The success of agentic AI rollouts will depend on whether underlying workflows are designed to effectively support it. IT departments should play a central role here—but effective coordination with operations teams is key.
Agentic AI, like a host of other technologies that came before it, is poised to transform healthcare. A quick scan of recent stories in this very magazine illustrates the sheer breadth of—and investment in—its promise: from improving patient services and reducing administrative burdens to managing denials, optimizing clinical knowledge infrastructure, and even disinfecting hospital rooms.
That doesn’t necessarily mean that healthcare organizations are prepared to harness its full power. A Microsoft study published last December in the New England Journal of Medicine found that only 3% of surveyed health system leaders are actively deploying agentic AI, even as ~75% are focused on pilots and exploratory efforts.
A critical reason these efforts fail is due to what AI advisor Stuart Winter-Tear calls “translation debt,” or the hidden operational work created when AI is layered into existing workflows. Executives think something is automated away only to find that work has been redistributed into more coordination, exception handling, reconciliation, and oversight tasks.
Healthcare IT leaders know this better than anyone. At less digitally mature organizations, a string of technology transformations has left IT teams saddled with an overabundance of applications that don’t deliver real ROI. With agentic AI serving as an extension of these initiatives, it’s understandable that IT departments may not want to own new rollouts.
That, however, would be a mistake. IT’s technical know-how is pivotal in redesigning the workflows, outlining the use cases, and establishing the governance structures that generate successful outcomes. The challenge is to bridge the gap between the technical and operational sides of an organization. IT needs a contextual understanding of the operational workflows in which they’re embedded, while operational teams need to be more technically savvy.
Fortunately, taking direction from prior digital platform implementations can help.
Four Lessons for Healthcare IT Leaders
Healthcare IT departments have been built up through large platform implementations, with many today still allocating much of their time to electronic health record (EHR) support.
This makes EHR and related digital transformations the ideal proving ground from which to draw lessons to help IT collaborate with operations to redesign workflows—and ensure agentic AI reaches its full potential.
Here are four key lessons to remember:
1. Manage AI translation debt with effective governance.
With EHR, shadow systems proliferated: despite a comprehensive enterprise platform, clinicians and administrators continued to rely on spreadsheets, manual trackers, and internal communication channels to manage handoffs, exceptions, and workflow gaps. No wonder that a 2016 study found physicians were spending two additional hours of EHR and desk work for every hour of direct clinical face time.
The takeaway: as soon as you introduce an AI agent, all the implicit coordination, exception handling and context previously internalized by humans must be made explicit, with clear and structured governance in place. IT leaders should work with operations teams to clarify who has oversight and access, map human and agent responsibilities against different risk levels, implement technical controls, and monitor what’s happening in real time.
2. Manage fragmented data infrastructure.
An AHRQ-funded study revealed that a leading Computerized Provider Order Entry (CPOE) system facilitated 22 types of medication-error risks. A major culprit? Information errors generated by fragmented data and hospitals’ various information systems.
This issue is already plaguing health systems’ AI initiatives. The Microsoft study notes that a multi-year effort to deploy an AI scheduling agent faltered due to poor data infrastructure, while another system’s multi-agent tumor-board application pilot succeeded only after integrating imaging, pathology, and genomic data into a single platform that allowed agents to reason across unified patient records.
IT leaders must work to convince their colleagues that AI efforts are not a one-off project and can’t be siloed away. Agentic AI only works with enterprise-wide cooperation and interoperable technology infrastructure.
3. Secure buy-in from frontline workers.
In the early 2010s, Robert Wood Johnson University Hospital’s EHR effort failed dramatically—ICU nurses, for instance, had no information about it until the day it went live—due in large part to a lack of support from nursing leaders. This isn’t an isolated case: despite the fact that nurses are the largest EHR workforce, they are often the least considered in implementation design.
When Robert Wood tried again—this time, successfully—the hospital empowered these workers by creating the Sunrise Clinical Manager End User Council, which included representatives from every nursing unit and the executive power to approve all decisions related to EHRs. Similar structures that establish buy-in and participation from frontline staff are necessary in determining whether specific agentic AI applications will be able to generate measurable benefits. IT can play a vital role here, leveraging its technical expertise to train and educate employees.
4. Proving—and measuring—ROI is key
IT and operations teams often jockey for credit when it comes to new technology initiatives and their associated cost savings. That has the potential to not only create costly risks (e.g., redundancies, a lack of governance, etc.) but also steep challenges in linking new tools to revenue improvements.
It’s vital, then, that IT teams understand how to tie agentic AI investments to ROI by taking into account a wide range of metrics, from reductions in labor expenses and denials to patient experience improvements, better clinical outcomes, and employee retention.
We Need to Get This Right
Getting the work redesign right for agentic AI tools will have far-reaching benefits. After all, for an increasingly burnt-out—and in-demand—clinical workforce, successful implementations can remove busy work, improve patient care, and ultimately lead to higher job satisfaction.
IT can’t sit on the sidelines. They must work alongside operations teams to successfully push agentic AI strategies forward. By heeding lessons from past digital transformation efforts, hospitals and health systems can better manage these rollouts and deliver sorely needed wins to the industry at large.
About James McHugh
James McHugh, Managing Director at BRG, is a seasoned healthcare technology and automation expert. He specializes in technology automation, performance improvement, EMR implementations, and revenue-cycle management, blending operational experience with technical expertise to help hospitals and health systems improve their operational and financial performance.
About Patrick Higley
Patrick Higley is a Director in the BRG Healthcare practice, where he advises healthcare clients on digital/AI investment and adoption strategy. He works with health system and industry executives to translate emerging technologies into measurable results, helping organizations build digital capabilities across clinical and operational functions.
