The Insurer's Service Delivery Dilemma: Evaluating Your Options in 2025

TLDR;

Customer expectations are rising, and traditional staffing models are failing to scale efficiently. Insurers face five viable options for service delivery: (1) hire more humans, (2) outsource to another firm, (3) DIY with general AI tools like ChatGPT, (4) adopt a generic voice/CCaaS platform, or (5) implement insurance-native, omnichannel AI that talks and takes action within your systems while maintaining compliance. Among these, Option 5 provides the strongest balance of quality, cost efficiency, and risk control—if implemented with clear rules, integrations, and escalation paths.

The Five Models—A Quick Comparison

The Human-Centric Approach

Advantages

The human-centric model relies on hiring experienced insurance professionals to handle customer interactions. This approach offers several key benefits:

1. Deep Insurance Knowledge: Experienced customer service representatives (CSRs) understand policy nuances and compliance requirements. For complex coverage questions or claims situations, their expertise is invaluable.
2. Relationship Building: Insurance remains a relationship-driven industry. Human agents excel at creating personal connections that drive retention and referrals.
3. Flexibility and Judgment: Complex scenarios often require nuanced human judgment to navigate gray areas effectively.
4. Cultural Alignment: Employees can authentically represent your brand values and products in every interaction.

Challenges

Despite its strengths, the human-centric approach presents significant hurdles:

1. Linear Scaling Costs: Service costs increase proportionally with call volume. This model lacks leverage as your business grows.
2. Availability Constraints: Traditional business hours limit accessibility for after-hours calls or emergencies.
3. Quality Inconsistency: Human agents have varying skill levels and performance fluctuations that impact customer experience.
4. Talent Shortages: Hiring experienced insurance professionals is increasingly difficult due to competition and turnover rates.

Outsourcing: Leveraging External Expertise

Advantages

Outsourcing customer service to a third-party firm offers scalable solutions for insurers facing high interaction volumes. Key benefits include:

1. Cost-Effective Scalability: Outsourcing firms can handle large volumes of routine inquiries at lower costs compared to in-house teams.
2. Access to Trained Professionals: Many outsourcing providers specialize in insurance services and offer trained staff familiar with industry processes.
3. Operational Flexibility: Outsourcing allows insurers to focus on core competencies while delegating customer service operations to experts.

Challenges

While outsourcing provides scalability, it also introduces risks:

1. Limited Control: Insurers may lose some control over the brand experience and customer interactions.
2. Quality Concerns: Service quality can vary depending on the outsourcing provider’s training and oversight mechanisms.
3. Compliance Risks: Outsourcing firms may not fully align with your regulatory requirements or data privacy standards unless carefully vetted.

DIY AI: Building with Generic Tools

Advantages

The democratization of AI tools like ChatGPT makes it tempting for insurers to build their own solutions internally. Benefits include:

1. Cost Control: Direct access to AI models is cost-effective for organizations with technical resources.
2. Customization Flexibility: DIY solutions allow complete control over conversational flows and integrations tailored to specific needs.
3. Learning and Innovation: Developing internal AI capabilities builds organizational knowledge and fosters innovation.
4. Rapid Prototyping: Modern AI tools enable quick proof-of-concept development without vendor dependencies.

Challenges

DIY approaches come with hidden complexities:

1. Integration Burden: Connecting AI tools to policy systems and CRMs requires significant technical expertise.
2. Insurance Domain Limitations: Generic AI models lack insurance-specific knowledge and require careful validation of responses to avoid errors or liabilities.
3. Resource Requirements: Building and maintaining AI solutions demands specialized technical talent that many insurers lack internally.
4. Compliance Gaps: Insurance-specific regulatory requirements often exceed the capabilities of generic platforms without extensive customization.

Generic Voice/CCaaS Platforms

Advantages

Platforms like RingCentral offer pre-built communication tools enhanced with basic AI capabilities for general business use cases:

1. Proven Infrastructure: Established platforms provide reliable voice processing and integration capabilities built on tested foundations.
2. Faster Implementation: Pre-built features enable quicker deployment compared to custom development efforts.
3. Broader Feature Sets: Generic platforms often include comprehensive tools like telephony systems, conferencing, and collaboration features alongside AI capabilities.
4. Vendor Support: Professional implementation services and ongoing updates reduce internal resource requirements for maintenance.

