revolutions. Consider the introduction of electricity: when it was first developed, many were hesitant—concerned about safety, unfamiliar with its workings. It was not until leaders invested in educating people and building trust that electricity became ubiquitous, transforming industries. The telephone followed a similar path. Initially dismissed as a novelty, the telephone only became indispensable when people began to understand its broader potential. The internet, too, was once met with skepticism—early adopters had to convince others that this emerging technology could reshape industries. It took investment in infrastructure, education, change management, and cultural shifts for the internet to evolve into the global information highway on which we now rely.

AI stands at a similar crossroads. Too often, it is seen as a regulatory hurdle, or a novelty to be captured in use cases, or confined to niche, esoteric projects. But just as electricity, the telephone, and the internet reshaped our world, AI’s true potential will only be unlocked when we address the human capital, training, and organizational systems that hold it back. It is not enough to focus on the technology itself—we need to build trust, manage the change process, educate people, and reimagine how organizations and systems adapt to fully embrace what AI can offer.

The Federal AI Mindset: Navigating People, Systems, and Culture for Success

In the federal health space, agencies like the FDA, NIH, and CMS are already showing AI’s potential in critical areas. Whether it is accelerating drug approvals, enhancing real-world evidence (RWE) analysis, predicting public health crises, or detecting fraud in healthcare systems, the groundwork is being laid for AI to solve some of the most complex challenges in healthcare. However, AI’s true potential goes beyond its technological capabilities—it depends on how we navigate people, systems, culture, and the change management required for meaningful adoption.

For example, the FDA is using AI for postmarket surveillance, monitoring drug safety through real-time analysis of patient data. At the NIH, AI is advancing precision medicine, helping researchers sift through vast genomic datasets to deliver personalized treatments. CMS is leveraging AI to detect patterns of fraud, waste, and abuse, improving efficiency in Medicare and Medicaid programs. Yet, while these examples show promise, technology alone is not enough. Success in these initiatives depends on building systems that support AI, creating the right organizational culture, and equipping the workforce with the skills, training, and mindset to harness AI’s potential.

One of the most pressing challenges is data readiness and interoperability. Across federal health systems, data is often siloed, unstructured, and fragmented, making it difficult to unlock the insights AI can offer. To unlock the full potential of AI, federal health systems must prioritize AI/ML-ready data and interoperability. This means investing in data integration strategies that bridge the gaps between siloed data, making it easier to extract valuable insights. Encouraging inter-agency collaboration and standardizing data formats across systems will be key to achieving this goal, enabling AI to truly drive innovation.

Beyond technical integration, a cultural shift is needed—one that fosters trust in AI and ensures that teams feel empowered and knowledgeable enough to work alongside new technologies. AI literacy, human capital development, and a collaborative culture are essential for AI to move from being seen as a technical tool to becoming a catalyst for deeper innovation.

AI is a People-Driven Transformation, Not Just Technology

The importance of the people factor cannot be overstated. AI systems are only as effective as the people who design, manage, and use them. This is especially true as Generative AI (GenAI) models become more common, helping to automate tasks but still requiring human oversight to ensure thoughtful use. Federal health agencies need to foster a culture of collaboration, trust, and continuous learning, ensuring that the workforce is not only technically skilled but adaptable and open to AI’s evolving nature.

Regulatory bodies like the FDA and CMS face the challenge of balancing ethical oversight with the flexibility to innovate, ensuring AI’s deployment is both responsible and transformative. Issues like cybersecurity, privacy, and bias mitigation require careful management, demanding a multidisciplinary approach that balances technical, ethical, and human considerations.

In this context, AI’s true potential is about more than just technology. It is about how we align systems, build the right organizational culture, and empower people to make AI work over the long term. AI-driven predictive analytics can help agencies like the CDC anticipate public health crises, while AI in precision medicine can revolutionize patient care by tailoring treatments to individuals. But without human-centered strategies to address bias, over-reliance, or system failures, even the most advanced AI models will struggle to deliver meaningful impact.

Unlocking AI’s Full Potential: Addressing the Human and Systemic Factors

Reluctance and caution surrounding AI stem from deeper cultural and systemic barriers. Unlocking AI’s full potential requires organizations to address these challenges head-on. It is not enough to simply invest in hardware or algorithms—or tools like Generative AI. What’s required is a fundamental shift in how we think about AI and the systems we build around it. Organizations need to create environments where people feel empowered, knowledgeable, and capable of exploring what AI can truly do. The real focus should be on adapting human behaviors, culture, and organizational structures to support AI-driven innovation, not just relying on outdated frameworks or viewing AI as a regulatory checkbox.

Fostering a culture of experimentation, collaboration, and risk-taking is essential. AI is not a one-size-fits-all solution, and neither should its adoption strategies be. Every organization, much like psychiatrist Irving Yalom’s idea of unique therapy for every patient, needs its own tailored approach to AI. The key is creating systems and processes that allow for flexibility, creativity, and exploration.

The Art of the Possible: Reimagining Systems and Culture

Organizations need to stop thinking small. AI is not just about optimizing existing processes—it’s about reimagining them. Federal agencies, particularly in healthcare, must create space to explore the “art of the possible.” This means focusing not just on the technology but also on how to build trust, empower people, and create organizational systems that allow AI to thrive.

If we want AI to be more than just a regulatory compliance tool, we need to shift the narrative. Investing in leadership that is willing to explore AI’s potential beyond niche uses, creating educational frameworks that demystify AI for stakeholders, and building systems that encourage experimentation are critical. Only by changing the cultural and organizational structures surrounding AI can we unlock its full promise.

Conclusion: From Caution to Transformation

AI has the power to reshape healthcare, industry, and society at large. But that transformation will only happen if we move beyond seeing AI as just another technology to regulate. The future of AI is not about chips or infrastructure—it is about addressing the human factors, organizational systems, and cultural shifts that hold it back.

History shows us that technological breakthroughs are often delayed not by the technology itself, but by the people and systems that are slow to adapt. If we stop fearing what AI might disrupt and instead focus on what it can enable, we can unlock its true potential. The question is not whether AI can transform healthcare—the question is whether we are ready to change the systems around it to let it.