Insurers have made real progress in deploying AI across distribution, underwriting, and claims functions—and many are seeing improvements in customer experience, speed, and operational efficiency. But as meaningful as these gains may be, they hide the opportunity for a deeper transformation.
Beyond making routine insurance work more efficient, AI will change the nature of talent within the industry, including such issues as what work will remain for people to perform, how companies will build human expertise, and where accountability will sit. The insurers that win will not be those that deploy AI fastest, but those that proactively redesign talent around it.
The ongoing incorporation of AI into insurance processes is exerting pressure on the traditional insurance talent model. Historically, insurers have built expertise through volume, exposure, and sequential, people-led workflows, with underwriters, claims professionals, distribution teams, managers, and risk leaders connecting fragmented processes.
AI-led workflows will challenge that system in multiple ways. Most notably, they will shift routine execution away from people and concentrate human work on the exceptions, including the judgment, calls, and decisions that demand human ownership. The result will need to be a fundamentally different approach to achieving high-quality decisions. Throughout the workflow, decisions will depend less on individual handling of routine files, and more on the system’s built-in logic, controls, and accountability.
In dealing with this new environment, insurers face a choice. One option is to layer AI onto existing workflows and capture incremental efficiency. The other is to redesign the structure of work, the process of decision ownership, and the development and deployment of talent. The gap between those two paths is significant. By BCG's estimate, roughly 70% of AI's potential value comes from change management, adoption, and the redesign of talent and operating models, while only 30% comes from AI tools and technology itself. The implication, already validated by leading insurers, is clear: AI strategy cannot be separated from talent strategy.
AI Is Replacing Work, Not Automating It
Most insurers currently treat AI as a tool to improve existing workflows—accelerating submission intake, improving claims triage, or guiding distribution decisions. But that view is too narrow. AI’s potential goes far beyond merely upgrading aspects of the current workflow. Increasingly, AI will be able to take over execution itself.
Straight-through processing will become the default method for clear, high-volume requests, with AI capturing and structuring data at the source and embedding guidance directly into decisions and workflows in real time. As a result, humans will largely be removed from routine execution. (See Exhibit 1.)
The Work That Remains
Human involvement is likely to become increasingly event-driven, focusing on exceptions, ambiguity, and high-stakes decisions. Work will shift away from completing specific tasks and toward owning outcomes, with people overseeing, validating, and intervening in decision systems that run continuously. AI won’t just make existing insurance work faster. It will change the kinds of work that people do. (See Exhibit 2.)
In underwriting, the current model might require underwriters to review 200 straightforward submissions each month. The future model will have them focus instead on 10 or 15 edge cases that involve novel exposures and unusual characteristics. That work is likely to be more engaging, but also more cognitively demanding and operationally risky. This shift mirrors the difference between general practice and emergency medicine: a general practitioner performs routine check-ups and minor procedures, while the emergency room deals exclusively with high-risk, time-critical cases.
In today’s claims model, managers and employees handle every situation. The future model will leverage AI to manage triage and fraud detection in clear-cut cases, freeing employees to work on negotiated settlements, policy ambiguities, litigation scenarios, and cases where understanding the nuances of the claimant's situation is essential to resolution.
In distribution, employees in the future will no longer spend time on data entry. Instead, they will focus on understanding client objectives, anticipating needs before clients articulate them, and structuring solutions that AI-assisted tools can’t deliver on their own.
These are not isolated workflow updates. Together, they will collapse the apprenticeship model, concentrate accountability, and make human intervention rarer but more consequential. Without deliberate redesign, the traditional talent model will break.
Five Ways AI Breaks the Traditional Talent Model
As employees shift from processing routine tasks at volume to owning outcomes, AI will reshape the insurance talent model in five ways, each pulling at a different thread:
- Expertise will erode. Insurance has long built judgment through repetition and volume. As routine work disappears, the route that junior staff traditionally take to become seasoned experts will vanish. The pipeline will breaks at its foundation: as AI removes routine work, entry-level roles will shrink, cutting off the earliest and most formative stage of judgment development. Insurance will no longer be able to rely on learning by doing, and this will create a structural paradox: expertise will become more valuable at the very time when it becomes harder to build. To act effectively on the decisions that remain, people will need to understand how AI systems reach their conclusions—not as a specialized skill, but as a baseline requirement of the role. Fewer people will handle high-stakes interventions, so the quality of talent will matter more, and the cost of a weak bench will rise with it.
- Ownership will fragment. When human involvement becomes exception-based, accountability gaps will open quickly in the absence of clear decision rights. For instance, who will own the underwriting decisions that the system makes? Who will be responsible for automated claims outcomes? These questions call for the designation of well-defined owners before decision systems begin to operate continuously.
- Human work will become more intense. As AI absorbs routine processing, the work that remains for people will grow more complex. It will often involve ambiguity, high stakes, and emotionally sensitive situations. Human involvement is likely to shift toward fewer but weightier moments such as negotiations, contested exceptions, and decisions that set precedents. These roles may be more engaging and have higher value, but they will also be more demanding. Cognitive load will rise as the routine repetitions that once helped people calibrate their judgment fall away. Accountability for a growing share of high-impact decisions will fall on a shrinking number of employees—a combination that, left unmanaged, can breed decision fatigue, judgment inconsistency, and burnout. It might also increase attrition and make roles harder to fill, accelerating the loss of critically important expertise.
- Accountability will concentrate. As routine decisions become systemic, formal accountability for outcomes will land on a smaller set of workers, even though the decisions will come from models, data pipelines, and controls that no one person operates directly. The span of accountability will widen as direct control narrows. As AI amplifies the reach of individual decisions, human judgment will become a risk vector. Errors will occur less frequently, but their impact will be broader and harder to contain. This evolving situation will increase the importance of traceability. Decisions will need to be observable and explainable even when no individual human is directly executing them. And because each automated decision will scale across thousands of cases, the consequences of any single misjudgment will scale with it, resulting in a growing gap between who is answerable for outcomes and who can actually shape them in the moment.
