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AI and the Future: The Critical Role of Quality Assurance

Authors

Kartik Sakthivel, Ph.D., MS-IT/MS-CS, MBA, PGC-IQ
Vice President & Chief Information Officer and Regional Chief Executive Officer – Asia West
LIMRA and LOMA
ksakthivel@limra.com

Neil Bendov
Vice President Marketing, Emtech Group, Inc.

July 2025

The insurance industry is undergoing a digital transformation, and artificial intelligence (AI) is playing a major role in reshaping quality assurance (QA).

As insurance companies strive to improve efficiency, ensure compliance and meet rising customer expectations, AI is becoming an essential tool for modernizing the QA process. The future of AI in insurance QA software promises to be both innovative and impactful.

Modernizing QA

Quality assurance is critical in insurance. Any errors or bugs in software can lead to financial losses, compliance issues or damaged customer trust. Traditionally, QA in insurance has relied heavily on manual testing. This approach, while thorough, is slow, labor-intensive, and increasingly impractical as systems grow more complex.

Poor QA comes at real costs. The 1:10:100 rule illustrates the increasing costs associated with resolving issues caused by bad data. According to the 1:10:100 rule, it costs $1 to fix bad data at the point of creation (prevention cost). The same bad data, if it enters a development/test environment during the software development cycle, costs $10 to remediate. This is known as the cost of correction or correction cost. If that same bad data goes undetected and enters a live production environment and is later reported as a defect during operations, it costs $100 to fix. This is known as the failure cost. In other words, if your QA can detect a defect in your development cycle, the cost to remediate this defect will be $1; but if the same bug makes it to your live environment undetected, it will cost $100 to resolve.

As companies continue to digitize, the traditional ways of testing will no longer be enough. What lies ahead is not just the automation of repetitive tasks, but a complete transformation of the QA process powered by intelligent systems. The future of AI in insurance QA promises smarter, faster, and more proactive approaches that can keep pace with the increasing demands of regulatory compliance, customer expectations and complex digital infrastructure.

Predictive Testing

In the coming years, AI will move quality assurance from being a reactive process to a predictive one. Instead of finding bugs during or after testing, AI systems will anticipate where problems are most likely to occur before the code is even written. By analyzing data from past defects, user feedback, development history and system behavior, AI will be able to forecast high-risk areas in upcoming releases. It will allow QA teams to prioritize their efforts more effectively, preventing many issues before they emerge and saving insurance carriers time, money and reputational risk.

Another breakthrough will come from self-evolving QA systems. Today, automated test scripts often break when the software changes. In the future, AI-powered platforms will automatically adapt to changes in code, interface design or functionality. These systems will be capable of recognizing modifications, understanding their impact and updating tests on their own. They will monitor live applications and learn from user interactions, continuously refining their testing strategies to focus on the areas that matter most.

AI will also enable advanced simulation of user behavior at a scale that is currently impossible. Insurance products serve a wide range of users, and each customer interacts with digital platforms in unique ways. Future AI systems will generate thousands of realistic test scenarios using synthetic user personas, designed from real policyholder data and behavioral patterns. These simulations will test how systems handle a variety of real-world actions, providing a much deeper level of validation than static test cases.

Compliance Intelligence

Regulatory compliance will benefit significantly from AI’s capabilities as well. Today, ensuring that insurance software complies with evolving regulations is largely a manual task. AI systems of the future will be able to interpret regulatory documents and automatically verify that software systems align with the applicable rules. These systems will adjust to regulatory changes in real time, giving insurance carriers confidence that their platforms remain compliant without the need for continuous manual intervention.

The way QA professionals interact with testing systems will also change. Rather than eliminating QA roles, AI will elevate them. QA professionals will evolve into strategic partners who interpret AI outputs, guide intelligent systems, and align testing with business goals. Their work will become more about making decisions and less about performing repetitive tasks. With AI handling execution, QA teams will collaborate more closely with developers, designers, compliance leaders, and customer experience teams to ensure that quality is deeply embedded in every product decision.

Looking even further ahead, the insurance industry could eventually adopt zero-touch QA environments, where AI autonomously manages the entire testing life cycle. These systems will decide what to test, generate and execute the tests, identify defects, propose solutions, and even push code updates without human involvement. This future may still be years away.

AI will also integrate quality assurance with real-time monitoring in production. Insights gathered from live user behavior will feed back into the QA process, allowing continuous learning and improvement. Systems will identify emerging patterns, flag anomalies, and adjust testing priorities dynamically. This feedback loop will create a smarter, more responsive QA environment where testing doesn't stop at deployment but continues to evolve with the product and the user base.

Strategic Future

The future of AI in insurance QA is more than a technological upgrade. It represents a fundamental change in how quality is defined, delivered, and maintained. It’s a shift from scripted routines to intelligent systems that learn, adapt, and collaborate. It’s about turning QA into a continuous, data-driven, and business-aligned process that keeps pace with change.

Insurance carriers who embrace this future will gain a significant advantage. They’ll be able to launch products faster, respond to regulatory shifts with ease, and offer digital experiences that are more reliable and personalized. AI will improve the QA process and redefine it. And for insurance companies committed to innovation, that future can't come soon enough.

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