As health systems across the U.S. continue to explore how artificial intelligence fits into their operations, a frequent point of concern is whether a solution can be trusted in a clinical context.
How will an AI-powered solution affect patient outcomes? How will it impact staffing and workflow? These are all valid questions that should be addressed at an organizational level even before the search for a solution is in the works.
Scaling Challenges in AI Projects
Not having clear answers before deployment can lead to difficulties scaling a project. A 2026 survey from clinical platform company Carta Healthcare found that 71% of healthcare organizations that are seeing the value in their AI projects are not scaling at pace due to several factors.
44% named integration with an electronic health records system as a top barrier, 37% named lack of executive sponsorship or budget, and 33% named competing organizational priorities. Clinical trust, ongoing cost, and regulatory concerns all tied at 26% as a major barrier.
The Role of Governance in AI Adoption
Such impediments can and should be addressed when a health system has a robust AI governance structure with multidisciplinary participation that includes physicians and nurses from the start. In order to scale AI, organizations must have clear governance in place.
Otherwise, projects never leave the piloting phase, user adoption never grows, and investments are squandered. Clinical users have expressed that when they’re expected to adopt a new technology or process, they’re less likely to do so if the solution was handed to them as mainly an IT directive rather than a collaborative project.
The question should not be, “How can we adopt the latest technology?” but rather, “How can we understand a common problem, with all the right context in a clinical environment, that must be solved?” That’s a key part of AI governance: understanding stakeholder pain points, risk, and the impact to workforce.
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To build trust, clinicians must be involved in governance. If an organization wants to solve a clinical problem such as documentation burden, physicians and nurses should be at the table. IT teams should be embedded with a nurse to understand where the bottlenecks are that can be solved with technology.
When organizations don’t have clinicians — especially nurses — involved in governance, there is resistance to AI adoption. A project is then viewed as a cost-cutting measure or a tool for replacement. Those concerns must be addressed immediately.
Measuring AI Success and Data Readiness
Organizations have room to grow when it comes to workforce training and communication of AI programs. For instance, through the governance process, leadership should ensure that education and additional help desk support are baked into a solution’s deployment.
Another piece of governance includes clear measurements for ROI. Has your organization defined how it will measure success, and is that definition understood and clear to all involved? Those measurements could be in better clinical outcomes, reduced workflow complexity, or improved data processes.
That’s why the most common AI use cases in healthcare in recent years have been around ambient clinical documentation, back-office administrative automation, and medical imaging and clinical decision-making support. These use cases have measurable outcomes in time saved, tasks reduced or changed, or patients supported.
Even before healthcare organizations can shop around for an AI solution, they must also address data quality. Data readiness is foundational when layering on an AI tool to eliminate inefficient processes and workflows, rather than exacerbating them. Improving data governance will also help to address clinicians’ concerns around trust.
Strategic Partnerships in AI Implementation
A strategic partner can help clinicians build trust in AI by addressing inefficient processes, establishing governance, modernizing data, and targeting high-value problems. Such a partner can assist organizations in measuring success, ensuring agreement on definitions and goals, and providing change management support through training and a network of AI champions to improve adoption.