From predictive analytics to large language models to autonomous agentic systems, the potential for transformation is massive—but so are the challenges. To help insurers plan effectively, we’ve outlined the pros and cons of three major AI categories: traditional AI, Generative AI (Gen AI), and Agentic AI.

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Traditional AI

(e.g. machine learning, predictive analytics)

Traditional AI refers to rule-based systems and machine learning algorithms that analyze structured data to support decision-making. These tools are already widely used in insurance—for example, to predict claim severity, calculate customer churn risk, or straight-through-processing based on historical loss data. Unlike newer forms of AI, traditional AI focuses more on classification, regression, and analysis of historical data, rather than content generation or autonomous behavior.

Today, traditional AI is relatively mature and well-established in the insurance space. Many carriers have successfully deployed predictive models in areas like pricing, fraud detection, and retention, often as part of larger digital transformation initiatives. The focus now is on refining these models, expanding use cases, and embedding with modern core systems to maximize long-term value.

Pros:

• Improved Risk Assessment & Pricing: AI models analyze large datasets to better predict risk and set accurate premiums, which helps insurers create more competitive pricing that truly reflects customer risk.
  
• Fraud Detection: Unusual patterns in claims data are flagged in real-time to detect potential fraud, and false-positive identifications are reduced, which improves customer experience.
  
• Claims Triage: Automated systems sort and route claims based on severity, making the process faster and helping adjusters focus on more complex cases.
  

Cons:

• Data Quality & Bias: AI is only as good as the data it's trained on, so bad or biased data can lead to unfair or inaccurate decisions.
  
• Regulatory Scrutiny: Because AI decisions can be made in a black-box, insurers cannot file products ahead of time to get regulator approval.
  
• High Implementation Costs: Getting started with AI can be expensive, especially when you factor in infrastructure, training, and hiring the right talent.
  

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Generative AI

(e.g., large language models like ChatGPT)

Generative AI refers to systems that can create new content—text, code, images, even video—based on large training datasets. In insurance, Gen AI can be used to draft claims summaries, respond to policyholder inquiries, or summarize underwriting guidelines. Popular tools include models like OpenAI’s ChatGPT or Google’s Gemini, which are increasingly being embedded in customer service, knowledge management, and documentation workflows.

While generative AI is still in the early adoption phase in insurance, its capabilities have advanced rapidly over the past year. Most carriers are experimenting with small-scale pilots—such as internal knowledge assistants or chatbot enhancements—but many are also navigating concerns about data privacy, hallucinations, and industry-specific regulations. The next wave of adoption will likely depend on better fine-tuning, insurance-product models, and stronger governance controls.

Pros:

• Enhanced Customer Service: AI chatbots can handle simple policyholder questions 24/7, which keeps response times fast and frees up human agents for more complex needs.
  
• Document Ingestion: Drafting policy summaries and compliance docs happens in seconds, saving teams hours and reducing the chance of human error.
  
• Underwriting Assistant: Gen AI can pull together insights and summaries from internal data, helping staff make faster, better-informed decisions.
  
• Improved Knowledge Base: Teams gain instant access to manuals, guidelines, and historical info, cutting down on time spent digging for answers.
  

Cons:

• Hallucinations: These models sometimes make up facts or give incorrect answers, which can lead to confusion—or worse, compliance issues.
  
• Data Privacy Risks: Handling sensitive policyholder or claims data with Gen AI requires strong controls to avoid violations or misuse.
  
• Limited Insurance Context: Out of the box, Gen AI doesn’t understand the nuances of your insurance products, so it needs fine-tuning to be truly useful.
  

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Agentic AI

(AI systems that act autonomously toward goals)

Agentic AI takes things a step further; it allows systems to act autonomously toward predefined goals. Rather than waiting for input, agentic systems can complete tasks—such as automatically sending a follow-up email for a missing claims document or rebalancing workflows based on PTO. These agents combine research, planning, and action, and often work alongside human teams as intelligent digital coworkers capable of learning over time.

Agentic AI is a fairly recent development and is still in its infancy when it comes to insurance applications. While use-cases like intelligent scheduling or automated workflows are starting to emerge, most insurers are still in the exploratory stage. That said, as tools evolve to offer more contextual memory and reasoning abilities, agentic systems are expected to play a significant role in redefining how insurers manage complex, multi-step processes across the policy lifecycle.

Pros:

• End-to-End Automation: Agentic AI can manage entire workflows—from FNOL to settlement—which reduces handoffs and keeps everything moving smoothly.
  
• Proactive Outreach: Automated follow-ups with customers or adjusters help prevent delays and keep everyone on track.
  
• Adaptive Decision-Making: These systems learn from past outcomes and adjust accordingly, making workflows smarter and more effective over time.
• Workforce Augmentation: With humans in control, agentic AI works alongside human teams like intelligent digital coworkers, helping insurers scale without adding headcount.
  

Cons:

• Governance & Oversight: Letting AI act independently means insurers need strong controls in place to avoid emergent risk.
  
• Liability Concerns: If an AI system makes the wrong call, it’s not always clear who’s responsible, which can be a legal nightmare.
  
• Cultural Resistance: Teams might be hesitant to trust or adopt agentic assistants, so change management is key to making it work.
  

Why BriteCore Is the Right Partner for Forward-Thinking Insurers

BriteCore is a strategic partner for P&C insurers looking to embrace the full spectrum of AI capabilities—from traditional predictive models to cutting-edge generative and agentic capabilities. As a cloud-native, API-first core insurance platform, BriteCore provides the modern infrastructure and data accessibility required to power AI-driven decision-making across underwriting, claims, and customer service. Its flexible, configurable architecture allows insurers to seamlessly embed third-party AI tools or train their own models, while ensuring transparency, governance, and growth. With a track record of continuous innovation and a deep understanding of insurer needs and market trends, BriteCore positions its clients to not only adopt AI effectively today but to evolve with it as the technology reshapes the future of insurance.

Dive deeper into how modern core insurance platforms empower the next wave of intelligent automation; The Foundation for Agentic AI: API-First Core Insurance Platforms—a closer look at why the right core technology is essential for unlocking the full potential of agentic AI.