Episode #158
Apr 3, 2025
Why Identifying the Right Pain Points is Key to AI Success
Preetha Sekharan from Unum
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Preetha Sekharan from Unum shares her expertise on why identifying the right pain points is key to AI success. Discover how to pinpoint the most impactful AI use cases, gain buy-in from stakeholders, and navigate the challenges of implementing AI in insurance.
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Artificial intelligence (AI) has become a game-changer in the insurance industry. But as exciting as the technology is, simply implementing AI isn’t enough to drive meaningful business impact. The key lies in identifying the right pain points — and solving them strategically. In episode 158 of The InsurTech Geek Podcast, Preetha Sekharan, VP, Digital Incubator, Applied AI & Transformation at Unum shared her invaluable insights on how AI is reshaping insurance, why trust matters, and what the future holds for AI in the industry.
Meet Preetha Sekharan
Preetha Sekharan’s career trajectory is a masterclass in combining technical expertise with business strategy. Born and raised in India, Preetha earned her degree in electrical engineering from the National Institute of Technology, Rourkela, before moving to the U.S. to pursue her MBA at Duke University. This blend of technical and business education laid the foundation for her success in the AI and insurance industries.
Her career path includes working at Infosys, McKinsey, and Booz & Company, where she gained exposure to various industries, including retail, consumer goods, and logistics. However, her experience with healthcare payers and insurance companies during her consulting years sparked her interest in the insurance industry.
“Insurance is a data nerd’s dream,” Preetha notes. “It’s complex, data-intensive, and highly regulated — making it an ideal space for AI to thrive.”
Preetha joined Unum to lead its digital strategy and AI innovation, where she now heads the Digital Strategy and Incubation team. Her work focuses on identifying business problems that AI can solve, implementing AI-driven solutions, and ensuring the organization builds trust in AI among employees and stakeholders.
The Importance of Identifying the Right Pain Points
One of the biggest challenges in implementing AI successfully is knowing where to apply it. Preetha emphasizes that AI implementation should always be business-driven, not technology-driven.
“I’m passionate about not doing technology for the sake of technology. It has to be in the context of a business problem,” she explains.
When Unum began exploring AI, they took a strategic approach. They started with a high-visibility use case that had the potential to deliver quick results and demonstrate AI’s value.
Why Picking the Right Use Case Matters:
✅ Visibility: Selecting a prominent issue ensured that AI adoption would be noticed at the executive level.
✅ Feasibility: The problem needed to be complex enough to show AI’s capability but not so unpredictable that it would fail.
✅ Impact: Success with the initial project would create momentum for future AI adoption across the organization.
Unum’s first AI implementation focused on the claims process — an area where AI’s predictive capabilities could streamline operations and improve outcomes.
“We picked claims because it was a burning platform. Solving it successfully built momentum and created believers in AI.”
Building Trust in AI
AI adoption hinges on trust — and building that trust is not automatic. Preetha notes that trust must be earned through transparency and user involvement.
Co-Creation with End Users
Unum involved claims specialists directly in the development process. This co-creation ensured that AI was designed with real-world needs in mind and gave end-users a sense of ownership over the solution.
“When users are part of the process, they start to see AI as a helpful tool rather than a threat.”
Governance and Risk Management
Unum also established strong governance frameworks to manage AI risk and compliance. Preetha describes working closely with legal and risk teams to ensure that AI models were reliable and compliant with regulations.
“We knew we had to make the brakes strong if we wanted to go fast.”
Avoiding Over-Reliance
Interestingly, Preetha mentions that with generative AI, trust has shifted from skepticism to over-reliance.
“With ChatGPT and similar tools, the challenge is now preventing people from trusting AI too much without applying human judgment.”
The Shift Toward AI-Native Business Models
Most insurance companies have integrated AI into their existing workflows — but Preetha believes the real transformation will come from rethinking business models altogether.
“We’ve been fitting AI into existing systems — now it’s time to think like an AI-native company.”
What an AI-Native Insurance Company Looks Like:
✅ Fully automated claims processing using AI-powered agents
✅ Predictive analytics to anticipate customer needs
✅ Hyper-personalized customer service and communication
✅ Dynamic pricing models based on real-time data analysis
Preetha emphasizes that this will require not only technological innovation but also a shift in organizational thinking.
“We need to stop anchoring ourselves to how things work today and start imagining how things could work in an AI-driven future.”
AI’s Potential to Move from Risk Management to Risk Prevention
AI’s ability to analyze vast amounts of data and identify patterns makes it well-suited for risk prediction. Preetha believes the next frontier in insurance will be using AI to prevent claims before they happen.
“Insurance will shift from just managing risk to predicting and preventing it.”
For example, AI could analyze wearable health data to provide early warnings about potential health issues, helping insurers and customers avoid costly claims.
“We already see this happening in workers’ compensation and life insurance. AI will give us the ability to reduce both the frequency and severity of claims.”
The Future of AI in Insurance
Preetha predicts that the future of AI in insurance will be defined by:
- Extreme Automation – AI-powered agents and robotic process automation (RPA) will handle routine tasks, freeing human employees to focus on complex issues.
- Hyper-Personalization – AI will enable insurers to provide highly customized products and services.
- Seamless Customer Experiences – Customers will engage with insurers through AI-powered platforms that anticipate their needs and provide proactive solutions.
- Ecosystem Integration – AI will connect insurers with healthcare providers, financial services, and other stakeholders to create a seamless customer journey.
“We’re just at the beginning of this AI transformation. The next wave will be even bigger.”
Conclusion: AI’s Transformative Potential
AI is already reshaping the insurance industry — but the most profound changes are yet to come. Preetha Sekharan’s insights underscore the importance of identifying the right business problems, building trust through user involvement, and reimagining business models from an AI-native perspective.
As AI becomes more sophisticated and integrated into business processes, the companies that succeed will be those that view AI not just as a tool, but as a foundation for new ways of working.
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