5 Things Healthcare Leaders Want from AI in 2025 and Beyond

The role of artificial intelligence (AI) in healthcare continues to evolve at a rapid pace and healthcare executives are rightfully responding with both optimism and caution.  

From large hospital systems to community-based behavioral health providers, leaders are closely monitoring AI’s potential to transform care delivery. They are asking specific, measurable questions: Will this technology reduce administrative burden? Can it enhance patient outcomes? Is it trustworthy, explainable and safe? 

According to a 2024 HIMSS–Medscape report, 86% of health systems are now using some form of AI, with the majority applying it to detect clinical patterns and generate insights that humans might overlook. A separate 2024 McKinsey analysis showed that more than 70% of healthcare stakeholders—including providers, payers and health tech vendors—are actively adopting generative AI solutions. These trends reflect a broader industry movement toward digital transformation, but they also highlight a growing list of expectations. 

Put simply: Healthcare leaders are not seeking novelty. They are seeking value and efficiency. They want AI to solve real problems without creating new ones, and they are setting the bar higher than ever. The following themes reflect key expectations that emerged from field insights and executive conversations across care settings. 

1. Support for Clinicians and Human-Centered Care 

One of the most frequently cited goals for AI adoption is to reduce the administrative burden on clinical staff. Documentation remains a top contributor to burnout, often requiring evenings and weekends to complete. Leaders are calling for AI tools that streamline repetitive tasks, surface relevant information in context and allow clinicians to spend more time in direct patient care. 

In one study, clinicians using AI scribes like Bells Virtual Scribe saved an average of three hours per week on after-hours documentation—a clear indication that ambient AI can reduce administrative burden and allow clinicians to better engage during patient encounters. 

2. Transparency, Trust, and Governance 

For healthcare organizations to move forward with AI, they must be confident in how decisions are made and how data is managed. Executives increasingly demand that AI tools be explainable, auditable and governed by clear ethical standards. Concerns include where data is stored, who has access to it and how AI-generated outputs influence care decisions. 

This demand for transparency is rooted in a growing awareness of AI’s limitations. High-profile examples of AI hallucinations—including erroneous diagnoses of fictional conditions—have reinforced the need for robust oversight and clinician control. A 2025 Philips survey found that 63% of clinicians believe AI can improve patient outcomes, yet only 48% of patients agree.  

However, when clinicians explain how AI is used, 79% of patients report increased comfort. This underscores the importance of trust and transparency at every level. 

3. Integration with Existing Systems and Workflows 

AI solutions must enhance, not complicate, existing clinical workflows. That means seamless integration into electronic health records (EHRs), billing systems and quality reporting tools. Poorly integrated tools risk duplication of effort and workflow disruption, which can reduce adoption rates and compromise care quality. 

Healthcare leaders are placing a premium on AI that supports interoperability and connects to a broader ecosystem of data. Embedded tools that generate real-time suggestions within the EHR, alert providers to care gaps or flag compliance issues are far more likely to be embraced. Integration is not a technical preference—it is a strategic necessity. 

4. Real-Time Intelligence to Improve Outcomes

Healthcare organizations are seeking actionable insights that can be applied in the moment—not weeks later. AI-powered tools that identify patients at risk, suggest timely interventions or alert care teams to emerging gaps in care are seen as highly valuable. These applications go beyond retrospective reporting and support a more proactive, predictive approach to care delivery. 

This real-time decision support is especially critical in high-stakes areas such as behavioral health, chronic disease management and acute care coordination. Leaders want AI that functions as a clinical partner, delivering insights that elevate both quality and timeliness of care. 

5. Mitigation of Financial and Compliance Risk 

With the rise of value-based care and increasing payer scrutiny, financial risk is an ever-present concern. AI can play a key role in supporting compliant documentation, identifying missing elements before claims are submitted and reducing the likelihood of audits or takebacks. 

Executives are keenly aware of the financial implications of documentation gaps. By flagging potential issues early, AI can help protect reimbursement rates and safeguard both revenue and clinical integrity. In this context, AI becomes not just a clinical tool, but a critical component of revenue cycle management. 

 

A Clear Set of Expectations 

As the pace of AI adoption accelerates, so do the expectations placed on it. Healthcare leaders are no longer evaluating AI based solely on its potential—they are demanding measurable outcomes. They want tools that are explainable, interoperable and clinically meaningful. They want partners who offer transparency, governance and staff support. 

Most of all, they want technology that aligns with the values of healthcare itself: safety, trust and human connection.