Where Healthcare Business Leaders Should Invest In Operational AI

This voice experience is generated by AI. Learn more.This voice experience is generated by AI. Learn more.Jason Considine is the President of Experian Health, a leader in revenue cycle management solutions.

getty​Healthcare organizations have plenty of opportunities to invest in and use AI to advance the operations and business side of providing care. In fact, investment in AI healthcare overall, including the clinical side, was more than $1 billion last year. The question is no longer whether AI belongs in the investment portfolio. Now comes the harder part: deciding what deserves to scale. The best place to start is with the problem, not the technology.

Every investment, especially in the fiscally squeezed healthcare system, competes for limited resources and organizational attention. With the median hospital operating margin index at just 2.5%, innovation can test an organization’s risk tolerance and may slow progress. Leaders need a strong use case and clear measures of value to determine whether the investment solves meaningful problems and produces sustainable results. Additionally, scaling innovation does not stop at the software. Leaders need to invest in people and processes.

A successful AI pilot is only the beginning. Deloitte’s 2026 U.S. Health Care Outlook found that only about one-third of healthcare organizations operate AI at scale. Closing that gap requires both capable technology and an organization prepared to adopt it.

A pilot answers one question: Can this work?

Scaling requires several answers: Does it solve a priority problem? Can we quantify the improvement? Does it fit into the way people actually work? Do our teams have the training and authority to use it? Can we support it operationally and monitor performance as conditions change?

Leaders should define the outcome before selecting the technology and decide in advance what evidence would support broader adoption. In the case of the revenue cycle, that could be fewer denials, fewer manual interventions, faster reimbursement, reduced cost to collect, increased staff capacity or a better patient experience. The measure will vary by use case, but the principle should not.

AI doesn’t need to be deployed everywhere at once. Leaders can start with the strongest business case, learn from real-world use and then scale deliberately as the technology, workflows and workforce mature together.

In my experience, pilots stall less often because the technology cannot perform and more often because the organization has not redesigned the work around it. The strongest programs give employees hands-on opportunities to test new tools, reduce handoffs and rethink the workflow from the desired outcome backward. They also establish clear human oversight and feedback loops so teams can evaluate output quality, intervene when necessary and improve performance over time.

Some of the best investment opportunities are hiding in the most frustrating parts of healthcare.

Consider the revenue cycle. Our company’s 2025 State of Claims survey found that 90% of claim denials are reworked with at least some human review before resubmission. At the same time, only 56% of respondents believe their current claims technology meets their revenue cycle demands.

That represents an enormous amount of human capacity being directed toward correcting problems after they occur.

This is where I believe the investment equation needs to change. Instead of asking where AI can automate another task, ask where it can prevent unnecessary work from happening in the first place.

On the front end, that can mean using AI and advanced data capabilities to improve the accuracy of patient information, eligibility and insurance coverage before a claim ever moves downstream. Elsewhere in the revenue cycle, it can mean identifying claims at risk of denial earlier or helping teams prioritize the work most likely to produce reimbursement.

The value is not automation for automation’s sake. The value is changing the workflow so fewer people have to touch the same problem over and over again. Technology investments that eliminate avoidable work can effectively create capacity without requiring organizations to continually add staff. The objective isn’t to remove people from the equation or cut jobs. It should be to make better use of their expertise, solve complex problems and support patients.

Before making the next investment, I think there’s another question worth asking: Will this technology simplify the environment or add another layer to it?

Healthcare already has deeply fragmented technology ecosystems. Solving each problem in isolation can create a new generation of disconnected tools that staff must navigate, integrate and manage. What I have seen is that fragmentation rarely announces itself during a pilot. It shows up later in duplicate work queues, inconsistent data and additional systems to monitor.

A compelling use case is not enough. Leaders should evaluate how the technology fits within their full workflow, enterprise architecture and data strategy. That assessment should address interoperability, data quality and governance and the organization’s ability to measure performance over time. This requires a cross-functional C-suite discussion involving the CIO, COO, CFO and CISO along with clinical and operational executives. ​

The most valuable AI investments should be measured by what they give back to the organization. That can mean time back to staff, fewer avoidable touches for administrative teams, revenue that would otherwise be lost or more resources available to focus on care. Executives should make those gains explicit.

This shifts the focus from adopting more AI or chasing the latest capabilities to redesigning work around meaningful results. With margins tight and demands continuing to grow, healthcare organizations cannot afford innovation without impact. They can use AI to remove friction, recover capacity and reinvest in higher-value work, creating compounding value that informs the next investment.​

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