In a few short years, healthcare organizations moved from experimental AI pilots to system-wide adoption. According to NVIDIA, 70% of healthcare organizations reported actively using AI in 2026, up from 63% in 2025. However, the real story isn’t just about adoption, but application.
The healthcare industry quickly moved from “How do we automate?” to “How do we personalize?”
Moving from standard AI, such as generic chatbots and scheduling management, to personalized AI is the difference between customer service and clinical excellence. Organizations looking to build a cutting-edge place to work and receive care need to expand their use of AI throughout the entire healthcare workflow, especially the patient journey.
At the same time, AI brings significant benefits to both the healthcare system and the patients. According to the Deloitte 2026 Global Health Care Outlook, 64% of healthcare leaders are reducing costs by standardizing and automating workflows.
Patients Bring High Expectations into Each Interaction
One of the biggest shifts in healthcare is that patients no longer compare their hospital experience with others', explained Leah Gabbert, Marketing Director of Global Industry Solutions at Nutanix. Instead, they compare it to their experience using Amazon or Netflix, both of which deliver a seamless and personalized experience.
“Generic advice doesn’t drive behavior change,” said Gabbert. “However, when AI-driven care pathways are tailored to a patient’s specific recovery speed and lifestyle, readmission rates drop.”
When asked about the risks to organizations that don’t focus on using AI in innovative ways to drive value to both patients and providers, Gabbert shared three key concerns:
Talent drain: Top-tier clinicians are fleeing organizations that “bury them in clicks.” They will migrate to “AI-first” hospitals where they can actually spend time with patients.
Margin compression: Without the 64% cost-reduction potential offered by AI-standardized workflows, legacy hospitals will struggle to remain financially viable as reimbursements tighten.
Cyber-Vulnerability: Legacy AI systems are easier to “poison” or hack. Leveling up AI includes leveling up the infrastructure—like Nutanix Unified Storage—to ensure data is immutable and resilient.
AI Tools Transforming Clinical Decision-Making and Provider Support
AI’s ability to mine data from multiple sources gives providers more complete and immediate insight into patients’ histories and needs. By using AI in the decision-making process, healthcare providers can process and analyze volumes of data to help improve patient outcomes.
Mahmee, which operates AI-powered maternal health clinics in Southern California, uses AI for chart summarization and risk stratification. The clinics use AI to analyze patient data, such as pregnancy history, in real time as part of a risk assessment before every visit to note red flags and determine which patients need further assessment.
AI-technology automatically triggers interventions when traditional care would have stopped, and it also improves fetal monitoring accuracy to reduce unnecessary C-sections. Doulas, lactation consultants, and nurses are then freed from administrative work so they can focus entirely on direct patient care.
“Technology is only as equitable as the system it’s built on,” said Melissa Hanna, CEO of Mahmee. “What we’ve built at Mahmee is a coordination layer that makes our human team smarter and more present, not a replacement for them.”
What AI-Powered Patient Experiences Look Like Today
Personalization is one of the most deeply felt AI enhancements for providers and patients alike. Jessica Echeverri, MSW, a practicing psychotherapist and owner of three mental health clinics, uses AI throughout the patient journey. These implementations demonstrate how AI can enhance clinical care while maintaining the human therapeutic relationship that drives outcomes, she explained.
When prioritizing outreach efforts, her clinics use AI-powered lead scoring to identify which prospective clients are most likely to complete intake and maintain engagement. Between patient visits, clinicians implement AI-driven patient monitoring, which analyzes text-based check-ins for mood changes, crisis language, or medication adherence issues.
Additionally, the clinics’ AI tools personalize homework assignments and coping strategies based on individual progress patterns.
How AI Is Streamlining Healthcare Administration and Operations
Many healthcare organizations overlook ways that AI can improve administrative efficiency and operations, which support an improved provider and patient experience. Gabbert says that by using predictive Revenue Cycle Management (RCM), healthcare systems can now predict which insurance claims are likely to be denied before they are submitted, allowing staff to fix errors proactively and shortening the payment cycle.
Because supply chain directly impacts patient care, organizations are increasingly using predictive analytics to reduce delays and errors that can impact patient care. For example, Gabbert says that because hospitals can now predict spikes in respiratory illnesses weeks in advice, they can ensure that PPE and medication stocks are optimized when they are most needed.
Managing AI Healthcare Tech: The Role of Hybrid Multicloud
For healthcare organizations implementing AI systems, there are foundational questions about how to manage them. With diverse sets of AI applications powering a range of capabilities, the need for a centralized, scalable, flexible, and secure environment to house them is critical.
Hybrid multi-cloud has emerged as the optimal solution. A blend of on-premises, private cloud, and public cloud environments gives organizations the flexibility to place AI applications and data in the most appropriate computing environment while maintaining a connected infrastructure.
For example, sensitive patient data can be kept on-premises or in a private cloud to comply with privacy regulations while computationally intensive AI tasks, such as large-scale data analysis or training machine learning models, can leverage the vast resources of public clouds.
As healthcare organizations’ data needs grow, especially with the increasing use of AI and big data analytics, the hybrid multi-cloud model enables healthcare providers to expand their storage and computing capabilities without significant upfront investments in physical infrastructure.
They can also tailor their IT environments to the specific needs of different AI applications. For example, they can use cloud-based services for rapidly deploying and testing new AI tools, then seamlessly integrate those tools into their existing on-premises systems.
What Is the Future of AI in Healthcare?
Healthcare organizations looking to meet patient and employee expectations of digital and in-person interactions must quickly look to add new use cases to their workflows. As the technology and digital interactions in our daily life increase in personalization, the expectations will only continue to rise. By building the infrastructure to support AI use cases and continuing to implement innovative use cases, healthcare systems can provide the best possible care to each and every patient.
Editor’s note: Learn how Nutanix helps healthcare organizations streamline operations with a secure, unified and scalable platform.
This is an updated version of the article originally published on March 5, 2024.
Jennifer Goforth Gregory is a contributing writer. Finder her @byJenGregory.
Michael Brenner contributed to the original article. His work has appeared on sites such as Forbes, Entrepreneur Magazine and The Guardian. Follow him @BrennerMichael.
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