5 Reasons Why Meaningful AI Matters in Healthcare

Artificial intelligence is no longer experimental in healthcare. It is fully operational. Reporting by McKinsey shows that in a 2024 survey, more than 70% of respondents from healthcare organizations said they were pursuing AI integration. Similarly, a 2025 survey conducted by the National Institute of Health indicated that 100% of respondents reported adoption of AI documentation tools, with more than half indicating a “high degree of success” with the tools. That pace far exceeds earlier waves like electronic health record (EHR) adoption, signaling a structural shift in how care is managed. Against that backdrop in a recent webinar “Meaningful AI: Leading with Purpose”, Netsmart leaders Tom Herzog, COO, and Matthew Arnheiter, SVP of Innovations, emphasized a critical distinction in effectively navigating this new landscape: success with AI is not just about the technology itself but about how it is applied with purpose. 

1. AI must start with people, not technology

One of the clearest themes from the leadership discussion is that, if it is to be successful, AI strategy cannot be tech-first. It must be people focused. Organizations that set their sights solely on tech tools risk creating complexity without value. Instead, leaders are encouraged to prioritize how augmented intelligence (AI) can empower clinicians, improve workflows, and enhance patient outcomes. As Herzog put it, “this is not a tech-first strategy. It’s a people-first strategy.” 

This framing shifts the conversation from automation replacing roles to augmentation improving them. It also addresses the underlying fear many staff feel about AI by grounding adoption in tangible benefits to their daily work. 

2. The pace of change is fundamentally different 

Healthcare has seen major innovation waves before, but the velocity of AI adoption is unprecedented. What once took decades now unfolds in years or even months. Arnheiter noted that technologies like EHRs took roughly seven years to reach widespread adoption while AI solutions are achieving similar penetration in a fraction of that time. 

This compression creates both opportunity and pressure. Organizations must adapt faster but also rethink traditional implementation timelines, governance models and training approaches to keep up. 

3. AI expands capability rather than just reducing cost 

A common misconception is that AI is primarily about efficiency or cost reduction. While those benefits exist, the more meaningful impact is capability expansion. AI enables tasks that were previously impractical, such as timely clinical insights, automated documentation and proactive decision support. In addition to helping reduce costs, these automation benefits go beyond balance sheets, impacting care providers and the individuals they serve by freeing up more time for care teams to be with clients instead of wading through administrative roadblocks. 

As Herzog explained, “we’re adding valuable capabilities back to the system, things that weren’t easily done before.” 

This distinction matters. Organizations that focus only on savings risk underutilizing the potential of AI, while those that invest in new capabilities can tap into new opportunities for how providers can use the time they save in a more meaningful way. 

4. Different AI modalities unlock different value 

Not all AI is the same, and understanding the different categories helps clarify where value comes from. Arnheiter outlined four key types: predictive, generative, prescriptive and agentic AI. Predictive models forecast outcomes. Generative AI creates content like clinical notes. Prescriptive systems recommend actions to change outcomes. Agentic AI goes further by taking autonomous actions across workflows. These layers of functionality work together to support providers across the care journey.   

5. Change management is the real challenge 

Technology is only part of the equation. The harder problem is organizational change. AI challenges long-standing workflow assumptions and roles across every department. 

Herzog framed it directly: “this is probably the biggest change management exercise we’re going to have.” 

Success depends on aligning leadership, engaging staff and redesigning processes around new capabilities. Without that alignment, even the most advanced AI tools will stall before delivering meaningful value. 

Bringing direction to disruption 

AI represents a turning point for healthcare but its impact depends on intentional leadership, implementation and adoption. The organizations that succeed will be those that move beyond experimentation and focus on purposeful implementation grounded in people outcomes and sustainable change. 

As Herzog summarized, the goal is to move “from disruption to direction.”