Enterprise AI Readiness in Healthcare: Development and Preliminary Evaluation of a Five-Domain Survey Framework

Hospitals and health systems are increasingly turning to artificial intelligence, the branch of computer science that enables machines to learn from data and make decisions. These tools promise to help doctors diagnose diseases faster, manage hospital resources more efficiently, and reduce the administrative burden on staff. However, simply buying or testing a new software program does not mean a hospital is ready to use it effectively across its entire operation. There is a significant difference between having a technology and having the organizational capacity to use it safely, fairly, and at scale. Just as a car requires not only an engine but also a driver, fuel, roads, and traffic laws to function, an artificial intelligence system needs a supportive environment to succeed. Without the right leadership, data quality, trained staff, and clear rules, even the most advanced tools can fail or cause harm.

A recent study by Ajmal Aminee at California State University explores this gap by developing a new way to measure "Enterprise AI Readiness." Instead of counting how many artificial intelligence projects a hospital has started, the researcher asked leaders to evaluate their organization's overall preparedness across five specific areas. The study surveyed 150 leaders from hospitals, medical centers, and technology firms, followed by in-depth interviews with 28 of them. The goal was to determine if a hospital's readiness in these areas actually connects to how well they implement artificial intelligence, how easily they can expand its use, and whether they see real benefits from it. The findings suggest that while many organizations are strong on paper with their strategies and governance, they often struggle with the practical work of training staff and integrating these tools into daily patient care.

The researcher built a framework based on five interconnected pillars of readiness. The first is leadership and strategy, which looks at whether executives have a clear plan and the knowledge to guide artificial intelligence efforts. The second is data, technology, and infrastructure, examining if the hospital has clean, accessible data and the computer systems needed to run these tools. The third pillar is workforce and organizational readiness, focusing on whether the doctors, nurses, and administrators understand the technology and feel prepared to use it. The fourth area is governance, security, and responsible artificial intelligence, which checks if the organization has rules to protect patient privacy, prevent bias, and ensure accountability. The final pillar is clinical integration and evaluation, measuring whether the tools fit smoothly into patient workflows and if the hospital can track whether they are actually improving outcomes.

When the researcher analyzed the survey responses, they found that readiness was not evenly distributed across these five areas. Leadership and strategic readiness received the highest scores, with an average rating of 3.89 out of 5. This suggests that hospital executives are generally aware of the importance of artificial intelligence and have begun to formulate strategies. Governance and security also scored relatively well at 3.71, indicating that many organizations have established committees and policies to oversee these technologies. However, the scores dropped significantly for the more practical aspects of implementation. Workforce readiness averaged 3.34, and clinical integration and evaluation received the lowest score at 3.29. This pattern reveals a common trend: hospitals are good at planning and setting rules, but they are less prepared to train their staff and weave these tools into the complex, real-time flow of patient care.

The study also investigated whether these readiness scores mattered for actual success. The data showed a strong link between overall readiness and three key outcomes: how successfully artificial intelligence was implemented, how easily it could be expanded to other departments, and whether the organization felt it was gaining real value. Organizations that scored higher across all five domains reported greater success in deploying these tools and scaling them up. Specifically, leadership and strategic readiness had the strongest independent connection to implementation success, followed closely by the ability to integrate tools into clinical workflows. This indicates that having a clear vision from the top is crucial, but it is not enough on its own; the tools must also be usable by the people on the front lines.

To understand the numbers better, the researcher spoke with the leaders who had taken the survey. The interviews confirmed the survey results but added important context. Many leaders admitted that while they had ambitious strategies and formal committees, the day-to-day reality was often more difficult. They described challenges with fragmented data systems that did not talk to each other, a lack of specific training for staff, and the difficulty of getting new tools to fit into existing patient routines without causing disruption. One leader noted that moving from a small pilot project to a full-scale system required much more than just proving the technology worked; it required reliable data, clear ownership, and sustained support. Another pointed out that governance often stopped at the approval stage, with less attention paid to monitoring the tools after they were deployed or holding people accountable for their performance.

The study concludes that becoming "AI-ready" is not a single event but a continuous process of building and aligning capabilities. It is not enough for a hospital to adopt a new technology; it must also ensure its staff is trained, its data is clean, its rules are enforced, and its workflows are adapted. The research suggests that many healthcare organizations are currently ahead in strategy and governance but lag behind in the human and operational work required to make artificial intelligence work in practice. While the study provides a useful framework for identifying these gaps, the author cautions that the results are based on self-reported perceptions and need further testing over time. Nevertheless, the findings offer a clear message for healthcare leaders: to realize the full potential of artificial intelligence, they must look beyond the technology itself and focus on building a supportive, capable, and integrated organization.