The underwriting function within insurance is at a significant turning point. AI and automation now sit at the centre of how risk is assessed, priced, and managed. This shift is changing the role of the underwriter from a largely manual, experience-led position into one that also relies on confidence with data, technology, and cross-functional collaboration.
For boards, hiring teams and senior executives, this change is not only operational; it is strategic. Many carriers upgrade systems and tools but do not update how they hire or grow the people who use them. At Eliot Partnership, we believe companies need to rethink underwriting through a clear understanding of the skills and behaviours required for the future.
This blog provides a practical guide for leaders. It explains how the underwriting role is changing, what that means for talent and capability, and how insurers can build a workforce ready for the decade ahead.
Underwriting Under Pressure
Underwriters are working in conditions marked by rising loss volatility, growing regulatory demands and a steady increase in data volume from digital distribution. These pressures make accuracy, speed, and judgement more important than ever. At the same time, technology is reshaping how underwriting work gets done, and the industry is facing a clear shortage of the skills needed to support that shift.
Key findings from industry research include:
- AI is driving full workflow redesign, not simple task automation (McKinsey).
- Digital underwriting techniques have reduced life insurance question sets by more than 70% in some cases (McKinsey).
- 74% of insurance CEOs are concerned about digital skills shortages (PwC).
- 90% of insurance executives agree on the urgency of reinventing the employee value proposition to reflect human-machine collaboration. (Deloitte).
- 55% of insurance executives believe underwriting shortages will limit growth in the coming year (Insurance Thought Leadership).
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These findings show a clear pattern. Underwriting is being reshaped by new technology at the same time that skilled talent is becoming harder to find. This combination sharpens the need for a new type of underwriter and a new approach to hiring and development.
The Rise of the Hybrid Underwriter
A “hybrid underwriter” brings together traditional strengths with new capabilities. They use sound judgement and understand market conditions, but they also understand model outputs, know how to interpret dashboards, and can work comfortably with data teams. The modern underwriter is expected to see how decisions affect portfolio results, broker relationships and customer outcomes. They also need to work confidently with colleagues from actuarial, engineering and analytics.
This combination matters because the volume of information underwriters handle has increased sharply. AI-driven underwriting has already reduced the average decision time for standard policies to roughly 12.4 minutes, according to industry commentary.
The same analysis reports accuracy gains of up to 43 percent when AI is used to support decision making. AI leadership also correlates with improved financial performance. McKinsey found that insurers who lead on AI adoption generate roughly 6.1 times greater shareholder returns than their peers.
These gains rely on people with the right mix of skills and behaviours.
How AI Is Changing Underwriting Work
Traditional underwriting centred on manual review, rules engines and spreadsheets. Modern underwriting looks different. Submissions can be sorted by AI, documents scanned for key insights and pricing suggestions delivered through predictive models. Underwriters are supported by NLP tools that review medical, legal or engineering content in a fraction of the time previously required. Real-time data feeds allow underwriters to check conditions or risk factors quickly.
Decerto reports that AI-supported underwriting workbenches can reduce processing time by half and improve accuracy by around 30 percent. This shift means the underwriter’s day changes. Instead of spending lengthy periods reviewing documents or performing manual checks, more time is spent on complex risks, broker conversations, product work, pricing refinements and model oversight. The work becomes more judgement-led and more connected to wider business goals.
What This Means for Hiring
Many job descriptions still reflect what underwriting looked like ten or even twenty years ago. Modern underwriting demands people who can understand risk and pricing, but also interpret analytics, communicate clearly about model outputs and collaborate across teams. Hiring managers should look for curiosity, learning ability, confidence with data tools and the ability to handle complex information while staying grounded in underwriting fundamentals.
This is why potential now matters as much as experience. Some of the strongest hybrid underwriters come from backgrounds where they have worked with analytics or multi-team projects, even if they have fewer years of underwriting tenure. Eliot Partnership explores this further in ‘Experience Isn’t Enough: Why Insurance Leaders Must Commit to Continuous Learning.’
A clear competency framework helps interviewers evaluate talent in a consistent way. It should reflect technical capability, sound judgement, commercial understanding, collaboration skills and the ability to learn new tools and methods. Organisations that recruit against these skills put themselves in a strong position to support future growth.
Upskilling Current Underwriters
The skills gap cannot be solved by hiring alone. Many carriers are now investing in underwriting academies, data literacy training and structured rotations with analytics teams. Others use internal “automation champions” to support adoption inside underwriting teams.
PwC and CEO survey data show that capability shortages can limit progress even when technology investment is strong. Without structured upskilling, automation becomes a system upgrade rather than a genuine improvement in how underwriters work.
Leaders can make progress by mapping current and future skills, focusing training on core capabilities such as analytics interpretation and model awareness, updating performance measures and giving underwriters time to collaborate with specialist teams. These themes align with guidance in Eliot Partnership’s ‘How to Build a Smarter Leadership Assessment Strategy.’
Leadership, Culture and Governance
Strong leadership is essential. Underwriters need to feel that automation supports them. Leaders should show interest in model outputs, performance dashboards and new data sources, and take the time to ask the right questions.
AI also brings new governance responsibilities. Academic research highlights concerns around model bias and fairness in data-heavy underwriting.
Source: https://arxiv.org/abs/2401.11892
Boards must understand how models are tested, how override rules work, how bias is monitored and how data lineage is tracked. This requires leaders who understand the mix of underwriting and technology, not only one side of the equation.
Cross-functional work is another essential factor. Strong underwriting decisions increasingly depend on collaboration with actuaries, engineers, data scientists and distribution teams. Carriers that support this way of working gain more value from their technology investments.
What Boards and Executives Must Prioritise
Workforce planning must match the pace of AI adoption. McKinsey notes that small adjustments are not enough when workflows change at a rapid pace.
Boards should evaluate whether they have a clear plan for hybrid roles, whether training budgets align with transformation goals and whether leadership assessment tools have been updated. Underwriting talent is central to risk-taking, and leadership profiles must reflect a mix of underwriting, data and change experience.
Succession planning also needs attention. The CUO of the future will require experience in risk, pricing, analytics awareness and the ability to lead multi-team work. Updating leadership frameworks now ensures long-term resilience.
Underwriting in 2030
McKinsey forecasts that AI will be deeply embedded across underwriting, pricing and claims. Human judgement will still play a central role. Complex risks, broker relationships, exceptions and portfolio steering all require decisions that cannot be automated.
New underwriting career paths are likely to appear, including automation leadership roles, portfolio data specialists or analysts who sit between product, pricing and underwriting. These roles show how underwriting is widening rather than shrinking.
As AI tools become more common across the industry, talent becomes the differentiator. Carriers that invest early in hybrid underwriting teams will gain the greatest advantage in accuracy, speed and client experience.
Conclusion
AI and automation are reshaping underwriting, but the most important changes relate to people. Carriers need underwriters who understand both risk and data, and leaders who can guide teams through change. Boards must update succession plans and governance frameworks to reflect new responsibilities.
Underwriting will remain a strategic advantage for insurers who invest in skills, ambition, and capability. The future belongs to hybrid underwriting teams, and the time to prepare is now.
