The Rising Demand for 24/7 AI Support in Life Insurance

The Rising Demand for 24/7 AI Support in Life Insurance

Life insurance brokers are no longer just advisors—they’re digital partners in a 24/7 client economy. As customer expectations evolve, so must the tools that serve them. AI-powered support is no longer optional; it’s a strategic imperative for brokers who want to stay competitive, efficient, and client-focused.

Leading insurers like Sun Life, Manulife, MetLife, and Prudential are already deploying AI to handle high-volume, routine inquiries—policy status checks, premium calculations, document retrieval, and onboarding. These systems are reducing operational friction and freeing brokers to focus on complex, high-value interactions.

• 7% reduction in phone-aided benefit verification calls (Principal)
• 25–50% faster claim cycle times for non-contestable claims
• 53% decline in employee growth among U.S. life insurer peers in 2024

These numbers aren’t just trends—they’re signals of a transformation already underway. According to Bloomberg Intelligence, AI is driving measurable cost savings and productivity gains across the sector.

AI is not replacing brokers—it’s empowering them. The most successful deployments use a human-in-the-loop model, where AI handles initial triage and routine tasks, while human brokers step in for emotionally sensitive or complex situations. This balance preserves empathy, ensures compliance, and strengthens trust—critical in life insurance.

A real-world example: Principal’s NLP-powered chatbot reduced phone-aided verification calls from 50% to just 7%, proving that AI can handle repetitive tasks at scale without sacrificing accuracy or service quality.

As AI adoption accelerates, brokers must act now. The next phase isn’t just automation—it’s intelligent, empathetic, and seamless support. The question isn’t if you’ll adopt 24/7 AI—but how quickly you’ll do it.

Core Challenges in Deploying AI: Compliance, Legacy Systems, and Empathy

Core Challenges in Deploying AI: Compliance, Legacy Systems, and Empathy

Life insurance brokers stand at a crossroads: AI promises 24/7 support, faster service, and reduced workload—but only if key hurdles are overcome. Without addressing compliance risks, outdated infrastructure, and the irreplaceable value of human empathy, AI integration can backfire.

AI in life insurance operates under strict scrutiny. The EU AI Act, enforceable from August 2026, classifies AI used in underwriting and pricing as "high-risk", demanding rigorous governance, documentation, and impact assessments. In the U.S., 45 states and territories enacted AI-related legislation or resolutions in 2024, ranging from anti-discrimination rules to state-level AI task forces. These frameworks demand more than technical compliance—they require ethical design, transparency, and auditability.

• High-risk classification under the EU AI Act applies to AI in pricing and underwriting
• 45 U.S. states passed AI-related laws or formed task forces in 2024
• Data privacy laws (e.g., GDPR, CCPA) require strict handling of sensitive client information
• Regulatory reporting must be built into AI workflows to ensure traceability
• Compliance-by-design is no longer optional—it’s foundational

A broker ignoring these rules risks fines, reputational damage, and client loss. As highlighted by InsuranceNewsNet.com, compliance must be embedded from day one.

Even with the best AI strategy, legacy systems older than 20 years remain a critical barrier to integration. These outdated platforms lack modern APIs, struggle with data interoperability, and often cannot support real-time AI workflows. According to Will Wood of INSTANDA, “Life and health companies and advisors cannot experience the full benefits of AI while relying on legacy systems built more than 20 years ago.”

• Systems over 20 years old hinder AI integration
• Data silos prevent seamless AI-CRM and AI-policy system communication
• Manual workarounds increase error rates and reduce efficiency
• Modernization efforts must begin in 2025 to unlock AI potential
• Integration with CRM, e-signature, and policy admin tools is non-negotiable

Without modern infrastructure, AI becomes a digital band-aid—not a strategic upgrade.

In life insurance, emotions run deep. Death claims, policy changes, and financial planning are not transactional—they’re deeply personal. AI must be trained to detect emotional cues and respond with empathy-by-design, adjusting tone and response based on context. As DigiQT notes, AI systems are being built to guide beneficiaries through grief with compassion.

• AI must detect emotional tone in client messages
• Responses should adapt to sensitive topics like death or illness
• Human brokers must step in for complex or emotional cases
• Human-in-the-loop models preserve trust and compliance
• Empathy is a competitive differentiator, not a feature

A real-world example: Principal reduced phone-aided benefit verification calls from 50% to 7% using NLP-powered chatbots—yet still routed emotionally charged inquiries to human brokers. This hybrid model delivers speed and care.

Moving forward, brokers must balance automation with humanity—using AI as a co-pilot, not a replacement. The next section reveals how to build that system right.

How to Deploy a 24/7 AI Support System in Your Brokerage

How to Deploy a 24/7 AI Support System in Your Brokerage

Life insurance brokers are no longer just advisors—they’re digital transformation leaders. With AI platform spending projected to grow 800% from 2023 to 2028, the time to act is now. Leading firms like Principal and Manulife are already using AI to slash repetitive work, reduce claim cycle times by up to 50%, and free brokers to focus on high-value relationships.

