The healthcare financial experience is too fragmented for the level of accuracy, efficiency, and trust the system requires. Payers, providers, and patients all feel the effects: avoidable disputes, delayed claims resolution, administrative burden and confusion around what is owed, paid and reimbursed.
For Zelis CEO Amanda Eisel, modernizing the healthcare financial experience starts with making these processes less complicated. Better alignment across payments, pricing intelligence, reimbursement logic and data can help create more efficient, fair and effective healthcare financial operations. AI has an important role to play, but only when it is applied to specific problems and tied to measurable outcomes.
Although AI has emerged as one of the most promising technologies in healthcare, Eisel emphasized that the conversation has to move beyond experimentation. Organizations need to understand where AI can improve existing workflows, where it can reduce friction, and where human oversight and governance remain essential.
“As we look at our ecosystem, the common thread is that every payer — regardless of size — is leaning on technology vendors to shape their AI strategy. The largest nationals may have the teams to build some capabilities themselves, but they’re still evaluating external partners. For the rest of the market, that vendor relationship becomes even more central,” Eisel noted. “The conversations that I’m having with payers these days are less about the technology itself and more about ‘tell me about the use cases, tell me about how AI can function within our workflows and the technology that we have today.’ That’s what really leads to impact.”
Eisel noted that many payers are looking to larger technology partners, like Zelis, that have the infrastructure, workflow expertise, data governance, and AI governance capabilities needed to support responsible adoption. That is particularly important in healthcare, where AI tools often rely on sensitive data and where trust can be undermined quickly if security, accuracy, or transparency falls short.
A 2025 Zelis survey showed that 71% of payers are actively using AI and nearly half are piloting use cases. A recent report by the National Association of Insurance Commissioners also found that many AI and machine learning use cases focus on claims adjudication, including claims automation, insights and recommendations for claims approval, high-dollar claim risk assessment, and claims processing. The Zelis survey showed that 23% of payers have embedded AI as a central operational component.
One year later, Eisel said the industry is becoming more disciplined about where AI can create value. The focus is increasingly on which problems AI can solve more effectively, which use cases can scale, and how organizations can balance innovation with cost, governance, and trust.
That is especially important because AI is not just a technology decision. It is an operating model decision. For payers, responsible adoption requires clear governance, strong data controls, defined success metrics, and a practical understanding of how AI will function inside claims, payments, reimbursement, and administrative workflows.
Corporate governance and oversight committees are essential to ensuring pilots are done responsibly, assessed against clear metrics, and tied to a shared definition of success.
“Payers [are] saying, ‘Yes, I want to adopt AI but I need to do it responsibly,’” said Eisel. “[By] setting up their own governance boards, spending a lot of time understanding how the underlying technology works, it gives confidence in data governance and security. That’s where a lot of the focus is.”
Zelis has an AI governance team that takes a cross-functional approach, incorporating client, legal, financial, technical and operational perspectives. For Eisel, that kind of governance is central to building trust in how AI is developed, deployed and measured.
The industry cyberattack in 2024 underscored how vulnerable healthcare operations can become when critical infrastructure is disrupted.
“I think it really changed our industry,” Eisel said. “We realize that when there are security breaches, it can bring the system to a halt. All the more reason that everybody is really focused on it. It’s a lived experience.”
So what should meaningful AI value look like in healthcare finance?
Eisel said one priority is building trust through more accurate data and stronger payment integrity. Zelis uses AI to help identify anomalies and potential fraud indicators before payment is made, supporting more informed decision-making and helping reduce avoidable friction across the payment process.
Another priority is data standardization. Fragmented data creates waste, rework, disputes, and administrative burden. AI, when paired with integrated workflows, can help organizations identify patterns, improve consistency, and reduce some of the administrative costs that slow down the healthcare system.
“The key is to start by asking, ‘What value do we create — for the organization, for our clients, and ultimately for the healthcare system?’ That’s what makes AI so exciting,” Eisel explained. “It gives us a path to solving healthcare challenges that previously felt unsolvable. The more we stay focused on creating meaningful value, the greater the opportunity to build a better healthcare system.”
Eisel advises other health tech leaders to begin with the payer problem they are trying to solve, then determine whether AI can solve that problem better, faster or more consistently. The goal should not be AI adoption for its own sake, but to improve the workflows, decisions, and operational outcomes that matter most.
That includes remaining grounded in operational and clinical needs, while recognizing that AI adoption requires changes in how teams work.
“Think about AI holistically. It’s not just a technology shift, it involves a transformation that changes how companies operate, how teams work, and how people adapt. That’s why our AI transformation is co-led by our chief people officer. Success with AI ultimately depends on how well people embrace and integrate it into the way they work. That’s the framing I recommend people take.”
For payers, the path forward is not simply to move faster with AI. It is to move more deliberately: prioritizing use cases that improve accuracy, reduce administrative friction, strengthen payment integrity, protect sensitive data, and build trust across the healthcare financial ecosystem. Payers are stewards of their members’ data, and technology partners have a responsibility to support that stewardship with the right infrastructure, governance, and operational discipline. As AI becomes more embedded in healthcare finance, the organizations that create the most value will be those that connect innovation to measurable outcomes and use it to make the system work better for everyone it serves.
Image: BlackJack3D, Getty Images
