Building Trust in AI for Healthcare Organizations

Building Trust in AI for Healthcare Organizations

A Framework for Providers and Payers

Introduction

The phrase "Get me to a live person" is a common refrain in reports analyzing user interactions with AI-driven customer service agents. It highlights a fundamental challenge: people often don’t trust AI to deliver fast, accurate, helpful, or empathetic responses. In healthcare, where decisions directly impact lives, this lack of trust carries even greater weight. If patients, clinicians, nurses, physicians, payers, and administrators don’t trust AI, they may reject its use no matter how advanced or beneficial the technology could be. 

Artificial Intelligence (AI) is revolutionizing healthcare by improving decision-making, boosting operational efficiency, and personalizing patient care. However, to unlock its full potential, healthcare organizations must actively build trust among all stakeholders while navigating challenges like job displacement fears, regulatory gaps, and technical limitations. This white paper outlines strategies to foster trust in AI, ensuring the perspectives of providers, payers, administrators, and patients are prioritized, and emphasize ethical, effective integration into healthcare systems. Drawing on trends and insights from 2023–2025, we’ll explore practical steps to overcome barriers and maximize AI’s transformative potential.

Understanding Trust in AI for Healthcare Organizations

A major barrier to AI adoption in healthcare is the fear that it will replace human roles, potentially leading to job losses for professionals, administrators, and support staff. This concern, supported by studies showing automation’s impact on repetitive tasks (e.g., a 2024 report estimating ~15% of administrative roles could be automated by 2030), can fuel resistance and slow implementation. AI should be viewed not as a replacement for human expertise, but as a tool to enhance effectiveness, reduce workload, and allow professionals to focus on high-value tasks. This will sometimes require proactive reskilling and redeployment programs to mitigate displacement risks. 

Simply stating this vision isn’t enough. Trust must be demonstrated through action and engagement. When implemented thoughtfully, AI can automate repetitive administrative tasks, lighten documentation burdens, and support clinical decisions. For example, a 2025 pilot at a major U.S. hospital system used AI to reduce physician documentation time by ~20%, improving job satisfaction while preserving roles. Rather than displacing jobs, AI can empower healthcare professionals by improving efficiency, reducing burnout, and enhancing satisfaction but, organizations must address displacement fears head-on with transparent communication and training initiatives. 

Trust hinges on transparency, accountability, reliability, fairness, and a patient-centered approach but, it’s also shaped by cultural attitudes, past data breaches, or skepticism toward technology in healthcare. In regions like the European Union, stringent regulations like GDPR heighten privacy concerns, while in the U.S., interoperability challenges and historical mistrust in health tech (e.g., EHR frustrations) can foster mistrust. Addressing both technical and human factors, while considering regional and cultural difference, healthcare organizations can drive better decision making, improve patient outcomes, and create sustainable models balancing cost, quality, and accessibility.

Strategies for Building Trust in AI

Broadening the Perspective Beyond IT

Many organizations treat AI as a tool within their IT departments which can restrict its impact to the greater organization. While IT is essential for AI’s implementation, AI adoption in healthcare must be an organization-wide effort involving clinicians, administrators, payers, process owners, and patient advocates. These stakeholders play a critical role in shaping how AI supports healthcare delivery and improves patient outcomes. 

Expanding AI ownership beyond IT ensures solutions are developed with input from daily users increasing trust and effectiveness. When clinicians, nurses, payers, administrators, and patients collaborate with technical teams, AI becomes more aligned with real-world needs, integrates seamlessly into workflows, and delivers user-friendly, cost-effective solutions. Challenges like differing priorities or power dynamics (e.g., between leadership and frontline staff) must be managed through inclusive change management as resistance can arise if stakeholders feel excluded.

Effective AI Enablement

AI enablement shouldn’t be an afterthought or relegated to the end of deployment. Instead, stakeholders must be engaged early to understand how AI enhances their work and how they can contribute to its reliability and trustworthiness. Enablement should be an ongoing process of education, feedback, and adaptation, not a one-time event. 

Focus enablement on practical applications, not technical complexities. Clinicians, administrators, and patients don’t need deep knowledge of AI algorithms, but they may benefit from basic technical literacy (e.g., understanding data privacy or model limitations) to build confidence. A 2025 survey of U.S. healthcare providers found that 60% felt more trusting of AI after basic training on its practical benefits and limitations. AI should reduce, not increase, documentation burdens by automating repetitive tasks and prioritizing patient care over data entry.  Also, successful enablement and adoption occurs when programs are accessible across diverse cultural and regional contexts.

