In summary
- There is a discernable move from reactive to proactive patient care
- AI has an important role in delivering human-centered healthcare
- Patient engagement is being redefined for a consumer-driven age
- Governance, compliance and ethics are vital in strategies for AI in healthcare
- Behavioral insights and predictive analytics are enabling new healthcare delivery models
The challenge for our healthcare sector is clear: build services that are intelligent, resilient, and human‑centered – without losing trust or equity.
In the first of our series exploring the healthcare trends for 2026, we look at the role of artificial intelligence (AI) in addressing this challenge and why there is more to consider than technology alone.
AI now supports clinicians at the point of care [1], [2], [3], automates back‑office workflows [4], predicts population‑level health risks [5], [6], and accelerates research and precision medicine. Its applications span clinical decision support, speech‑to‑note, medical imaging, virtual assistants, claims processing, scheduling, epidemiology, and drug discovery.
AI has become the red thread running through the transformation of healthcare-not a future vision, but a reality embedded across the sector today.
How healthcare organizations can successfully implement AI
There is no doubting the potential benefit of embedding AI in healthcare systems. However, success requires more than technology alone.
Sustainable AI implementation requires both strategic vision and operational readiness. Organizations need an AI strategy aligned with business goals and supported by leadership, addressing governance, compliance and ethics.
For sustainable operational implementation, organizations need:
- A strategy that adheres to regulations, such as GDPR and HIPAA, to ensure patient privacy and secure data handling
- A reliable and robust infrastructure, adequate data provision and management, and interoperability
- Change-management, ensuring that employees and patients alike trust AI and adopt the technology
- A guarantee of fair and equitable outcomes across diverse patient populations by mitigating algorithmic bias.
How has AI moved from science fiction to day-to-day reality in the transformation of healthcare?
The future of healthcare is AI-powered, patient-centered, and data-driven.
What was mere science-fiction just a few years ago is now becoming deeply embedded in healthcare systems. Organizations must act now to overcome systemic challenges and meet the expectations of patients and staff alike.
The following two trends evidence the growing uptake of digital solutions, necessitating a need to balance technology with human governance and new strategies for insight-powered patient care.
Trend 1: A move from reactive to proactive healthcare
An aging population and the burden of chronic disease have created a complex healthcare landscape that is only going to increase in scale.
This will demand a strategic and intelligent response from healthcare providers going forward – one that takes them from a reactive to a proactive stance.
For decades, health systems have been structured around a reactive logic: treat disease when it advances, intervene in crisis, and rely on hospitals as the central node of care delivery.
That architecture is now collapsing under the weight of rising multimorbidity, escalating chronic disease costs, widening inequities, and a rapidly aging population. Multimorbidity is when a patient has two or more chronic health conditions.
We are seeing a demographic shift where 1 in 6 people globally will be over 60 by 2030 [7] and the 80+ cohort will triple by mid‑century [8]. At the same time, adults aged 55+ already account for more than half of health spending [9].
This is concentrating system costs in that part of the population that would benefit most from earlier targeted prevention.
Aging itself is not the crisis; it’s only a catalyst.The real challenge is that most health systems still intervene too late.
Why is prevention‑first healthcare no longer optional?
Noncommunicable diseases already account for the majority of global health spending, set to cost an estimated $47 trillion by 2030 [10]. The majority of those costs are avoidable, because they stem not from sudden illness but from predictable, long‑tail deterioration.
Most older adults live with multiple conditions, exposing the inadequacy of care pathways built around single diseases rather than whole‑person risk [11].
A prevention‑first operating model (PFOM) replaces the “react‑and‑rescue” paradigm with continuous risk identification, early detection, proactive intervention, and home‑based support. This shift is no longer aspirational, but a system‑sustainability strategy.
Encouragingly, emerging technologies now make preventive care scalable, personalized, and economically viable. It seems that technology has finally caught up with the prevention vision, as the examples in our FAQs section illustrate – see FAQs, below.
This breakthrough is the result of digital maturity, intelligence and infrastructure in combination.
How will federated and explainable AI transform patient outcomes with next-generation prevention?
