From hype to value: aligning healthcare AI initiatives and ROI

As AI transformation remains top of mind for healthcare leaders, I’ve noticed two common pitfalls plaguing new entrants and early adopters.

1. Those in the early stages are often susceptible to the “ready, fire, aim” approach – quickly identifying a tool and searching for a problem to match.
2. Early adopters are having trouble defining clear return on investment (ROI), which may go beyond financials.

These pitfalls are reflected in our data as well. 36% of health systems lack a formal AI prioritization framework, and a recent Vizient benchmarking survey found the top barrier to implementing AI is a lack of clear ROI.

A successful AI strategy must include a clear prioritization framework and a deeper understanding of value. With this in mind, here is an example of one organization’s success and three steps to move beyond the hype and maximize ROI.

Nebraska Medicine’s success

When faced with 600 competing AI initiatives, Nebraska Medicine didn’t chase the “new, new thing.” They focused their efforts on a few high-impact goals like workforce optimization and streamlined surgical operations. Beyond the financial return on investment, the initiatives drove systemwide value: optimized capacity, reduced clinician burden and enhanced patient experience.

Their secret wasn’t just smart algorithms. It was the discipline to treat AI as an operational investment. That rigor translated into real patient impact. A 2500% increase in discharge lounge use wasn’t only a workflow win—it was an experience breakthrough. Faster discharges meant smoother care transitions and less time waiting in beds.

That kind of impact comes from intentional, strategic execution. Too often, organizations invest in pilots without a roadmap to sustained value. Here are three steps top health systems take to drive measurable results.

Three steps to drive focus and value:

1. Align AI to strategic and operational goals

AI projects with the strongest ROI start with a clear business problem. AI should never be implemented just because it’s available. It should serve a purpose, whether that’s solving workforce challenges, improving diagnostic efficiency or strengthening revenue cycle performance. That alignment to enterprise goals is what drives results.

One integrated health system starts the digital deployment process by evaluating how new AI initiatives align with system priorities. A committee reviews each initiative and must clearly define its end users, integration needs and support strategy for user adoption. That clarity ensures fewer false starts and stronger long-term results.

2. Adopt a new ROI framework to realize AI’s full potential

Health systems need a broader definition of value, especially when it comes to AI.

At Stanford Health Care, new AI models are evaluated using an internally developed FURM assessment, which assesses how fair, useful and reliable models are. The framework includes ethical review, workflow simulations, financial projections and deployment feasibility analysis to measure AI solutions before and after implementation.

While Stanford’s assessment provides broad governance standards, Sg2, a Vizient company, developed a focused framework that reflects how digital health (including AI) creates return.

This framework moves beyond cost savings to what organizations gain: capacity, quality and long-term sustainability.

Bottom line: A structured, repeatable assessment process helps organizations measure AI success beyond dollars saved.

3. Move from isolated pilots to systemwide value

Too many organizations are stuck in pilot mode. As I mentioned, our data show 36% of health systems don't have an AI prioritization framework. This leads to aimless piloting and underutilized potential.

Here are steps successful leadership teams take to move from pilot to scale:

• Establish a cross-functional AI governance team that includes finance.
• Create a prioritization process that includes success metrics and guidelines for when projects are scaled or stopped.
• Embed ROI timelines and metrics in every AI proposal.
• Track AI projects like operational investments: phased, measured, accountable.
• Train leaders to evaluate AI with an "impact-first" mindset.

At Premier Health, new tech projects are treated with the same rigor as core operations. Top initiatives are prioritized quarterly by the Operational Excellence team—based on speed to value—and are rigorously measured against both hard and soft ROI. Those that fall short are shut down swiftly, with lessons quickly re-integrated into future projects.

Making AI count

There is no doubt AI holds tremendous potential for healthcare organizations, but the value can’t be realized without a proactive, strategic approach.

To maximize that ROI, leaders must ask fundamental questions like: Are we solving the right problems? Are we measuring what matters? And are we building for scale?

AI’s value in healthcare is real. It’s measurable. But it requires structure, strategy and a disciplined focus on outcomes. Ask the tough questions, define your outcomes and lead with purpose. That’s how we create lasting value.