Naresh Ramaswamy, SVP of Technology and Chief Architect, explains that successful AI adoption in insurance requires a balance of modernized data pipelines and human-centric change management.
Insurance AI rises or falls on orchestration. Claims and service interactions demand empathy and judgment, so automation only works when it operates within clear governance guardrails and a unified data foundation. Leaders who align technology, data architecture, and workforce readiness turn AI into measurable operational gains, while fragmented pilots stall under regulatory pressure and siloed systems.
He emphasizes that successful AI adoption in insurance requires a combination of technology, governance, and human-centered design.
- The productivity prize: Development teams report gains from AI-assisted coding. “At Virtusa, we see at least a 40 to 50 percent productivity improvement,” Ramaswamy explains, citing tools such as Copilot, Bedrock, and Gemini that accelerate output and reduce repetitive work. Scaling these gains enterprise-wide requires coordinated oversight of privacy, model risk, and evolving state-level AI regulations. Proactive governance creates stability, allowing teams to move faster with confidence.
- Higher autonomy: Organizations are learning to define where AI operates independently, where it supports decision-making, and where human judgment remains essential. Ramaswamy compares enterprise AI to autonomous vehicle models such as Waymo. “Even the most advanced systems operate at Level 4 autonomy, where human oversight remains available by design. We are still years away from a completely autonomous CSR. There is no such thing as fully autonomous software at the moment."