Introduction

• Is the auto insurance industry still stuck in the slow lane? While cars are getting smarter with sensors, cameras, and AI-driven autopilot features, the insurance processes tied to them often remain outdated and painfully manual. Why is filing a claim still a paperwork nightmare in the age of automation? Why do customers have to wait days for decisions that AI can make in seconds? The truth is, auto insurers can no longer afford to ignore the speed, accuracy, and intelligence that AI agents in auto insurance bring to the table. From real-time accident detection to instant claim approvals, AI agents in auto insurance aren’t just a futuristic concept they’re the upgrade the motor insurance industry desperately needs. The real question is: will insurance companies accelerate with AI or be left behind in the dust?

What Are AI Agents?

• AI agents are intelligent software programs designed to perceive their environment, process data, make decisions, and take actions to achieve specific goals all with minimal or no human intervention. These agents are built using advanced technologies like machine learning, natural language processing (NLP), computer vision, and robotic process automation (RPA). Think of them as digital coworkers that can automate complex tasks, learn from new data, and improve over time. Whether it’s answering customer queries, analyzing images, or optimizing workflows, AI agents are capable of working around the clock with precision and consistency. (Want to Know More About What are AI agents?)

• AI agents in auto insurance are specialized digital assistants that automate and optimize core insurance operations related to vehicles such as underwriting, claims processing, customer support, risk assessment, fraud detection, and policy recommendations. These agents use real-time telematics, driving behavior data, vehicle sensors, and historical claims information to make intelligent decisions.

• For example, they can detect accidents as they happen, estimate repair costs using image recognition, or adjust premiums dynamically based on how safely someone drives. In short, AI agents in auto insurance are transforming slow, manual processes into fast, smart, and personalized experiences making insurers more efficient and policyholders more satisfied.

What are the Challenges Faced in Traditional Auto Insurance ?

• Auto insurance has been a staple of road safety and financial protection for decades but is the system still fit for today’s fast-moving, tech-driven world? Despite rapid advances in vehicle technology and customer expectations, many auto insurers remain tethered to outdated processes, manual operations, and legacy systems. From delayed claims to fraud-prone evaluations and rigid policy structures, the traditional model is showing its cracks. Drivers want speed, transparency, and personalization yet what they often get is paperwork, long wait times, and frustration.

1. Delayed Accident Response and Claims Initiation

• In traditional auto insurance, there’s no mechanism to instantly detect when an accident occurs. The claims process typically begins only when the policyholder manually reports the incident. This delay in awareness hampers the insurer’s ability to act quickly, leads to extended processing timelines, and increases the chances of disputes or misreporting. Without real-time incident detection, insurers often miss the critical window where intervention could reduce losses and enhance customer experience.

2. Inability to Use Telematics Data Effectively

• Modern vehicles generate an abundance of telematics data including driving speed, braking patterns, acceleration, route history, and more. However, traditional insurers lack the infrastructure or systems to harness this data meaningfully. As a result, they miss out on critical insights that could personalize pricing, predict risk, or automate claims. This gap in leveraging available data puts them at a disadvantage against tech-enabled competitors.

3. Static Premium Pricing Models

• Traditional premium calculations rely on static data like driver age, car model, or ZIP code, rather than real-world driving behavior. This often leads to unfair pricing cautious drivers end up paying the same or more than risky ones. Without usage-based pricing models or real-time assessments, insurance becomes less accurate and equitable, reducing customer satisfaction and limiting the insurer’s ability to compete on personalization.

4. Lengthy Vehicle Inspection Processes

• Before issuing policies or settling claims, insurers often require physical inspection of vehicles. This involves scheduling appointments, manual evaluations, and report writing a time-consuming and labor-intensive process. These inspections can delay policy issuance or claim settlement, frustrating customers and increasing operational costs.

5. Challenges in Handling Multi-Vehicle Accidents

• Accidents involving multiple vehicles require insurers to determine shared liability, cross-reference multiple claims, and validate complex sequences of events. Traditional methods, which rely on verbal statements, police reports, and manual analysis, often lead to long delays and inconsistent outcomes. This complexity can result in legal disputes and increased claim expenses.