The AI4HealthyAging [1] consortium led by Capgemini is a blueprint for what next‑generation prevention looks like. Using federated learning, the model predicts early risk signals for stroke or heart failurewithout moving sensitive patient data between institutions. Explainability techniques ensure clinicians understand why a model triggers an alert, building trust at the point of care. This is prevention delivered through privacy-preserving intelligence – not more clinics or more beds.
These models anticipate the data‑sharing architecture envisioned by the European Health Data Space (EHDS) [12]. This will enable secure and harmonized cross‑border health-data reuse for prevention, research, and policy across Europe – see FAQs below.
How do governance and trust help to create proactive health systems?
But algorithms alone do not create prevention‑first systems. Enterprise adoption requires governance, trust, human‑AI collaboration, and organizational redesign.
Together, these capabilities shift health systems from reactive to proactive, from episodic to continuous, and from hospital‑first to home‑centered.
Yes, societies are aging, multimorbidity is rising, caregivers are overstretched [13]. But these are not the root problem – they are signals urging systems to abandon outdated reactive care models.
Three recommendations to accelerate preventive healthcare strategies
To help healthcare organizations make AI real on the journey to preventive healthcare, we recommend the following actions:
- Treat prevention as an investment thesis, not a moral argument. Shift away from viewing prevention as “good to have” and start treating it as the system’s primary economic lever.
- Make AI the frontline partner in care, not just a back‑office experiment. The question is no longer whether AI works, it’s whether leaders will redesign workflows, accountability, and governance so that early‑risk signals actually change decisions at the point of care.
- Build the data foundation now or accept that prevention will remain fragmented forever. Without interoperable, privacy‑preserving, federated data infrastructure, prevention will continue to be episodic and reactive. Leaders who fail to build this backbone today will lock their systems into a decade of incrementalism.
What questions should leaders ask about preventive healthcare?
- What partnerships are we forming across government, community, aging services, and industry to deliver prevention beyond the walls of healthcare?
- How are we preparing our organizations culturally for a world where care begins before symptoms appear?
- Are we ready to operationalize AI for early detection at scale with governance, explainability, and workforce integration baked in?
- How are we ensuring that prevention‑first models reduce inequity rather than unintentionally widen it?
A prevention‑first architecture is the only scalable, humane, economically viable pathway forward. And AI, federated data, and digital‑home models are the enablers that make it real, not theoretical.
Trend 2: Redefining engagement in healthcare – from patients to consumers
Consumer expectations are reshaping healthcare. Success now depends on insight-powered engagement and preparing for AI maturity, while safeguarding trust and equity.
Patients expect healthcare interactions to be intuitive, transparent, and personalized. Yet most digital tools were designed for clinicians, not consumers.
Frustration is measurable: 63% of patients would switch providers due to poor communication, and 47% have avoided scheduling appointments because of phone-based delays, sometimes leading to emergencies [14].
The challenge is clear: convenience and transparency are no longer optional.
How will AI-driven care shape a consumer-like patient experience?
From 2026, the differentiator will be insight-powered engagement. AI is advancing rapidly, but its mainstream adoption will take time.
Leading organizations are using behavioral insights and predictive analytics to anticipate needs, personalize outreach, and simplify access. These strategies improve loyalty and operational efficiency while laying the foundation for AI-driven care in the future.
At the same time, consumers are taking AI into their own hands through tools like ChatGPT for health queries, making it do-or-die for providers to offer safe, trusted alternatives.
What are the regulatory implications of implementing new technology?
Privacy and trust remain critical. WHO warns that AI adoption in health is outpacing legal safeguards, with only 8% of countries having liability standards [15].
Patients want transparency: 53% feel comfortable using AI for healthcare queries, but confidence depends on clear governance and ethical design [16].
The following cautionary tale evidences early regulatory dilemmas. Clinically validated AI mental health companion, Woebot was shut down in June 2025. The decision was driven by regulatory challenges and the lack of clear FDA guidance for AI-based mental health tools, especially those using large language models. This was despite measurable clinical outcomes reducing depressive symptoms in post-partum women and college students, supported by RCTs. It had served more than 1.5 million users [17], [18].
