Healthcare’s Next Big Decision: Preparing for a Smarter Future

The healthcare industry is standing at an inflection point. Patients expect more personalized care. Clinicians are stretched thin. Technology is advancing faster than many organizations can adapt, and the pressure to do more with fewer resources shows no signs of easing. For healthcare executives, the challenge isn’t simply keeping up with change. It’s deciding what kind of organization they want to lead on the other side of it.

As 2026 draws to a close, that question takes on new urgency. The decisions made now will influence everything from how patients experience care to how employees do their jobs, how organizations invest their resources and how prepared they are to navigate the next wave of disruption. Waiting for the future to become clearer isn’t a strategy. Neither is chasing every new trend.

So where should leaders focus their attention before the calendar turns? Members of the Senior Executive Healthcare Think Tank, a curated group of experts in patient experience, workforce strategy, policy, quality, equity and healthcare technology, offer their perspectives on the decisions that matter most. They explore what it takes to build organizations that are not only equipped for what’s next but better prepared to deliver on the promise of healthcare.

Turn Consumer Health Data Into a Patient Engagement Advantage

Patients increasingly arrive at healthcare appointments with information collected outside traditional clinical settings. Wearables, continuous glucose monitors, independent laboratory testing and personalized wellness programs are changing how people understand and manage their health. Healthcare organizations must decide whether they will actively participate in this evolution or leave the opportunity to competitors.

Mick Safron, CEO and Founder of Biohackers World, says executives should establish a formal policy for accepting and using consumer-generated health data.

“Decide now how your organization will serve patients who already manage their own health,” he says. “Consumers are buying wearables, glucose monitors, lab panels and longevity protocols on their own, then showing up with data their clinicians often can’t use.”

The disconnect represents both a clinical challenge and a business opportunity. Patients increasingly expect healthcare professionals to help interpret information they collect independently, rather than simply address health problems after they develop.

“At Biohackers World, we meet thousands of these people each year, and their message is consistent: They want a doctor who partners on prevention, not one who only treats disease,” Safron says.

His recommendation is to establish clear guidelines for which consumer-generated data clinicians will accept, how they will interpret it and which preventive services the organization will offer.

Research supports the growing relevance of this decision. A 2026 study published in JAMA Network Open found that approximately 41% of U.S. adults used wearable devices for health management in 2024, up from 30.2% in 2020. Yet actual sharing of wearable data with clinicians remained relatively limited. The findings highlight an opportunity for providers to create more intentional processes for incorporating patient-generated information into care.

Organizations that develop these capabilities can strengthen patient relationships while exploring new preventive care services. As Safron explains, “Organizations that do will win a loyal, self-paying audience in 2027. Those that wait will lose it to direct-to-consumer clinics that already speak its language.”

“The initial wave of AI hype has triggered massive capital expenditure without a corresponding blueprint for value realization.”

– Rajani Kumar Sindavalam, Systems Engineering Leader at HCL America Inc.

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Make AI Investments Accountable for Measurable Results

Healthcare organizations have moved quickly to explore AI, but experimentation alone does not guarantee meaningful improvements. Executives must distinguish between technology investments that generate excitement and those that produce measurable operational or clinical value.

Rajani Kumar Sindavalam, Systems Engineering Leader at HCL America Inc., recommends making formal project governance and employee training mandatory components of every AI investment.

“Healthcare executives must mandate that every AI investment be driven by a rigid project charter, explicit scope boundaries and a comprehensive employee training strategy,” Sindavalam says.

Without those requirements, organizations risk investing in tools without understanding how they will improve existing processes or enhance services.

“The initial wave of AI hype has triggered massive capital expenditure without a corresponding blueprint for value realization,” he explains. “Over the past two years, the vast majority of organizations have failed to answer the fundamental question of how these investments translate into measurable process optimizations or product enhancements.”

Healthcare leaders should therefore require each proposed initiative to establish its objectives, implementation boundaries, ownership, workforce preparation requirements and success measures before funding is approved.

For executives, the message is that AI investments should be managed as strategic business initiatives rather than disconnected technology experiments.

Sindavalam believes this discipline will help organizations move beyond experimentation: “Imposing strict governance and mandatory upskilling now will shift organizations from reactive experimentation to disciplined execution, ensuring long-term operational resilience and tangible ROI by 2027.”

“These critical aspects of an enterprise’s AI architecture are foundational for the safe, effective and scalable deployment of AI in healthcare.”

– Mark Francis, Founder and CEO of CaregiverZone, Inc.

