Why This Matters – Now

Across specialty markets in the UK / Europe, APAC / Asia and the US, underwriters still spend too much time wrestling with inputs instead of shaping portfolios. Market analyses are consistent: ~40 percent of underwriter time is spent on administrative tasks, such as normalizing PDFs and spreadsheets, re-keying data and reconciling fields.¹ Another survey emphasizes that underwriters spend ~35 percent of their workweek on tasks that could be automated.²

At the same time, only a small minority of insurers have scaled Artificial Intelligence (AI) enterprise-wide. According to a recent global survey, only 7 percent of insurance companies have successfully scaled AI across their enterprise.³ Data readiness and integration remain the most frequently cited blockers. The consequences are visible everywhere: Slower quotes, capacity leakage, higher control overhead and missed pricing nuance on the risks that matter most.

The thesis is clear: Underwriting transformation will not be driven by tools bolted onto messy workflows. It will be re-shaped by data-first, process-disciplined and AI-augmented operations – anchored by an Underwriting Copilot that amplifies human judgment, not replaces it.

Today’s Reality: The Cost of Friction

The drag is quantifiable:

• Time Dilution: ~30–40 percent of an underwriter’s day is spent on non-core administrative tasks; in some organizations, the share is even higher.

• Cycle Time Drag: Quote turnaround stretches on complex risks, SLAs are missed and aging queues build, directly reducing speed-to-decision.

• Conversion & Revenue Leakage: Slower responses and incomplete, back-and-forth case preparation depress hit / win rates and push attractive opportunities elsewhere.

• Cost & Re-work: Duplicate data entry and reconciliations inflate controllable expense; re-work and exception handling consume scarce expert capacity.

• Control Exposure: Late, manual sanctions / ESG checks increase the risk of misses; inconsistent evidence raises audit remediation overhead.

• Portfolio Impact: Limited time for selection and pricing nuance results in sub-optimal mix, unnoticed accumulations and weaker portfolio balance.

• Data Friction: Unstructured, inconsistent inputs create downstream delays and errors – becoming a primary source of leakage across the underwriting workflow.

The business impact globally is significant: Slower turnaround reduces conversion; capacity is lost as expertise is consumed by administrative work; decision quality is blunted by incomplete data; and manual controls inflate costs and operational risk.

Bottom line: Today’s model systematically dilutes the scarcest resource in specialty – underwriter time and attention.

Underwriting 2030: What “Good” Looks Like

By 2030, the specialty underwriting model must look fundamentally different. Three principles will define success:

How AI, Agentic AI and Generative AI (Gen AI) re-shape the underwriter’s day:

The impact of this shift will be measurable:

• ~30-50 percent reduction in quote turnaround times⁴

• ~20-35 percent increase in productivity^5,6 by shrinking the administrative drag and automating routine checks

• 5-10-point improvement in hit / win rate^7,8 due to responsive quoting and cleaner submissions

• Significant reduction in manual data entry – based on WNS’ experience, administrative re-keying accounts for nearly 40 percent of underwriter workload today. Automating this effort releases capacity for judgment and negotiation

• Near-total automation of sanctions, ESG and compliance checks⁹ with explainable outputs and audit trails

• Portfolio discipline strengthened¹⁰ through real-time accumulations, concentration alerts, scenario testing and portfolio-aligned signals

Impact by 2030: Underwriters spend the majority of their time interpreting signals, shaping portfolios and documenting rationale, not wrangling data or chasing status.

How We Get There: The WNS Underwriting Workbench

This future of underwriting is already being built. Over the past several years, WNS has worked with clients across regions to prove the efficacy of the model through the Underwriting Workbench – a domain-first platform that integrates data, process, controls and intelligence into a single underwriter experience.

At its core is the UW Smart Assist, the evolved Underwriting Copilot within the Workbench UI. Powered by Agentic AI and Gen AI, it stays context-aware across cases, portfolios and process stages to guide underwriters with real-time intelligence.

Conclusion: A Future Defined by Judgment and Foresight

By 2030, underwriting will not be defined by how quickly documents are processed, but by how effectively data, process and intelligence are orchestrated. The winners will be those who free underwriters from administrative drag and re-direct their focus toward judgment, foresight and portfolio discipline.

AI creates real value only when processes are disciplined and data is trusted. The WNS Underwriting Workbench — with the Underwriting Copilot at its core — turns today’s fragmented, document-led reality into a data-first, insight-rich model. It elevates underwriter judgment, strengthens governance and delivers scalable growth now while laying the foundation for Underwriting 2030 in every major specialty market.

Discover how the WNS Underwriting Workbench, with its embedded Copilot, can transform underwriting from fragmented to data-first. Speak with our specialists to learn more.

References

1. Why Underwriters Don’t Underwrite Much | Insurance Thought Leadership

2. The Underwriter of the Future | BCG

3. Insurance Leads in AI Adoption. Now It’s Time to Scale | BCG

4. The Algorithmic Advantage: How Underwriting Is Being Transformed in the Insurance Industry | Celent

5. The Potential of Gen AI in Insurance: Six Traits of Frontrunners | McKinsey & Company

6. How Insurers Can Supercharge Their Strategy with AI | BCG

7. How Data and Analytics Are Redefining Excellence in P&C Underwriting | McKinsey & Company

8. The Future of AI in the Insurance Industry | McKinsey & Company

9. Are Insurers Truly Ready to Scale Gen AI | Deloitte

10. Global Insurance Report 2025: The Pursuit of Growth | McKinsey & Company