Agentic AI in Insurance: From Competitive Advantage to Competitive Baseline: How Dremio Fuels Agentic AI at Scale

The insurance industry is undergoing a structural shift. What was once a slow moving, data heavy sector is now being reshaped by real time intelligence, automation, and advanced analytics powered by artificial intelligence. Agentic AI is no longer a futuristic concept or a “nice to have” innovation, it is rapidly becoming the competitive baseline that separates market leaders from laggards.

Across claims, underwriting, and pricing, insurers are using AI to transform how decisions are made, how quickly they are executed, and how accurately they reflect real world risk. At the center of this transformation is the ability to unify and activate data, both internal and external at scale. This is where modern architectures like agentic lakehouses, including platforms such as Dremio, are becoming foundational.

The Rise of Real Time Claims Intelligence

Claims processing has historically been one of the most manual, time consuming, and costly operations in insurance. Agentic AI is changing that dynamic in a fundamental way.

Today, insurers are deploying agentic AI models that can:

• Detect fraud in real time using behavioral patterns, anomaly detection, and network analysis.
• Automatically triage claims and route them to the appropriate adjusters based on complexity, severity, and risk.
• Extract and analyze unstructured data from images, documents, and voice transcripts.
• Provide instant damage assessments using computer vision, particularly in auto and property claims.

The result is a shift from reactive claims handling to proactive, intelligent orchestration. Instead of waiting days or weeks for claims decisions, insurers can now process many claims in minutes, while simultaneously improving accuracy and reducing leakage.

For example, after a major weather event, agentic AI systems can ingest satellite imagery, policy data, and historical loss patterns to prioritize high risk claims instantly. This not only improves customer experience but also ensures that resources are allocated where they are needed most.

Precision Underwriting in a Volatile Risk Environment

Underwriting is becoming more complex as risks grow more dynamic. Climate change, cyber threats, supply chain disruptions, and shifting demographics all contribute to a more volatile risk landscape.

Agentic AI enables insurers to move beyond static underwriting models toward continuous, data driven risk assessment. By integrating diverse data sources, such as IoT sensors, geospatial data, credit signals, and third party datasets, agentic AI can produce far more granular and accurate risk profiles.

Key advancements include:

• Real time risk scoring based on continuously updated data streams.
• Automated underwriting decisions for low and mid complexity policies.
• Enhanced risk segmentation using machine learning models that uncover nonobvious patterns.
• Explainable agentic AI models that provide transparency into underwriting decisions for regulatory compliance.

This level of precision allows insurers to write better business, avoid adverse selection, and respond more quickly to emerging risks. It also empowers underwriters to focus on complex cases where human judgment is most valuable, rather than spending time on routine evaluations.

Dynamic Pricing and Intelligent Rate Optimization

Pricing has traditionally relied on historical data and periodic updates. In today’s environment, that approach is no longer sufficient.

Agentic AI driven pricing models enable insurers to:

• Adjust rates dynamically based on real time risk signals.
• Incorporate external data such as weather trends, economic indicators, and behavioral data.
• Optimize pricing strategies across segments to balance growth and profitability.
• Simulate multiple scenarios to understand the impact of pricing changes before implementation.

This shift toward dynamic pricing intelligence allows insurers to remain competitive while protecting margins in the face of rising loss costs. It also creates opportunities for more personalized products and usage based insurance models, particularly in auto and health segments.

Agentic AI as the New Competitive Baseline

The cumulative impact of agentic AI across claims, underwriting, and pricing is clear: insurers that successfully deploy agentic AI are pulling ahead.

They are:

• Settling claims faster and with lower costs.
• Selecting risks more accurately.
• Pricing policies more competitively and profitably.
• Delivering better customer experiences.

Meanwhile, insurers that lag in agentic AI adoption face increasing pressure, not just from traditional competitors, but from digitally native entrants and insurtech firms that are built around data and AI driven automation from day one.

In a world of rising loss costs, regulatory scrutiny, and customer expectations for speed and transparency, AI is no longer optional. It is the mechanism through which insurers turn vast amounts of data into faster, smarter, and fully explainable decisions.

The Role of Agentic Lakehouses

To realize the full potential of agentic AI, insurers must solve a foundational challenge: data fragmentation.

Insurance data is typically spread across legacy systems, data warehouses, data lakes, and third party platforms. This fragmentation makes it difficult to access, unify, and operationalize data at scale, especially in real time.

Agentic lakehouses are emerging as a solution to this problem. They combine the flexibility of data lakes with the performance and governance of data warehouses, while also enabling AI agents to interact with data directly.

In this model:

• Data remains in place but is made accessible through an AI semantic layer.
• Agentic AI models and agents can query, analyze, and act on data without complex data movement.
• Governance and security are enforced consistently across all data sources.
• Real time and batch data can be used together seamlessly.

This architecture is particularly well suited for insurance, where data diversity and regulatory requirements are both high.

Why Dremio Stands Out

Among the platforms enabling agentic lakehouse architectures, Dremio offers a compelling combination of performance, flexibility, and cost efficiency.

For insurers, Dremio provides several key advantages:

• Minimal data movement: Dremio allows insurers to query data where it lives, reducing the need for costly and risky data duplication.
• High performance analytics: Its query acceleration capabilities enable real time insights across massive datasets.
• Open architecture: Built on open standards, Dremio avoids vendor lockin and integrates easily with existing tools and AI frameworks.
• Strong governance: Fine grained access controls and data lineage support regulatory compliance and auditability.
• Cost efficiency: By eliminating redundant data pipelines and storage, Dremio significantly reduces total cost of ownership.

Most importantly, Dremio enables insurers to operationalize AI faster and with less risk. By providing a high performance agentic AI semantic data layer, it allows AI models and agents to access the data they need without the delays and complexities of traditional architectures.

The Path Forward

The insurance industry is at an inflection point. Agentic AI is no longer an experimental capability, it is the foundation of modern insurance operations.

Insurers that invest in agentic AI, supported by robust data architectures like agentic lakehouses, are positioning themselves to thrive in an increasingly complex and competitive environment. Those that do not, risk falling behind, not gradually, but rapidly.

The winners in this new era will be the organizations that can turn data into decisions in real time, at scale, and with full transparency. They will be the ones who treat AI not as a feature, but as core infrastructure.

And increasingly, they will be the ones who choose platforms like Dremio to make that transformation both achievable and sustainable.