Beyond ChatGPT: The Next Wave of AI Models Reshaping Insurance

Executive Summary

Large Language Models (LLMs) like ChatGPT introduced the insurance industry to generative AI’s potential, but they represent just the beginning. The next generation of AI technologies is emerging with capabilities that go far beyond text generation. For insurance executives, understanding these developments is critical for strategic planning, competitive positioning, and avoiding the risk of building strategies around soon-to-be-outdated technology.

This article examines five key AI developments that will define the next 3-5 years: agentic AI systems, multimodal models, small language models, reasoning-focused architectures, and embodied AI. Each presents specific opportunities and challenges for carriers, wholesalers, and agencies.

Key Takeaways:

• Agentic AI will move insurance operations from “AI-assisted” to “AI-executed” workflows
• Multimodal models will transform claims processing by analyzing text, images, and video simultaneously
• Small language models offer cost-effective, secure alternatives to cloud-based LLMs
• Reasoning models provide the transparency and explainability regulators demand
• Success requires flexible AI strategies, not commitment to any single technology

The LLM Plateau: Why the Industry Needs to Look Beyond Today’s Technology

Since ChatGPT’s public debut in late 2022, insurance organizations have invested heavily in LLM-based solutions for customer service, policy document analysis, and underwriting support. These investments have delivered value, particularly for routine correspondence and information retrieval. However, LLMs have fundamental limitations that become apparent in production environments.

Current LLMs excel at pattern recognition and text generation but struggle with multi-step reasoning, consistent accuracy, and autonomous action. They require constant human oversight, can’t reliably perform mathematical calculations, and hallucinate with enough frequency to make fully automated decisions risky. For an industry built on precision, compliance, and accountability, these limitations matter.

According to Gartner’s 2024 research, organizations that limit their AI strategy to current-generation LLMs risk finding themselves with technical debt within 24 months as more capable systems become standard. The question isn’t whether to adopt LLMs, but how to build an adaptable AI infrastructure that can incorporate next-generation capabilities as they mature.

Five Technologies Defining the Next Wave

1. Agentic AI: From Assistance to Execution

What It Is: Agentic AI systems combine language understanding with the ability to take autonomous action. Unlike LLMs that respond to prompts, these systems pursue goals across multiple steps, use tools and APIs, make decisions within defined parameters, and learn from outcomes.

Insurance Applications:

For claims processing, agentic AI could receive a first notice of loss, automatically pull the relevant policy details from the management system, verify coverage, request necessary documentation from the policyholder, analyze submitted photos or videos, calculate initial reserves, and route complex cases to human adjusters while handling straightforward claims end-to-end.

In underwriting, agents could continuously monitor risk factors for commercial accounts, automatically request updated information when risk profiles change, adjust coverage recommendations based on real-time data, and flag accounts requiring human review based on predefined criteria.

Implementation Considerations: Agentic AI requires robust governance frameworks. Insurance executives must define clear boundaries for autonomous action, establish approval workflows for high-value decisions, implement comprehensive audit trails, and maintain human oversight for exceptions and appeals. The regulatory implications are significant, particularly regarding accountability when AI makes coverage or claims decisions.

2. Multimodal AI: Understanding the Complete Picture

What It Is: Multimodal AI processes multiple types of data, including text, images, video, audio, and structured data, within a single model. Rather than using separate systems for different data types, these models understand relationships across formats.

Insurance Applications:

Property claims present an ideal use case. When a policyholder submits a homeowner’s claim for wind damage, a multimodal AI could simultaneously analyze the written description, review photos and video of the damage, compare against pre-loss property images, cross-reference with weather data, assess repair estimates, and generate a comprehensive claim evaluation. All of this happens in one pass, rather than requiring separate tools for each data type.

For auto claims, these systems could process accident scene photos, dash cam footage, police reports, witness statements, and vehicle sensor data together, identifying inconsistencies and patterns that single-modality systems miss.

Business Impact: McKinsey’s 2024 analysis found that multimodal AI systems reduce claims processing time by 40-60% compared to traditional workflows and by 25-35% compared to text-only LLM implementations. The accuracy improvements are equally significant, with fraud detection rates improving by 30-45% when all available data types are analyzed together.

3. Small Language Models: Efficiency and Security for Independent Operations

What It Is: Small Language Models (SLMs) are specialized AI systems designed to run on local hardware rather than requiring cloud infrastructure. While they handle narrower tasks than massive LLMs, they deliver faster response times, better data security, lower operating costs, and the ability to function without internet connectivity.

Insurance Applications:

For independent agencies, SLMs address two critical pain points: cost and data security. An agency-specific SLM could be trained on policy language, carrier guidelines, and common customer questions, then run entirely on agency servers. This provides instant responses to producer questions about coverage details, automated email drafting for standard client communications, and policy comparison tools without sending confidential client data to external servers.

