The insurance industry is facing a pivotal moment as artificial intelligence (AI) reshapes every aspect of business operations—from claims processing to customer experience. To thrive in this fast-evolving landscape, insurers must rethink traditional processes and adopt adaptable, AI-powered frameworks.
On a recent episode of the Unstructured Unlocked podcast, Rory Yates, Chief Strategy Officer at EIS, shared critical insights into how insurance providers can leverage AI to build a competitive edge. Drawing from his wealth of experience in the insurance industry, Yates explored how generative AI and data fluidity can drive operational adaptability, improve customer satisfaction, and help insurers manage risk in an increasingly unpredictable world.
Listen to the full podcast episode here: Unstructured Unlocked Podcast
Here’s a deep dive into the conversation and actionable insights for insurers ready to unlock the power of AI.
Structured systems meet generative AI
The insurance industry has long relied on structured data to manage policies, claims, and pricing. But with the advent of generative AI, Yates describes a seismic shift akin to “dropping a boulder into a lake.” “It ripples everywhere,” he explained on the podcast. “You have something unexpected and powerful that’s great for non-deterministic tasks, but now insurers need to rethink their systems to integrate structured and unstructured data seamlessly.”
He explains, “Generative AI introduces non-deterministic capabilities, challenging insurers to rethink their systems while blending structured data with event-based and unstructured insights.”
This evolution requires insurers to move beyond rigid, policy-driven frameworks and invest in platforms that prioritize data fluidity. Data fluidity refers to the ability to integrate and interpret diverse data sources quickly, resulting in more dynamic business operations. Yates elaborates, “We’re focused on ensuring adaptability—our event-streaming-based approach enables us to connect structured and unstructured data seamlessly.”
Use case spotlight: fraud detection
AI’s data analysis capabilities are already revolutionizing fraud detection. Generative AI can analyze behaviors and data patterns in real-time, significantly speeding up the identification of fraudulent claims. For example, AI tools can combine structured data from policies with contextual signals to flag anomalies more accurately and efficiently than traditional methods. By leveraging such insights, insurers can mitigate risks and improve trustworthiness.
Yates shared an intriguing use case where new AI technologies analyze subtle changes in a caller’s voice, caused by physiological responses such as increased blood flow in the vocal cords during deception. “It’s fascinating,” he said. “This technology, once used by intelligence agencies, is now beginning to revolutionize fraud detection in insurance—quickly validating suspicions during telephone interviews.”
By applying this generative AI approach to anomaly detection, insurers can address undetected or “invisible” fraud, which Yates identified as “the real risk” to insurers. “There’s billions of fraud going undetected,” he noted. “AI allows us to analyze the patterns we previously ignored, bringing greater transparency and trust.”
Related Content: 5 Resolutions for insurers in 2025: Embrace the decision era
The growing need for adaptability in insurance
Success in today’s insurance market is no longer about maintaining baseline operations—it’s about adaptability. Yates notes, “Adaptivity will be the defining competitive characteristic of insurance, he stated. “It’s no longer enough to maintain the status quo. Insurers must respond dynamically to regulatory shifts, market changes, and evolving customer needs.”
Yates also mentions, “The ability to quickly adjust to risk, market shifts, or regulatory changes will separate leaders from laggards.”
One area where adaptability is particularly valuable is claims management. Traditional processes often follow rigid workflows, limiting responsiveness to unique circumstances. However, intelligent AI-based orchestration tools offer dynamic claims management. For example, these systems can assess eligibility in real-time or seamlessly adjust workflows to accommodate evolving regulatory requirements.
“Claims processing is an area ripe for transformation,” Yates explained. “AI can intelligently orchestrate decisions, providing insurers with the ability to adapt faster than legacy systems could.”
Enhancing ecosystem flexibility
EIS is helping insurers leverage AI and ecosystems to improve adaptability. Yates emphasizes, “We’ve built platforms designed to widen ecosystems, allowing insurers to integrate new tools and partnerships without being constricted by existing infrastructure.”
This adaptability ensures insurers can continuously enhance their offerings while scaling their AI capabilities.
Schedule a 1-on-1 demo of Indico Data’s Decision Automation Platform to see how AI integration can fortify your claims management systems.
Overcoming “pilot purgatory” with strategic AI implementations
One common obstacle to AI adoption in insurance is what Yates calls “pilot purgatory.” Insurers are often hesitant to fully implement AI due to fears of obsolescence or untested ROI. This limbo results in wasted time and resources on proof-of-concept projects that never materialize into wide-scale benefits.
