Why Healthcare Still Isn’t Ready for AI

Artificial intelligence (AI) is often heralded as the next frontier in healthcare—promising everything from faster diagnosis to personalized patient care. But despite near-universal recognition of its potential, the reality is that most healthcare organizations are far from ready. According to Cisco’s AI Readiness Index, while 97% of health leaders believe AI is essential to their future, only 14% are equipped to deploy it effectively today.

What’s holding healthcare back? The answer lies in deep-seated, foundational challenges that should be addressed before AI can truly transform patient outcomes.

Data Quality and Infrastructure Limitations

AI thrives on data, but healthcare’s digital backbone is still faces challenges related to interoperability and technological advancement. Patient information is frequently siloed in disconnected electronic health record (EHR) platforms—making it difficult, if not impossible, for AI tools to access a comprehensive view of the patient journey.

Even when data is accessible, it may be unstructured, incomplete, or gathered primarily for billing purposes rather than clinical care. Further, organizations may not have invested in secure, unified data platforms or data lakes capable of supporting robust AI analytics. In those situations, algorithms are often trained on partial or outdated information, undermining their accuracy and reliability.

Example: A regional hospital group and Cisco customer that was attempting to deploy a predictive analytics tool for readmissions found that their data was scattered across multiple systems and locations, with no single source of truth.

Governance, Trust, and Explainability

For clinicians, trust in AI should be non-negotiable. Yet AI solutions may operate as “black boxes”—delivering recommendations without clear, interpretable reasoning. This lack of transparency can make it difficult for doctors to understand, validate, or act on AI-driven insights.

Compounding the challenge, regulatory frameworks are still evolving and uncertainty with compliance standards can make healthcare organizations hesitant to commit. There are also pressing ethical concerns. For example, algorithmic bias can unintentionally reinforce disparities in care.

Finding: Cisco research found that clinicians often bypass AI-generated risk scores because the platforms lack “explainability,” leaving providers unable to validate the automated insights against established medical protocols during critical care moments.

Workforce and Cultural Resistance

Even the most advanced technology is only as effective as the people who use it. Healthcare organizations that lack the in-house expertise to implement, validate, and maintain AI solutions face challenges in finding enough data scientists, informaticists, and IT professionals, and frontline clinicians may not have the training or confidence to trust AI-driven recommendations.

Furthermore, AI tools may not fit neatly into established clinical workflows. Instead of saving time, they can add new steps and complexity—fueling frustration and pushback from already-overburdened staff. The culture of healthcare, rooted in evidence and caution, can be slow to embrace the rapid pace of AI innovation.

Example: A regional maternal-fetal health initiative led by academia, community, and government leaders seeking to leverage AI for longitudinal care faces barriers to adoption as clinicians fear professional value erosion and internal IT teams resist implementation of AI due to a lack of training and data privacy concerns.

Conclusion: Bridging the Readiness Gap

Healthcare’s AI revolution is coming—but only for those who lay the groundwork. The sector should prioritize data quality and interoperability, invest in transparent and trustworthy AI governance, and empower their workforce to confidently leverage new technologies.

Cisco’s Professional Services Healthcare Practice is uniquely positioned to help organizations address these challenges:

• • Data and Infrastructure Modernization:
    Cisco assists with designing secure, interoperable data architectures, integrating legacy systems, and building robust platforms for AI-driven analytics.
  • AI Governance and Trust Services:
    Our experts help organizations through ethical AI adoption; and the implementation of transparent, explainable AI solutions—building clinician and patient trust.
  • Workforce Enablement and Change Management:
    Cisco provides tailored training, workflow redesign, and ongoing support to help facilitate adoption, upskilling your teams to thrive in the age of healthcare AI.

By addressing these foundational barriers today, healthcare organizations can unlock the promise of AI tomorrow—for better outcomes, greater efficiency, and a healthier future for all.

Interested in learning more?

• Join Cisco at HIMSS 2026 March 9-12, 2026 in Las Vegas! Visit us at booth 10922 in the AI Pavilion to experience live demonstrations of our newest solutions. Engage in one-on-one conversations with Cisco experts to discuss your organization’s needs and discover how our AI-ready infrastructure is empowering the future of healthcare. Learn more here.
• Contact Cisco’s Professional Services Healthcare Practice CXHealthcareBD@cisco.com to accelerate your AI readiness journey.