Over the past year, nearly every AI conversation in healthcare has started the same way: Which model should we use?

It’s a fair question. Large language models have matured rapidly, and their capabilities are impressive. But in production environments, the model is rarely what determines success. Interoperability does.

Across payer, provider, and digital health implementations, we’ve seen a consistent pattern: organizations invest heavily in AI reasoning before ensuring they can reliably deliver the right clinical signal into the right workflow at the right moment.

When the foundation isn’t ready, even strong models underperform.

That reality is what led us to develop the SONG Framework — a practical lens for evaluating whether your custom AI solutions are truly positioned to scale.

Signal: The Hidden Constraint

Most AI pilots perform well in controlled demos. They struggle in live environments. Not because the reasoning is flawed, but because the signal is incomplete.

A lab result that syncs 45 minutes late can alter a decision. An API that performs well in testing may throttle under real-world load. A medication history that arrives via fax introduces silent gaps.

These aren’t rare edge cases. They’re operational realities.

Having a FHIR endpoint doesn’t guarantee agent-ready data. APIs may update in batches rather than in real time. Terminology mappings may be inconsistent. External systems may be only partially connected. Consent policies may restrict access in ways that are not immediately visible.

An AI agent built on partial inputs will not necessarily fail dramatically. It will simply generate output that appears confident but rests on unstable ground.

Before comparing models, organizations should ask a more foundational question: Can we reliably access clean, timely, longitudinal clinical signal? If not, the model choice is secondary.

Orchestration: Where Adoption Is Won or Lost

Even with strong signal, AI fails if it disrupts the workflow.

Clinicians don’t adopt tools that add clicks, create new logins, or require blanket review of every output. We’ve seen well-designed agents ignored because they weren’t embedded at the right trigger point. We’ve also seen promising logic create fatigue because every recommendation required the same level of oversight, regardless of risk.

Production AI requires orchestration discipline. That means embedding agents within existing workflows, routing outputs based on confidence, and distinguishing between low-risk automation and high-risk escalation. When orchestration is weak, AI shifts the burden instead of reducing it.

This distinction rarely appears in early demos. It becomes clear in production.

Normalization: Structure Is Not Meaning

FHIR standardizes format, but it doesn’t guarantee shared meaning.

In practice, one system may document “glucose” while another records “blood sugar.” Units may differ across lab networks. Local codes may map imperfectly to national standards.

Humans navigate these inconsistencies intuitively. Automated systems do not.

Without deliberate semantic normalization, including terminology mapping, unit conversion, and ambiguity handling, AI reasoning degrades. The system may function technically, but patterns are missed, and conclusions become less reliable.

Structural interoperability is necessary. Semantic consistency is what makes AI dependable.

Governance: The Lifecycle Question

AI in healthcare isn’t static. Guidelines evolve. Payer policies change quarterly. Formularies shift. Regulatory requirements expand. An agent configured for today’s environment can drift within months.

Drift rarely presents as a dramatic failure. It appears gradually in override rates, subtle inconsistencies, and outdated logic. Without monitoring and version control, it can surface later in audits or performance reviews.

Deploying a custom AI solution isn’t a one-time initiative. It’s an operational commitment. Versioning, monitoring, audit trails, and update governance must be built into the architecture from the start.

This lifecycle discipline is often overlooked early on, yet it determines whether an AI system remains an asset or becomes a liability.

The 2025–2027 Convergence

The urgency behind this architectural shift is increasing. CMS-0057-F mandates FHIR-based prior authorization APIs by 2027, while TEFCA continues reshaping national data exchange expectations. At the same time, administrative burden remains unsustainable, and AI capabilities continue to mature.

These forces are converging in a narrow window.

Organizations that treat interoperability as a compliance task will meet minimum standards. Those who treat it as an architectural strategy will position themselves to scale AI responsibly. The difference is readiness.

A More Sustainable Approach to Healthcare AI

AI will continue to improve. Models will become more capable. But no model can compensate for delayed data, fragmented workflows, inconsistent semantics, or unmanaged drift.

Before launching another pilot, healthcare organizations should pause and evaluate the infrastructure beneath it:

  • Is the signal reliable?
  • Is the workflow ready?
  • Is the data normalized?
  • Is governance defined?

The SONG Framework (Signal, Orchestration, Normalization, and Governance) was developed to help leaders answer those questions and assess architectural readiness before scaling AI initiatives.

To explore these dimensions in more detail and see how they apply to prior authorization, medication reconciliation, chronic disease management, and payer data exchange, download the full white paper here.

Healthcare AI will scale. The organizations that strengthen their foundations first will scale it responsibly.