6. High Incidence of Fraudulent Damage Claims

• Auto insurance is particularly susceptible to fraud from staged accidents to inflated repair estimates. Traditional systems rely heavily on human judgment and physical inspection, which can be easily manipulated. Without digital forensics,

7. Lack of Real-Time Policy Adjustments

• Policyholders often experience changes in vehicle usage like switching to remote work or going on a long road trip. Traditional auto insurance doesn’t accommodate real-time policy adjustments, forcing customers to stay locked into fixed terms regardless of usage changes. This lack of flexibility results in dissatisfaction and pushes users to seek usage-based insurance alternatives.

8. Inconsistent Repair Network Management

• Once a claim is approved, insurers typically direct customers to garages or repair shops for service. In traditional systems, this network is loosely managed, with limited tracking of quality, pricing, or turnaround time. This can lead to overbilling, substandard repairs, and customer dissatisfaction, with insurers footing higher-than-necessary bills.

9. No Instant Damage Assessment

• In the event of a claim, customers often have to wait for a surveyor to physically inspect the damage. This process is not only slow but also depends on the availability and objectivity of the evaluator. Without AI-driven image analysis or digital claim tools, damage assessment becomes a bottleneck in the overall process.

10. Disputes Over Liability in Accidents

• Determining fault in accidents is often contentious. Traditional insurers must depend on subjective reports, police documentation, or witness statements, all of which can be conflicting. Without access to dashcam footage, telematics, or smart accident reconstruction tools, insurers face delays and potential legal escalations, adding to costs and damaging customer trust.

11. Limited Visibility into Vehicle Condition History

• Insurers often issue policies or process claims without full knowledge of a vehicle’s service history, accident record, or modifications. This lack of transparency can lead to inaccurate risk assessment, higher fraud susceptibility, and over- or under-insurance, all of which directly affect profitability and pricing strategy.

12. Inadequate Integration with Traffic & Law Enforcement Systems

• Traditional insurers operate in silos and are rarely integrated with regional traffic departments or law enforcement databases. This limits their ability to verify accident claims, check for traffic violations, or flag stolen vehicles in real time. As a result, fraud detection and risk analysis remain reactive and incomplete.

13. Paper-Heavy Documentation Process

• Despite digital transformation trends, many insurers still rely on physical documents for KYC, policy issuance, and claims. This not only delays processes but also increases the chances of errors, document loss, and customer frustration. Additionally, maintaining paper records adds to storage and compliance costs.

14. Lack of Proactive Risk Alerts for Policyholders

• Traditional systems do not provide real-time risk alerts to drivers, such as warnings for dangerous driving patterns, upcoming bad weather, or accident-prone zones. By missing out on these preventive insights, insurers lose the opportunity to reduce claim frequency and improve driver safety both of which could positively impact profitability.

15. Limited Claims Tracking Transparency

• After filing a claim, policyholders often find themselves in the dark about its progress. Traditional systems provide little to no real-time updates, forcing customers to make repeated follow-ups. This lack of visibility damages trust, increases support call volumes, and lowers overall customer satisfaction.

16. Poor Post-Accident Experience

• Many traditional insurers fail to assist customers after an accident beyond claim filing. Services like towing, emergency transport, or identifying nearby approved garages are typically not included, leaving policyholders stranded and confused at a critical moment. This gap in post-accident support undermines customer loyalty.

17. Difficulty in Managing Commercial Vehicle Fleets

• Insuring and managing a fleet of commercial vehicles presents its own challenges tracking mileage, monitoring driver behavior, logging accidents, and managing bulk claims. Traditional systems lack centralized dashboards or automation to handle these complexities, resulting in inefficiencies and missed revenue opportunities in the commercial auto segment.

What are the use cases of AI agents in auto insurance?

• The auto insurance industry is undergoing a major transformation, and AI agents in auto insurance are driving the change. As vehicles become smarter and customer expectations rise, traditional processes simply can't keep up with the demand for speed, accuracy, and personalization. Whether it’s automating claims, detecting fraud in real time, or offering usage-based pricing, AI agents in auto insurance are no longer just an innovation they’re a competitive necessity. These intelligent digital assistants are revolutionizing every touchpoint of the auto insurance journey, helping insurers reduce costs, enhance customer satisfaction, and make faster, data-driven decisions.

1. Instant Claims Processing with Image Recognition

• AI agents in auto insurance can instantly analyze photos of vehicle damage submitted by policyholders using computer vision. Trained on thousands of accident images, these agents can assess the severity of damage, estimate repair costs, and even approve low-risk claims on the spot. This removes the need for human surveyors in many cases, dramatically reducing claim turnaround time and improving customer satisfaction.

