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Elevance Health CDIO Ratnakar Lavu outlines three AI priorities at AHIP 2026
Elevance's AI priorities: simplify member navigation, cut provider admin work, equip employees with timely info. It never uses AI for denials, routing any non-approval to a human.
At AHIP 2026 last week, Elevance Health's Chief Digital Information Officer Ratnakar Lavu outlined the insurer's three priorities for artificial intelligence: simplifying member navigation, reducing provider administrative burdens, and equipping employees with timely information. The presentation shows how a large health plan is applying AI to support human decision-making rather than replace it.
AI is drawing more investment across health insurance, but for Elevance the focus stays on using technology to advance what the company is trying to achieve. As payers explore AI for Insurance tasks like claims processing and benefits automation, Lavu described a deliberate, member-first approach.
Simplifying the member experience
Elevance built a "ChatGPT-like experience" called Sydney inside its mobile app to answer members' questions about coverage, costs, and provider quality. Lavu gave the example of a member preparing for knee surgery who typically struggles to connect the dots between benefits and out-of-pocket costs.
"A member can ask a question and say, 'I have a knee surgery, do my benefits actually cover that knee surgery or not?' … It understands who you are, it understands your benefits, it understands what you're asking for, and then provides the right insight: 'Yes, it is covered. This is how much you'll pay. And then, by the way, there's a provider who's nearby who actually can provide quality care.' Because we also have enough information about which providers are providing quality care for our members and at what cost," Lavu said.
Simplifying the provider experience
For providers, the company is using AI to accelerate prior authorization approvals. Lavu made clear that the system is designed only to speed positive decisions. "We don't use AI for denials, so anytime the AI cannot result in an approval, it actually routes it to a human," he said. "There's always a human in the loop on this. But then we really want to get the approvals going fast, so that the provider can actually focus on the care for the member."
Supporting employees with timely information
Inside the call center, AI aggregates administrative and clinical information about the caller and presents it to associates so they can resolve issues more quickly. Lavu said the system goes beyond simple summarization: "We've done a lot of work in the call center. When a member calls in, we understand who the member is. We aggregate all the information, administrative and clinical, and then provide it to the associates, so they can actually quickly resolve the member issues. And a lot of intelligence goes into that with AI. It is not just summarization, but it has enough insights about the member and their longitudinal journey with us." After each call, Elevance analyzes call summaries to gauge satisfaction and reaches out to members proactively if needed.
For call center teams looking to adopt similar systems, an AI Learning Path for Call Center Supervisors can help translate these tools into measurable improvements.
Why this matters for healthcare professionals
Elevance's AI strategy offers a concrete model for how insurers can lower administrative friction without letting algorithms decide care denials. For clinicians and health administrators, the insistence on keeping a human in the loop for any non-approval may shape expectations about payer-provider trust and what responsible AI adoption looks like inside health plans. The approach also signals that AI's short-term value in insurance may lie in removing routine obstacles-not in automating clinical judgment.