Challenges

Generic platforms struggle to meet the unique needs of insurers:

1. Limited Customization: These solutions lack flexibility for insurance-specific workflows or compliance requirements.
2. Insurance Context Gaps: General-purpose AI doesn’t understand industry terminology or processes like FNOL intake or policy servicing without significant adaptation efforts.
3. Integration Limitations: Connecting generic platforms to legacy insurance systems often requires custom development work that negates speed advantages.
4. Compliance Concerns: Generic platforms may not meet insurance-specific regulatory standards or provide audit trails required for operations in this industry.

Insurance-Native AI: Purpose-Built Solutions

Advantages

1. Compliance-Driven Escalation: Purpose-built platforms like Liberate understand when conversations cross into territory requiring licensed agents. Unlike platforms that focus purely on conversation automation, they're built with sophisticated triggers that recognize coverage questions, policy modifications, or claims decisions that legally require human oversight.
2. Domain Expertise: Purpose-built platforms understand insurance processes, terminology, and compliance requirements. They can handle complex scenarios like FNOL intake, policy servicing, and claims status inquiries without custom development.
3. Compliance by Design: Insurance-specific platforms build in regulatory requirements, audit trails, and data protection measures required for insurance operations. Compliance becomes a feature rather than a challenge.
4. Complete Task Automation: True purpose-built platforms don't just handle conversations—they complete entire workflows. Liberate, for example, operates omnichannel across phone, email, SMS, and chat while directly integrating with your policy administration system to actually perform tasks like policy updates, payment processing, and claims status changes. Rather than simply providing information, these platforms act as virtual employees who can access your source of truth and execute complete transactions.
5. Scalability with Quality: Purpose-built platforms can handle volume increases without quality degradation. Whether you process 100 calls or 10,000 calls monthly, service consistency remains constant.
6. Risk Management: Insurance-specific platforms understand the liability implications of AI in insurance contexts. They provide appropriate escalation triggers, human handoff protocols, and quality controls that protect both customer relationships and regulatory compliance.
7. Compliance-Driven Escalation: Purpose-built platforms like Liberate understand when conversations cross into territory requiring licensed agents. Unlike platforms that focus purely on conversation automation, they're built with sophisticated triggers that recognize coverage questions, policy modifications, or claims decisions that legally require human oversight.

Challenges

While powerful, insurance-native platforms require careful implementation:

1. Higher Upfront Investment: These solutions demand greater initial investment in terms of integrations and change management but deliver long-term cost efficiencies at scale.
2. Vendor Dependency: Choosing a specialized platform introduces dependency risks tied to the vendor’s innovation roadmap and stability.
3. Market Maturity: The insurance-specific AI market is still evolving rapidly; vendors may vary significantly in sophistication.

Strategic Decision Framework

To choose the right approach for your organization, evaluate these criteria based on your operational realities:

1. Current Scale & Growth Trajectory: Human teams may suffice for low interaction volumes (<1k/month), but automation becomes essential for higher volumes or rapid growth.
2. Technical Resources: DIY approaches require significant technical expertise; honest assessment of your capabilities is crucial.
3. Compliance Requirements: Complex regulatory environments favor purpose-built solutions with insurance-specific features.
4. Integration Complexity: Legacy systems may demand specialized integration capabilities that generic platforms struggle to provide.
5. Risk Tolerance: Consider your organization’s comfort level with vendor dependencies and operational changes.

Conclusion

The customer service delivery challenge facing insurers isn’t temporary—it’s structural. Rising expectations for instant service paired with increasing costs demand a sustainable solution that balances automation with human expertise.

Among the five options outlined above, insurance-native omnichannel AI offers the most compelling mix of scalability, compliance strength, and cost efficiency—provided it’s implemented thoughtfully with clear rules and integrations.

Ultimately, the future belongs to MGAs, brokers, and carriers that can deliver exceptional customer experiences at scale while bending their cost curves downward through smart automation choices.

The choice isn’t human vs AI—it’s humans amplified by technology tailored specifically for the insurance industry’s unique needs.