2. Telematics-Based Dynamic Pricing

• AI agents in auto insurance leverage real-time telematics data from vehicles to understand driving behavior such as speed, cornering, braking, and mileage. This allows insurers to move away from traditional static pricing models and instead offer personalized, usage-based premiums. Safe drivers are rewarded with lower rates, while high-risk behaviors can trigger pricing adjustments or alerts, improving fairness and encouraging safer driving.

3. Automated Fraud Detection

• AI agents in auto insurance use behavioral analytics and historical data to flag suspicious claim patterns, such as repeated claims, staged accidents, or inflated repair costs. These agents can detect inconsistencies in claim reports, cross-check vehicle histories, and use anomaly detection algorithms to proactively reduce fraud. This minimizes financial losses and ensures genuine claims are processed faster.

4. 24/7 Customer Support via AI Chatbots

• AI agents in auto insurance, in the form of AI-powered virtual assistants, are available round-the-clock to help customers with queries about their policy, coverage, claim status, and more. These chatbots can guide users through tasks like buying a policy or filing a claim without human involvement. By automating common queries, insurers reduce operational costs and provide consistent, immediate service.

5. Real-Time Accident Detection and Emergency Support

• Connected with IoT devices and telematics, AI agents in auto insurance can detect accidents the moment they happen by monitoring sudden deceleration or airbag deployment. Once triggered, they can automatically alert emergency services, notify the insurer, and start claim initiation. This real-time response significantly improves driver safety and accelerates the claims process.

6. Personalized Policy Recommendations

• AI agents in auto insurance analyze a customer’s driving history, vehicle type, demographics, and preferences to suggest the most relevant policies. Instead of generic plans, users are offered tailored coverage options that align with their actual needs. This not only improves conversion rates but also enhances customer satisfaction by avoiding over- or under-insurance.

7. Driver Behavior Monitoring and Risk Alerts

• AI agents in auto insurance can continuously monitor real-time driving behavior such as phone usage, rapid acceleration, or frequent braking and send alerts when risky patterns are detected. This encourages better driving habits and reduces the likelihood of accidents. Over time, this proactive monitoring also helps insurers reduce the frequency of claims.

8. AI-Powered Underwriting Automation

• Traditional underwriting can take days, but AI agents in auto insurance can evaluate an applicant's driving record, claim history, telematics data, and even credit scores in seconds. They apply consistent rules and machine learning models to approve or deny applications automatically. This drastically reduces underwriting time and ensures consistent, data-backed decisions.

9. Repair Network Coordination

• After a claim is approved, AI agents in auto insurance can recommend nearby authorized garages, schedule repairs, and track the repair timeline. They can also confirm part availability and update the customer with live status updates. This reduces manual coordination and ensures a smoother post-claim experience.

10. Fleet Management Optimization for Commercial Vehicles

• AI agents in auto insurance help businesses monitor fleet vehicles in real time by analyzing usage patterns, wear and tear, and driver behavior. They can also predict maintenance needs, automate claim reporting, and adjust insurance coverage dynamically based on vehicle activity. This reduces costs and improves operational efficiency for commercial auto insurers.

11. Voice-Enabled Claims Filing

• Using speech recognition and NLP, AI agents in auto insurance can guide users through filing a claim using voice commands via phone or mobile app. This enables faster, hands-free claims reporting, particularly useful in stressful post-accident situations. It also improves accessibility for customers who may not be comfortable using digital interfaces.

12. Predictive Claim Risk Scoring

• AI agents in auto insurance can assess incoming claims and assign them a risk score based on historical patterns, location, vehicle type, and customer behavior. High-risk or potentially fraudulent claims are flagged for manual review, while low-risk claims can be fast-tracked. This helps insurers allocate resources efficiently and manage claims more effectively.

13. Automated Policy Renewals

• AI agents in auto insurance monitor upcoming policy expirations and analyze the customer’s driving history, claim activity, and profile changes to generate renewal offers. They can initiate communication with policyholders, recommend changes, and even auto-renew low-risk policies, reducing customer churn and administrative overhead.

