AI adoption in healthcare has accelerated rapidly. Providers are embedding AI across administrative, operational and clinical workflows, increasing the need for data foundations that can support these technologies safely and at scale. Generative AI (GenAI) remains part of this landscape, particularly in areas such as documentation, summarization and patient communication, alongside predictive and other AI capabilities.

The shift is already visible in clinical practice. The American Medical Association's (AMA) 2026 survey found that 81% of physicians use AI in their practices, more than double the 38% reported in 2023. 

As adoption increases, the challenge for healthcare providers is increasingly one of readiness: whether their data is accessible, interoperable, well-governed and trusted enough to support AI across real-world workflows.

AI adoption moves into everyday healthcare

Healthcare providers are moving from isolated experimentation toward operational deployment. HCLTech's observations from HIMSS 2026 similarly point to healthcare organizations progressing from pilots toward scaled AI adoption, with governance, interoperability and cybersecurity shaping the next phase. 

Adoption still varies by organization, workflow and clinical risk. Administrative applications such as documentation and summarization are advancing quickly, while higher-risk clinical use cases require stronger validation, governance and human oversight.

Across that spectrum, data readiness is a common dependency. AI cannot deliver reliable insights when the underlying information is fragmented, inaccessible or poorly governed.

Data modernization: The critical foundation for AI

A key challenge healthcare organizations face in scaling AI is modernizing the data infrastructure that supports it.

“Many healthcare providers still operate on legacy systems that are not equipped to handle the demands of AI. These systems often store data in silos, making it difficult to access and use for AI applications. As a result, organizations are increasingly focusing on data modernization efforts to ensure that their data is AI-ready,” says Dr. Sandesh Prabhu, Global Head-Life Sciences and Healthcare Practices at HCLTech.

Data modernization goes beyond moving information from legacy systems onto newer platforms. Providers need to improve data quality, interoperability, governance and access so information can move securely across clinical and operational workflows.

Several priorities are central to this effort:

• Data governance and quality: Establish clear ownership, quality controls and governance to make clinical and operational data reliable, traceable and appropriate for AI use 
• Breaking down data silos: Connect data across electronic health record (EHR) systems, imaging, laboratories and operational platforms so clinicians and AI applications can access relevant context 
• Modernizing data platforms and interoperability: Build scalable data architectures and standards-based integration that allow information to move securely across systems and workflows 

The importance of these foundations is increasingly recognized. The OECD's 2026 report on scaling AI in health identifies fragmented data foundations as a significant barrier and says health data needs to be findable, accessible, interoperable and reusable. It also describes secure, interoperable and adaptive technical infrastructure as a cornerstone of scaling AI. 

The wider value of digital modernization also extends to care delivery. In 2025, McKinsey Health Institute noted that the growing use of digital technologies in healthcare can support a more comprehensive understanding of individual and population health and, in turn, could enable more accurate diagnoses, timely treatments and personalized care. 

AI in healthcare: From efficiency to patient and clinician impact

As healthcare organizations expand their use of AI, providers need to evaluate applications according to their data requirements, clinical risk and measurable outcomes. Operational efficiency remains important, but the value case increasingly includes clinician experience, patient access and quality of care.

Common use cases include:

• Operational efficiency: Automating administrative workflows such as scheduling, billing and supply chain management 
• Clinician workflows: Supporting documentation, chart summarization and other tasks that contribute to administrative burden 
• Clinical decision support: Analyzing patient information and surfacing relevant insights for clinician review 
• Patient navigation and education: Helping patients navigate services, schedule appointments and access appropriate educational information 

Reducing clinician documentation burden is one area where AI is beginning to show measurable value. A 2026 JAMA Network Open study of 1,547 clinicians found that ambient AI use was associated with moderate improvements in productivity and efficiency, including reduced time spent on notes and a sustained decline in after-hours documentation.

The patient experience is connected to the same data foundation. Giving clinicians access to relevant, accurate information at the point of care can support better-informed decisions and more personalized interactions. AI can help surface that information, but its usefulness depends on the quality, completeness and context of the underlying data.

Building trust into data and AI

Trust is fundamental to healthcare AI because every AI system depends on data. Providers need confidence in where that data came from, whether it is accurate and representative, how it is protected and how AI-generated outputs are validated.

Responsible AI needs to extend across the full data and AI lifecycle, from collection and governance through deployment and ongoing monitoring.

Key priorities include:

• Governance and accountability: Establish clear ownership for data, AI models and decisions supported by AI 
• Privacy and security: Protect sensitive patient information through appropriate controls throughout the data lifecycle 
• Clinical validation and transparency: Give clinicians appropriate visibility into how tools perform and where human oversight is required 
• Continuous monitoring: Track performance, bias, safety and data quality as models and clinical environments change 

Trust concerns remain significant even as adoption grows. In the AMA's 2026 survey, 86% of physicians identified data privacy and 88% robust safety and efficacy validation as critical to broader AI adoption. The survey also found that 85% want to be consulted or directly involved in decisions about AI adoption. 

AI readiness: The need for education and training

Technology and data foundations alone are not enough. As AI becomes more embedded in healthcare delivery, AI literacy is becoming a core workforce capability.

Healthcare organizations need to help clinicians, operational teams and leaders understand how AI works, where it can add value and where its limitations require human judgment.

Key steps include:

• Developing AI literacy programs: Provide role-specific training for technical teams, clinicians and other employees 
• Clinician and leadership involvement: Involve the people who will use AI in selecting, evaluating and governing new tools 
• Continuous learning: Keep teams current as AI capabilities, governance requirements and clinical evidence evolve 

Building this knowledge alongside modern data foundations can help providers integrate AI into workflows without separating technology decisions from the clinical and operational environments in which they will be used.

The role of strategic partners in healthcare data and AI adoption

Strategic partners can help healthcare organizations navigate the relationship between data modernization, AI implementation and governance.

At HCLTech, we support healthcare organizations across the data and AI transformation journey:

• Data modernization: Assisting healthcare organizations in modernizing their data infrastructure to make it AI-ready
• AI consulting: Helping healthcare organizations select the right AI use cases, build their AI strategy, and identify suitable vendors
• Providing sandbox environments: Creating safe environments where healthcare organizations can test AI solutions before deploying them at scale

“Healthcare organizations are increasingly looking for partners who can offer end-to-end AI solutions, from data modernization to implementation and governance,” says Prabhu.

Building the foundations for healthcare AI

The next phase of healthcare AI depends on the foundations beneath it: modern, interoperable data, strong governance, trusted workflows and a workforce equipped to use AI responsibly. Providers that connect these foundations to measurable clinical and operational outcomes will be better placed to scale AI safely.

Data modernization is central to that effort. It gives AI access to reliable context and supports trust through stronger governance and traceability. It also helps clinicians access and act on information at the right point in care. Success should ultimately be measured through better patient outcomes, stronger clinician experience and more efficient healthcare operations.