The Growing Burden of Routine Calls: Why Brokers Need a New Approach

The Growing Burden of Routine Calls: Why Brokers Need a New Approach

Commercial insurance brokers are drowning in repetitive calls—policy status checks, renewal reminders, claims updates, and quote requests. These high-volume, low-complexity inquiries consume 85% of agent time on non-value-added tasks, leaving little room for strategic client engagement. The result? Burnout, delayed responses, and eroding client satisfaction.

• 55% of inbound calls are for information retrieval (e.g., policy details, coverage limits)
• 35% involve transaction execution (e.g., payment confirmations, document requests)
• 10% require issue resolution, often escalating due to poor first-contact resolution

These patterns are not unique to one firm—80% of routine calls in commercial insurance are now prime candidates for automation, according to industry benchmarks. Yet, most brokers still rely on human agents to handle them, creating a bottleneck that impacts both efficiency and client experience.

A real-world example: a mid-sized commercial insurer processed 12,000+ monthly calls with 70% related to policy status or renewal dates. Manual handling led to average response times of 48 hours, with 22% of clients reporting frustration. After deploying an AI voice agent for renewal reminders and status checks, the firm reduced response time to under 10 minutes, and client satisfaction rose by 38%—as reported by Leader’s Edge.

This shift isn’t just about speed—it’s about freeing brokers to focus on high-value work like risk assessment and relationship building. With AI handling the repetitive, brokers can deliver more personalized, strategic service. The next step? A phased, use-case-driven rollout that starts with the highest-impact workflows—like First Notice of Loss (FNOL) or renewal alerts—ensuring measurable ROI before scaling.

AI Voice Agents as Your 24/7 First Line of Defense: The Solution

AI Voice Agents as Your 24/7 First Line of Defense: The Solution

Imagine a tireless, always-on assistant that handles routine client calls—24 hours a day, 7 days a week—without fatigue, errors, or downtime. For commercial insurance brokers, AI voice agents are no longer science fiction. They’re now a strategic necessity, automating high-volume, low-complexity interactions to free brokers for higher-value work.

According to Plavno’s case study, AI voice agents resolve inquiries 85% faster, reduce claims resolution time by up to 75%, and cut operational costs by 35–42%—with 3–6× ROI in the first year. These aren’t hypothetical gains. They’re real results from insurers already deploying AI at scale.

Inbound calls in commercial insurance are dominated by repetitive tasks: - Policy status checks
- Renewal reminders
- Claims updates
- Quote requests
- First Notice of Loss (FNOL)

Synthflow’s research confirms that 55% of calls are information retrieval, 35% transaction execution, and only 10% issue resolution—making them ideal candidates for automation. By offloading these tasks, brokers reclaim time to focus on risk assessment, underwriting, and relationship-building.

One Fortune 100 insurer deployed an AI voice agent to manage renewal reminders and FNOL calls. The system handled up to 20 million calls annually, reducing missed renewals and accelerating claim intake. With seamless CRM integration, agents accessed real-time data to provide accurate updates—without human intervention. The result? A 45% faster claims resolution time and a 35% improvement in client retention, as reported in Plavno’s client case.

• 24/7 availability across 100+ languages
• Deep integration with CRM, claims, and billing systems
• Predictive routing to escalate complex cases to humans
• Compliance-first design with GDPR, SOC 2, and ISO 27001 support
• Managed AI employees that reduce hiring costs by 75–85%

As Plavno’s Solution Architect emphasizes, “AI is not about replacing brokers—it’s about enabling them to focus on high-value tasks.”

The shift isn’t about automation for automation’s sake. It’s about strategic leverage—using AI to handle the repetitive so brokers can excel at the human-centric, high-impact work that defines their value.

Ready to turn your call center into a 24/7 first line of defense? The next step is a phased, use-case-driven rollout—starting with renewal reminders or FNOL. The tools, data, and ROI are already here.

How to Implement AI Voice Agents: A Phased, Real-World Roadmap

How to Implement AI Voice Agents: A Phased, Real-World Roadmap

Inbound calls in commercial insurance are dominated by repetitive tasks—policy checks, renewal alerts, claims updates, and quote requests—draining broker bandwidth and delaying client responses. AI voice agents are no longer futuristic; they’re operational tools delivering 85% faster inquiry resolution and 35–42% lower operational costs, with 3–6× ROI in the first year according to Plavno. The path to success isn’t a full-scale rollout—it’s a strategic, phased journey.

