The insurance industry is facing a critical challenge driven by significant staffing shortages and rising turnover rates. Data from the U.S. Bureau of Labor Statistics suggests the industry is expected to lose nearly 400,000 workers through attrition. This trend highlights the urgent need to backfill an aging workforce and bridge the worker gap, especially as retaining employees for tedious back office work becomes increasingly difficult amid shifting regulatory and customer requirements.
While there has been plenty of hype surrounding artificial intelligence (AI), the real opportunity today lies in using AI agents to strategically fill this impending claims management workforce shortage. By focusing on practical, proven use cases, carriers can determine what tasks can be automated, what will remain a human function, and how AI agents can interact to maximize the benefits for the workforce and overall back-office throughput. The goal is to incorporate the human-in-the-loop so that AI is safe and actually used. Let AI agents do the boring, repetitive tasks so adjusters can focus on judgment, negotiation, and empathy. Humans will be kept in command via review queues and escalation rules.
Here are three ways AI is actually changing claims management and where humans still matter most:
1. AI Handles the Clerical, So People Handle the Critical
The biggest gains in efficiency come from removing friction so that claims professionals can spend more time on strategy, empathy, and problem solving. AI is currently adding real value in focused, repetitive areas and big data applications. Success can be measured by metrics such as intake resolution rate (% calls/emails fully handled by agent), AHT (average handle time) delta (minutes saved per claim) and error rate on field extraction (when extracting knowledge from data).
Key practical and proven use cases where AI is delivering value today include:
- Omnichannel claim intake across email, SMS, and telephony, with entity capture (name, policy, plate/ID) and automatic case creation.
- Knowledge-mining and data processing over large document sets per claim/patient; agents extract tasks and schedule nudges for upcoming visits or missing paperwork.
- Risk signals and fraud triage by comparing millions of claims to spot outliers for SIU review.
- Subrogation and recovery automation: detect subrogation opportunities from facts, generate demand letters, track recoveries.
These applications highlight the concrete ways AI can address the rising difficulty of retaining employees for back-office work.
2. Keeping AI Safe and Trusted Through Human-in-the-Loop Design
As AI systems handle more aspects of the claims process, it is paramount that organizations design systems where humans stay in control, ensuring both safety and trust. This approach is known as human-in-the-loop design, where the AI assists but the human remains in control.
To keep AI safe and trusted, organizations must prioritize the following design principles:
- Confidence Thresholds and Guardrails: These are necessary to decide when AI acts independently versus when it escalates the task to a human. (For example: LLM as a judge "license-plate number ≥0.95, name ≥0.90"→ auto-apply vs queue)
- Designing the Handoff: Claims leaders must focus on designing the precise interaction and transition between the human and the AI, rather than just the underlying model. An incremental adoption example of this is seen in IVR systems with forwarded calls serving as human escalations.
- Trust as a Feature: Transparency, explainability, and auditability must be prioritized at every step. This means showing the sources for information, not just providing answers.
3. Driving Adoption – Because Tools Only Matter If They're Used
AI tools can only deliver strategic advantage and address the workforce gap if they are actually incorporated into daily workflows. Focusing on adoption over mere availability is crucial. Successful incorporation depends on leveraging behavioral and cultural levers.
Agents should join like a new teammate: they sit in channels, see only the data they're allowed to see, and can @mention humans when confidence is low. Companies that route people to a separate 'AI dashboard' will lose adoption; companies that embed agents into existing flows win.
The drivers of real adoption can be broken down into three areas:
- Ability: AI solutions must meet users in their existing workflows; employees should not be asked to change tools. For example, AI functionality should be integrated within claims management systems or email platforms like Outlook.
- Motivation: Organizations must identify champions within the workforce and highlight peer success stories to drive internal motivation.
- Prompts: Adoption can be encouraged through in-workflow nudges, such as prompting a claims adjuster when creating a plan of action note. Other effective reminders include in-system messages, like "You saved 2.5 hours using AI drafting this week," or social/peer prompts sharing success stories.
By focusing on these three foundational approaches, the insurance industry can strategically leverage AI to address its critical staffing shortage and elevate the remaining workforce to focus on high-value, strategic functions.
A Note about Privacy, Security, and Governance
AI in claims is cultural, not just technical: every claimant is a human with an inviolable right to privacy. Agents should be designed to honor that first, then apply industry controls.
- Privacy principles: Data minimization by default; purpose-bound processing; least-privilege access; explicit consent for recordings; subject access and deletion flows.
- Security controls: Encryption in transit and at rest; envelope key management with regular rotation; short, business-justified retention windows; immutable audit trails; per-tenant isolation and row-level security; tamper-evident logs for model/tool outputs.
- Governance: Data Processing Agreements and BAAs where required; vendor due diligence; model/version change logs; approved "never-autonomy" actions; periodic access reviews.
- Regulatory alignment: Designed to align with HIPAA principles for PHI, GDPR for EU data rights, and SOC 2 control families for security and availability.
- Human accountability: High-impact actions require human approval; overrides and escalations are attributed to specific users; exceptions are reviewed in weekly ops.
