By Abhishek Dwivedi, Senior Vice President and Global Head of Insurance, Xebia
GenAI is no longer experimental in insurance. From quote generation to claims triage and document intelligence, enterprise-grade pilots have become embedded workflows. However, the business impact remains inconsistent. Most insurers have crossed the automation threshold without capturing proportional margin gains. This isn’t a failure of tools. It’s a failure of orchestration design. Systems have evolved to handle volume and variance but not fiscal consequences.
Where margin actually slips
Margin erosion is rarely driven by weak underwriting or poor pricing anymore. It begins with unowned overrides, ambiguous escalations, and the absence of traceability across decision points. In many cases, AI systems operate with high throughput but zero economic feedback. A GenAI assistant may generate 200 quotes an hour, but if 30 percent are later overridden for priority advisors with no audit linkage, the system accelerates drift instead of containing it.
Case in point: The invisible override loop
Consider a mid-sized Indian health insurer that deployed GenAI copilots to assist underwriters. Quote velocity improved significantly. However, within one quarter, the loss ratio rose, not fell. Analysis revealed manual intervention in 38 percent of quote cases. These overrides were not tracked. They weren’t tied back to eventual claims. Pricing teams had no input, and underwriter incentives were not aligned to claim outcomes. The GenAI stack functioned perfectly on surface metrics but silently undermined profit control.
Execution is the new control layer
Fixing this did not require additional tools. It required architectural maturity. Copilot outputs were redesigned to include pricing band sensitivity. Override behaviour began triggering daily alerts to governance nodes, not just monthly reviews. Document automation logs were tied to advisor clusters showing repeat rework. Escalation ladders were constructed to redirect ambiguous clauses to expert copilots or humans based on risk level. Once fiscal telemetry entered the loop, override frequency fell by over 15 percent and per-policy margin began to correct.
From insight to economic attribution
Customer360 is no longer about unified views or NPS alignment. It is the scaffolding for decision traceability. Every interaction must be recorded with SLA impact, FTE consumption, and cost attribution. If an advisor’s onboarding leads to three times more document rework, the system must trigger retraining of document pipelines or sales workflows. If CX intervention resolves a claim faster but requires excessive deflection, that cost must be allocated, not absorbed silently. Attribution is not about punishment. It is about economic clarity.
Redefining role accountability
Insurers that mature execution architecture move away from abstract ownership. They assign accountability with context. The Chief Underwriting Officer does not just monitor quote output but overrides variance by segment. Claims heads don’t just measure turnaround time but track resolution drift by cause. Data leaders ensure copilots include feedback loops mapped to actual loss ratios. The COO stops looking at workflow compliance alone and begins measuring orchestration friction across systems and personas. AI becomes the proxy, not the excuse.
From automation to accountability: A new mandate for insurers
This next phase of insurance modernisation is not about being GenAI-first —it’s about being margin-first. The most successful insurers will not be the ones with the most copilots, but the ones who make every signal accountable, every deviation traceable, and every override a design decision—not a blind spot. When AI outputs generate cost impact logs, and workflows embed fiscal telemetry, the organisation stops drifting and starts governing. Not by manual control, but by architecture that speaks the language of margin.