Transparency and Explainability

AI models must be designed for explainability, enabling healthcare professionals and administrators to understand how decisions are made. Open communication about AI’s role, limitations, and processes builds confidence and reduces skepticism. Using interpretable AI tools, such as those providing clear decision rationales, can address concerns, but challenges like “black box” algorithms or proprietary systems may persist, requiring ongoing dialogue with stakeholders. 

Involve stakeholders early during pre-deployment and engage them during the process to build transparency and foster understanding and initial trust. This is especially helpful in regions with a higher level of skepticism due to history of data misuse. For instance, a 2024 EU study highlighted that patients were 30% more likely to trust AI tools with clear explainability features aligned with GDPR.

Breaking Down Healthcare Silos

Healthcare’s fragmented nature limits AI’s potential. Interoperability complexity exists between siloed AI systems, electronic health records (EHRs), and payer systems is vital for seamless communication and data sharing. A unified approach to AI adoption across providers and payers can improve care coordination, payment efficiency, and overall healthcare delivery.

However, achieving interoperability remains challenging due to legacy systems, proprietary software, and differing regulations across regions. A 2025 report noted that only 25% of U.S. healthcare organizations believe they can achieve full EHR-AI integration citing cost and technical barriers. Collaborative engagement of stakeholders helps overcome resistance as they are a part of the solution. Organizations must invest in standards (e.g., HL7 FHIR) and pilot projects to demonstrate success while addressing regional disparities.

Regulatory and Compliance Considerations

AI in healthcare must comply with regulations like HIPAA, GDPR, and FDA guidelines for medical devices to ensure safety, security, and ethics. It’s imperative that organizations establish governance structures to oversee compliance and collaborate with regulators to meet evolving standards. However, regulatory frameworks can lag AI innovation, creating gaps in trust such as unclear guidelines for AI in diagnostics or patient monitoring.  

Building trust requires demonstrating a commitment to patient privacy and responsible AI use from the outset, but organizations must also proactively engage regulators and industry bodies to address emerging risks. For example, a 2024 FDA update emphasized new AI oversight requirements, urging pilot testing to ensure safety, a step some organizations find resource-intensive but critical for trust.

Measuring AI Success and Trust Over Time

Trust in AI must be continuously assessed using measurable metrics, such as provider satisfaction, patient outcomes, administrative efficiency, and error reduction. Feedback loops involving clinicians, administrators, and patients enable ongoing improvements and reinforce long-term trust in AI systems. 

A 2025 study of U.S. hospitals adopting AI for diagnostics found that tracking provider satisfaction (via surveys) and patient outcomes (e.g., reduced readmission rates) increased trust by ~40% over two years. However, organizations must account for regional differences (e.g., patients in Asia may prioritize outcome data, while EU patients emphasize privacy metrics) ensuring metrics are culturally relevant and transparent.

Ethical and Fair AI Use

AI systems must be trained on diverse datasets to avoid biases that could harm certain patient groups. Regular audits should evaluate AI for fairness and ethics, incorporating clear policies to prevent discriminatory outcomes. Again, early involvement of clinicians, administrators, and patients will help mitigate risks and build trust. Note that challenges with data scarcity in underserved regions and biased historical datasets exist and need to be considered. 

A 2024 global health AI report highlighted that ~35% of AI models showed bias toward majority populations underscoring the need for diverse training data and audits. Organizations can partner with patient advocacy groups to ensure fairness, addressing cultural and regional disparities in data representation.

Stakeholder Buy-In and Change Management

AI adoption succeeds with active participation from leadership, frontline workers, and administrators. Change management strategies, like pilot programs, targeted training, and transparent communication, build confidence and reduce resistance. However, achieving universal buy-in can be difficult due to competing priorities, resource constraints, or power dynamics (e.g., leadership vs. frontline staff). 

Engaging stakeholders early, addressing concerns, and showcasing AI’s benefits (e.g., a 2025 pilot reducing nurse burnout by ~15%) are key. Organizations must also manage resistance through tailored strategies, such as regional training programs or incentives, to account for cultural differences in adoption readiness.

AI’s Role in Patient Engagement

AI can enhance patient engagement through personalized health insights, automated reminders, and virtual assistants for self-care. To encourage adoption and trust, healthcare organizations must provide patients with educational resources and clear communication about AI’s role in their care.

