Artificial intelligence (AI) will reshape white-collar work whether companies are ready or not. In most industries, the conversation stops at automation and efficiency.
In insurance, the question is more consequential: will AI be used primarily to cut costs and headcount, or to help people stay healthier, more resilient, and in work for longer?
The answer will shape not just the future of insurance, but the future of knowledge work itself.
Unlike almost every other industry, insurance is economically aligned with human wellbeing.
Insurers make money when people stay healthy, employed, and resilient.
In theory, better outcomes for people should translate directly into better outcomes for businesses.
In practice, the system rarely works that way.
Human and commercial cost
Employers spend heavily on employee benefits and wellbeing programmes, yet engagement remains stubbornly low while stress and burnout continue to rise.
In the UK, more than six in ten employees now show symptoms of burnout, such as exhaustion and disengagement, and in 2024 Mental Health First Aid England found nearly eight in ten reported moderate-to-high stress levels at work.
In the US, more than half of workers were burned out, with many citing stress as a factor undermining productivity, retention and long-term performance, according to Eagle Hill Consulting research.
The human cost is obvious. The commercial cost is mounting.
Brokers, meanwhile, struggle with manual processes layered on top of fragmented legacy systems. Insurers face worsening risk and higher claims costs.
The goals across the system are aligned, but the signals that matter in everyday life and work are poorly understood, and even more poorly acted on.
AI can change that equation, but only if it is applied differently.
So far, most insurers are using AI the same way other industries are: to automate call centres, streamline claims, and reduce headcount.
These efficiencies are real, but they miss the more consequential opportunity.
The most powerful capability modern AI offers is not task automation, but contextual understanding, the ability to recognise patterns across behaviour, work, and life that traditional systems struggle to see in isolation.
Modern knowledge work generates a steady stream of weak signals: longer hours, fewer breaks, disrupted sleep, rising stress, declining engagement.
Individually, these signals are easy to dismiss. Taken together, they reveal whether pressure is sustainable or quietly becoming long-term risk.
Earlier intervention
Historically, insurance has only entered that story at the end, when stress becomes burnout and burnout becomes a claim.
By that point, people are already out of work, employers are managing absence and churn, and insurers are paying for failure rather than preventing it.
AI creates the possibility of intervening much earlier.
Not with generic wellbeing programmes or static risk scores, but with personalised, timely support grounded in real patterns of behaviour.
For individuals, this means guidance during genuinely difficult transitions, not a PDF of resources sent after something has already gone wrong.
For employers, it offers clearer insight into workforce wellbeing, enabling better decisions about benefits, communication, and workload design.
For insurers, it opens the door to shifting from reactive claims management to proactive risk reduction.
Elements of this shift are already emerging.
Across insurance and employee benefits, there is growing interest in moving beyond point-in-time assessments toward systems that learn continuously from everyday behaviour.
Reinsurers are exploring preventative frameworks. Insurers are beginning to talk seriously about validated models rather than theoretical pilots.
Employers facing rising mental-health claims and productivity challenges are looking for approaches that focus on early intervention rather than crisis response.
The incentives have always existed. What AI changes is the precision and timing with which they can be acted on.
Short-term cost optimisation
Yet the industry remains largely focused on short-term cost optimisation.
In conversations with insurance executives, investors, and operators over the past year, a consistent pattern emerges: AI is framed primarily as an efficiency lever, a way to process claims faster, staff call centres more cheaply, or reduce manual work.
That framing is understandable and dangerously incomplete.
Employee benefits and insurance were designed for an era when the dominant risks at work were physical. Knowledge work has fundamentally altered that risk profile.
Today, the greatest drivers of claims and lost productivity are psychological: stress, burnout, and the erosion of boundaries between work and life.
These are risks traditional insurance systems were never built to see, let alone manage.
AI will reshape white-collar work regardless.
The question is whether insurers use it only to shed jobs, or also to keep people healthier, more resilient, and in work for longer.
Turning that ambition into reality will not be easy.
Aligning data, incentives, and execution across insurers, employers, and individuals is hard.
Most transformations fail not because the vision is wrong, but because systems are not designed to support it.
But the opportunity is real.
Insurers that use AI to help people adapt, rather than simply to automate, can build businesses that are more resilient, more differentiated, and more valuable over time.
In a world where disruption is permanent, systems built only to react will always arrive too late.
AI offers insurance a choice. I know which path I will be taking.
