The healthcare financial experience remains too fragmented for the accuracy, efficiency, and trust the system requires, and Zelis CEO Amanda Eisel sees AI as one way to reduce administrative burden - but only when it's tied to specific problems and measurable outcomes. Eisel, speaking about the state of AI adoption among healthcare payers, said the conversation must move beyond broad experimentation and toward targeted use cases within existing claims, payments, and reimbursement workflows.
"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," Eisel said. "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."
A 2025 Zelis survey found that 71% of payers are actively using AI and nearly half are piloting use cases. Another report from the National Association of Insurance Commissioners noted that many AI and machine learning efforts focus on claims adjudication, including automation, insights for approval decisions, and high-dollar claim risk assessment. For medical billers and coding teams, this pattern means that AI is reshaping claims processing and payment integrity workflows at an accelerating pace.
Governance and trust as table stakes
Eisel stressed that responsible AI adoption depends on governance structures that go beyond the IT department. Payers are establishing internal oversight committees to vet pilots, define success metrics, and ensure data governance keeps pace with the technology. "Payers [are] saying, 'Yes, I want to adopt AI but I need to do it responsibly,'" she said. "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 built an AI governance team that brings together client, legal, financial, technical, and operational perspectives. Eisel pointed to the 2024 industry cyberattack as a catalyst - a lived experience that showed how critical infrastructure can halt systems entirely. The memory of that disruption has sharpened the industry's attention to security, accuracy, and transparency in any AI tool that handles sensitive data.
Where AI creates operational value
Eisel sees practical value in two areas: identifying anomalies and fraud indicators before payments go out, and reducing fragmentation caused by data that exists in disconnected systems. AI can spot patterns that manual review misses, allowing payers to make more informed decisions and cut avoidable disputes. Pairing AI with integrated workflows also helps standardize data and lower the administrative costs that inflate the back office.
She cautioned against adopting AI for its own sake. "The key is to start by asking, 'What value do we create - for the organization, for our clients, and ultimately for the healthcare system?'" Eisel said. "It gives us a path to solving healthcare challenges that previously felt unsolvable."
AI is not just a technology shift - it's an operating model change
Eisel advised health tech leaders to frame AI as a transformation that must change how teams work, not simply a tool they plug in. "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."
For payers, that means the path forward is not about speed but about deliberate prioritization: choosing use cases that strengthen payment integrity, reduce administrative friction, protect member data, and build trust across the financial ecosystem.
Why this matters for healthcare professionals
As AI becomes more embedded in claims, payments, and reimbursement, healthcare professionals - from medical billers to revenue cycle managers to compliance officers - will encounter tools that change daily workflows. Eisel's approach underscores that the organizations creating the most value will be those that connect AI to specific operational outcomes rather than chasing broad automation promises. For anyone whose work touches the healthcare financial pipeline, the difference between a well-governed AI tool and an unchecked experiment will show up in fewer disputes, clearer payment logic, and less time spent untangling administrative errors. The practical takeaway: ask not just what AI can do, but how it will function inside the technology and processes your team already relies on.
Your membership also unlocks:
