Healthcare organizations have spent years connecting systems and standardizing data exchange. As artificial intelligence moves from experimentation to enterprise adoption, those investments are taking on a new role. Fast Healthcare Interoperability Resources (FHIR) is no longer simply an interoperability standard. It is becoming one of the foundations that enables AI to understand, interpret, and apply healthcare data in meaningful ways.

Artificial intelligence has become the defining conversation in healthcare boardrooms. Health systems are exploring ambient documentation, clinical decision support, operational copilots, intelligent scheduling, revenue cycle automation, and countless other applications that promise to improve efficiency while easing the burden on clinicians. Payers are investing in predictive models to improve care management and member engagement. Life sciences organizations are accelerating drug discovery, clinical trial design, and pharmacovigilance with increasingly sophisticated AI capabilities.

Yet beneath the excitement lies a reality that many organizations are encountering sooner than expected. The challenge is no longer identifying compelling AI use cases. But rather creating an environment where those technologies can consistently produce reliable, trusted, and clinically meaningful outcomes.

This realization highlights an important shift in how healthcare leaders should think about interoperability.

For years, interoperability was largely viewed as an IT initiative focused on moving information between electronic health records (EHRs), laboratory systems, imaging platforms, pharmacies, and revenue cycle applications. Success was measured by whether information could be exchanged between systems with fewer manual interventions and less custom integration. Those efforts were essential, but they were designed to solve yesterday's problem.

Artificial intelligence has introduced a new one.

AI systems are not simply consuming data. They are interpreting relationships, recognizing patterns, generating recommendations, and supporting decisions that affect both clinical and operational outcomes. Those capabilities depend on far more than data availability. They require information that is consistent, structured, discoverable, and understood within the proper clinical context.

This is where FHIR is beginning to take on an entirely new level of strategic importance.

The next challenge isn't access to data. It's access to meaning.

Healthcare has never struggled to generate information. In fact, the industry generates over 2.5 quintillion bytes of clinical data every day. This significant output makes healthcare the world's fastest-growing data sector, producing about 30% of the world’s total data volume. Every patient encounter produces diagnoses, medications, laboratory results, imaging studies, clinical notes, referrals, care plans, billing information, and countless operational data points. The volume of information continues to grow as remote monitoring, wearable devices, virtual care, and digital therapeutics become more integrated into everyday care delivery.

The difficulty has always been transforming that information into something usable across an increasingly complex healthcare ecosystem.

Even organizations with mature interoperability programs often manage hundreds of applications that were implemented over many years, each with its own data structures, workflows, and business rules. While those systems may technically exchange information, they frequently do so in ways that require significant interpretation before another application, or an AI model, can use that information confidently.

That distinction matters because AI does not experience healthcare the way clinicians do.

A physician reviewing a patient chart naturally fills in gaps using years of clinical experience, institutional knowledge, and contextual understanding. AI systems rely entirely on the information they receive. With approximately 80% of EHR data stored as unstructured information, AI depends on standards like FHIR to connect, normalize, and preserve clinical context across disparate systems. Without that foundation, fragmented data inevitably leads to fragmented insights.

Healthcare leaders are beginning to recognize that the effectiveness of AI depends less on selecting the right model than on building an information architecture capable of supporting it.

Why FHIR changes the conversation

FHIR was developed to simplify healthcare interoperability through standardized APIs and a common approach to representing healthcare information. For many organizations, its value has traditionally been measured by faster integrations, improved application development, and easier information exchange.

Those benefits remain important, but they no longer tell the complete story.

FHIR provides something AI desperately needs: consistency.

Rather than forcing intelligent systems to interpret dozens of proprietary interfaces and inconsistent data structures, FHIR creates a common representation of patients, encounters, medications, observations, procedures, and countless other clinical resources. Information becomes easier to retrieve, easier to understand, and significantly easier to combine across multiple systems without losing its clinical meaning.

This is particularly important as organizations move beyond isolated AI pilots and begin embedding intelligent capabilities across the enterprise. Clinical documentation assistants require complete medication histories. Care management platforms depend on longitudinal patient records. Revenue cycle automation relies on structured clinical and administrative information. Population health analytics require consistent representations of patient encounters across multiple care settings.

FHIR does not eliminate these challenges, but it provides a standardized foundation that makes solving them considerably more achievable.

HL7 has increasingly emphasized that AI and FHIR should be viewed as complementary technologies, arguing that standardized health information creates the conditions necessary for AI to operate safely and effectively within healthcare environments.

