Health systems are slowing AI scale-up because governance, clinician involvement, data readiness and clear success metrics are often missing. Addressing those gaps up front makes deployments more likely to move from pilot to routine use.
Most AI pilots stall: a 2026 Carta Healthcare survey found 71% of organizations that see AI value are not scaling projects.
Top technical and organizational barriers: EHR integration (44%), lack of executive sponsorship or budget (37%), and competing priorities (33%).
Include clinicians early and often: multidisciplinary governance with physicians and nurses reduces resistance and frames AI as a clinical support, not headcount replacement.
continue to explore how artificial intelligence fits into their operations, a frequent point of concern is whether or not a solution can be trusted in a clinical context. These are all valid questions that should be addressed at an organizational level even before the search for a solution is in the works. Not having clear answers before deployment can lead to difficulties scaling a project.
If an organization wants to solve a clinical problem such as documentation burden, physicians and nurses should be at the table. It should be about addressing inefficient processes, establishing governance, modernizing data as needed and targeting high-value problems. 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.
Get the Report Listen Pause As health systems across the U.S. How will an AI-powered solution affect patient outcomes? In order to scale AI, organizations must have clear governance in place.
Medical students may be comfortable using digital tools, but that does not mean they are prepared for the electronic health record demands of residency. New clinicians must navigate the EHR to review extensive patient histories, manage orders and document decisions while keeping pace with clinical workflows. Meanwhile,
Artificial intelligence is influencing healthcare before many appointments even begin. New research from Zanda, a practice management software platform for allied health professionals, found that 70.2% of practitioners had clients reference AI-generated health information during consultations in the past 12 months. T
Healthcare organizations across the U.S. are rolling out chatbots powered by artificial intelligence to answer patient questions, guide people to care and help navigate the system. The logic is understandable. If patients are already using ChatGPT and other AI tools for health information, healthcare organizations natu
<figure data-wp-context="{"imageId":"6aafff4e72ebd"}" data-wp-interactive="core/image" data-wp-key="6aafff4e72ebd" class="wp-block-image size-full wp-lightbox-container"><img data-recalc-dims="1" data-attachment-id="17191" data-permalink="https://digitalthoughtdisruption.com/2026/09/20/clinical-decision-support-ai-need
The following is a guest article by Baha Zeidan, CEO at Azalea Health Healthcare has always been a human business. Yet, somewhere along the way, the technology designed to help clinicians began demanding more of their attention than their patients. Healthcare teams started serving technology instead of it serving them.
Takeaways AI’s greatest near-term impact in many healthcare organizations may be in administration, particularly as financial pressures persist and experienced employees continue to be difficult to recruit and retain in the industry. Healthcare employers are increasingly integrating AI across the employment lifecycle,
Loading more related stories...
Open the app view to save this story, compare related coverage, and continue from the same source.