AI Readiness in Healthcare: What Happens When the System Isn’t Ready
- Carole W. Kamangu
- 5 hours ago
- 5 min read
Across healthcare and public health organizations, leaders are actively investing in artificial intelligence, testing tools and launching new initiatives but the way work actually gets done inside their organizations is not evolving at the same pace. This disconnect is happening because AI is being introduced quickly to keep up with innovation, while the underlying dynamics in the decision-making process, the priorities that are set, and the way teams operate have not been addressed.
What is Already Happening with AI in Many Organizations
In most organizations today, what may appear like structured adoption of artificial intelligence is, in reality, not the case as teams are testing and using AI in inconsistent ways across the system.
AI is already embedded in scheduling systems, documentation workflows, clinical decision-making and operational processes. The problem many organizations currently experience no longer has to do with the accessibility of AI tools to their workforce but more with how each team is using it differently, often without a shared approach. One team may use an approved tool, while an individual from the same team uses whatever is more user-friendly or easiest to access. In many cases, leadership might not fully be aware of how AI is being used day-to-day, even as adoption continues to grow.
AI Readiness is Misinterpreted at the Leadership Level
Most organizations misinterpret what AI readiness actually means. It’s often defined in terms of access to tools or the ability to deploy them quickly. Leaders ask which platform to adopt, how fast they can integrate it, or how much operational capacity they can replace. The answers to those questions are not indicators of readiness. They simply reflect a focus on tools and rapid deployment instead of how the organization actually functions and can benefit from successful implementation.
AI readiness is not about becoming tech experts. It's about the responsibility leaders have to ensure that adoption is aligned with the mission of their organizations and focused on meeting the needs of the communities they serve.
“AI readiness is not about becoming tech experts. It's about the responsibility leaders have to ensure that adoption is aligned with the mission of their organizations and focused on meeting the needs of the communities they serve.”
AI readiness is also not about selecting the right tool or application, it’s not a solution for over-hiring, or a replacement for a lack of technical skills within the workforce. It’s about leadership and requires leaders to understand how work is currently happening across their organizations and whether that way of operating can support new technology without creating organizational risks.
AI Readiness Reveals Gaps Within Health Systems
Many Organizations adopt AI to streamline their processes and save costs without focusing on what it actually does. AI does not fix how an organization operates but it makes existing issues more visible.
If operations already run smoothly, AI can strengthen performance. But when teams are already working around gaps, managing delays, duplicating work, or making decisions without clear direction, AI brings those issues to the surface. Leaders need to be ready for what AI will expose about their current system before their organizations adopt any sort of technology.
“Leaders need to be ready for what AI will expose about their current system before their organizations adopt any sort of technology.”
Organizations struggling after AI adoption are not dealing with a technology issue. What they’re seeing is existing problems, like unclear decision-making, disconnected leadership teams, and inconsistent processes, becoming more visible and harder to manage.
AI Adoption Increases Risk in Misaligned Systems
When different parts of the organization are already operating in silos, AI introduces more variation in how decisions are made and how work gets done instead of creating consistency.
For example, healthcare organizations may roll out AI tools or training options to reduce clinical workload for nurses and physicians and public health organizations may spearhead AI adoption to enhance surveillance and data analysis for epidemiologists. Without clear expectations on how they should be used and how sensitive information must be handled, organizations are introducing new risks into the equation. As a result, teams may choose different tools, use them differently, produce inconsistent outcomes, and in the worst case scenarios, mishandle sensitive enterprise data. In some cases, individuals use AI independently without oversight, creating what is known as shadow AI.
At that point, the organization is no longer managing adoption. It’s trying to catch up with how AI is already being used. What initially looked like innovation becomes exposure to patient and enterprise data leakage, inconsistent decision-making across leadership teams, and potential financial or reputational consequences.
Efficiency from AI Use Doesn’t Mean the System Works
One of the most persistent assumptions driving AI adoption is the belief that efficiency is primarily achieved through workforce reduction. Leaders often ask how many roles can be replaced or how quickly operations can become leaner. This thinking treats AI like a staffing solution, when in reality it is interacting with how work is already structured.
AI does not replace the need for a well-functioning system. It speeds up whatever is already happening whether that’s efficient workflows or broken ones. If work is already coordinated and consistent, AI can improve performance. When teams are duplicating efforts, working around operational bottlenecks, or depending on informal methods, AI amplifies the speed and significance of these issues.
System Pressure Increases AI Implementation Risks
In healthcare and public health organizations, risk is very much a reality. These entities are already functioning under the weight of financial limitations, workforce shortages, rising demands, and stringent regulatory oversight.
Introducing AI into these contexts without considering the actual workflow adds further complexity to existing stressors. Issues that were previously manageable, such as delays, inconsistent processes, or communication breakdowns, become more common and noticeable. Over time, this leads to higher costs, strained teams, and adverse patient care outcomes.
AI Readiness Starts with Leadership
Organizations seeing better results with AI adoption are not always adopting the most advanced tools but they are laser-focused on establishing strong systems. They’re also clear on how decisions are made and how teams are expected to work together before introducing new technology. To strengthen organizational alignment, every leader at the senior level like the department level should be involved and contribute to establishing a strong AI governance and adoption framework for the entire organization as part of its AI readiness efforts.
“Every leader at the senior level like the department level should be involved and contribute to establishing a strong AI governance and adoption framework for the entire organization.”
AI cannot be successfully introduced by one department alone. It requires leaders across the organization to agree on how decisions are made, how tools are used, how information is transferred and protected, and what outcomes are expected. When leadership is not aligned in how they operate, AI adoption perpetuates those silos instead of correcting them.
AI Makes System Gaps More Visible
When AI is introduced into a strong system, it can improve efficiency, support faster decision-making, reduce operational burden, and contribute to positive outcomes. When it’s not, the opposite happens. Teams experience inconsistent results, more confusion around processes, and increasing pressure to keep up with new tools that don’t match their skillsets.
AI is not a distant dream for health systems, it’s already part of how they operate today. For organizations moving forward with adopting AI at scale, it’s important to understand whether their systems are prepared for what AI will reveal and what readiness interventions should be implemented.
If AI is accelerating what a system already bears underneath the surface, it’s important for leaders to understand what is at stake and figure out what their systems are actually designed to produce before moving forward with investing in AI tools, workforce training and scalable adoption.
What Can Leaders Do Now?
To understand what organizational strengths and weaknesses can be amplified by AI adoption in your healthcare or public health organization, our AI Readiness in Healthcare Operations Scorecard will help reveal the gaps that need to be addressed before moving forward.
Comments