As health technology leaders finalize 2026 strategies, AI’s transition from hype to health-tech infrastructure will shape capital allocation, product roadmaps, and workforce planning across every segment of the industry. AI is shifting from experimental pilots to the infrastructure layer that underpins discovery, diagnostics, and care delivery across medical devices, biopharma, digital health, and diagnostics. As executives look back on 2025, the question is no longer whether to use AI, but how to operationalize it safely, at scale, and in ways that create durable value and trust.
From Buzzword to Backbone
AI’s role in health care has expanded rapidly, moving beyond isolated use cases to becoming a foundational capability across value chains. Adoption inflected in 2024–2025, with surveys showing most U.S. physicians already using AI tools, especially for administrative relief and clinical support.
Deloitte projects the global AI in health care market will grow from about $39 billion in 2025 to more than $500 billion by 2032, with North America holding nearly half of the current market share.¹
For health tech executives, this shift reframes AI from a “nice-to-have” to a strategic infrastructure decision, comparable to EHRs and cloud platforms a decade ago.
What 2025 Taught HealthTech Leaders
The experience of 2025 offers critical lessons as organizations plan their 2026 AI roadmaps.
- Structural pressures intensified. Workforce shortages, margin compression, and rising expectations for hybrid and home-based care forced organizations to seek automation and decision support at scale.²
- Digital maturity gaps became visible. Deloitte and McKinsey, among others, found that while many organizations piloted generative AI, only a small minority deployed AI enterprise-wide; the gap between leaders and laggards is widening.1,3,6
- Trust, governance, and liability issues surfaced. The AMA and Health Affairs emphasized that liability, safety, bias, and transparency concerns remain major barriers to clinical AI adoption.4,7
Executives who treated 2025 as a learning lab, testing AI agents in narrow workflows while building governance and change management muscles, are now better positioned to scale responsibly in 2026.
Where AI Becomes Infrastructure in 2026
AI is now embedded across the life sciences and the health tech stack, from molecule to market to bedside.
- R&D and Biopharma Innovation
AI and generative AI are compressing timelines and reshaping how biopharma invests in discovery and development.
- AI adoption is predominantly concentrated in the early stages of drug discovery. In particular, machine learning, molecular modeling, and deep learning, have proven instrumental in accelerating compound selection, optimizing lead candidates, and supporting personalized therapeutic strategies.3,9
- For a deeper dive into peer-reviewed studies reporting AI applications directly linked to drug development phases, specifically from target identification to preclinical and clinical development, we found this June 2025 abstract published in PubMed Central, From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes, to provide insightful research.
For biopharma and medtech R&D teams, AI infrastructure now includes integrated data platforms, model development pipelines, and partnerships with specialized AI firms.
- Diagnostics, Devices, and Decision Support
Regulators, clinicians, and patients are increasingly encountering AI “under the hood” in imaging, monitoring, and diagnostic workflows.
- AI-enabled devices and algorithms are augmenting radiology, cardiology, pathology, and lab medicine, often as part of workflow-embedded decision support rather than standalone tools.1,2
- Multimodal systems combine imaging, EHR data, sensor streams, and genomics to generate richer insights, a capability highlighted in 2026 tech trend forecasts as a key source of competitive advantage.²
Device and diagnostics companies must now think of AI as part of their core product architecture, from sensor design and edge compute to cloud analytics and integration with clinical systems.
- Care Delivery, Hybrid Models, and AI Agents
Provider organizations are embedding AI into care models, with growing emphasis on agents that coordinate tasks across the patient journey.
- Deloitte’s 2026 U.S. health care outlook points to digital health, AI, and cross-industry collaboration as central strategies for future-ready care models, including hospital-at-home and virtual-first pathways.²
- AI is particularly valuable for reducing administrative burden, supporting triage, and guiding resource allocation, critical in an era of workforce constraints.4,5,7
In 2026, AI agents are expected to handle more end-to-end workflows such as symptom intake, scheduling, prior authorization preparation, and follow-up outreach, while escalating complex decisions to clinicians.
Key Challenges on the Road to AI Infrastructure
As AI becomes embedded in infrastructure, the risks and complexities also scale.
- Regulatory and compliance uncertainty. New AI-focused regulations and guidance (such as the EU AI Act globally and evolving U.S. frameworks) require robust risk management, documentation, and post-market surveillance for AI-enabled tools.1,7
- Trust, bias, and explainability. AMA and National Academy of Medicine experts emphasize the need for safe, effective, and trustworthy AI, including transparent model behavior, bias mitigation, and clinician-centered design.5,7
- Workforce readiness and change fatigue. Health Affairs identifies building an “AI-competent” workforce as a strategic priority, spanning clinicians, operations, and IT.⁷ McKinsey similarly notes that scaling gen AI in life sciences requires new roles, training, and operating models.⁶
Organizations that underinvest in governance, education, and culture risk stalled pilots, clinician pushback, or reputational harm, even if the underlying technology is strong.
Strategic Moves for HealthTech Executives
For leaders in medical devices, biopharma, digital health, and diagnostics, the shift from hype to infrastructure demands deliberate strategy across five critical dimensions.
- Define Your AI North Star
Executives should anchor AI investment around a clear value thesis tied to their specific segment.
