Healthcare leaders are betting big on AI to improve patient outcomes and operational efficiency. Investment is flowing into diagnostic algorithms, GenAI models for deeper clinical insight, predictive analytics and patient engagement tools. However, in most healthcare systems, the impact of these investments remains limited. AI pilots might show promise, but scaling those benefits across care delivery is an uphill battle.

Scaling challenges are not a result of the technology. AI often delivers accurate insights in controlled environments. The issue lies in how it is introduced into the realities of clinical work. In healthcare, that means physician liability, complex care hierarchies, rigid legacy systems and fragmented data. Without a product-aligned operating model that aligns teams, processes and technology around patient care, AI risks becoming a bolt-on tool that fails to change results on the ground.

The healthcare AI potential and the adoption gap

In the right setting, AI can support earlier diagnoses, anticipate patient deterioration, personalize treatment and improve capacity utilization. GenAI can help clinicians synthesize complex histories and evidence for faster, better‑informed decisions while reducing administrative burden.

However, these results are the exception, not the rule. Healthcare providers face adoption barriers that are deeply rooted in the way care is delivered:

• Physicians are legally accountable for clinical decisions and are cautious about tools they cannot fully trust.
• Electronic health record systems are designed to meet compliance requirements, not to deliver real-time, AI-driven insights.
• Treatment decisions require coordination across attending physicians, residents, nurses and specialists, each with different authority levels.
• Valuable data is trapped in departmental silos spanning clinical, operational and patient-generated sources.

These challenges make it harder for AI to become part of the everyday clinical process. To realize AI’s full potential in healthcare, the industry needs a structural change that enables true integration into care delivery.

How a product-aligned operating model enables AI in healthcare

A product-aligned operating model (PAOM) addresses these adoption barriers by restructuring around patient care pathways rather than traditional departments. For example, instead of separate teams for cardiology, radiology and nursing, a cardiovascular care pathway team would bring together everyone involved in that patient journey.

In this model, physicians, nurses, IT specialists, data scientists and operational staff work together with shared accountability for the same patient outcomes. Each professional maintains their clinical decision authority, and AI tools are embedded directly into their existing workflows. This could involve surfacing GenAI-generated guidelines at the point of care, integrating AI-driven risk scores into discharge planning or utilizing Agentic AI to automate follow-up tasks.

By organizing around pathways, data from multiple sources flows more consistently into a single view of the patient, improving data readiness and increasing the relevance of AI outputs. This model also ensures regulatory requirements are met while reinforcing trust that clinical judgment remains with licensed providers.

A leading example is how Mass General Brigham works with Philips to embed AI into imaging workflows. Insights are integrated into the diagnostic process within established protocols, letting care teams act faster without compromising compliance or physician control.

Implementing AI in a PAOM framework

Adopting AI in the life sciences and healthcare industry is as much about execution discipline as it is about technology. A PAOM provides the structure, but leaders must steer its implementation with clear priorities.

• Prioritize the right pathways: Focus first on clinical areas where AI can improve patient outcomes without disrupting accepted workflows. Chronic disease management and elective procedures often present strong candidates because they have repeatable processes and measurable endpoints.
• Co-design AI tools with clinicians: Involving physicians early ensures that AI outputs align with how clinical decisions are made and ensures that liability boundaries are respected. This builds trust and increases adoption rates. HCLTech’s The Blueprint to AI‑led Operating Model report notes that only 17% of organizations fully utilize customer feedback, but those with a product‑aligned model are far more likely to integrate it into design from the start (70% vs. 54%). This means patient input, clinician perspectives and real‑world outcomes can continuously shape AI tools, including GenAI and Agentic AI applications, so they stay relevant and trusted.
• Invest in the infrastructure to support AI: Robust, interoperable systems are essential so data can be accessed in real time across care settings. Even the most advanced AI models cannot deliver value if the supporting systems are outdated or disconnected.
• Lead the cultural shift: Moving from department-first to patient-first requires leadership to set clear expectations about multidisciplinary collaboration. Leaders must reward teams for shared outcomes, not only departmental performance.
• Measure what matters: AI performance in healthcare should be assessed through patient-centric metrics such as readmission rates, treatment adherence and patient-reported experience scores. Operational efficiency gains are valuable, but they should never replace patient outcomes as the primary measure of success.
• Automate beyond clinical decisions: Agentic AI can be applied to administrative processes like scheduling, resource allocation and discharge coordination. This reduces the time clinicians spend on tasks that do not require medical judgment and frees more time for direct patient care.

Competitive advantage through AI-enabled care pathways

When deployed through a product-aligned model, AI becomes an integral part of the care delivery process. Multidisciplinary teams supported by relevant data and context-specific AI can reach decisions faster, improve diagnostic accuracy and ensure smoother care transitions. Patients benefit from services that feel coordinated across what were once disconnected functions. From GenAI-powered clinical insights to Agentic AI-enabled process automation, clinicians gain tools that reduce their workload while maintaining control over clinical decisions.

The competitive advantage is twofold. First, healthcare organizations can achieve measurable improvements in patient outcomes, which strengthens their reputation and patient loyalty. Second, they can reduce operational costs by optimizing the use of clinical resources and eliminating unnecessary steps in the care delivery process.

Organizations that combine AI with a product-aligned approach will raise the standard in both patient care and operational efficiency, positioning themselves as leaders in the new age of healthcare.