Rob Salter

Summary

Artificial Intelligence (AI) is rapidly emerging as the critical enabling technology for the transformation of the healthcare and life sciences (HCLS) sectors. It offers an unprecedented opportunity to shift from reactive, fee-for-service models to proactive, value-based health care (VBHC) by improving clinical outcomes, reducing costs, and enhancing patient and provider experiences. The industry is moving toward a vision of “Intelligent Health”, an AI-driven, hyperpersonalised future characterised by real-time decisions, continuous data insights, and seamless interoperability.

Key AI technologies are at the forefront of this shift. Generative AI (GenAI) is seeing rapid adoption, with 97% of healthcare provider respondents in a 2025 Gartner survey having already deployed it or expecting to by 2027, primarily for administrative and clinical documentation tasks. AI Agents, defined as autonomous or semiautonomous software entities, represent the next frontier, poised to become a goal-driven digital workforce that automates complex processes in areas like care management, claims processing, and revenue cycle performance.

However, significant structural and cultural barriers impede the path from pilot projects to enterprise-scale deployment. Lack of data readiness is the most frequently cited nonfinancial barrier to GenAI ROI across providers (51%), payers (61%), and life sciences organisations (55%). Other critical challenges include inadequate governance frameworks, the difficulty of translating productivity gains into measurable ROI, low AI literacy among employees, and executive misalignment on AI strategy.

To realise AI’s full potential, organisations must adopt a disciplined, strategic approach. This requires establishing a formal AI strategy aligned with business objectives, building robust AI governance through frameworks like AI Trust, Risk, and Security Management (TRiSM), and investing in foundational capabilities. These foundations include AI engineering, a unified discipline encompassing DataOps, ModelOps, and DevOps, to scale AI solutions reliably, and FinOps for AI to manage unpredictable costs. Crucially, success hinges on fostering an AI-ready workforce through comprehensive AI literacy programs and creating an organisational culture that embraces data-driven decision-making.

——————————————————————————–

The AI Revolution in Healthcare and Value-Based Health Care

AI is not merely an incremental improvement but a foundational technology poised to redefine healthcare delivery. Its integration is indispensable for realising the promise of advanced Value-Based Health Care (VBHC) models, which reward quality outcomes and cost-efficiency over the volume of services.

The Vision of Intelligent Health

Gartner defines Intelligent Health as the future of the HCLS industry, which is “AI-driven and hyperpersonalised, with real-time decisions made possible by continuous insights from ambient data-gathering and interoperable data-sharing.” This vision is built on four key imperatives:

1. Precision: Optimising experience, health, and financial outcomes through hyperpersonalised engagement and decision-making, including precision medicine and workflow optimisation.
2. Equity: Ensuring no unjust differences in healthcare access, inclusion, or outcomes, supported by technology designed for equity.
3. Ethics: Upholding rigorous standards where technology choices are fair, transparent, explainable, accountable, secure, and human-centric.
4. Interoperability: Establishing the seamless, secure, and standardised exchange of health data as the cornerstone of digital transformation.

AI as the Enabler of Advanced VBHC

Traditional VBHC models often remain reactive and limited by fragmented data. AI overcomes these constraints by enabling a shift from reactive to proactive care.

“AI is not just another tool; it’s the critical enabling technology for achieving the promise of advanced VBHC models.”

Key AI-enabled capabilities in VBHC include:

• Proactive Patient Identification: Analysing electronic health records (EHRs), imaging, and genomic data to identify high-risk patients for timely, personalised interventions.
• Comprehensive Data Integration: Pulling data from diverse sources to identify trends, flag inconsistencies in clinical records, and create a complete patient view.
• Administrative Automation: Automating tasks like clinical documentation, coding, and data entry, freeing professionals to focus on patient care.
• Dynamic Care Pathways: Developing personalised care plans that adapt in real-time based on patient response, preferences, and evolving risk factors.
• Enhanced Experience: Automating tasks and personalising engagement to improve both patient and provider experiences.

Organisations that deploy AI across multiple domains, rather than in isolated point solutions, demonstrate the highest return on investment (ROI).

AI Use Case in VBHC	ROI Potential	Implementation Complexity	Primarily Benefits
Predictive risk stratification	High	Medium	Both Comissioner & Provider
Automated coding	High	Low	Both Comissioner & Provider
Intelligent prior authorisation (iPA)	High	High	Both Comissioner & Provider
Remote Patient Monitoring (RPM) analytics	High	Medium	Provider

Pivotal AI Technologies and Their Applications

Several emerging AI technologies are poised for significant impact across the healthcare and life sciences landscape. According to Gartner’s “Emerging Tech Impact Radar,” these technologies vary in their maturity and time to mainstream adoption but share the potential to drive transformation.

