The Missing Layer in Healthcare AI: Execution

I often get the question about how we compare with the work being done by the giants of AI.

OpenAI. Anthropic. Google. And more…

My answer is always the same. We build on the shoulders of the giants.

The progress these giants have made in artificial intelligence over the last few years is extraordinary. Large language models can interpret complex documents, summarize vast amounts of information, and reason across data in ways that would have seemed impossible not long ago. Without those breakthroughs, companies like ours would not exist.

They're also moving into the world of healthcare. In early 2026, both OpenAI and Anthropic launched dedicated healthcare initiatives: ChatGPT Health (OpenAI), OpenAI for Healthcare (enterprise platform), Claude for Healthcare (Anthropic).

These platforms allow users or healthcare organizations to connect medical records, lab results, and wellness data directly to AI assistants and analyze them conversationally.

This marks an important transition: healthcare AI is moving from experiments and benchmarks to real deployment.

But these launches also reveal something critical: The big model companies are building AI interfaces to healthcare data — not operational infrastructure for healthcare systems.

While these giants are building intelligence, they are not building healthcare operations. And healthcare breaks because the system itself is difficult to run.

The Reality of Healthcare Operations

If you run a healthcare organization, you know this reality well. Your day is not spent asking philosophical questions about artificial intelligence. Your day is spent dealing with things like:

• Claims that were rejected for unclear reasons
• Explanation of benefits documents arriving in dozens of formats
• Eligibility checks that require logging into multiple payer portals
• Prior authorization workflows that stall patient care
• Operational tasks moving between systems that were never designed to work together

Your operations do not lack information, they are overwhelmed by execution. The real challenge is not knowing what needs to happen. It is getting all the pieces of the system to actually move.

The Invisible Machinery of Healthcare

Healthcare is often described as a clinical industry. But operationally, it behaves more like a logistics network. Information moves between systems. Tasks move between people. Documents move between organizations. A single patient visit can trigger dozens of operational steps.

• Eligibility verification
• Coding
• Claim submission
• Payment reconciliation
• Denial management

Multiply that by thousands of visits. What you get is one of the most complex operational systems in any industry.

What AI Actually Changes

Artificial intelligence is often discussed in dramatic terms. Will AI replace physicians? Will machines diagnose diseases? Those questions dominate headlines.

But the most immediate impact of AI in healthcare may be far more practical. AI can help run the operational machinery of healthcare.

Parsing complex documents like EOBs, coordinating workflows across systems, reconciling payments and claims, routing tasks automatically. In other words, AI can help orchestrate the daily execution that keeps healthcare organizations functioning.

Assistants vs Infrastructure

Most AI tools entering healthcare today behave like assistants. They answer questions, explain information, summarize records — and that is valuable.

But healthcare operations require something different.

They require infrastructure. Infrastructure does not just explain the system, it helps run the system.

It coordinates tasks, moves data between systems, handles exceptions, keeps workflows moving. That is where the real opportunity lies.

Why Smaller Healthcare Operators Need This Most

Large hospital systems have enormous technology budgets. Small and mid-sized healthcare operators do not. Yet they face the same operational complexity. Sometimes even more.

They interact with the same payers. The same billing processes. The same regulatory requirements. But they often lack the engineering teams needed to build sophisticated infrastructure.

This is where AI-native operational systems become transformative. They allow smaller organizations to operate with capabilities that previously required massive technology investments.

The Next Phase of Healthcare

Healthcare has already gone through one major technology transformation: the digitization of records. Electronic health records changed how information is stored. But they did not fundamentally change how work flows through the system.

The next transformation will be operational. Healthcare organizations will move from manual coordination to AI-orchestrated execution. Tasks that once required human intervention will move automatically through workflows.

Humans will remain essential. But their role will shift: from processing tasks to overseeing systems.

Building the Execution Layer

This is the layer we focus on.

Not replacing clinicians. Not building another dashboard.

But helping healthcare organizations run the operational systems that sit behind care.

Because the future of healthcare will not be defined only by better models or smarter algorithms.

It will be defined by how intelligently we design the systems that people work inside.

The giants of AI are building the engines. Our job is to help healthcare organizations actually drive the car.

At XY.AI Labs, we're proud to be that execution layer with the proper infrastructure for the small and medium size operators.

— Sam De Brouwer, Co-Founder, CEO