Healthcare organizations are investing in AI, but most of it is piecemeal.
While AI spending in healthcare nearly tripled to $1.4 billion in 2025, typical healthcare systems coordinate dozens of different vendors for AI and automation solutions, from ambient AI scribes to AI-driven pre-authorization apps.
This “vendor sprawl” causes more problems than it solves, as managing these point solutions strains IT resources. Additionally, using multiple vendors for AI and automation misses the point of AI and automation: improving quality, improving patient experience, and streamlining efficiency.
However, while 62% want one comprehensive AI partner, only 13% have one, according to a recent industry report.
Fortunately, healthcare leaders are now waking up to the fact that vendor sprawl is undermining the promised ROI. As new AI applications and use cases enter the market, it’s important to reframe your approach to technology adoption and reconsider your broader organizational goals.
The rise and fall of fragmented “point solution” AI approaches
The past decade has seen an explosion in the number of health information technology (IT) applications used across health systems.
According to the 2026 College of Healthcare Information Management Executives (CHIME) Leadership Survey, more than 40% of healthcare organizations use 75 or more healthcare operations applications. Another recent study shows that health systems use an average of 18 different EHR vendors across locations, departments, and acquired operations.
However, while federal policy has long encouraged and encouraged hospitals and clinicians to adopt interoperable EHRs (through CMS’s Interoperability Acceleration Program and ONC’s accreditation requirements under the 21st Century Cures Act), there is still no comparable framework governing AI.
The HTI-1 final rule was the first step. It sets baseline transparency and risk management standards for predictive tools built into ONC-certified health IT, including EHRs used by most hospitals and physicians in the United States. However, their scope of protection is narrow. These apply only to AI that EHR vendors incorporate into their own certification systems, and not to standalone tools that hospitals purchase and add separately.
In other words, the very critical solutions that hospitals are amassing are outside the scope of the one federal regulation that governs medical AI today, and even that regulation targets transparency, not interoperability. There are no mandates yet that require AI tools to connect with each other or with the systems around them, and the rules are still evolving.
Not surprisingly, the proliferation of AI point solutions can be attributed to AI’s success in mitigating serious problems. For example, physician burnout is visibly influenced by the AI scribes around them. However, each time a separate AI point solution is adopted, the task of integration becomes more difficult.
According to a 2026 industry survey of senior leaders at medium and large U.S. health systems, 69% of senior health system technology leaders cite vendor management and integration as the biggest barrier to implementing AI solutions, with some organizations spending 26-50% of IT staff time on it. Further, only 4% report having sufficient resources to maintain that level of oversight.
A more consistent AI approach
Reducing a stack of siled vendor solutions to just a handful of solutions that work together seamlessly is a difficult task. But through a more comprehensive AI implementation approach and deeper vendor vetting, it is possible.
Improving AI capabilities starts with choosing care partners that look across the spectrum of possibilities. Big picture. Optimize efficiency from the moment a patient or care coordinator books an appointment, until the claim is processed, and throughout the entire continuum of care.
Not all vendors can oversee multiple AI use cases. Additionally, it may be OK for some health systems to take a point-solving approach to testing water at this time.
But over time, working with a vendor that understands the “big picture” and how multiple AI-powered systems work together will become increasingly important.
Most medical institutions understand this. As noted in the CHIME study, 75% of healthcare leaders say operating across multiple tools is central to the challenge of modernizing healthcare systems.
The right care partner understands the twists and turns of the healthcare regulatory environment and stays one step ahead of all specifications, interoperability standards, and compliance requirements. The ideal partner understands how ambient AI tools can not only listen, but leverage clinical evidence and ancillary data to ensure clinician concerns are documented and substantiated. The same ideal partner understands that while the quality of notes is important, other aspects of documentation and data exchange are equally important.
Moving away from point solutions isn’t always fun. We’re excited to check out the next hot new AI application with sexy curb appeal and the potential to solve hospitals’ biggest challenges.
But the right AI care partner offers something more sustainable: multiple tools that work well together and cover the entire patient encounter cycle. This type of care partner helps hospitals move away from a “point” approach to maximize outcomes and ROI.
For most organizations, simply adding to the AI stack is not sustainable. A better way forward is to replace point solutions that don’t work well together and invest in a smarter approach with the right AI vendor who understands the full spectrum of care and wants to build the AI to support it.