- Medtech: Focus on AI-enhanced devices and cloud-connected platforms that improve outcomes, reduce procedural time, and integrate seamlessly with provider workflows.²
- Biopharma: Prioritize AI in target discovery, trial design, and real-world evidence generation, aligning with portfolio strategy and therapeutic areas.3,8,9
- Digital health: Use AI to differentiate in engagement, personalization, and longitudinal care journeys, moving beyond point solutions.2,6
- Diagnostics: Invest in AI-powered interpretation, automation, and near-patient testing that shortens time to diagnosis and supports precision medicine.1,2
A concise “AI mission statement” clarifies where the organization intends to lead, partner, or simply comply.
- Build Enterprise-Grade Data and Compute
AI infrastructure is only as strong as the underlying data and compute strategy.
- Deloitte’s Tech Trends 2026 highlights an “AI infrastructure reckoning,” with organizations rethinking compute, storage, and network architectures to optimize inference economics.10
- Scaling gen AI in life sciences requires robust data foundations, including integrated, high-quality datasets across R&D, manufacturing, and commercial functions.⁶
Executives should prioritize:
- Modern data platforms that unify clinical, operational, and real-world data with strong governance.
- Thoughtful decisions about on-prem, cloud, and edge infrastructure that balance cost, latency, and regulatory requirements.
- Institutionalize AI Governance and Risk Management
Leading organizations are formalizing multidisciplinary AI governance structures rather than treating AI as a series of isolated IT projects.
- The AMA has released AI governance toolkits and emphasizes physician involvement across the AI lifecycle: design, development, standards-setting, and clinical integration.5,1
- Health Affairs and National Academy of Medicine identify four action areas: trustworthy use, AI-competent workforce, evidence-based AI research, and clear liability frameworks.⁷
Practical steps include:
- Establishing an AI oversight committee with representation from clinical, legal, compliance, IT, and operations.
- Implementing standardized risk scoring, monitoring, and post-market surveillance for AI tools, including third-party solutions.
- Invest in People, Not Just Platforms
The shift from hype to infrastructure is ultimately a human change-management challenge.
- AMA survey data show physicians see clear advantages in AI but still call for stronger oversight, liability clarity, and integration with clinical workflows.4,5
- Health Affairs emphasizes education and training to build AI literacy across the workforce, from frontline clinicians to executives.⁷
Priority actions:
- Co-design AI tools with clinicians, lab professionals, and care teams to ensure usability and safety.
- Offer targeted training on AI basics, limitations, and responsible use, emphasizing augmentation rather than replacement.
- Align AI with Market Access, Partnerships, and Talent
As AI becomes infrastructure, competitive advantage will hinge on ecosystem strategy and talent.
- Deloitte’s life sciences and health care outlook underscores cross-industry collaboration between tech, payers, providers, and life sciences as essential to scaling AI-enabled models.2,12
- McKinsey finds that organizations successfully scaling gen AI in life sciences are rethinking operating models, governance, and talent profiles, including new roles like AI product owners and translational data scientists.⁶
Executives should:
- Pursue strategic partnerships with cloud providers, AI technology firms, and data platforms to accelerate time-to-value.
- Update talent strategies to attract and retain AI-savvy leaders and technical experts while upskilling existing teams.
Why This Matters for 2026 Planning
Leaders who treat AI as a core capability, not an add-on, will be positioned to improve outcomes, reduce friction, and unlock new growth models across medical devices, biopharma, digital health, and diagnostics.
This article launches Talencio’s 10-part “Navigating 2026” series, beginning with AI as health-tech infrastructure and continuing through hybrid care, advanced therapies, data interoperability, cybersecurity, and beyond. As each trend unfolds, the organizations that integrate AI thoughtfully into their infrastructure, governance, and talent strategies will define the next era of health technology.
#AIinHealthcare #HealthTech #MedTech #Biopharma #DigitalHealth #Diagnostics #GenAI #HealthcareInnovation #2026Trends #SeriousTalent
Sources
- Deloitte Insights – “2026 Global Health Care Outlook”
- Deloitte Insights – “2026 US Health Care Outlook”
- Deloitte Insights – “Tech Trends 2026” and AI infrastructure analyses
- American Medical Association – “AMA Augmented Intelligence Research”
- HealthcareITNews “AMA recommends a risk-based approach in its new AI governance framework”
- McKinsey & Company – “Scaling gen AI in the life sciences industry”
- Health Affairs – “Artificial Intelligence in Health and Health Care: Priorities for Action”
- National Library of Medicine – “From Lab to Clinic: How Artificial Intelligence (AI) is Reshaping Drug Discovery Timelines and Industry Outcomes”
- Deloitte Insights – “AI Infrastructure Compute Strategy”
- American Medical Association – “AMA position on the 2025 federal government AI action plan” and “Augmented intelligence in medicine”
- Deloitte – “2026 Life Sciences and Health Care industry insights report”
- Talencio – “Navigating 2026: The Top 10 Health Technology Trends Every Leader Should Watch”
About the Author
Paula Norbom is the Founder and CEO of Talencio, an executive search and staffing firm serving health technology companies. She has worked in the health technology industry for over 30 years.