14. Smart Document Processing

• Customers often submit documents like licenses, registration papers, and invoices. AI agents in auto insurance, equipped with OCR and NLP, can read, extract, and verify key information from these documents in seconds. This reduces errors from manual data entry, speeds up policy issuance and claim processing, and improves operational accuracy.

15. Geolocation-Based Premium Adjustments

• AI agents in auto insurance can adjust premiums in real time based on where a driver spends most of their time, such as parking in high-theft areas or frequently traveling on dangerous roads. These location-based insights make coverage more accurate and ensure the insurer is pricing risk appropriately.

16. AI Agents for Subrogation Analysis

• In complex accidents where another party is at fault, AI agents in auto insurance can analyze evidence like traffic data, police reports, and vehicle sensors to determine liability. This speeds up subrogation — the process of recovering costs from the third party’s insurer and helps insurers recover losses more efficiently.

17. Digital Twin for Accident Reconstruction

• AI agents in auto insurance can use telematics and sensor data to reconstruct the sequence of events in an accident. This digital twin helps establish fault, validate claims, and even train autonomous claim adjudication systems. It’s especially useful in multi-vehicle or high-impact collisions where clarity is critical.

18. Sentiment Analysis for Customer Feedback

• AI agents in auto insurance can analyze emails, chat transcripts, surveys, and social media comments to gauge how customers feel about their insurance experience. These insights allow insurers to identify pain points, improve communication strategies, and increase customer satisfaction in a data-driven way.

19. Cross-Sell and Upsell Recommendations

• Based on driving behavior and customer profiles, AI agents in auto insurance can suggest relevant add-ons like zero depreciation, engine protection, or roadside assistance at the right moment. These intelligent upsell strategies not only improve customer value but also boost insurer revenue with minimal effort.

20. Automated Regulatory Compliance Monitoring

• Insurance is heavily regulated, and staying compliant with regional laws is critical. AI agents in auto insurance can monitor processes in real time, flag non-compliant actions, and generate audit-ready reports. This ensures the insurer remains compliant with changing legal frameworks and avoids penalties.

What Are Benefits of Adopting AI Agents In Auto Insurance ?

• The auto insurance industry is rapidly evolving, and at the center of this transformation are AI agents in auto insurance intelligent digital tools that are reshaping how insurers operate, interact, and deliver value. As customer expectations rise and competition intensifies, traditional methods simply can’t keep pace with the demand for faster claims, personalized coverage, and fraud-proof processes. AI agents in auto insurance offer a smarter alternative by automating routine tasks, enabling real-time decision-making, and delivering hyper-personalized customer experiences.

1. Faster Claims Processing

• One of the most significant advantages of AI agents in auto insurance is their ability to accelerate claims processing. Traditionally, claims involve multiple steps from damage assessment to document verification and repair approval often leading to delays. AI agents in auto insurance can instantly process submitted images, assess the extent of damage using computer vision, cross-check policy coverage, and even authorize low-risk claims without human intervention. This reduces claim turnaround time from days to minutes, drastically improving the customer experience and reducing the insurer's administrative burden.

2. Improved Risk Assessment

• Conventional underwriting relies heavily on demographic data such as age, gender, and vehicle type. AI agents in auto insurance, however, go a step further by incorporating real-time telematics and behavioral analytics. They assess how, when, and where a person drives including braking patterns, speed consistency, and daily mileage to create a precise risk profile. This results in more accurate premium pricing, minimizes the chances of under- or over-insurance, and allows insurers to segment customers based on actual risk exposure.

3. 24/7 Customer Support

• AI agents in auto insurance, deployed as virtual assistants or chatbots, ensure that customer support is available 24/7. Whether it's a query about policy details, the status of a claim, or coverage recommendations, these agents can handle thousands of simultaneous conversations without downtime. This not only reduces the strain on customer service teams but also ensures that customers get immediate, consistent, and accurate responses even outside of business hours boosting overall satisfaction.

4. Reduced Fraud and Losses

• Insurance fraud is a major challenge, especially in auto insurance where staged accidents, inflated repair bills, or false claims are common. AI agents in auto insurance are trained to detect anomalies in claim patterns using predictive analytics and machine learning. They can identify red flags such as inconsistencies in claim reports, frequent high-value claims from the same individual, or repair estimates that deviate from the norm and alert fraud investigators in real time. This proactive detection reduces fraudulent payouts and protects the insurer’s financial health.

