Healthcare’s next operating model

Healthcare is entering a new phase of reinvention. The sector is no longer adopting digital tools at the margin; technology is becoming the operating backbone of care delivery, clinical decision support, patient engagement, and even medical equipment.Across the source set, a consistent pattern emerges: value is shifting toward AI-enabled workflows, connected software platforms, and increasingly intelligent physical systems that can extend scarce clinical capacity while improving experience and precision.

For industry leaders, the implication is clear. The question is no longer whether AI and digital innovation will reshape healthcare, but how quickly organizations can redesign operating models, talent, infrastructure, and trust frameworks to capture the upside. Those that treat AI as an enterprise transformation, rather than a collection of pilots, will be better positioned to lead.

1. AI is moving from automation to agency

The most important shift is the transition from conventional automation to agentic AI. BCG describes AI agents as systems that can “autonomously plan and execute tasks with minimal human oversight,” with implications spanning patient care, clinical workflows, and drug discovery. In parallel, Deloitte reports that nearly a third of life sciences and healthcare organizations already see agentic AI as having a significant impact on organizational strategy.

This is a material change in the role of technology. Instead of simply supporting clinicians and administrators, digital agents are beginning to coordinate decisions across fragmented systems. Accenture points to agentic architectures that connect legacy and new platforms, centralize functions previously handled by overburdened professionals, and could orchestrate care pathways in real time across imaging, records, triage, and specialist workflows.

The strategic takeaway is that AI’s value is expanding from task efficiency to operating-model redesign. That raises the bar for execution. BCG argues successful innovators follow a 10-20-70 model: 10 percent of effort on algorithms, 20 percent on technology and data, and 70 percent on people and processes. In healthcare, that distribution is especially relevant because transformation success depends as much on workforce redesign and adoption as on the model itself.

2. Productivity is now the proving ground for AI in healthcare

Healthcare’s workforce imbalance makes productivity the sector’s most immediate AI use case. Accenture finds that 83 percent of healthcare executives are piloting generative AI in pre-production environments, yet fewer than 10 percent are investing in the infrastructure required for enterprise-wide deployment. At the same time, 83 percent cite employee efficiency as a top priority, and 77 percent expect gen AI to boost productivity.

The urgency is reinforced by structural supply-demand pressure. Accenture highlights that about 900,000 registered nurses are expected to leave the profession by 2027, while the US could face a physician shortfall of roughly 139,000 by 2033. Demand is rising as well: the population aged 60 to 90, among the heaviest users of healthcare, is expected to increase by 45 percent over the next 20 years.

These constraints explain why ambient documentation, clinical co-pilots, and language-based automation are moving to the forefront. BCG notes that ambient AI scribes are reducing physicians’ documentation burden, while AI assistants synthesize patient data, symptoms, and research to improve productivity and reduce diagnostic errors. Accenture estimates that 40 percent of healthcare working hours are devoted to language-based tasks that gen AI can transform; 17 percent of tasks can be fully automated and another 23 percent can be augmented.

The message for leaders is pragmatic: healthcare does not need to wait for fully autonomous care models to realize value. The near-term prize lies in scaling AI against administrative load, clinician capacity, and knowledge work bottlenecks.

3. Patients are becoming active decision-makers and digital channels are gaining power

A second structural trend is the rebalancing of influence toward the patient. BCG notes that close to half of US adults use health apps and roughly a third use wearable devices, creating a broader stream of real-world data that providers can analyze for prediction and personalization.

Bain shows how this is changing commercial and care dynamics. Globally, about half of patients now advocate for a specific treatment, and around a quarter of healthcare professionals say patient requests inform treatment decisions. More than half of patients see value in AI-based tools for treatment information, while nearly one-third of HCPs say they use such tools frequently. In the US, direct-to-patient platforms are also gaining traction, with 25 percent of patients having tried them and appreciating the convenience.

This patient shift is not just a marketing issue; it is a channel-power issue. Bain argues that AI is becoming a new gatekeeper of information, while BCG expects pharmaceutical and medical device companies to build more direct-to-patient relationships. The competitive implication is that healthcare organizations will increasingly need strategies that integrate clinical trust, consumer-grade digital experience, and AI-mediated engagement.