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Put Human Accountability at the Center of AI Governance

As AI systems become more sophisticated, healthcare executives must consider not only what these technologies can accomplish but also who remains responsible when they influence clinical decisions or take action within operational workflows.

Mark Francis, Founder and CEO of CaregiverZone, Inc., emphasizes that healthcare organizations need clearly defined AI governance and operational models before expanding their use of increasingly autonomous technologies.

“To ensure that their organizations are positioned to thrive in 2027, healthcare executives should ensure that their AI governance and operational models are buttoned-up,” Francis says.

That means establishing human authorization requirements at critical decision points, particularly when agentic AI is involved in clinical care. These systems may perform increasingly complex tasks, but their capabilities should not eliminate human responsibility.

Francis also stresses the importance of accountability throughout the technology lifecycle.

“It also includes the implementation of processes to ensure that all AI-driven actions are auditable, fully traceable and report to a single human owner,” he explains.

For executives, this means establishing clear approval processes, maintaining records of AI-driven decisions and assigning an identifiable individual responsibility for each system’s operation and oversight.

These safeguards are key as healthcare organizations introduce AI into workflows involving clinical recommendations, patient communications and administrative decisions.

“These critical aspects of an enterprise’s AI architecture are foundational for the safe, effective and scalable deployment of AI in healthcare,” he says.

Make Governance Part of Every AI Investment Decision

While accountability is essential once an AI system is deployed, healthcare executives should address governance much earlier—when deciding whether an initiative deserves investment in the first place.

Jordan Henry, Founder and Chief AI Ethicist at Veritas AI Consulting, advises leaders to establish nonnegotiable requirements for every proposed AI initiative.

“Before year-end, healthcare executives should decide that no AI initiative advances without an accountable executive owner, defined safety standards and measurable clinical or operational outcomes,” Henry says.

His recommendation addresses a common leadership challenge: Organizations can become so focused on the potential of AI that they overlook the operational requirements necessary to implement it safely and effectively.

“Make governance part of the investment decision, not an afterthought,” he emphasizes.

That principle requires executives to evaluate more than a vendor’s capabilities or projected financial savings. They should consider whether the technology has been appropriately validated, whether employees understand how to use it and whether its integration into existing workflows will improve performance.

Henry identifies three particularly important requirements: validation, workforce training and safe workflow integration.

“The strategic advantage is not adopting more AI. It is knowing which applications improve care, reduce administrative burden and justify their cost,” he explains.

This is important when financial and workforce resources are already stretched. An AI application that creates additional work, introduces unnecessary risk or fails to demonstrate measurable benefits may undermine the very objectives it was intended to support.

“This approach positions organizations to scale what works in 2027 and beyond while protecting patients, staff and scarce resources.”

Align AI Adoption With Workforce and Care Delivery Goals

Healthcare’s workforce challenges are unlikely to disappear simply because organizations introduce new technology. Clinician burnout, staffing shortages, administrative demands and growing patient expectations require leaders to rethink how work gets done.

Vikas Gupta, Technical Manager at HCL America Inc., recommends aligning technology capabilities with the practical needs of healthcare operations.

“One decision healthcare executives should make before year-end is to establish a clear enterprise strategy for AI-enabled care delivery and workforce transformation,” Gupta says.

The distinction between an enterprise strategy and a collection of disconnected pilots matters. Individual applications may offer localized improvements, but organizations need a broader plan for determining where AI can make the greatest difference.

Gupta points to the pressures driving this transformation: “Healthcare organizations face growing pressure from workforce shortages, clinician burnout, rising costs and increasing patient demand.”

Executives should identify high-impact use cases where technology can reduce repetitive administrative tasks, support clinicians and improve patient access. They must also invest in training so employees understand how new tools affect their responsibilities and workflows.

“Leaders who move beyond AI pilots and invest in governance, workforce training and high-impact use cases can reduce administrative burden, improve patient access and enhance operational efficiency,” Gupta explains.

Importantly, workforce transformation should not be treated as a technology deployment exercise. Employees need opportunities to develop new skills, understand changing responsibilities and participate in implementation decisions.

The ultimate objective is to improve the delivery of care, not simply automate existing processes. Gupta says organizations that connect AI adoption with measurable clinical and business outcomes will be better positioned to provide high-quality, patient-centered care in 2027 and beyond.

“Most healthcare organizations enter the new year with ambitious plans for clinical automation and predictive analytics, only to watch them stall because patient, claims and pharmacy data remains trapped in overnight batch cycles.”

