AI is increasingly being introduced into healthcare to improve clinical decision-making, enhance patient outcomes, streamline administrative processes, and increase operational efficiency. AI can support healthcare organizations by analyzing large volumes of data, assisting with diagnosis and treatment decisions, automating routine tasks, and enabling more personalized and timely patient care. However, while the potential benefits are significant, scaling AI across healthcare organizations is far more complex than implementing individual AI solutions.
The values of AI in healthcare
AI value in healthcare begins with operational efficiency
Healthcare organizations are increasingly adopting AI to automate repetitive administrative and clinical tasks, allowing healthcare professionals to work more efficiently and make faster, better-informed decisions. Common applications include generating clinical notes from patient-provider conversations, automating medical coding and billing, streamlining scheduling and patient communications, analyzing medical images to support diagnosis, and providing clinical decision support.
As a result, many organizations are prioritizing AI investments in areas where benefits can be realized quickly and measured clearly. According to Forrester research commissioned by FPT, the most widely realized AI benefits today are workforce productivity (53%), faster decision-making (51%), and improved operational efficiency (49%).
However, the importance of these benefits is expected to diminish significantly over the next one to two years, with fewer than 10% of organizations identifying them as future priorities. This shift suggests that as AI becomes embedded in everyday workflows and routine administrative tasks become increasingly automated, efficiency gains are no longer viewed as a differentiator. Instead, they are becoming an expected part of the operational baseline.
Then, the focus is shifting toward governance, security, and risk management
As AI adoption matures, healthcare organizations are placing greater emphasis on benefits related to governance, security, reliability, and risk management. This transition reflects the unique demands of the healthcare sector, where organizations manage highly sensitive patient data, operate under strict regulatory requirements such as HIPAA and GDPR, and depend on complex clinical processes where inaccurate or biased AI outputs can directly impact patient outcomes.
The growing focus on governance is also influenced by evolving local regulations, which often impose country-specific requirements around data privacy, AI accountability, and clinical decision-making. As a result, successful AI adoption requires not only technical expertise but also deep healthcare domain knowledge to ensure compliance and align AI initiatives with both regulatory and patient care objectives.
Consequently, organizations are increasingly looking to AI not only as a productivity tool but also as a means of strengthening security, resilience, and operational oversight. While only 10% of organizations report having already realized benefits in security, risk management, and resilience, 41% expect AI-enabled monitoring, threat detection, and response capabilities to deliver value within the next one to two years.
A similar pattern is emerging in governance. Although only 5% of organizations have already achieved governance-related benefits from AI, expectations for future impact are high. More than one-third (36%) anticipate realizing governance improvements within the next six months, while a further 27% expect AI to enable more consistent governance and control over the next one to two years.
Scaling AI: What challenges is the healthcare industry facing?
As AI adoption expands from isolated use cases to organization-wide deployment, healthcare leaders must address a new set of barriers related to cybersecurity, data readiness, regulatory compliance, operational resilience, and workforce adoption
Cybersecurity emerges as the top challenge
Cybersecurity is the most frequently cited obstacle to scaling AI. Among 57 surveyed executives, 47% identified AI-related security risks and expanded attack surfaces as their top challenge. This concern is particularly significant in healthcare, where AI systems rely on large volumes of highly sensitive patient data and often interact with complex clinical and operational systems. As AI becomes embedded across workflows, organizations face growing risks related to unauthorized access, data leakage, model manipulation, and other AI-specific threats.
Across regions, this challenge is consistent. Nearly half of respondents in North America (46%) identified cybersecurity as a major concern, with similarly high proportions reported in EMEA (42%) and APAC (41%). These findings highlight that securing AI systems has become a global priority for healthcare organizations seeking to scale adoption responsibly.
Data readiness remains a critical foundation
Beyond security concerns, many healthcare organizations continue to struggle with the data challenges that underpin successful AI deployment. In both North America and EMEA, 46% of respondents identified insufficient data readiness as a major barrier to scaling AI. AI systems depend on access to large volumes of accurate, consistent, and well-governed data. However, healthcare organizations frequently encounter fragmented systems, inconsistent data standards, and interoperability limitations that make it difficult to create a reliable data foundation.
In North America, patient information is often distributed across multiple organizations and technology platforms. Despite progress in interoperability, many providers still face challenges consolidating complete and consistent datasets, limiting AI's ability to generate accurate insights at scale.
In EMEA, the challenge is further compounded by the diversity of healthcare systems, data standards, languages, and regulatory requirements across countries. Variations in data protection policies and interpretations of GDPR can create additional uncertainty around how health data can be shared and utilized. As a result, organizations often struggle to develop AI solutions that can be deployed consistently across multiple markets. Initiatives such as the European Health Data Space (EHDS) reflect ongoing efforts to address these barriers through common standards, governance frameworks, and infrastructure to support more seamless health data exchange across Europe.
FPT - Your Trusted AI partner to transform healthcare industry
To help the healthcare industry accelerate with AI, FPT introduced MediSight, an AI-powered framework designed to help healthcare providers, medical device manufacturers, and pharmaceutical organizations accelerate digital transformation, strengthen regulatory compliance, and unlock actionable insights from healthcare data. Built on FPT's FleziPT - AI-first ecosystem and guided by CASAN, FPT's framework for progressing from AI experimentation to AI-native operations, MediSight enables healthcare and life sciences organizations to scale AI adoption across care delivery.
The framework is structured around three core layers. The Healthcare Insight Platform (HIP) provides a cloud-native, interoperable data foundation supporting standards such as HL7 v2, CDA, FHIR, and DICOM, with built-in capabilities for data mapping, de-identification, and clinical terminology standardization. MediSight Crew comprises autonomous AI agents, digital workers, and healthcare-focused assistants that support administrative, clinical, and compliance activities. MediSight Verse offers a growing portfolio of applications spanning clinic management, claims administration, virtual care, patient engagement, clinical trials, medical imaging, documentation, and healthcare analytics.
Designed for healthcare-grade security, MediSight supports industry requirements including HIPAA, HITRUST r2, and ISO 27001. The framework can be deployed across major cloud environments and integrated with leading healthcare systems, enabling greater data traceability, streamlined compliance processes, accelerated product development, and more efficient patient care.