For carriers, SLMs enable field adjusters to work with AI assistance even in areas with limited connectivity, performing initial damage assessments, generating preliminary reports, and accessing relevant policy information without cloud dependence.

Cost Implications: Accenture’s recent analysis indicates that SLMs can reduce AI operational costs by 60-80% for specific use cases compared to cloud-based LLM APIs. For small to mid-sized agencies operating on tight margins, this cost difference determines whether AI adoption is financially viable.

4. Reasoning Models: Transparency for Regulatory Compliance

What It Is: Reasoning-focused AI systems emphasize logical step-by-step problem-solving rather than pattern matching. These models show their work, explain their conclusions, and provide audit trails that satisfy regulatory scrutiny.

Insurance Applications:

Underwriting decisions require justification. A reasoning model evaluating a commercial property risk would document each factor considered: building construction type and fire rating, proximity to fire stations and water sources, security systems and loss prevention measures, claims history and risk management practices, and local crime and weather patterns. The system would then explain how these factors combined to reach a coverage recommendation and premium calculation.

For compliance officers, reasoning models provide the transparency that state insurance departments and federal regulators increasingly demand. When an applicant questions a decline or rate increase, the insurer can provide a clear, documented explanation of the decision-making process.

Regulatory Advantage: The National Association of Insurance Commissioners (NAIC) has emphasized that AI systems used for underwriting and claims decisions must be explainable and auditable. Reasoning models align with these requirements in ways that “black box” neural networks do not. Carriers that adopt transparent AI systems position themselves favorably as regulation inevitably tightens.

5. Embodied AI: Physical Risk Assessment and Prevention

What It Is: Embodied AI combines artificial intelligence with physical systems including drones, robots, and IoT sensor networks. These systems collect and analyze data from the physical world, enabling proactive risk management rather than reactive claims processing.

Insurance Applications:

Commercial property insurance could be transformed by IoT sensor networks that continuously monitor buildings for water leaks, temperature anomalies, electrical issues, and security breaches. When problems are detected, the AI system alerts property managers and policyholders, potentially preventing claims before they occur.

For property inspections, AI-powered drones can assess roof condition, identify damage patterns, measure square footage, and detect issues invisible from ground level. The combination of visual inspection and AI analysis produces more consistent, thorough assessments than manual inspections alone.

Long-Term Strategic Value: While embodied AI requires more infrastructure investment than software-only solutions, it shifts the insurance model from reactive to proactive. Deloitte’s research suggests that preventing one major commercial property loss through early detection delivers more value than processing ten routine claims more efficiently. For carriers willing to make the investment, embodied AI creates genuine competitive differentiation.

Integration Challenges: Why Legacy Systems Matter

The insurance industry’s enthusiasm for next-generation AI must be tempered by operational reality. Most carriers and agencies run on core systems that are 15-30 years old, built long before API-first architectures became standard. Integrating advanced AI with these legacy platforms presents significant challenges.

Data Integration: Modern AI systems require clean, structured data. Many insurance systems store information in outdated formats, use inconsistent naming conventions across platforms, lack comprehensive APIs, and maintain incomplete historical records. Before deploying sophisticated AI, organizations must address fundamental data management issues.

Workflow Redesign: Advanced AI doesn’t simply accelerate existing processes; it enables entirely new workflows. This requires rethinking job roles and responsibilities, revising approval authorities, updating compliance procedures, and retraining staff. The organizational change management challenges often exceed the technical implementation challenges.

Vendor Ecosystem: The insurance technology landscape includes hundreds of vendors, many offering “AI-powered” solutions. Evaluating these offerings requires understanding whether they use genuinely advanced AI or simply rebadged existing technology, how they handle data security and privacy, whether they integrate with your existing agency management or core systems, and what their long-term viability is as the technology evolves.

Building a Future-Ready AI Strategy

Insurance executives should approach next-generation AI with informed pragmatism. The following framework helps organizations prepare for technological change without overcommitting to immature solutions:

Start with Clear Use Cases: Identify specific operational bottlenecks where advanced AI provides measurable improvement. For agencies, this might be producer support and client communication. For carriers, claims processing and underwriting efficiency typically offer the highest near-term ROI.

Prioritize Interoperability: Invest in AI solutions that work with your existing systems rather than requiring wholesale replacement. API-first architectures, standard data formats, and vendor-agnostic platforms provide flexibility as technology evolves.

Develop Internal Expertise: The most successful insurance AI implementations combine vendor solutions with internal capability. This means training staff to understand AI capabilities and limitations, building data science teams that understand insurance operations, establishing AI governance committees with business and technical representation, and creating ongoing education programs as technology advances.

