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As healthcare organizations rethink the role of artificial intelligence, the greatest gains are emerging from technologies that translate complex information between people and systems while strengthening data governance and operational efficiency.
Artificial intelligence is redefining administrative and operational infrastructure in healthcare as technology companies look beyond clinical applications. The goal is to address longstanding inefficiencies in the movement of information between payers, providers, and patients. Administrative delays can influence payments, clinical workflows, and ultimately patient care. This is where the central challenge is no longer whether AI should be adopted, but how it can be implemented responsibly.
The most meaningful advances are emerging in areas where human effort has traditionally been required to bridge communication gaps between disconnected systems. Rather than replacing established, deterministic workflows, AI is being used to eliminate the manual translation work that slows claims processing, payment cycles, and operational decision-making.
AI Delivers the Greatest Value where People Absorb Inefficiencies
Complexity in healthcare creates countless friction points that require staff to interpret and convert information from one format into another. Employees routinely review faxes, emails, and phone conversations before manually entering or translating information into machine-readable systems. This leads to bottlenecks that consume valuable time and resources.
As a result, translation tasks have become one of AI’s most valuable applications. Instead of attempting to replace predictable, rules-based processes, organizations are using AI to automate the exception handling that has historically depended on human intervention, allowing employees to focus on higher-value work while improving efficiency across healthcare operations.
Targeting Healthcare’s Data Bottlenecks
Health IT platform Availity is among the companies focusing its AI strategy on these administrative pain points. Connecting payers and providers across the country, the company sees the greatest opportunity in addressing the moments where manual intervention interrupts otherwise efficient workflows.
“The biggest opportunity is not where the highest volume of transactions is, because those mostly need to stay deterministic. The opportunity is really where there are humans that absorb inefficiencies. When I look for AI use cases, I do not go look where there are algorithms; I go look where the people are spending that time. Because at the end of the day, generative AI is a translator back and forth between human language and machine language,” said Michael Privat, Chief Data & Engineering Officer, Availity.
That philosophy extends to common operational challenges, including situations where a provider misses a receipt, an accounting cycle stalls, or staff members must manually resolve exceptions before work can continue. AI, in these scenarios, functions as a translator that accelerates information flow between humans and digital systems without disrupting deterministic processes.
Availity also distinguishes between simply improving existing workflows and fundamentally redesigning them. According to Privat, organizations that limit AI to incremental efficiency gains risk overlooking its broader transformative potential.
“We often use AI to incrementally optimize instead of truly disrupting. There is some value in incremental optimization; it helps you win the race to the bottom. But the real value, the one that becomes very visible, is when you disrupt enough to win the race to the top. Using AI to optimize existing workflows is a lazy way to use AI. It works, but you just do not extract as much value,” he said.
Strong Governance is the Foundation for Responsible AI
While AI presents significant opportunities, successful deployment depends on building the right foundation before implementation begins. Simplified systems and disciplined data governance are essential prerequisites because poorly managed data can cause AI to amplify existing operational problems rather than solve them.
For healthcare organizations handling sensitive information, governance is not simply a compliance requirement but a core element of responsible innovation.
“Data governance is paramount. If you are going to give access to your data to AI, you better have governance in place, or else you are pretty much guaranteed you will leak data. Security and those sorts of things are baked in our DNA; we would not even consider applying AI to something that we do not feel has enough guardrails around it,” said Privat.
That emphasis reflects a broader industry recognition that AI delivers its greatest value only when supported by secure data practices and streamlined systems capable of producing reliable outcomes.
Building Healthcare’s Next Generation of Operations
Healthcare’s AI evolution is now centered on supporting human judgment rather than replacing it. By removing the administrative friction that slows communication between people and systems, AI enables healthcare professionals to spend less time translating information and more time making informed decisions.
Organizations that combine strong data governance with simplified technology environments and a willingness to rethink longstanding processes are likely to influence the industry’s next phase of innovation.
