Laying the Foundation for Smarter AI in Clinical Care: 4 Essentials for a Thoughtful Strategy | Point of view

Leaders in the healthcare sector are investing heavily in AI. Eighty-five percent experiment with or implementing AI tools to improve care and operations, with global spending expected to reach $164 billion by 2030. Yet clinicians are rightly cautious.

In a global survey, only 38% of frontline professionals said that current solutions meet real clinical needs. Almost half fear that if AI is poorly deployed, diagnostics could slow down (46%) and burnout could increase due to additional non-clinical work (46%). The task for hospital and health system leaders is to deliberately choose so that AI lightens the burden rather than adding to it.

So what’s worth adopting and how can you deploy it without overburdening already overworked teams? Here is a practical roadmap built around four essential elements.

1. Start with proven, high-impact use cases

Start where the pain is deepest and the evidence is strongest: time- and morale-draining work. Administrative Burden and Burnout Cost U.S. Healthcare Systems About $1.5 Million $4.6 billion per year. Targeting these burdens first can produce immediate results. Agentic AI tools are already excellent to tasks such as scheduling and tracking appointments, reminders, insurance pre-authorizations and documentation, which consume countless hours of staff time.

By entrusting this work to agentic AI, clinicians and staff save valuable time. At the same time, hospitals and health systems can reduce no-shows, speed up approvals and, most importantly, improve patient outcomes.

2. Balance short-term wins with long-term transformation

Pilot projects and point solutions are important. But they will not solve structural problems such as data fragmentation and aging systems. More than three quarters of clinicians report wasted clinical time due to incomplete or inaccessible patient information.

About a third lose almost an hour per shift. This represents an impressive 23 work days per clinician each year. In the short term, AI can help by filling some of these gaps (such as integrating data from multiple sources or flagging missing information). But ultimately, this requires modernizing IT infrastructure and phasing out systems that trap data in silos.

At the same time, modernize data architecture and interoperability so that future capabilities are built on a solid foundation. Think of AI as part of a broader digital transformation, not a scattershot of applications. On this basis, full integration by 2030 could automate a large part of the administrative burden and, therefore, potentially double patient capacity. To achieve this future, hospital CIOs and CMIOs must address AI initiatives as part of a broader digital transformation, not just as isolated application deployments.

3. Hire for transformation, not tradition

Technology fails without proper leadership. As health systems expand their AI initiatives, success depends on leaders who can fill clinical needs with technical innovation and drive process change that leverages the best of human and AI capabilities.

Look for hybrid talent that combines clinical understanding, product acumen, data savvy, agile ways of working, and human-centered design. Consider non-traditional candidates from technology, finance or other rapidly evolving industries to complement in-house clinical expertise and create roles that can drive AI strategy across silos.

Many organizations are also partnering with technology companies and hyperscale cloud providers rather than building everything in-house. In a survey, 61% of AI enthusiasts favored partnerships with third parties.

It is equally important to put governance in place as early as possible. A clear AI governance framework keeps efforts aligned with clinical priorities, establishes safety and ethics guardrails, and focuses attention on measurable outcomes. Finally, large organizations are developing highly reliable AI-based systems using collaborative learning programs that bring collective expertise to the challenge.

4. Break down cultural resistance to change

The “culture of no” is real. New tools fail when those who need to use them don’t trust them.

Involve doctors, nurses, and other end users in the process from the start: co-design workflows, pilot them in real-world settings, and run feedback loops that actually change the product.

Address legitimate concerns head-on: accuracy, bias, and accountability. More than 75% of clinicians remain unclear as to who is responsible when the AI ​​makes a mistake. Be explicit about validation methods, known limitations, and the place of human oversight in the workflow. Trust grows when safety nets are visible in daily practice.

Making AI Work: A Practical Path Forward

A smarter AI strategy starts small, thinks big, and stays human.

Focus on high-value use cases first, especially those that alleviate administrative burdens and burnout. At the same time, invest in the plumbing: interoperable data, modernized infrastructure and disciplined governance.

Recruit leaders who can bridge the gap between medicine and technology, and empower them to manage change with transparency and rigor. And involve clinicians in the process as early as possible so that AI becomes a helping hand and not another burden.

Do this, and the benefits will come in two waves. In the short term, you streamline operations, reduce absences and bottlenecks and give caregivers time back. Over time, you create the conditions to safely adopt more advanced features as they mature because the data pipelines, guardrails, and culture are ready.

There will be skepticism to overcome, processes to rethink, and existing systems to dismantle. But with a well-founded plan, AI becomes a practical driver for better patient care and a more sustainable healthcare system, rather than the latest hype cycle.

Angela Adams is CEO of Inflo Health.