4. Healthcare is becoming more software-centric, including in medical systems and equipment

The source set also points to a deeper architectural shift: healthcare is moving from isolated hardware and legacy systems toward connected, software-centric ecosystems. McKinsey coverage in Healthcare Digital notes that software platforms are becoming increasingly embedded in the healthcare ecosystem, giving providers and payers new routes to operational efficiency through workflow automation, interoperability, and better use of previously siloed data.

That same logic is extending into care environments and equipment. Accenture describes a future in which digital agents coordinate with imaging systems, medical records, and specialists in real time. Deloitte goes further, arguing that “physical AI” is evolving robots from pre-programmed machines into adaptive systems that perceive, learn, and operate autonomously in complex environments, with particular promise in MedTech. It cites a UK surgical robotics developer integrating Nvidia’s IGX Thor AI platform to improve computing power, situational awareness, and real-time recommendations during surgery.

Taken together, these examples suggest that the next generation of healthcare equipment will derive more of its value from software, data, and intelligence layers rather than from hardware alone. For leaders in providers, MedTech, and life sciences, this means competitive advantage will increasingly depend on digital architecture, interoperability, cybersecurity, and the ability to keep products and clinical systems continuously improving through software and AI. That is also why Deloitte emphasizes the importance of AI infrastructure, data sovereignty, latency, resilience, and AI-native IT redesign as deployment moves from proof of concept to production.

5. Trust is becoming the critical scaling condition

As AI becomes more embedded in clinical and operational decisions, trust is shifting from a soft consideration to a core strategic requirement. Accenture states that 81 percent of healthcare executives believe they need to prioritize a trust strategy in parallel with their technology strategy. Its broader argument is that healthcare’s next technology chapter will only scale if patients trust the experience, clinicians trust the protocols, and organizations can demonstrate ethical, secure, and clinically sound deployment.

This is especially important because the sector is moving toward more autonomous and more embodied forms of AI. Agentic systems, biometric interfaces, digital humans, and robotics all widen the opportunity set, but they also heighten concerns around governance, transparency, privacy, and accountability. Deloitte makes a similar point: ambition alone is insufficient in a regulated industry, and success depends on navigating legacy systems, regulations, and organizational change.

For executives, the implication is straightforward. Trust cannot be treated as a downstream compliance exercise. It has to be designed into the architecture, workflows, and patient experience from the start.

The road ahead

The future of healthcare will be shaped by three converging forces: AI that can act, patients who expect to participate, and medical systems that are increasingly software-driven, connected, and intelligent. In the near term, the largest gains will likely come from productivity, documentation, data integration, and patient engagement. Over time, however, the bigger shift may be structural: healthcare organizations evolving from fragmented institutions into adaptive digital ecosystems that predict needs, personalize care, and continuously learn.

Industry leaders should therefore focus on five priorities: move beyond pilots, build fit-for-purpose AI infrastructure, redesign workflows around human-plus-machine collaboration, strengthen direct and trusted patient engagement, and prepare for a world in which more value is created in software and intelligence layers across both care delivery and equipment. Organizations that do this well will not simply digitize today’s healthcare model; they will help define the next one.

At LINO Consulting & Research, we help healthcare organizations translate AI ambition into scalable, real-world impact.
Let’s discuss how your organization can lead this transformation.

Reference:

Accenture. (2025, March 10). Gen AI amplified: Scaling productivity for healthcare providers.
https://www.accenture.com/us-en/insights/health/gen-ai-amplified-scaling-productivity-healthcare-providers

Accenture. (2025). Accenture technology trends 2025: Healthcare.
https://www.accenture.com/us-en/blogs/health/accenture-technology-trends-2025-healthcare

Bain & Company. (2024). Pharma commercialization in the age of AI and active patients.
https://www.bain.com/insights/pharma-commercialization-in-the-age-of-ai-and-active-patients/

Boston Consulting Group. (2026). How AI agents will transform health care.
https://www.bcg.com/publications/2026/how-ai-agents-will-transform-health-care

Deloitte. (2026). Tech trends 2026: Life sciences and healthcare.
https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/perspectives/tech-trends-2026-life-sciences-healthcare.html

Healthcare Digital. (2024). McKinsey: AI and data are redefining US healthcare efficiency.
https://healthcare-digital.com/news/mckinsey-ai-data-are-redefining-us-healthcare-efficiency