5. Personalized Customer Experiences

• AI agents in auto insurance can deeply personalize the insurance experience by analyzing customer behavior, preferences, and risk profiles. For instance, if a user drives mostly within city limits and during off-peak hours, the AI agent may recommend a policy that reflects lower risk. It can also suggest additional coverage options such as zero depreciation or roadside assistance based on individual needs. This level of personalization enhances user engagement, boosts conversions, and helps build long-term loyalty.

6. Lower Operational Costs

• AI agents in auto insurance automate repetitive and time-consuming tasks like data entry, document verification, claims sorting, and policy updates. By reducing the need for manual processing, insurers can cut down significantly on staffing costs and operational inefficiencies. Moreover, AI reduces error rates in data processing, avoiding costly mistakes and rework. These savings allow insurance companies to reinvest in innovation and customer service improvements.

7. Real-Time Decision Making

• With access to telematics, traffic data, and customer behavior insights, AI agents in auto insurance can make informed decisions in real time. For example, if an accident is detected via vehicle sensors, the AI agent can instantly initiate a claim, notify emergency services, and provide the customer with nearby repair options. Similarly, if driving patterns change (like reduced mileage), the agent can adjust the premium dynamically. This agility improves responsiveness and positions the insurer as a proactive service provider.

8. Scalable and Consistent Service

• Human agents are limited by capacity, time zones, and fatigue. In contrast, AI agents in auto insurance can scale effortlessly, handling thousands of customer interactions simultaneously while maintaining consistent quality and tone. Whether it’s onboarding new customers, assisting with claims, or explaining policies, AI ensures that every user receives uniform service a critical factor in brand reputation and customer retention.

9. Better Fleet Management for Commercial Insurers

• For commercial auto insurance, managing fleets presents complex challenges, including monitoring vehicle usage, ensuring timely maintenance, and assessing driver behavior. AI agents in auto insurance can aggregate and analyze data from GPS, telematics, and onboard diagnostics to give fleet managers actionable insights. This helps prevent breakdowns, reduces the number of claims, and allows insurers to offer dynamic premiums based on real-time usage creating a win-win for both parties.

10. Enhanced Regulatory Compliance

• Insurance is a highly regulated industry, and non-compliance can lead to hefty fines and legal repercussions. AI agents in auto insurance can be trained to follow specific regulatory frameworks from KYC norms to claim documentation and data privacy. They automatically log every customer interaction, ensure required documents are collected, and flag any policy inconsistencies. This reduces compliance risk, ensures audit-readiness, and keeps insurers aligned with constantly evolving legal standards.

What challenges are faced by auto insurance companies in the adoption of AI agents?

• While AI agents promise to revolutionize auto insurance with automation, accuracy, and personalized experiences, their adoption is not without hurdles. Despite growing interest and investment, many insurers struggle to move beyond pilot projects and into full-scale implementation. From legacy systems and poor data quality to regulatory concerns and internal resistance, several roadblocks stand in the way of a seamless AI transformation. Understanding these challenges is critical not to slow progress, but to ensure AI adoption is strategic, compliant, and future-ready. In this section, we explore the key obstacles insurers face when integrating AI agents into their auto insurance workflows.

1. Legacy Systems and Infrastructure

• One of the biggest hurdles in adopting AI agents in auto insurance is the presence of outdated IT infrastructure. Most traditional insurers rely on legacy core systems built decades ago, which were never designed to support AI technologies, APIs, or real-time data processing. Integrating AI agents into such environments often requires a complete overhaul of backend systems, data architectures, and internal workflows. This transition is not only costly but also time-consuming, creating a significant barrier for insurers looking to modernize operations.

2. Data Quality and Availability

• AI agents rely on massive amounts of clean, structured, and real-time data to function effectively. Unfortunately, many auto insurers struggle with fragmented or poor-quality data collected from disconnected sources like policy documents, claims logs, or customer service records. In the absence of standardized telematics or behavioral data, AI models may produce inaccurate or biased outputs. Without reliable datasets, the core capabilities of AI agents like risk assessment, fraud detection, or claim automation become compromised.

3. High Implementation Costs

• The cost of adopting AI agents extends far beyond just purchasing software or tools. It includes infrastructure upgrades, cloud service integration, data cleaning, model training, and hiring or upskilling technical teams. In addition, insurers must allocate resources to testing, compliance verification, and ongoing maintenance. For small- to mid-sized insurance companies, these high upfront investments can be prohibitive, especially when ROI is not immediate or guaranteed.

