By Zack Tisch

In healthcare, there is no shortage of enthusiasm for artificial intelligence (AI).

Healthcare leaders today no longer need to be convinced of AI’s utility. Instead, they are more interested in figuring out how their organizations can scale AI and use it to boost worker productivity.

For example, a recent report from McKinsey & Company found that 50% of U.S. healthcare organizations had implemented generative AI at the end of 2025, up from 47% in 2024 and 24% in 2023. Notably, for the first time since McKinsey initiated the survey, all respondents said they had at least some plans to pursue generative AI, indicating a decrease in organizational hesitation to use the technology.

Given AI’s strong potential to improve the quality of patient care, drive lower costs, and deliver greater administrative efficiency, healthcare leaders’ growing embrace of the technology is understandable.

However, despite the eagerness to adopt AI in healthcare, it is essential that leaders proceed with caution. Indeed, in my experience working over the past two decades with organizations like Stanford, MD Anderson Cancer Center, and UCLA Health, I have observed that those that gain the most value from technology typically are not the earliest adopters. Rather, they’re the ones that clearly define the problems they need to solve and take a disciplined approach to aligning technology with their business goals.

With that in mind, here are five tips healthcare organizations should keep in mind when considering AI adoption:

Plan beyond immediate needs: Too often, organizations choose technology to solve today’s problem without considering whether it will remain relevant in a few years, even as healthcare evolves rapidly. Like the shift from paper charts to EHRs, new technologies such as AI reshape workflows, staffing, and care delivery.

The right solutions address current challenges while adapting to future changes, integrate across departments, align with how teams work, and can be measured without adding unnecessary complexity.

Establish governance before scaling: Many organizations adopt multiple AI tools across teams, leading to duplication, inconsistent practices, and siloed learning that limits impact. To avoid this, they need clear ownership for evaluating technology, consistent risk review, defined success metrics, and shared insights across the enterprise.

Without that structure, even promising tools struggle to move beyond isolated pilots. Strong governance doesn’t hinder innovation; it ensures investments translate into meaningful, operational value.

Artificial Intelligence Homepage