– Mahendran Chinnaiah, Digital Healthcare Architect at a major U.S. healthcare and pharmacy services firm

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Modernize Data Infrastructure Before Scaling Innovation

Even the most promising healthcare technology investments can struggle when the underlying information infrastructure cannot support them. Fragmented systems, delayed data exchanges and outdated integrations can prevent organizations from accessing the timely information needed for effective decision-making.

Mahendran Chinnaiah, Digital Healthcare Architect at a major U.S. healthcare and pharmacy services firm, recommends addressing this foundational challenge before the end of the year.

“Commit to sunsetting legacy batch feeds and fragile point-to-point integrations in favor of unified, real-time data pipelines,” Chinnaiah says.

His perspective highlights a problem that often receives less executive attention than visible technology investments: the infrastructure connecting clinical, claims and pharmacy information.

“Most healthcare organizations enter the new year with ambitious plans for clinical automation and predictive analytics, only to watch them stall because patient, claims and pharmacy data remains trapped in overnight batch cycles,” he explains.

When information is delayed, clinicians and operational teams may be working with outdated records. That can undermine everything from proactive care management to administrative automation.

Chinnaiah recommends replacing fragmented data exchanges with modern pipelines that support continuous information updates. The investment can create a stronger foundation for clinical and operational improvements.

Chinnaiah explains the operational benefit: “Deciding to overhaul your core data plumbing before year-end eliminates the silent latency and reconciliation failures that drag down daily operations.”

He adds that continuous data availability can unlock investments across the organization: “When system data updates continuously rather than once every 24 hours, downstream investments—from proactive care gap alerts to automated prior authorization—can finally deliver real value.”

Make Patient Safety and Quality the Organization’s North Star

Technology, infrastructure and operational efficiency are important, but healthcare executives must never lose sight of the fundamental purpose of their organizations: delivering safe, high-quality patient care.

David Zechman, President of The Zechman Group LLC, and retired hospital president and CEO, brings a hospital leadership perspective to this priority. His experience leading healthcare organizations informs his recommendation that executives make measurable patient outcomes the central focus of their year-end decisions.

“Ensure that patient safety, satisfaction and quality metrics are exceeding expectations by the end of 2026,” Zechman says.

Rather than simply maintaining current performance, he encourages executives to establish higher expectations for the year ahead.

“Subsequently, the new 2027 thresholds for these three metrics are set so that they exceed 2026 results,” he explains.

This recommendation creates a practical framework for continuous improvement. Leaders should review performance across patient safety, satisfaction and quality, identify gaps and establish specific targets that challenge their organizations to improve.

It also provides an important counterbalance to the excitement surrounding new technology. AI adoption, automation and infrastructure modernization should ultimately support better patient experiences and outcomes.

“This is the most important reason why healthcare organizations exist, which is patient care,” he says.

Financial performance and operational efficiency matter, but neither should come at the expense of quality or patient safety.

As Zechman reminds executives, “We can never take this for granted as healthcare providers and professionals.”

Preparing Healthcare Organizations for 2027

• Establish a formal policy for incorporating consumer-generated health data. Define which patient-generated information clinicians will accept, how it will be interpreted and which preventive services the organization will provide.
• Require a business case and workforce training plan for every AI investment. Establish project charters, defined objectives and measurable return-on-investment expectations before approving funding.
• Assign human accountability for AI-driven decisions and actions. Ensure AI systems have appropriate authorization requirements, audit trails and clearly identified human owners.
• Make safety and measurable outcomes prerequisites for AI adoption. Require validation, workforce preparation and workflow assessments before allowing new applications to advance.
• Connect AI adoption with enterprise workforce transformation. Prioritize applications that reduce administrative burden, improve access and support employees in delivering better care.
• Invest in modern, interoperable data infrastructure. Replace fragile integrations and delayed batch processes with reliable, real-time data exchange capabilities.
• Raise patient safety, satisfaction and quality targets for 2027. Evaluate 2026 performance and establish higher benchmarks that reinforce continuous improvement.

Building a Healthcare Organization Ready for What’s Next

Healthcare doesn’t need more initiatives simply for the sake of keeping pace. It needs leaders willing to ask harder questions: Are we making care better for patients? Are we giving clinicians the tools and support they need to succeed? Are our investments solving real problems, or simply adding another layer of complexity? The answers should shape what organizations prioritize, fund and measure as they move into 2027.

The opportunity is bigger than adopting the next breakthrough technology. It’s building healthcare organizations that are more responsive to patients, more supportive of their workforce and better equipped to adapt when the next disruption arrives. The executives who make those decisions now won’t just be preparing for the future of healthcare. They’ll be helping determine what that future looks like.