4. Regulatory and Compliance Concerns

• Insurance companies must operate within strict regulatory frameworks, and the adoption of AI adds another layer of complexity. AI models used for pricing, underwriting, or claims must be explainable and non-discriminatory a difficult feat for complex algorithms like neural networks. Regulators may require justification for every automated decision, especially when it affects customers financially. Meeting these compliance expectations while still leveraging the speed and power of AI agents remains a delicate balancing act.

5. Lack of Skilled Talent

• Deploying AI agents requires a combination of skills that are rarely found in a single team. Insurers need data scientists, AI engineers, machine learning specialists, business analysts, and legal experts all with a deep understanding of insurance operations. The talent shortage in AI is already acute, and finding professionals who also understand the complexities of the insurance domain makes it even more challenging. This talent gap slows down innovation and forces insurers to rely heavily on external consultants or vendors.

6. Resistance to Change Within Teams

• Introducing AI agents can trigger fear and resistance among existing staff, especially those in claims processing, underwriting, or customer support roles. Employees may worry that AI will replace their jobs or reduce their influence within the company. This internal resistance can result in poor adoption, lack of collaboration between teams, and even active pushback against AI initiatives. Change management and transparent communication are crucial but often overlooked elements of AI adoption.

7. Integration with External Ecosystems

• AI agents don’t operate in isolation they often need access to external data sources like vehicle telematics platforms, repair networks, law enforcement records, and third-party KYC providers. Integrating these systems into the insurer’s environment is technically complex and may involve data standardization, security protocols, and regulatory clearances. Poor integration can limit the functionality of AI agents and reduce the accuracy or speed of their decision-making.

8. Concerns About Accuracy and Trust

• While AI agents can significantly improve efficiency, any wrong or biased decision like rejecting a valid claim or inflating a premium can damage an insurer’s reputation. Customers and internal stakeholders may not fully trust AI decisions, especially if the logic behind those decisions isn’t transparent. Building trust in AI systems requires not just technical reliability, but also strong explainability, user-friendly interfaces, and fallback mechanisms involving human intervention.

9. Ethical and Bias Issues in AI Decision-Making

• AI agents can unintentionally perpetuate or even amplify biases present in historical data. For example, if past claim approvals were unintentionally skewed based on location or demographic factors, the AI may learn and replicate these biases. This raises serious ethical concerns, especially in a heavily regulated industry like insurance. Without robust bias mitigation frameworks, ethical governance policies, and continuous auditing, insurers risk facing public backlash and legal challenges.

10. Customer Education and Acceptance

• For many policyholders, especially those unfamiliar with technology, interacting with AI agents may feel impersonal or confusing. Some customers prefer the reassurance of a human advisor, particularly during sensitive moments like filing a claim after an accident. If AI agents are not well-explained or perceived as unreliable, customers may lose trust in the insurer altogether. Education campaigns, hybrid models (AI+ human), and clear communication are essential to encourage adoption and ease customers into the digital experience.

Conclusion

• The future of auto insurance is no longer about if AI agents will be adopted — it's about how fast and how strategically insurers can implement them. As vehicles evolve and customer expectations soar, traditional insurance models are being stretched to their limits. From delayed claims and outdated risk assessments to rising fraud and operational inefficiencies, legacy systems simply can’t keep up with today’s demand for speed, precision, and personalization.

• AI agents in auto insurance present a powerful solution — enabling insurers to automate intelligently, deliver real-time decisions, personalize coverage, and dramatically enhance both customer and internal experiences. Their ability to optimize everything from underwriting and claims to customer support and regulatory compliance marks a true paradigm shift in how auto insurance is delivered.

• However, successful adoption is not without its hurdles. Legacy infrastructure, data challenges, internal resistance, and regulatory complexities continue to slow down progress. But for insurers who embrace the change with clear vision, ethical governance, and the right partnerships, AI agents in auto insurance can unlock a new era of efficiency, accuracy, and growth.

• The industry is at a critical juncture: accelerate with AI or risk being left behind. For forward-thinking insurers, AI agents in auto insurance aren’t just a technology upgrade — they are the competitive edge that will define the future of the industry.