Begin with a single, high-volume workflow where automation delivers immediate value. Ideal candidates include: - Renewal reminders (reducing missed renewals) - Policy status checks (handling 55% of information retrieval calls) - First Notice of Loss (FNOL) (accelerating claims initiation)

A Proof of Concept (PoC) can be deployed in 12–14 weeks, as demonstrated by Plavno’s MVP roadmap Plavno’s implementation timeline. This allows you to validate performance, cost savings, and client acceptance before scaling.

Example: A mid-sized broker used an AI voice agent for renewal reminders, reducing missed renewals by 28% in six months—without adding headcount.

An AI voice agent is only as smart as its data. Deep integration with CRM (Salesforce, HubSpot), claims platforms, and billing systems is non-negotiable Plavno’s case study. Without real-time access, agents can’t update records, schedule appointments, or resolve issues accurately.

Key integration requirements: - Real-time access to policy and claims databases - Two-way sync with CRM for lead tracking - Secure API connections with audit trails - Support for GDPR, SOC 2, and ISO 27001 compliance Plavno’s compliance framework

Tip: Use a provider that offers pre-built connectors for insurance-specific platforms to reduce deployment time.

AI agents must understand insurance-specific language—terms like “deductible,” “umbrella coverage,” or “subrogation.” Train models on historical call data and real policy documents to improve accuracy Plavno’s training approach. Implement continuous feedback loops to refine responses and handle edge cases.

Critical success factors: - Use domain-specific training data (not generic AI models) - Monitor for misinterpretations with a human-in-the-loop review - Retrain quarterly or after major policy changes

Not every call should be automated. Design predictive routing to escalate complex, emotional, or high-risk cases (e.g., fraud, health-related claims) to human brokers Strada’s guidance. This preserves trust, ensures compliance, and maintains brand consistency.

Best practice: Use sentiment analysis to detect frustration and trigger immediate human intervention.

For sustainable growth, partner with a specialized provider like AIQ Labs, Plavno, or Synthflow AIQ Labs’ three-pillar model. These firms offer custom AI development, managed AI employees, and compliance-first deployment, reducing costs by 75–85% compared to hiring full-time staff Plavno’s managed employee model.

Next step: Evaluate providers based on integration depth, compliance certifications, and real-world insurance case studies—not generic AI claims.

The shift from reactive to proactive service is now within reach. By starting small, integrating deeply, and scaling smartly, brokers can transform voice interactions into a strategic advantage—freeing themselves to focus on what they do best: building trust, managing risk, and growing relationships.

Best Practices for Trust, Compliance, and Long-Term Success

Best Practices for Trust, Compliance, and Long-Term Success

In the regulated world of commercial insurance, deploying AI voice agents isn’t just about efficiency—it’s about building trust, ensuring compliance, and securing long-term client relationships. Without a disciplined approach, even the most advanced AI can erode confidence, trigger regulatory scrutiny, or damage brand reputation. The most successful implementations aren’t defined by technology alone, but by ethical design, rigorous governance, and seamless human oversight.

Key pillars of responsible AI deployment include: - End-to-end data privacy compliance (GDPR, HIPAA, SOC 2, ISO 27001)
- Transparent audit trails for all AI interactions
- Human-in-the-loop protocols for sensitive or complex cases
- Brand-aligned voice personas that reflect your broker’s tone and values
- Continuous monitoring and retraining using real client interactions

According to Plavno’s case study, AI systems designed with governance frameworks ensure compliance while enabling 24/7 service. Similarly, Leader’s Edge research confirms that compliant AI adoption correlates with a 35% improvement in client retention—a direct result of trust built through consistency and security.

A real-world example from a mid-sized commercial insurer shows how structured compliance prevents risk. By integrating their AI voice agent with HubSpot CRM and claims systems, they ensured all policy and claim data remained within secure, auditable channels. When a client requested a claims update, the AI accessed real-time data, confirmed identity via multi-factor authentication, and delivered a response—while logging every step. This approach eliminated data silos and reduced compliance risk, proving that automation and regulation can coexist.

To maintain trust, seamless handoffs to human agents are essential. When a call involves fraud suspicion, emotional distress, or complex policy coordination, AI must escalate immediately. As Strada’s CEO emphasizes, “ROI is clearest when AI handles routine tasks—leaving brokers to manage the high-stakes, high-empathy moments.”

Moving forward, brokers must treat AI not as a cost-cutting tool, but as a strategic partner in service excellence. The next section explores how to select the right provider—and build a scalable, compliant AI ecosystem that grows with your business.