Investments in AI are beginning to change the requirements placed on the insurance workforce. As AI becomes embedded in daily operations, employees will need to interpret system-generated insights, manage exceptions, document AI-assisted decisions, and apply judgment in situations where automated tools lack sufficient context.
Shift recently examined 100 job postings from leading U.S. insurance organizations to understand how this transition is appearing in the hiring market. The findings show substantial demand for the people building AI capabilities. Seventy-two percent of AI, analytics, product, and technology roles explicitly referenced AI. Among operating roles in claims, underwriting, SIU, and subrogation, the figure was 6%.
The results suggest that insurers have entered the next stage of AI adoption. Technical teams are building the infrastructure and capabilities. The work now extends to preparing the broader organization to use those capabilities effectively.
Job descriptions reveal how work is evolving
Job descriptions serve several purposes. They attract candidates, define current responsibilities, and signal which capabilities an organization expects to matter in the future. Viewed collectively, they can also show how an industry is preparing for changes in technology and operating models.
The hiring patterns in Shift's research suggest that insurers have concentrated much of their visible AI hiring within teams responsible for building, implementing and supporting the technology. These roles require specialized expertise in data science, product development, analytics and engineering.
The impact of those investments will eventually extend across the business. Claims professionals, underwriters, investigators and other operational teams will increasingly use AI-enabled tools as part of their daily work. Their responsibilities will evolve as those systems become more deeply integrated into decisions and workflows.
That transition is beginning to appear in the hiring landscape. Operational job descriptions continue to emphasize core insurance capabilities such as judgment, communication, and customer service. Far fewer describe responsibilities involving AI-generated insights, exception management, documentation of AI-assisted decisions, or the responsible use of automated recommendations.
Preparing for AI-Enabled Insurance Work
Insurance expertise remains essential. Human judgment is still one of the industry's most valued skills: nearly eight in 10 job postings analyzed referenced judgment, discretion, complex decision-making, investigation, negotiation or problem-solving. More than three-quarters of operating-role postings emphasized these capabilities.
AI will change how that expertise is applied. As automated systems take on more routine tasks, employees can devote more time to complex claims, disputed liability, ambiguous policy language, and sensitive customer situations. These scenarios require context, experience, and decision-making that cannot be reduced to a system output.
The findings show that current job descriptions value judgment, although they rarely describe it in the context of AI-enabled work. Few operating-role postings mention AI-assisted decision support, and none reference the responsible use of AI.
Insurers can begin to define these capabilities more precisely. Judgment includes assessing whether an AI-supported recommendation makes sense in context. Investigation increasingly includes determining what additional information is needed when a system flags a claim, policy, provider or network. Communication includes explaining a decision clearly and empathetically when digital tools have informed the process.
In this environment, AI can increase the value of insurance expertise by helping professionals apply it more consistently and at greater scale.
The entry-level postings in the sample offer an early indication of how this development may occur. AI references were uncommon across claims roles, though some trainee positions specifically mentioned a willingness to learn or receive training in AI.
This language suggests that some insurers may plan to develop AI fluency through onboarding, internal learning and day-to-day use of AI-enabled tools. Such an approach could be especially valuable in an industry facing an experience and talent gap. AI can help newer employees access institutional knowledge, identify relevant information and develop sound decision-making practices more quickly.
The job-posting data cannot show how widespread these training programs are. It does, however, raise an important workforce question: how should insurers combine hiring, upskilling and workflow design to prepare operating teams for AI-enabled work?
Bringing Responsible AI into Operational Roles
Responsible AI appears rarely in insurance job descriptions. References to explainability, governance, transparency, and responsible use occur occasionally in analytics and technical roles. None of the operating-role postings analyzed included this language.
As AI becomes part of everyday insurance decisions, governance also becomes an operational capability. Employees using AI-supported systems may need to recognize when a recommendation warrants further review, determine when additional evidence is required, document how AI informed a decision, and escalate cases when a system lacks sufficient confidence or context.
Human-in-the-loop design, auditability and operational controls begin at the technology level, then continue through the workflow. Their effectiveness depends on the people applying them in real situations.
Many insurers may currently address responsible AI through technical, legal, risk or compliance teams. Greater adoption will create a need to translate those principles into practical expectations for claims professionals, underwriters, investigators and managers.
The Next Phase
The hiring data shows that insurers are investing in the technical talent needed to build and deploy AI. The next phase involves integrating those capabilities into the work of claims professionals, underwriters, investigators, and other operating teams.
Traditional insurance expertise will remain the foundation of these roles. Employees will apply that expertise in an environment shaped by AI-assisted insights, automated workflows and increasingly sophisticated decision-support systems. Job descriptions, onboarding, training and leadership expectations can help define how that work should be performed.
The value of AI will ultimately depend on the quality of the decisions it helps people make. Insurers that combine strong technology with experienced, well-prepared employees will be better positioned to improve consistency, manage complex cases and serve customers effectively.
AI transformation is therefore also a workforce transformation. Its success will depend on helping insurance professionals use new capabilities with judgment, confidence and accountability.