Yates offers a clear framework to break free from this cycle. He advises insurers to map AI use cases on an impact-risk matrix, identifying initiatives that are:
- High Value, Low Risk (e.g., call center automation or fraud detection)
- High Value, High Risk (requiring more governance and gradual adoption)
“Fraud detection or call center scripting can be a great starting point,” he said. “These are tasks with immense value and minimal regulatory risk, providing quick wins to build organizational confidence.” Starting with low-risk, high-value projects ensures quick wins, building confidence and demonstrating the viability of AI-driven transformations across an organization.
Real-world success story
One insurer achieved dramatic success by using generative AI for customer support call scripting. The AI tool provided real-time suggestions for call agents, improving resolution times and driving up Net Promoter Scores (NPS). This low-risk implementation enhanced customer satisfaction while showcasing AI’s immediate value. “It’s a classic example of high-value, low-risk implementation,” Yates explained. “And it set off a chain reaction, where the organization began to explore broader AI use cases after seeing tangible results.”
The key to AI success: human oversight and governance
While generative AI offers a wealth of opportunities, Yates advises caution, particularly in sensitive areas like underwriting and medical claims. “Just because AI can, doesn’t mean it always should,” he notes. “The risk of getting it wrong is too high when customer trust is on the line.”
To safeguard against missteps, insurers must establish robust governance frameworks and maintain human oversight in critical decision-making. Yates recommends three key strategies:
- Prepare Your Data: Ensure structured data is clean, organized, and ready for AI systems to analyze effectively.
- Enforce Governance Measures: Implement checks and approvals for AI-driven functions, especially customer-facing tools.
- Keep Humans in the Loop: Assign clear roles for human decision-makers in high-risk areas.
“When it comes to underwriting or claims involving medical data, you can’t afford to compromise customer trust,” Yates explained. He cited examples of insurers implementing AI to enhance—not replace—human oversight, ensuring critical decisions undergo a “human handshake.”
Related Content: Transforming insurance claims: insights from Ian Thompson, Strategic Advisor and Former Zurich Insurance Executive
Looking ahead: AI as a catalyst for insurance transformation
Yates envisions a future where AI empowers insurers to become more agile and customer-centric. Some potential advancements include:
- Real-Time Risk Assessments: AI-driven insights could allow insurers to price risk dynamically, moving beyond static annual policies.
- Proactive Loss Prevention: Predictive analytics and generative AI can help insurers notify customers of impending risks, enhancing trust and reducing claims.
- AI Fluency Across Teams: Building organizational fluency in AI tools will be essential for sustaining long-term competitiveness.
Yates believes that adaptability extends beyond technology. “Organizational design and AI fluency are just as critical as the tools themselves,” he emphasizes.
Start building your AI advantage today
The AI revolution is here, and insurers that adapt will secure their place as future leaders in the industry. From fraud detection to proactive risk management, the potential for transformation is unparalleled. However, success requires a balanced strategy that starts with low-risk projects while preparing for sustained innovation.
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Frequently asked questions
- How can insurers ensure data privacy and security when integrating AI into their operations? Insurers must implement strong encryption methods, adhere to data protection regulations like GDPR or CCPA, and establish AI governance frameworks to prevent unauthorized access. Continuous monitoring and auditing of AI systems can help detect potential vulnerabilities. Additionally, insurers should use anonymization techniques for customer data where possible and ensure AI models operate within strict ethical guidelines to prevent misuse or data breaches.
- How can insurers balance the cost of AI implementation with the expected return on investment? To justify AI investments, insurers should conduct impact assessments that quantify efficiency gains, fraud reduction, and customer experience improvements. Starting with low-cost, high-impact applications such as automating customer service interactions or detecting fraud can provide immediate financial returns. By reinvesting savings into more advanced AI applications, insurers can gradually scale up their AI initiatives without overwhelming budgets. Partnering with AI vendors who offer scalable and modular solutions can also help insurers manage costs effectively.
- How do regulatory changes impact AI adoption in the insurance industry? Regulatory bodies continuously update compliance requirements to address AI-driven decision-making risks, such as bias in underwriting or claims processing. Insurers must work closely with legal and compliance teams to align AI implementations with evolving regulations. Developing transparent AI models with explainable decision-making processes can help insurers meet regulatory expectations while maintaining customer trust. Staying engaged with industry groups and regulatory discussions can also help insurers anticipate and adapt to future compliance requirements.