Some patients may form distrust in AI due to privacy concerns, lack of personalization, or difficulty understanding outputs. A 2025 survey across North America and Europe found that 45% of patients were hesitant about AI-driven tools unless assured of data security and human oversight. Organizations can address this through user testing with diverse patient groups, transparent data policies, and culturally sensitive communication, recognizing regional variations (e.g., higher trust in tech-savvy markets like Japan vs. skepticism in regions with data misuse histories).

Conclusion

Building trust in AI for healthcare requires a comprehensive approach of benefits and risks. A successful AI healthcare implementation prioritizes transparency, ethics, collaboration, regulatory compliance, payer engagement, leadership, and patient involvement. Concurrently, it must navigate the diverse challenges like job displacement fears, regulatory gaps, and cultural differences. By adopting comprehensive best practices and strategies, providers and payers can foster confidence in AI, leading to better patient outcomes, more efficient care, and cost-effective healthcare systems.

Trust in AI is not a one-time achievement; it’s an ongoing commitment to transparency, collaboration, and refinement, balanced with realistic acknowledgment of barriers. Drawing on global trends from 2023–2025, healthcare organizations can turn AI into a transformative force, but only with proactive measures to address risks and regional disparities. Without trust, AI remains an underutilized tool rather than the game-changer it can be in healthcare.

How ServiceNow Supports AI Trust in Healthcare

As healthcare organizations navigate the complexities of AI adoption, ServiceNow provides a robust platform to support the key recommendations outlined in this whitepaper. By enabling transparency, automation, interoperability, and compliance, ServiceNow helps providers and payers build trust in AI while enhancing operational efficiency.

Breaking Down Silos with Seamless Interoperability

ServiceNow’s AI-powered healthcare workflows integrate with electronic health records (EHRs), payer systems, and other critical applications, breaking down data silos. By leveraging industry standards such as HL7 FHIR, ServiceNow ensures real-time data exchange, improving care coordination and operational efficiency.

Enhancing Transparency and Explainability

ServiceNow’s AI models prioritize explainability, offering intuitive dashboards and audit trails that allow healthcare professionals to understand AI-driven recommendations. This visibility builds confidence in AI decision-making, ensuring alignment with regulatory and ethical guidelines.

Driving AI Enablement with Intuitive User Experience

AI adoption thrives when users feel empowered. ServiceNow’s user-friendly interface and built-in AI training modules help clinicians, administrators, and payers understand and trust AI-driven workflows. With personalized role-based guidance, stakeholders can harness AI’s potential without technical complexity.

Proactive Governance and Compliance

Navigating AI regulations is a critical challenge for healthcare organizations. ServiceNow automates compliance tracking, risk assessments, and governance workflows, ensuring adherence to HIPAA, GDPR, and emerging AI regulations. This proactive approach mitigates risks and enhances stakeholder confidence.

Empowering Healthcare Workers, Not Replacing Them

AI should augment, not replace, healthcare professionals. ServiceNow’s automation capabilities reduce administrative burdens, allowing clinicians and administrators to focus on high-value tasks like patient care and strategic decision-making. By streamlining workflows and minimizing burnout, ServiceNow reinforces trust in AI’s role as a support tool rather than a disruptor.

Continuous Trust Monitoring with Real-Time Insights

Trust in AI is an ongoing process. ServiceNow’s analytics and feedback loops allow organizations to track AI adoption metrics, user satisfaction, and patient outcomes over time. By continuously refining AI-driven processes based on real-world data, ServiceNow helps healthcare organizations sustain trust and drive measurable improvements.

With ServiceNow, healthcare providers and payers can implement AI solutions that are ethical, transparent, and user-centric, ensuring AI is not just a technological advancement but a trusted partner in delivering better healthcare outcomes.

Authors note:  The momentum around AI is such that things have matured in the time you’ve spent reading this document. As a result, it is subject to change, and I look forward to collaboration and feedback on the topic.  Thank you for your time and attention. 

References / Sources: 

World Economic Forum (WEF) – Future of Jobs Report 2025

McKinsey & Company – AI in the Workplace 2025 or The Future of Work in Healthcare

Goldman Sachs – AI and Global Employment Impact (2023–2025)

Forrester – Generative AI Jobs Impact Forecast 2023 (Updated 2025)

HIMSS Reports and AI in Healthcare (2025)

AHA “3 Factors Driving AI Surge in Health Care” (2023)

Docus “AI in Healthcare Statistics 2025: Overview of Trends”

Pew “What the data says about Americans’ views of artificial intelligence”

Reports from PwC, MIT/Boston University academic studies