TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion and thought leadership area. Today’s topic is on moving AI tools from restricted pilots to full operational use, and what that entails in workflow design. The author, Inger Sivanthi, is the Chief Executive Officer of Droidal, an AI healthcare services company focused on revenue cycle and operational automation. His and the company’s work centers on responsible AI adoption that scales to real world clinical and operational workflows.

For the past few years, healthcare AI has been framed as a story about possibility. Leaders have asked which workflows could be automated, which decisions could be supported, and which longstanding bottlenecks might finally be addressed. Those questions were necessary, because they opened the door to experimentation and helped organizations test where AI might create value.

That phase is ending. The difference between organizations that will move forward and those that stall no longer lies in how many AI tools they pilot. It lies in how well they can embed AI into real workflows, governance structures, and enterprise systems. The next phase of healthcare AI will depend on operational execution, not on how advanced the models are.

Pilots rarely prepare you for real operations

Most pilots run under conditions that real operations never see. The workflow is narrow, the team is small, and someone is watching the numbers closely enough to catch problems before they compound. In that environment, even fragile AI tools can look impressive.

Production is different. Real healthcare operations are full of exceptions, staff changes, and system constraints. Tasks that start with simple data triage or routing quickly spread into clinical, scheduling, and financial workflows. The closer AI gets to patient care, safety, and financial outcomes, the more important the underlying operational design becomes.

A 2023 systematic review on barriers to AI in healthcare, published in PLOS Digital Health via the National Institutes of Health, shows that the main obstacles are not the algorithms themselves, but how they fit into existing workflows and governance structures. The model may work. The operational scaffolding does not.

Success depends on how you design workflows around AI

Healthcare AI becomes valuable only when it fits naturally into the way people already work. That requires more than a technical deployment. It requires clarity on who reviews the output, who handles exceptions, and how decisions are documented when the system is wrong.

When an AI system touches clinical documentation, care coordination, or billing decisions, the downstream consequences land on real people and real timelines. Delays or misrouted tasks can affect patient access, continuity of care, and financial performance. The closer AI gets to those outcomes, the more important it is to have a named person responsible for reviewing what the system produces and catching what it misses.

Connectivity between systems and teams is just as critical as the AI model itself. Point tools that sit outside the surrounding infrastructure often create new handoffs instead of removing work. The strongest operational use cases in healthcare are those that span pre‑service, clinical, and post‑service workflows. AI delivers the most value when it supports the full sequence, not just one isolated task.

Trust depends on how you govern AI in practice

Another reason execution is difficult is that organizations often treat adoption as an afterthought. Staff are expected to absorb new tools while maintaining the same pace, same targets, and same accountability. That usually leads to friction, workarounds, or quiet resistance.

AI changes the shape of work. It shifts who reviews information, who handles exceptions, and how quickly decisions move. People need clarity on those changes and confidence that the system supports them, not that it creates hidden complexity. The closer AI gets to high‑stakes decisions, the more important it is to have clear governance, oversight, and auditability.

The American Medical Association highlights that clear accountability, oversight, and governance are essential for AI to earn trust inside health systems. AI will not win trust through claims or marketing. It will earn trust by performing consistently inside real workflows, especially when pressure is high and staff have no time to compensate for gaps.

Operational execution is where AI succeeds or fails

The organizations that move ahead in healthcare AI will not be the ones running the most pilots. They will be the ones that turn AI into dependable operational infrastructure. That means embedding responsibility, transparency, and integration into the design from the start.

The next phase of healthcare AI will depend on how well organizations manage its operational execution, not on how advanced the models are. Healthcare leaders who treat AI as a workflow‑design problem, not just a technology problem, will be the ones for whom it actually works in practice.