AI and the Carrier-Agent Relationship

• October 22, 2025

By Dave Sterner

Artificial intelligence (AI) is fast becoming a strategic differentiator in the insurance industry, enabling carriers to unlock new efficiencies, enhance decision-making and deliver more personalized, data-driven customer experiences. The rapid growth of spending on AI technologies, along with increasing adoption rates, illustrates high expectations among global insurers.

The industry is spending $10-$20 billion annually on AI technologies, a figure expected to grow to $35 billion over the next three years, according to an average of forecasts from technology research and advisory firms. A recent Conning survey of insurance executives indicated that 55% have begun adoption of generative AI, while another survey by Celent indicates that 22% of insurers will have agentic AI in production by the end of next year.

Among carriers, there is a noticeable tendency for successful digitalization to coincide with independent agent partnerships. The most recent edition of the ACORD “Insurance Digital Maturity Study” showed that the top tier of digitally mature insurers was 25% more likely than average to prioritize independent agents and brokers as their primary distribution channel—and, tellingly, 40% more likely than the bottom tier of digital laggards.

For digitally enabled carrier-agent partnerships, AI will not just be a technological shift, but a foundational capability—one with the potential to drive growth, improve risk performance and future-proof the business in an increasingly digital and competitive market.

Much like other digital technologies, certain foundational elements are required in both carriers and independent insurance agencies alike to support effective and scalable AI solutions. Here are a few areas it can have an impact:

1) Strategy. Alignment with business strategy enables AI by ensuring that investments directly support the organization’s core goals, whether improving the customer experience, driving operational efficiency or accelerating innovation. When strategic priorities guide AI initiatives, they are more likely to receive executive support, integrate effectively with existing processes and deliver measurable business value.

This alignment also helps prioritize AI use cases with the highest impact, reduces the risk of isolated or redundant projects, and ensures that the supporting infrastructure, data and talent are focused where they matter most.

2) Technology. An agile, scalable and modern technology infrastructure provides the flexibility and computing power needed to process large volumes of data, deploy models quickly and adapt to evolving business needs. Cloud-based platforms, application programming interface (API)-driven architectures, and modular data pipelines enable rapid experimentation, integration and iteration. Scalability ensures that as AI workloads grow, the infrastructure can accommodate increased demand without compromising performance.

Meanwhile, modern tools and frameworks accelerate development and make it easier to manage and monitor AI solutions at scale. Together, these capabilities allow organizations to move from isolated pilots to enterprise-wide AI adoption efficiently and securely.

3) Culture. An organization that embraces digital innovation creates a culture and environment where AI can thrive. By fostering openness to change, encouraging experimentation and investing in digital capabilities, organizations are better positioned to identify valuable AI opportunities and implement them effectively.

A strong innovation mindset drives cross-functional collaboration, rapid prototyping and continuous learning—all critical for navigating the complexities of AI deployment. This proactive, forward-looking approach transforms AI from a theoretical possibility into a practical, high-impact tool for business growth.

AI and the Carrier-Agent Relationship

What does it all mean for independent agents? Thoughtful leverage of AI capabilities from both ends of the carrier-agent partnership has the potential to increase efficiency and effectiveness across a wide variety of areas:

1) Streamlined underwriting and submission intake. AI usage by carriers and agents means that agents will be able to extract data from unstructured or semi-structured documents, identify and address gaps and route submissions efficiently. This will improve processing time, accuracy and consistency.

2) Lead generation and prospecting. Agents can analyze large datasets, such as demographics, business filings, and property data, to identify high-propensity prospects and rank leads, enabling agents to focus on the most promising opportunities.

3) Customer service. AI-enabled chatbots will respond to frequently asked questions, guide users on policy renewals and answer coverage questions 24/7, ensuring support for agents and policyholders is available when needed.

4) Sales personalization. Agents can analyze client data to provide personalized policy recommendations and create customized outreach campaigns, thereby increasing both engagement and conversion rates.

5) Customer retention. AI tools will help agents identify policyholders at risk of lapsing, allowing agents to proactively engage with these clients and drive retention.

6) Document management. Agents can extract and organize data from various documents, categorizing files, checking for errors or compliance issues and tracking version changes to reduce mistakes and save time.

7) Workflow automation. Agents can route tasks to the right person, generate renewal reminders, and automate repetitive steps, such as form-filling or scheduling.

AI Challenges and the Role of Data Standards

Certain common obstacles and challenges must be addressed in successfully deploying AI capabilities. Fragmented data systems, inconsistent data quality, regulatory complexity and limited interoperability across internal and external platforms can slow down development, increase costs and limit the scalability and reliability of AI solutions.

Data standards—including data formats, exchange protocols and model governance—can help address these challenges. By aligning shared frameworks, organizations can accelerate deployment, ensure compliance and unlock broader ecosystem collaboration.

Here are three ways in which standards are supporting AI implementations across the insurance supply chain:

1) Underwriting. Standards help ensure that AI models receive quality, comparable data across sources, improving risk assessment accuracy and enabling automation.

2) Claims. Common taxonomies and classification structures enable AI systems to learn from larger, more diverse datasets, thereby enhancing fraud detection and streamlining triage.

3) Policyholder service. Standardized APIs and data exchange protocols make it easier to integrate AI-powered tools, such as chatbots, across back-end systems, delivering faster and more consistent service experiences.

As AI use expands to more complex and collaborative use cases, including real-time learning and agentic AI, a data standards-based approach will be essential to ensure consistency, trust and efficiency across the insurance ecosystem.

Dave Sterner is senior vice president of research and development at ACORD.