Generative AI (GenAI)

GenAI is rapidly moving from hype to practical application. The 2025 Gartner CIO and Technology Executive Survey indicates that AI is the top emerging technology for healthcare providers, with 85% planning to deploy GenAI by 2026.

• Primary Use Cases: The immediate priority is on administrative tasks such as clinical documentation and information summarisation to improve operational efficiency and reduce clinician burnout.
• Sub-disciplines:
  • Domain-Specific LLMs (DSLMs): Models trained on focused healthcare datasets to provide more relevant, accurate, and impactful results for tasks like medical image analysis and payer contract analysis.
  • Multimodal GenAI: Models that process and generate outcomes from numerous data types (text, images, video, speech), removing data barriers and enabling more comprehensive applications like holistic care plan generation.
  • GenAI Analytics: The use of LLMs to synthesize insights from vast amounts of health data, empowering the entire workforce by democratising access to data and reducing time to discovery.
• Hype Cycle Position: GenAI has entered the “Trough of Disillusionment,” indicating a maturing understanding of its limits and a shift toward ensuring scalable, consistent delivery.

AI Agents

AI Agents are considered the next frontier of AI, capable of acting as a goal-driven, digital workforce. Gartner defines them as “autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments.”

• Key Capabilities: AI agents can pursue complex goals and workflows with varying degrees of human supervision, distinguishing them from simpler intent-based chatbots. They can autonomously plan and take action across many aspects of healthcare operations.
• Sample Use Cases for Health Insurers:
  • Automation of care and utilisation management.
  • Claims, eligibility, and administrative processes.
  • Benefits administration.
  • Fraud, waste, and abuse (FWA) detection.
  • Provider contract and network management.
• Sample Use Cases for Providers:
  • AI virtual care assistants for post-discharge follow-up, symptom review, and medication adherence monitoring.
  • Improving revenue cycle performance.
  • Optimising clinical trial and workforce collaboration.
• Adoption Timeline: AI Agents are projected to reach early majority adoption in 3 to 6 years.

Key Emerging AI Technologies: Impact Radar Summary

Technology	Time to Mainstream Adoption	Predicted Impact & Adoption
Domain-Specific LLMs	1 to 3 Years	Very High
GenAI-Enabled Apps	1 to 3 Years	High
AI Agents	3 to 6 Years	High
Decision Intelligence	3 to 6 Years	Very High
Digital Twins	3 to 6 Years	High
Generative AI Analytics	3 to 6 Years	Very High
Health Data Management Platforms	3 to 6 Years	Very High
Multimodal GenAI	3 to 6 Years	Very High
Synthetic Data	3 to 6 Years	High
Ambient Intelligence	6 to 8 Years	High
Machine Customers	6 to 8 Years	High

Foundational Imperatives for Scaling AI

Moving AI initiatives from isolated pilots to enterprise-wide capabilities requires a disciplined focus on strategy, governance, data infrastructure, and operational practices.

The AI Strategy Mandate

An executable AI strategy is essential for aligning technology investments with business outcomes. Key components include:

• A Clear Vision: Formulated with C-level stakeholders, the vision must answer how AI is critical to the organisation’s future, given business goals and market conditions.
• A Prioritized Portfolio: Identify and prioritise a portfolio of concrete, business-related AI initiatives to realize value.
• An AI Operating Model: Set planning goals for building and maturing key AI capabilities in technology, data, organisation, literacy, engineering, and governance.
• Strategic Alignment: The AI strategy must be frequently aligned with complementary strategies, including digital/IT and data and analytics (D&A).

AI Governance and Responsible AI

As AI becomes more ubiquitous, robust governance is non-negotiable.

• Responsible AI (RAI): An umbrella term for making appropriate business and ethical choices when adopting AI. It encompasses fairness, bias mitigation, transparency, explainability, safety, privacy, and compliance.
• AI Trust, Risk, and Security Management (TRiSM): A Gartner framework that supports AI governance, trustworthiness, fairness, reliability, and security. Adopting AI TRiSM enables a dynamic approach to securing AI applications.
• AI Governance Platforms: Emerging as a distinct market, these platforms provide a central view of all AI applications and use cases, consolidate risk management, and automate approval workflows to enable safe scaling.