Equity must also not be overlooked. Digital transformation risks widening gaps for rural and vulnerable communities unless inclusion is embedded from the start. This means screening for digital needs, designing accessible interfaces, and co-creating solutions with indigenous and marginalized groups.
What questions should leaders ask about insight-driven patient care?
- How are we using behavioral insights and predictive analytics to personalize engagement today?
- What steps are we taking to embed transparency and convenience into every digital interaction?
- Are we preparing for AI maturity by building trust, governance, and data foundations now?
- How will we ensure accessibility and cultural safety for vulnerable and remote communities?
- How will we meet consumers where they are and aligned to their changing needs?
- How are we set up for ongoing change management as our modes and channels of engagement shift?
The answers will determine whether your organization leads in a market where experience, trust, and equity are as critical as clinical outcomes. What’s clear is that human-centered design and speed of innovation are essential to keep pace with consumer expectations.
Conclusion – and what’s next for a changing healthcare sector?
The world of healthcare is changing fast. Whether it is prevention-first models or expanded care delivery models and a consumer-centric approach to engagement, much of this change is being driven by advanced automation and AI-enabled resource optimization.
It is both an exciting time in healthcare and a moment of risk as providers strive to maintain trust and equity with effective governance, compliance and ethics embedded in AI strategies.
Further exploration of healthcare trends
This is just the start of a major transformation in healthcare. The next articles in this series will explore:
- How healthcare providers are considering the best way to embed virtual care into their health systems (telehealth, virtual wards and emergency departments, hospital-at-home, etc.)
- Why countering enormous financial pressure demands bold, technology-enabled strategies that deliver immediate savings without compromising care
- The role of digital models in clinical research and how electronic medical records systems can enhance data integrity
- How genomic medicine offers a new frontier in patient care, from prevention and diagnosis to individualized treatment
- Why genomics is still underutilized as an effective healthcare tool.
How can Capgemini help healthcare organizations move ahead confidently with AI?
We are preparing our clients for this new era as an AI-empowered, end-to-end advisory and transformation partner.
By connecting the trends – clinical, operational, financial, and experiential – discussed in this series of articles, we’re helping healthcare organizations translate disruption into measurable outcomes: safer and more accessible care, better experiences for patients and clinicians, and sustainable cost structures that support long term system resilience.
Look out for the next articles in this series exploring the healthcare trends in 2026.
FAQs
What examples are there of technology enabling the shift to preventive care?
Real‑world proof: The U.S. National Diabetes Prevention Program reduces progression to type 2 diabetes by 58% overall and 71% for adults 60+, with sustained long‑term benefits [19], [20].
Home‑first models: NHS England’s virtual‑ward framework has a target of >80% occupancy, signaling a structural pivot toward hospital‑level care delivered safely at home [21]
Remote monitoring: Robust evidence shows that home‑based monitoring after heart‑failure admissions reduces mortality and readmissions when implemented with fidelity. Australia’s RPM‑HF program demonstrates how early alerts can prevent crisis‑driven deterioration [22].
What examples are there of insight-driven care and personal choice?
Northwell Health (US) deployed predictive analytics to identify patients at risk of missing appointments. Tailored reminders and flexible scheduling reduced no-show rates by 30% and improved clinic throughput [23].
NHS UK introduced real-time cost estimates and personalized scheduling tools within its patient app. This transparency reduced billing disputes and improved satisfaction scores by 18% in pilot regions [24].
Lumen AI combines a portable breath analyzer with AI to measure whether the body is burning carbs or fat, delivering real-time metabolic insights. Users receive ultra-personalized nutrition and fitness plans, driving adoption among biohackers and wellness enthusiasts [25].
What is the European Health Data Space (EHDS)?
The EHDS is the first common EU data space dedicated to a specific sector – one of several created by the European Commission to collectively create a data sovereign, interoperable and trustworthy data sharing environment. The objective of the EHDS is to foster a health-specific data environment that supports a single market for digital health services and products across Europe.
How is Capgemini’s RAISE solution helping healthcare providers transform with AI?
Our Reliable AI Solution Engineering (RAISE) [26] offers an enterprise grade foundation for translating AI ambitions into measurable operational change by aligning three dimensions – Access, Adapt and Adopt.