Plan for Regulatory Evolution: Insurance regulators are still developing frameworks for AI governance. Organizations should document AI decision-making processes, maintain comprehensive audit trails, ensure human oversight of critical decisions, and actively participate in industry discussions about AI standards and best practices.

Adopt a Portfolio Approach: Rather than betting everything on a single AI technology, successful organizations maintain diverse AI capabilities. Current-generation LLMs for well-defined tasks, pilot programs testing next-generation technologies, partnerships with innovative insurtech companies, and internal research and development initiatives create resilience as the technology landscape shifts.

The Competitive Landscape: Who Moves First?

The insurance industry’s adoption of next-generation AI will not be uniform. Large national carriers have resources to invest in multiple AI initiatives simultaneously, experimenting with advanced technologies while maintaining current operations. Regional carriers and specialty insurers must be more selective, focusing on technologies that address their specific market niches.

Independent agencies face a different calculus. Many lack the technical resources for in-house AI development and depend on vendors and agency management system providers to incorporate advanced capabilities. This creates both risk and opportunity. Agencies that work with forward-thinking technology partners gain access to sophisticated AI without major capital investment. Those tied to vendors slow to innovate may find themselves at a competitive disadvantage.

The wholesale market presents unique opportunities for AI adoption. Wholesalers that implement advanced AI for risk assessment, quote generation, and market access can provide genuine value to retail agents beyond traditional relationship-based selling. Early adopters of reasoning models and multimodal AI for complex risk evaluation may capture market share as retail agents seek partners who can deliver faster, more accurate placement for challenging accounts.

Action Items for Insurance Executives

Immediate Actions (Next 90 Days):

• Assess your current AI investments and their technological foundations
• Identify three specific operational challenges where next-generation AI could deliver measurable value
• Evaluate your core systems’ readiness for AI integration
• Begin conversations with technology vendors about their AI roadmaps
• Establish an AI governance committee if you haven’t already

Short-Term Initiatives (Next 6-12 Months):

• Launch pilot programs testing one or two next-generation AI technologies
• Develop data quality improvement initiatives to support advanced AI
• Create or update AI governance policies addressing autonomous decision-making
• Invest in staff training on AI capabilities and limitations
• Join industry groups discussing AI standards and best practices

Long-Term Strategic Planning (12-36 Months):

• Build internal AI expertise through hiring or training
• Develop partnerships with insurtech companies and research institutions
• Plan system modernization initiatives that enable better AI integration
• Create competitive differentiation strategies based on AI capabilities
• Establish metrics for measuring AI’s business impact beyond cost reduction

Conclusion: Preparing for Continuous Evolution

The next wave of AI technologies after LLMs represents a significant opportunity for insurance organizations willing to move beyond the hype cycle and focus on practical implementation. Agentic AI, multimodal models, small language models, reasoning systems, and embodied AI each address specific limitations of current technology while introducing new capabilities relevant to insurance operations.

The key insight for executives is that AI technology will continue evolving rapidly. Organizations that build flexible, adaptable AI strategies will thrive. Those that overcommit to specific technologies or vendors risk finding themselves locked into soon-to-be-outdated systems.

The insurance industry’s cautious approach to new technology has historically served it well, preventing costly mistakes while allowing proven innovations to mature. That same prudent approach applies to next-generation AI, but with one critical difference: the pace of change demands faster decision-making than past technology cycles required.

The winners in the AI-enabled insurance market will be organizations that combine strategic vision with operational pragmatism, investing in promising technologies while maintaining the flexibility to adapt as capabilities evolve. The question isn’t whether your organization should prepare for the next wave of AI, but whether you’re moving fast enough to maintain competitive position as the industry transforms.

Sources

Gartner, “Hype Cycle for Artificial Intelligence, 2024” https://www.gartner.com/en/newsroom/press-releases

McKinsey & Company, “The State of AI in 2024: Generative AI’s Breakout Year” https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Accenture, “Technology Vision 2024: Human by Design” https://www.accenture.com/us-en/insights/technology/technology-trends-2024

Deloitte, “2024 Insurance Outlook” https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/insurance-industry-outlook.html

National Association of Insurance Commissioners (NAIC), “Principles on Artificial Intelligence” https://content.naic.org/article/principles-artificial-intelligence

Insurance Journal, “AI in Insurance: Beyond the Hype” https://www.insurancejournal.com/

Forrester Research, “The Future of AI in Insurance” https://www.forrester.com/

MIT Technology Review, “What’s Next for AI” https://www.technologyreview.com/topic/artificial-intelligence/

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