Data Readiness and Interoperability

AI initiatives are only as strong as the data infrastructure supporting them.

• AI-Ready Data: Data must be proven fit for a specific AI use case by assessing its representativeness, quality, lineage, and support for continuous validation. The fragmented nature of HCLS data makes this a primary challenge.
• Health Data Management Platforms (HDMPs): These modern platforms are critical accelerators for digital transformation. They unite disparate health data, deliver insights into workflows, and enable new operating models by leveraging cloud, data fabric concepts, and industry standards like HL7 FHIR.
• Trusted Research Environments (TREs): Secure, governed computational environments that enable organisations to share and analyse sensitive health data (clinical, claims, omics) without compromising privacy, unlocking access to global research networks.

AI Engineering and FinOps

Scaling AI requires new operational disciplines for both development and financial management.

• AI Engineering: A unified framework that brings together DataOps, ModelOps, DevOps, LLMOps, and AgentOps to enable a structured, repeatable factory model for operationalising AI solutions and shortening time-to-value.
• FinOps for AI: The application of financial operations best practices to manage the high and often unpredictable costs of AI services. This is crucial for optimising spend, ensuring financial accountability, and maximising ROI, as cost poses one of the greatest near-term threats to AI success.

Overcoming Barriers to AI Adoption

Despite heavy investment, most HCLS organisations remain stalled between pilots and limited deployments due to significant structural and cultural barriers.

Structural Barriers

These challenges relate to the foundational readiness of the organisation’s technology and processes.

• Data Readiness: The most common nonfinancial barrier to GenAI ROI across all HCLS sectors. Data is notoriously fragmented across different systems and formats (clinical notes, imaging, claims), and even narrow use cases quickly encounter issues with data quality, lineage, and integration.
• Governance Rigor: A lack of clear AI governance policies, risk controls, and accountability stalls deployment, especially in clinical contexts. Providers cite this as their second-highest barrier.
• Translating Value to ROI: Comissioners, in particular, struggle to convert nonfinancial benefits like time saved or improved user satisfaction into hard financial ROI. 58% report this as a top-three barrier.

Top Nonfinancial Barriers to GenAI ROI (Reported as Top 3)	Healthcare Providers	U.S. Payers	Life Sciences
Lack of data readiness	51%	61%	55%
Lack of governance rigor/accountability	49%	46%	40%
Inability to translate nonfinancial value to ROI	46%	58%	48%

Cultural Barriers

Scaling AI is as much about people and governance as it is about technology.

• AI Literacy: A critical gap exists between the need for AI skills and the workforce’s current capabilities. 81% of CIOs state that GenAI skill gaps impede their ability to meet 2025 objectives, and 63% of employees have not used GenAI in critical tasks.
• Executive Misalignment: Life sciences organisations, despite being more mature with GenAI use cases, significantly lag their peers in executive alignment on AI strategy and priorities (42% report this as a top barrier).
• Skills and Roles Investment: U.S. payers struggle disproportionately to invest in augmenting skills and creating new roles required for a data-driven model.

The Evolving Landscape: Timelines and Predictions

The AI landscape is evolving at a rapid pace, with regulatory bodies, technology vendors, and healthcare stakeholders all adapting to its transformative potential.

Strategic Planning Assumptions (2028-2030)

• Widespread Stakeholder Adoption: By 2028, over 70% of healthcare payers, providers, and consumers will adopt AI, rendering traditional pharma commercial models and their supporting technologies obsolete.
• Regulatory Acceleration in China: By 2030, China’s regulatory body (NMPA) will formalise a scoped agentic AI-powered pathway, reducing drug development timelines by 40% and catalysing similar AI review pilot programs globally.
• New Validation Frameworks: By 2028, 30% of life science organisations will implement new software testing frameworks to GxP validate AI-augmented solutions, optimising processes by 20%.

Case Study Spotlight: NHS England’s Digital Staff Passport

A prime example of digital transformation addressing operational inefficiency is NHS England’s Digital Staff Passport (DSP).

