ECU Health

Bennett Wall

Clinical Innovation Authority

Bennett Wall, MBA is a healthcare analytics and AI leader focused on advancing data-driven transformation at ECU Health. He leads enterprise AI initiatives, builds adoption strategies and helps healthcare teams use emerging technologies to improve operations, decision-making and patient care.

Making AI Adoption a Culture-Driven Transformation

The most meaningful lesson has been that adoption is earned, not issued. When we started our AI journey, the easy path would have been a bulk license buy and a top-down mandate. We deliberately chose the opposite. We built an AI Community of Practice (AI CoP), tied Microsoft 365 Copilot licensure to community engagement, and grew the program one user at a time. That patience produced near-universal adoption among our targeted users and, more importantly, a workforce that actually knows what to do with the tool. In healthcare, where workflows are sacred and the cost of distraction is measured in patient outcomes, you cannot shortcut culture. AI succeeds where people feel ownership over it.

Balancing AI Innovation with the Human Side of Patient Care

We treat AI as a teammate, not a replacement. Every use case we pursue has to answer one question first: does this give a clinician, nurse, or operator more time, clarity, or confidence to care for a patient? If the answer is no, we do not pursue it. We also keep humans in the loop on every decision that touches the patient. AI drafts, summarizes, and surfaces patterns; clinicians decide. Innovation that distances caregivers from patients is not innovation at ECU Health.

Overcoming the Challenges of Data-Driven Healthcare Transformation

Three challenges stand out. First, organizations often try to buy scale before earning it. Expensive licenses without an adoption strategy become shelfware and weaken confidence in future AI investments. Second, governance can lag behind technology, requiring leaders to build strong foundations around policy, privacy, and clinical safety while innovation continues moving forward. Third, workforce readiness remains critical. Rural and community health systems especially need to build AI capabilities internally while competing for talent in a highly competitive environment. A Community of Practice model helps address these challenges by creating learning, ownership, and shared responsibility.

How AI Is Changing Healthcare Leadership and Operational Strategy

AI is narrowing the gap between strategy and execution. Leaders can move from questions to insights and communication faster than before, raising expectations around judgment, ethics, and decision-making. At the operational level, AI is shifting healthcare organizations from simply collecting information to acting on it. The focus is moving beyond asking whether a dashboard exists to understanding what actions came from the insights it provides.

Building the Next Generation of Healthcare Technology Leaders

Emerging healthcare technology leaders should begin with the people closest to the work. Rather than waiting for a perfect enterprise-wide strategy, organizations should engage clinicians, analysts, and operators with real challenges and practical tools. Building AI literacy before expanding technology adoption creates stronger foundations for longterm success. Transparency about AI’s capabilities and limitations remains essential because trust is the foundation of meaningful healthcare transformation.

The goal is not simply to create an AI-enabled organization. The goal is to improve health outcomes and strengthen the communities’ healthcare organizations serve. AI is one of the tools helping move that mission forward.