5 Resolutions for insurers in 2025: Embrace the decision era

The insurance industry is in an interesting position currently. Legacy systems, complex regulatory landscapes, and ever-evolving customer expectations are challenging insurance companies to rethink how they operate. However, challenge brings opportunity: AI and automation are redefining workflows, improving decisioning, and delivering better customer experiences. As we step into 2025, another year in what we’ve termed The Decision Era, insurers have a clear mission—adopt intelligent technologies effectively to stay competitive. In this article, we’ll walk you through five actionable resolutions that will help you streamline operations, enhance risk management, and meet rising customer expectations head-on.

Resolution 1: Streamline document-intensive workflows with AI

Insurance workflows like underwriting risks, maintaining policies, and handling claims have long been burdened by document-heavy processes. Traditionally, insurers have relied on manual data entry and document review, which is both time-consuming and prone to human error. In an era where customers expect faster, more efficient service, these outdated practices put insurers at a disadvantage. However, advancements in AI and intelligent document processing (IDP) are transforming how insurers handle unstructured data and streamline their operations.

By adopting AI-driven automation, insurers can drastically reduce the time spent on manual tasks, allowing employees to focus on higher-value work such as decision-making, risk assessment, and customer engagement.

Automating submissions intake for faster underwriting

For example, a commercial insurer handling submissions intake can leverage AI to extract key details like coverage limits, policyholder names, and risk factors from unstructured documents. AI-powered data ingestion transforms previously manual processes, reducing submission-to-quote times and allowing underwriters to focus more on decision-making and less on administrative tasks.

Resolution 2: Improve claims processing and customer experience with AI

Claims processing is one of the most critical touchpoints in the customer journey for insurers. The speed, accuracy, and transparency of claims management can make or break customer trust. In times of crisis, such as natural disasters, insurers face an influx of claims that can overwhelm traditional workflows. By integrating AI into the claims process, insurers can handle high claim volumes more efficiently, prioritize cases by severity, and improve overall customer satisfaction.

Triage claims in real time to improve customer trust

AI systems can automatically triage claims based on severity, ensuring that high-priority claims receive immediate attention. For example, following a hurricane, insurers can automate payments to policyholders with severe property damage while routing lower-priority cases to human adjusters for more thorough review. This improves customer trust by ensuring faster payouts and reducing the average claims resolution time.

Additionally, AI can analyze submitted claims data—such as accident details, photos, supporting documents, and other relevant data like telematics—and automatically recommend whether a claim should be approved, denied, or flagged for further review. This significantly reduces the manual workload on claims adjusters and shortens the time it takes to deliver payouts, which is crucial in crisis scenarios when customers need immediate financial relief.

Related content: The power of submission clearance software in modern insurance workflows

Resolution 3: Strengthen decision-making for operational excellence

Customer expectations have shifted dramatically. Policyholders and brokers alike demand quick, accurate, and personalized interactions. Enterprise AI enables insurers to reimagine their decisioning processes, ensuring every interaction enhances the customer experience.

For example, an auto insurer could deploy an AI-powered solution to handle first notification of loss (FNOL) calls, not just for data collection but to enhance decision-making. By analyzing the information provided—such as accident details and uploaded photos—AI could prioritize claims based on severity, recommend approval or denial decisions, and flag high-risk cases for further review. This approach enables insurers to allocate resources effectively, focus human agents on complex or ambiguous claims, and streamline the overall claims process.

Ensuring policy alignment and regulatory compliance with explainable AI

But the benefits of AI go beyond the surface. In underwriting, explainable AI empowers insurers to make decisions that align more closely with policyholder needs. Underwriters can deploy adaptive AI agents that assess submission data, evaluate risk profiles, and autonomously recommend tailored policies based on contextual insights. For example, the AI might analyze a company’s risk exposure, such as equipment failure, and propose specific policy options. Similarly, it could assess a trucking company’s risk factors—like vehicle maintenance or route data—and dynamically prioritize commercial vehicle insurance.

Transparent AI-powered decisioning tools also help insurers feel confident in the outcomes. By using explainable AI, insurers can show how coverage limits or premium adjustments are determined, demystifying a process that often feels opaque to policyholders. These practices demonstrate that prioritizing customer-centric decisioning doesn’t just improve satisfaction—it also drives sustainable growth in a competitive market.

Resolution 4: Leverage AI to improve underwriting precision

Underwriting lies at the heart of any insurance operation. Balancing speed, precision, and profitability is a challenge—one that enterprise AI is uniquely positioned to solve. By leveraging predictive analytics and machine learning models, insurers can achieve a level of underwriting accuracy that was previously impossible.

For instance, a commercial property and casualty insurer might adopt AI to process property risk assessments and historical claims data for policy applications. Instead of relying solely on static risk models, the insurer’s AI system could evaluate dynamic data points such as property location, past maintenance records, and weather patterns. This approach would enhance underwriting accuracy, allowing the insurer to identify higher-risk properties more effectively and reduce claims losses through better risk stratification and proactive mitigation strategies.