• Problem: Inconsistent, time-consuming onboarding processes for staff moving between NHS facilities wasted an estimated 5 million hours annually, at a cost of £250 million. Staff had to repeatedly provide the same personal information and evidence of training.
• Action: NHS England, with partner Sitekit, developed the DSP, a decentralised digital identity service and mobile app. The DSP empowers staff to control their own certified, verifiable credentials.
• System Components: | Component | Function | Human resources web application | Used by recruiting organizations to identify staffing requirements. | Staff self-service web application | Used by staff to enter/update personal information and receive credentials. | Digital wallet | Used by staff to store and share certified credentials. |
• Results: The DSP streamlined staff movement, created a more efficient and cost-effective onboarding process, enhanced trust by giving staff control over their data, and established standardised interoperability between NHS organisations. This case highlights the power of digital solutions to solve longstanding challenges related to data sharing, trust, and operational efficiency principles central to the broader AI transformation.

Reference List

Adams, F. (2025). Infographic: How to Build a Smarter Digital Front Door for Healthcare and Life Sciences With AI (ID G00817884). Gartner, Inc.

Andrews, W., den Hamer, P., Ramos, L. and Sapp, C. (2025). A Strategic Guide to Maturing AI (ID G00834270). Gartner, Inc.

Benn, R. (2025). AI in Value-Based Care: Part 1 Roadmap Planning for Healthcare CIOs (ID G00835449). Gartner, Inc.

Benn, R., Dall’Occhio, A. and Potts, R. (2025). Healthcare Provider Trends: Imperatives for Intelligent Health (ID G00837547). Gartner, Inc.

Bishop, M., Craft, L., Gandhi, A. and Dennis, A. (2025). Generative AI ROI Benchmarks for CIOs Across Healthcare and Life Sciences (ID G00836085). Gartner, Inc.

Brethenoux, E. (2025). AI Leaders: Maturity Guide for AI Strategy (ID G00830986). Gartner, Inc.

Brethenoux, E., Buytendijk, F., Bant, A., Ramos, L. and Lopez, J. (2025). The Greatest Barrier to AI Value Isn’t Technology — It’s Your Comfort Zone (ID G00840188). Gartner, Inc.

Craft, L., Gandhi, A. and Adams, F. (2025). Intelligent Health: A Vision for the Healthcare and Life Science Industry (ID G00803238). Gartner, Inc.

Cribbs, J., Walk, V., Runyon, B., Craft, L. and Pessin, G. (2025). Top Technology Trends for Healthcare Provider CIOs in 2025 (ID G00824064). Gartner, Inc.

Dennis, A. and Harwood, R. (2025). Innovation Insight: Trusted Research Environments Accelerate Medical Advancements in the AI Era (ID G00840898). Gartner, Inc.

Gandhi, A., Harwood, R., Smith, J. and Shanler, M. (2025). Predicts 2026: Agentic AI Reshapes Competitive Advantage in Life Sciences (ID G00840609). Gartner, Inc.

Gartner, Inc. (2023). AI Governance Frameworks for Responsible AI: One-Minute Insights. Gartner, Inc.

Gartner, Inc. (2025). Strong Cybersecurity Partnerships Prevent AI Catastrophe (ID G00840201). Gartner, Inc.

den Hamer, P. and Bhati, R. (2025). The Pillars of a Successful Artificial Intelligence Strategy (ID G00805859). Gartner, Inc.

Jaffri, A. (2025). Analytics and Artificial Intelligence Primer for 2025 (ID G00822441). Gartner, Inc.

Khandabattu, H. and Tamersoy, B. (2025). Hype Cycle for Artificial Intelligence, 2025 (ID G00828523). Gartner, Inc.

Rivera, J. and Mehta, S. (2025). Emerging Tech Impact Radar: Artificial Intelligence in Healthcare (ID G00835420). Gartner, Inc.

Runyon, B. (2024). Case Study: NHS England’s Digital Staff Passport Improves Workforce Mobility (ID G00818379). Gartner, Inc.

Salgy, C., Eubank, A. and Adams, F. (2025). AI Agents: Use-Case Examples for Health Insurers (ID G00836642). Gartner, Inc.

Sicular, S., Krensky, P. and Judah, S. (2025). Artificial Intelligence Requires an Extended Governance Framework (ID G00805856). Gartner, Inc.

Singh, P. and Dev, S. (2022). Innovation Insight for AI-Enabled Diagnostic Imaging Interpretation (ID G00765732). Gartner, Inc.

Soyeb Barot, S., Chandrasekaran, A., Tamersoy, B., Agarwal, S., Dekate, C. and Brethenoux, E. (2025). Without AI Engineering, AI Pilots Stall and Fail to Deliver Value (ID G00840942). Gartner, Inc.

Ramos, L., Rice, L. and den Hamer, P. (2025). Without AI Literacy, ROI Plummets (ID G00840288). Gartner, Inc.