A strategic, phased rollout ensures compliance, efficiency, and client trust. Start with low-risk, high-volume tasks and scale with confidence.

Begin by mapping the most frequent client requests. These are ideal candidates for automation and include:

• Policy status checks
• Premium calculation requests
• Document retrieval (e.g., policy summaries, ID proofs)
• Onboarding step reminders
• Basic FAQ responses (e.g., “How do I update my beneficiary?”)

According to Bloomberg Intelligence, AI-driven automation has already reduced phone-aided benefit verification calls at Principal from 50% to just 7%—a clear signal that routine inquiries are ripe for AI handling.

This initial focus minimizes risk while delivering measurable efficiency gains.

Not all chatbots are built for life insurance. Prioritize platforms that:

• Use natural language processing (NLP) trained on insurance terminology
• Integrate with CRM systems (e.g., HubSpot, Salesforce) and e-signature tools (e.g., DocuSign)
• Support omnichannel access (web, mobile, messaging)
• Enable human-in-the-loop escalation for sensitive cases
• Are designed for compliance-by-design, especially under evolving regulations like the EU AI Act

As Will Wood of INSTANDA notes, legacy systems older than 20 years are a major barrier—so choose a platform that can bridge modern workflows with existing infrastructure.

Seamless integration is non-negotiable. AI must access real-time data across:

• Policy administration software
• CRM databases
• E-signature platforms
• Claims management systems

Without API-level connectivity, AI becomes a siloed tool—reducing value and increasing manual workarounds. DigiQT emphasizes that AI chatbots that integrate with these systems can automate straight-through processing, cutting claim cycle times by 25–50%.

Use event-driven architectures and secure, documented APIs to ensure data flows smoothly and securely.

AI doesn’t “understand” policy language by default. Train it using:

• Real past client interactions (anonymized)
• Standardized policy clauses and underwriting guidelines
• Regulatory requirements (e.g., GDPR, HIPAA, EU AI Act)
• Emotional tone detection for sensitive topics (e.g., death claims)

As McKinsey warns, change management represents half the effort in AI adoption. Continuous training using real feedback ensures accuracy and builds client trust over time.

Track success with measurable outcomes:

• First-response time (target: <10 seconds)
• Resolution rate for Tier-1 inquiries
• Escalation rate to human brokers
• Client satisfaction (CSAT) trends
• Reduction in manual workload per broker

While specific CSAT improvements aren’t quantified in current research, Bloomberg Intelligence confirms AI directly impacts service-center costs and claims-adjuster productivity—key indicators of success.

Use this checklist to evaluate your brokerage’s AI readiness across compliance, integration, scalability, and multilingual support—ensuring you’re set for a smooth, compliant rollout.

Download your free AI Chatbot Readiness Audit for Life Insurance Brokers to begin your transformation.

Best Practices for Sustainable AI Integration

Best Practices for Sustainable AI Integration

AI integration in life insurance isn’t just about automation—it’s about building a future-ready, compliant, and client-centric support ecosystem. The most successful brokers aren’t just deploying chatbots; they’re embedding AI as a strategic co-pilot that enhances human expertise, not replaces it.

Leading insurers like Manulife and Principal have already demonstrated that sustainable AI adoption hinges on three pillars: ethical design, continuous learning, and strategic partnerships. These aren’t optional add-ons—they’re foundational to long-term success.

• Prioritize empathy-by-design in AI interactions, especially during sensitive moments like death claims or policy changes.
• Maintain a human-in-the-loop model for complex or emotionally charged inquiries to preserve trust and compliance.
• Integrate AI with core systems—CRM, e-signature platforms, and policy administration software—to enable seamless, straight-through processing.
• Partner with specialized AI providers like AIQ Labs to navigate technical complexity and regulatory risks.
• Train AI continuously using real client feedback to improve accuracy and personalization over time.

According to Bloomberg Intelligence, life insurers are seeing tangible financial gains from AI—especially in service-center costs and claims-adjuster productivity. Meanwhile, InsuranceNewsNet.com warns that legacy systems older than 20 years remain a major barrier, urging modernization by 2025.

A real-world example: Principal reduced phone-aided benefit verification calls from 50% to just 7% using NLP-powered chatbots—proving that targeted AI deployment yields measurable efficiency gains. Yet, the company still relies on human brokers for complex case escalations, reinforcing the human-in-the-loop model.

Sustainable AI isn’t about one-time implementation. It’s an ongoing commitment to compliance, system integration, and ethical evolution. As the EU AI Act approaches enforcement in August 2026, classifying underwriting and pricing AI as “high-risk,” proactive governance is no longer optional.

Next: A step-by-step guide to deploying your own 24/7 AI support system—built on proven frameworks and real-world results.