Agentic AI for real-time risk adaptation

Agentic AI takes underwriting a step further by learning and adapting in real time. In the commercial insurance sector, underwriters dealing with high-value accounts often face nuanced risks that are hard to quantify. AI-powered systems analyze unstructured data such as safety reports, third-party audits, and even news articles to flag potential issues that might otherwise go unnoticed. For example, a manufacturer applying for coverage might have a history of compliance violations that wouldn’t show up in standard application data. Agentic AI not only identifies these risks but also suggests appropriate coverage terms, allowing underwriters to make informed, data-driven decisions.

The efficiency gains are just as compelling. A property-casualty insurer might integrate AI-driven document intake with its underwriting systems, reducing manual data entry. This would allow underwriters to handle more submissions without sacrificing quality, increasing both profitability and customer satisfaction.

Enhancing cross-border collaboration with cloud platforms

Cloud-native platforms also enhance collaboration across teams and geographies. As another example, an international health insurer could adopt a cloud-based AI solution to integrate its underwriting, claims, and policy management systems. This strategic move would result in a unified workflow that allows underwriters and claims adjusters across different countries to access the same real-time data, ensuring consistency and accuracy in decision-making.

Agentic AI further enhances scalability by adapting to changing conditions. As new data becomes available—whether from external sources like market trends or internal sources like policy renewals—agentic AI models adjust their outputs, empowering insurers to remain responsive and competitive. By investing in cloud-based AI solutions, insurers can achieve the flexibility and resilience needed to thrive in an ever-changing market.

Related content: Improve claims accuracy and enhance risk management through AI

Resolution 5: Strengthen regulatory compliance and data governance

Regulatory compliance remains a critical, yet often burdensome, aspect of insurance operations. With laws like GDPR in Europe and varying state-by-state regulations in the U.S., insurers face immense pressure to ensure data privacy, security, and transparency. Enterprise AI offers solutions that not only simplify compliance but also enhance operational efficiency.

For example, a multinational insurer might implement an AI-powered compliance monitoring system to handle the complexity of cross-border regulations. By automating tasks like data classification and audit trail generation, the system could guarantee compliance with international laws while reducing administrative overhead. Moreover, an AI model would be capable of flagging discrepancies in real time, enabling the company to address issues before they escalate into costly fines or reputational damage.

Improving data governance with agentic AI

Agentic AI adds another layer of value by continuously refining data governance strategies. By monitoring and adapting to changes in data usage patterns, agentic AI helps insurers maintain compliance with evolving regulations while optimizing data integrity. Insurance companies can use agentic AI to automate things like data retention policies, ensuring that sensitive customer information is stored, accessed, and deleted in compliance with both internal policies and external mandates. By embracing AI-driven compliance and data governance, insurers can turn regulatory challenges into opportunities for efficiency and trust-building.

Set your insurance company up for a successful 2025

As we step into 2025, the insurance industry faces both significant challenges and transformative opportunities. By embracing these five resolutions—streamlining document workflows, prioritizing customer-centric decision-making, enhancing underwriting precision, investing in scalability, and strengthening compliance—insurers can position themselves for long-term success.

Indico is proud to be at the forefront of this transformation. With our AI solutions, insurers can harness the power of IDP, agentic AI, and advanced decisioning to drive innovation and create competitive advantages. From improving efficiency in underwriting to elevating customer experiences, our platform is designed to meet the evolving needs of modern insurers.

Let 2025 be the year your company takes the next step toward operational excellence and customer-centric innovation. Whether it’s streamlining workflows, scaling operations, or ensuring compliance, Indico is here to help you achieve your goals and thrive in the rapidly changing landscape of insurance. Contact us today and discover how we can partner with you for a smarter, more efficient future.

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Frequently asked questions

• How can smaller insurers with limited budgets afford to implement and maintain AI-driven solutions like those described? Smaller insurers with limited budgets can still adopt AI-driven solutions by focusing on scalable and modular technologies that align with their immediate operational needs. Many AI platforms offer flexible pricing models, such as pay-as-you-go or subscription-based plans, which reduce the need for significant upfront investment. Additionally, leveraging cloud-based AI solutions can eliminate the costs associated with on-premise infrastructure, making implementation more affordable. Partnering with technology providers that specialize in working with smaller companies or offer tailored solutions can further streamline adoption while ensuring a positive return on investment.
• What are the specific risks or challenges associated with adopting AI and automation in insurance, and how can these be mitigated? The primary risks associated with adopting AI and automation in insurance include data security vulnerabilities, integration challenges with legacy systems, and the potential for biased decision-making from poorly trained AI models. These risks can be mitigated through several measures. First, investing in robust cybersecurity protocols ensures that sensitive customer data remains protected. Second, adopting open APIs or middleware can facilitate smoother integration with existing systems. Finally, insurers should prioritize explainable AI tools and invest in continuous model training using diverse datasets to minimize biases and maintain transparency in decision-making processes.
• Are there any real-world success stories or examples of insurance companies that have already implemented these resolutions effectively? A notable example of successful AI implementation in insurance is Lemonade, a digital-first insurer that uses AI to process claims and underwrite policies efficiently. For instance, its AI chatbot, Maya, handles customer onboarding, while AI Jim processes claims in minutes, reducing turnaround time and improving customer satisfaction. Similarly, Zurich Insurance has adopted AI-driven tools to streamline claims handling, achieving significant reductions in processing time and operational costs. These case studies demonstrate that leveraging AI not only enhances efficiency but also creates a competitive edge by delivering superior customer experiences.