- Governance will need to move in real time. Most insurers still govern their performance via after-action review. As AI drives continuous and at scale decision making, however, after-the-fact reviews will not be enough. The lag between a flawed decision and its discovery will become a span across which thousands of cases end up mispriced or mishandled. Consequently, oversight must to move in line with the decisions themselves.
Of these five changes, expertise development matters most. Without a new way to build judgment, the other four will become even harder to manage.
What Insurers Must Do to Redesign Their Talent
Reacting to these industry shifts in the nature of human work requires four moves: building expertise by design, establishing clear ownership of outcomes, supporting human work, and providing accountability and real-time governance.
Build expertise by design, not assumption. Future insurance professionals will develop their judgment less through volume and more through focused exposure to complex, nonstandard situations. Experience will have to come through a combination of structured mentoring, AI-assisted learning tools, and deliberate rotation across decision types.
Industries where mistakes can be catastrophic already work this way. At nuclear plants, operators may go years without facing a real emergency, so they train relentlessly on realistic simulators to keep their judgment sharp for the rare moment that demands it. This may prove to be the technique used to train new teams and roles in the insurance industry of the future, such as real-time monitoring teams; the deep judgment that these programs produce will provide the crucial—and critically scarce—input that governance will require.
Developing expertise will not be a matter of general upskilling. Rather, it will involve producing the few qualified people needed to sit in those seats. Insurers that invest early will build a sustainable advantage in decision making. Those that do not will find their human staff unprepared in the moments that matter most.
Clearly designate owners as coordination increases. Performance will depend more and more on how well different functions coordinate in real time, not on how well any one of them runs in isolation. Insurers that build tight, continuous coordination across groups will make faster and more consistent decisions at scale. Those that leave ownership ambiguous will see accountability gaps widen as decision volume grows. The key leadership challenge will be to define decision rights end-to-end across the system, not just within individual process steps. Without that clarity, inconsistent decisions and unmanaged exposure will proliferate.
Support human work. As workers shift from executing individual tasks to overseeing systems of decisions, organizations should invest in them accordingly. This means providing them with better tools, stronger decision support, clearer escalation paths, and active management of workloads.
Insurers should also assemble talent and capabilities into integrated decision systems that span functions. When underwriting insights inform distribution, claims patterns improve pricing, and customer interactions shape product design in real time, value will multiply.
Firms that build these systems will move faster, deliver better risk-adjusted outcomes, and achieve a level of responsiveness that fragmented competitors cannot match. However, integration alone will not create that value. Connected systems pay off only when the company has organized people in the right roles to govern them, improve them, and step in when they go wrong.
Promote accountability and real-time governance. Future AI-driven systems will operate continuously and at scale, and governance must evolve with them. The answer is to move from after-the-fact review to continuous, in-line control of decision quality. The most forward-thinking insurers will stand up small, senior, business-owned teams and embed them in each domain to monitor decisions as they occur.
An air traffic control tower offers a useful model. Aircraft follow established routes largely on their own, while controllers watch the whole field from their tower, catching conflicts early and stepping in only when conditions require their intervention.
In the insurance version of this model, each team would combine three roles: a senior business owner who is accountable for outcomes and is empowered to intervene; a domain expert who recognizes when results deviate from expected patterns and decides how to respond; and a data and technology lead who can trace a problem to its root and fix it in the system. The result is governance that can identify and intercept problems in the moment of the decision, rather than reconstructing what went wrong after a flaw has already spread across innumerable cases.
A Narrowing Window to Act
The window to act is narrowing as AI scales the speed, volume, and complexity of decisions and as the pipelines for experienced underwriting and claims talent weaken. These changes will produce a clear divergence: some insurers will use AI to improve existing models incrementally while others will redesign how they make, own, and govern decisions. Inevitably, the value creation gap between those two groups will compound over time. In many functions, a large share of the workforce is nearing retirement, and insurance is struggling to compete for next-generation talent against industries with stronger pull. Few insurers are thinking about this today—but those that wait will be forced to rebuild expertise in an environment where the apprenticeship model that once fed it is obsolescent.
Leading deliberately means treating talent redesign as a core element of AI strategy, not as a downstream change-management task. Four priorities deserve action now before the constraint becomes a crisis:
- Redesign how to develop expertise once routine work disappears, using structured exposure, simulation, mentoring, and deliberate rotation across decision types.
- Define decision ownership across business, technology, and risk, so that accountability remains clear as decisions become continuous and cross-functional.
- Invest in the sustainability of human work, giving employees the tools, support, and workload management they need to keep their high-strain, high-stakes roles viable.
- Build real-time, business-owned governance teams to monitor outcomes, diagnose issues, and intervene before flawed decisions can scale.
How a company allocates resources across these priorities will determine how well it competes with its industry peers.
In the near future, the constraint will no longer be what AI can do. It will be whether an organization has a talent model capable of leveraging AI effectively and at scale. Every insurer will have access to powerful AI models and platforms—but how these elements structure work, develop expertise, define decision rights, and govern decisions as human intervention becomes rarer and the stakes of each intervention rise will separate the winners from the also-rans.
Insurers that act early can redesign their talent models with intent. Those that delay will continue to layer powerful technology onto talent structures built for a different era, and they will fall behind faster-moving competitors. In an AI-led insurance model, talent is not merely a supporting capability. Instead, it is the constraint, the risk control, and the source of advantage.