AI is raising the stakes for interoperability

Much of today's discussion around generative AI focuses on large language models, foundation models, and intelligent agents. Those technologies are impressive, but they are only one component of a much larger ecosystem.

The organizations seeing the greatest value from AI are investing just as heavily in governance, data quality, interoperability, and information architecture as they are in the models themselves.

McKinsey has observed that healthcare organizations are moving beyond isolated AI implementations toward enterprise architectures capable of supporting multiple AI capabilities across clinical, operational, and administrative functions. That evolution depends on standardized, interoperable data rather than disconnected point solutions.

This is an important lesson for healthcare executives.

The question is no longer whether AI should become part of the organization. That decision has largely been made. The more pressing question is whether the organization's information architecture can support AI at enterprise scale without creating additional complexity or introducing unnecessary clinical risk.

FHIR is increasingly becoming one of the answers to that question.

Interoperability is becoming an enterprise capability

Perhaps the most significant shift occurring today is that interoperability is no longer confined to integration teams.

Clinical leaders depend on it to improve care coordination.

Operational leaders rely on it to streamline patient flow and optimize capacity.

Revenue cycle teams need consistent information to automate reimbursement workflows.

Researchers require standardized datasets to accelerate clinical discovery.

AI initiatives depend on it because fragmented information inevitably produces fragmented intelligence.

As these priorities converge, interoperability is becoming less about connecting applications and more about creating a connected enterprise capable of supporting continuous learning, informed decision making, and intelligent automation.

FHIR sits at the center of that evolution because it provides the shared language that increasingly allows people, applications, and AI systems to work from the same clinical understanding.

Life sciences faces the same challenge

Life sciences organizations are generating unprecedented volumes of research, genomic, manufacturing, clinical trial, and real-world evidence data. Bringing a new therapy to market still takes an average of 10 years and costs approximately $1.4 billion, with nearly 80% of those costs occurring during clinical development. AI has the potential to reduce both the time and cost required to develop new treatments, but only if it has access to complete, connected, and trustworthy data. That is why AI is quickly becoming part of everything from drug discovery and protocol design to patient recruitment, safety monitoring, and commercialization.

None of these efforts happen in isolation. Research data moves between pharmaceutical companies, contract research organizations, healthcare providers, regulators, laboratories, and commercial partners, each operating within its own systems and data models. Connecting those environments has long been one of the biggest obstacles to accelerating innovation.

FHIR is beginning to change that by making clinical information easier to exchange and reuse throughout the research lifecycle. Combined with modern cloud platforms and scalable data architectures, standardized healthcare data allows organizations to spend less time locating, cleaning, and reconciling information, and more time applying it to scientific discovery.

As precision medicine continues to evolve, organizations will increasingly need to bring together structured clinical records, genomic data, medical imaging, and real-world evidence into a single, usable view of the patient. The ability to connect those data sources consistently and at scale will shape how quickly new therapies are discovered, how efficiently clinical trials are conducted, and ultimately, how rapidly medical breakthroughs reach the patients who need them.

FHIR's biggest impact may still be ahead

Healthcare has spent decades investing in electronic health records, modernizing technology, and making it easier for information to move between systems. Those efforts were never easy, and they are far from finished. But they created something far more valuable than connected records alone: a data foundation that AI now depends on.

As organizations begin moving beyond AI pilots and into real-world adoption, one thing is becoming clear. AI is only as effective as the information it can access. When data is complete, standardized, and connected, AI can help clinicians, researchers, and administrators make faster, more informed decisions. When that data is fragmented or inconsistent, AI simply exposes the same problems that have challenged healthcare for years.

FHIR was never designed to power artificial intelligence. It was designed to make healthcare information easier to exchange between people, systems, and organizations.

That may end up being one of its most important contributions.

The future of healthcare AI will depend on more than increasingly sophisticated models. It will depend on whether organizations can build trusted data environments where clinical, operational, financial, and research information work together instead of living in separate silos. Governance, interoperability, and data quality will ultimately determine how much value AI can deliver.

FHIR helped healthcare learn how to share information. Its next chapter may be helping AI learn how to understand it.

TSG works alongside healthcare and life sciences leaders to modernize technology environments, improve interoperability, and build AI-ready data ecosystems that support better clinical, operational, and research outcomes. If your organization is evaluating its next step, get in touch with our experts to learn more about what we're seeing across the industry.