Healthcare AI’s real bottleneck isn’t intelligence — it’s integration

In 2012, MD Anderson Cancer Center began working with IBM on one of the most ambitious experiments in healthcare AI. The premise was compelling: combine the knowledge of a leading cancer center with Watson’s computing power and help physicians make better treatment decisions.

Five years and roughly $62 million later, MD Anderson allowed the contract to expire before Watson had been used to treat an actual patient. A university audit documented procurement problems, cost overruns and delays. The Journal of the National Cancer Institute also described the challenge of assimilating Watson into a hospital environment where important information lived in physician notes, medical shorthand and electronic records that the system could struggle to interpret.

I take a broader lesson from that history. Intelligence can be impressive in isolation and still create little operating value when it cannot understand the information around a process, participate in the work and hand the next action to the right system or person.

The same operating challenge has returned with agentic AI. Healthcare processes can cross clinical information, coverage rules, providers, payers, care teams and multiple operating systems. As CIO has recently noted in its coverage of enterprise architecture for agentic AI, traditional interfaces can move data between systems without supplying all the business context an agent may need to operate across them. In my experience, that is where the architecture problem begins.

The problem between the systems

Prior authorization is a useful example because it appears simple from the outside. A request is submitted and a decision comes back. Inside the enterprise, the process may depend on eligibility, clinical documentation, coverage requirements, information from a provider and a review that may require professional judgment. Each component can function well, and the overall process can remain slow because the difficult work often sits between systems.

Beginning in 2027, impacted payers face new CMS API requirements involving provider access, payer-to-payer exchange and prior authorization. The CMS Interoperability and Prior Authorization Final Rule requires impacted payers to implement certain FHIR APIs, with API compliance dates generally beginning January 1, 2027. The Prior Authorization API must support requests and responses and communicate whether a request is approved, denied with a specific reason or requires more information.

Those connections create a foundation for a more integrated operating environment. The information still has to enter a process that understands its context and knows where the work should go next. Software may be able to complete an action in one situation, while in another case it has to reach a person.

An AI agent could retrieve a clinical record, examine a coverage rule or recommend a next action. Its contribution depends on how well that intelligence fits into the complete operating process.

That is why I would be cautious about measuring AI progress by the number of agents deployed. Giving an agent access to five applications says very little about whether the underlying process improved. If people continue moving information manually and deciding where the work goes next, the organization has gained another intelligent capability without necessarily gaining much operating capacity.

Architecture should follow the work

I have spent years looking at automation through the way businesses operate. I tend to break an organization into its functions; understand the work each function performs and examine how information and responsibility move between them. The industry may change, although the discipline remains similar: start with the work.

For a healthcare CIO, that means understanding one complete process end to end. The CIO needs to know which information the process depends on, where it originates, who touches the work, which activities are repetitive and where judgment enters. From there, the architecture has to account for the system or person responsible for the next action.

These operating questions expose the difference between automating an isolated task and changing the way a complete workflow functions.

I organize that healthcare architecture around four layers: experience, intelligence, operations and secure data. Experience is where providers, payers, patients and care teams interact with the enterprise. Intelligence interprets information and can help understand what is happening or recommend an action. Operations carries that action through the organization by executing, routing, delegating or escalating the work. Secure data provides the records, rules and trusted information those functions depend on.

Their value comes from how the layers connect around the process being performed.

In prior authorization, clinical and coverage information has to be available when it is needed. Intelligence may help determine whether the information is complete and what it means in context. Operations then moves the work according to the result. Missing information can route the request back for completion, an automated action may allow the process to continue and a case requiring clinical judgment has to reach the appropriate professional with enough context to make that judgment.

Human involvement should be designed into the architecture from the beginning. Healthcare includes administrative work that automation can handle and decisions where professional responsibility stays explicit. Security, privacy, governance, compliance and monitoring belong in the same operating design. As agents participate in more of the work, the organization should be able to understand which information was used, what action occurred and where responsibility sits afterward.

This is where I think people-in-the-loop and agentic AI-in-the-loop become a useful operating model. The balance can vary across functions because the work itself varies.

Measure what changed

The measure I use for automation is what I call flex capacity. If one employee can handle 100 transactions and automation allows that role or team to handle 250, the organization has created additional capacity without adding people at the same rate. The business result is the increase in what the organization can do.

I have seen this in our own operating experience. Work that previously required roughly 500 people has been handled by approximately 100 to 150 people as automation changed the process. Coding, testing, data entry and data analysis are examples of activities where technology can reduce repetitive effort. Better visibility can also allow experienced people to oversee more work without the same administrative layers.

I would apply that thinking to healthcare workflows by establishing the operating baseline before adding agents. That baseline should show how many people touch the process, the volume the team can handle, the time spent moving information and the points where people intervene because the system cannot determine the next step. After automation, the same operating measures show whether capacity actually changed.

Greater volume from the same organization is one useful result. Reduced administrative work is another, particularly when clinicians and experienced professionals gain more time for work that requires their judgment. A large agent count provides much less information about the health of the operation.

The architecture has to support those outcomes across systems. An intelligent workflow may examine information, predict what could happen and recommend an action. Operations then carries the work to the appropriate destination. The path can involve software, a person or several systems depending on the process.

The 2027 CMS requirements give healthcare organizations a practical reason to improve important connections now. I would use that work to examine one complete operating process; from the information it depends on through the role intelligence can play and the points where professional judgment remains necessary. The resulting action has to reach the right destination, and the organization should be able to measure the change in operating capacity.

The earlier generation of healthcare AI taught me how closely intelligence depends on operating architecture. Today’s AI is considerably more capable, and healthcare interoperability continues to improve. CIOs have an opportunity to bring those developments together around the work itself.

The next advance in healthcare AI will be defined by how effectively the enterprise can put intelligence to work.