Insurance must redesign work around AI, not just add another tool
Generative and agentic AI are already reshaping how insurance work gets done. The question for insurers is whether leaders will actively direct that change or let it happen by default.
Since 2024, generative AI has moved beyond experimentation into enterprise workflows. It now creates content-text, images, code-and augments human productivity in ways traditional AI could not. Agentic AI takes this further: these systems perceive their environment, reason about it and take actions to achieve specific goals.
For insurance, the implications are concrete. Claims specialists will become "claims concierges" managing customer journeys. Underwriters will shift from evaluating individual risks to managing entire books of business. Sales teams will evolve into omnichannel distribution specialists. Finance professionals will become financial stewards working across finance, actuarial, IT and data functions.
But here's the catch: 45% of insurance leaders expect AI and digital investment to drive resilience and adaptability. Many still struggle to move beyond pilots due to skills gaps, technical debt and unclear ownership.
Two paths forward: augmentation or task ownership
AI in insurance currently takes two forms. The first is work augmentation: humans use AI to complete tasks faster, cut manual labor and speed up work. Fifty-four percent of employees already use AI for basic tasks like searching information; 38% use it to summarize documents.
Augmentation alone delivers limited value without changes to roles, incentives and performance expectations. Only 5% of employees currently use AI as a thought partner-the second path, agentic task ownership, where agents become part of the team with human oversight.
This shift requires explicit decisions about where accountability sits, when human judgment must override AI outputs and how regulatory expectations for explainability are met. It also demands a culture shift that training alone cannot achieve.
Four actions to build an AI-ready workforce
Plan AI capacity intentionally. Stop treating AI as incremental workload. Many people don't adapt as quickly as new expectations demand, especially in an aging workforce. Connect learning, skills and career pathways as one component of a broader employee value proposition.
Define skills by function. Traditional role definitions move too slowly. Defining skills at the functional level-claims, underwriting, operations-makes emerging needs like data literacy, AI oversight and workflow orchestration explicit and actionable. This approach strengthens alignment between strategy and talent.
Build transformation as capability. Organizations need change-capable people and processes to adapt. Lessons from pilots must translate quickly into scale. Leaders should model the use of AI as a thought partner and reward others for doing the same.
Make the "why" tangible. The workforce now expects personalized experiences around career, rewards and wellbeing. People must understand how to use AI day to day, see expectations clearly and understand the benefits. Successful adoption depends on trust, clarity and support.
The real test is leadership, not technology
Insurers that treat AI as just a technology deployment risk reinforcing existing ways of working and realizing only incremental gains. Those that treat it as a workforce and operating model transformation unlock step change improvements in productivity, customer experience and resilience.
That requires difficult choices: redefining roles, redistributing judgment, investing in new skills while protecting human expertise, and holding leaders accountable for modeling the best use of AI.
The insurance workforce of 2030 is being shaped now, one decision at a time. The question is whether today's choices prepare people to work alongside AI-or merely add another tool to yesterday's model.
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