2 posts tagged with "AI"

The 2025 Enrollment Technology Strategy Seminar (ETSS) brought together leading minds in benefits and enrollment technology to discuss automation, AI, and data-driven strategies shaping the industry's future. Over two days of insightful discussions, one theme stood out: The Power of Now—a call for immediate action to simplify processes, reduce inefficiencies, and enhance experiences for carriers, brokers, and employees alike.

At Uniblox, we left ETSS 2025 inspired and ready to take on the industry’s biggest challenges. From data best practices to AI-driven decisioning, here are our key takeaways from the event and how we’re helping move the industry forward.

Key Takeaways from ETSS 2025​

1. Data & Analytics: Crawl, Walk, Run​

A recurring question throughout the conference: Are we overloading the system with too much data? Organizations must first ensure data accuracy before scaling their analytics efforts. The key steps?

🔹 Crawl – Standardize data structures and clean up inconsistencies

🔹 Walk – Optimize data flows and minimize redundant inputs

🔹 Run – Leverage advanced analytics and AI to drive insights

2. Modernizing the Enrollment Process​

Enrollment shouldn’t be a once-a-year sprint—yet engagement outside of Open Enrollment is nearly nonexistent. Employees often make benefit decisions in under an hour with little guidance. We must do better.

Other challenges discussed:

🔹 Can billing and enrollment happen in parallel? Delays in OE create bottlenecks.

🔹 How long should enrollment take? One case study showed a two-week process—something that should be fundamentally optimized.

3. API vs. EDI: Are We Solving the Right Problems?​

The conference sparked heated discussions around API adoption versus legacy EDI integrations. A key takeaway:

🔹 API adoption is growing, but errors still persist—are we spending enough time fixing the root cause instead of just migrating to APIs?

🔹 Tracking is critical: How many companies track EDI vs. API error rates to see what’s actually improving?

🔹 Schema design matters: Bad data flows won’t be fixed by switching from EDI to API—errors must be tackled at the core.

One of the most important discussions at ETSS 2025 was around how systems handle bad data. Should data be rejected outright, or should errors be flagged while allowing workflows to continue?

4. AI Is Here—But Are We Ready?​

AI is not a future concept—it’s happening now. But while companies are increasing AI investments, many lack training programs and real-world use cases.

🔹 AI must be practical: “People like you bought this” algorithms haven’t driven real value in benefits selection. Instead, AI should focus on decision-making efficiency.

🔹 AI-powered automation should prioritize accuracy: AI alone won’t fix bad data—it must be part of a broader strategy.

🔹 Who’s responsible for AI-driven decisions? The relationship between data teams and service teams must be stronger than ever.

Final Thoughts: The Power of Now​

The benefits industry is evolving rapidly, and waiting for future solutions is no longer an option. The key takeaways from ETSS 2025 were clear:

💡 Data must be optimized, not just collected.

💡 APIs are the future, but only if we fix underlying data issues.

💡 AI will transform benefits—if implemented thoughtfully.

💡 Enrollment must be an ongoing experience, not just an annual event.

At Uniblox, we’re not just discussing these challenges—we’re actively solving them by:

🚀 Streamlining underwriting and enrollment through AI-powered automation

🔗 Optimizing API and EDI data flows to ensure seamless integration

📊 Providing real-time insights that drive meaningful improvements

⚡ Building a frictionless, eCommerce-like benefits experience

The time to act is now. Together, we can reshape the benefits industry and create a smarter, more connected future.

LIMRA Annual 2024 was full of AI buzz. Will AI replace jobs? Who is leading the pack in the insurance industry? Who is behind and how long can you wait to mitigate risks?

The answer: AI is a tool, not an employee. And the wielders of powerful tools built to solve niche problems typically succeed.

The State of AI in Insurance​

According to LIMRA, investment in AI is high and growing, with 100% of carriers experimenting with GenAI solutions and 75% with active or planned pilots and implementations of AI. However, this by no means suggests that AI adoption is mature. In fact, AI is in its infant stages when it comes to carrier adoption, with many challenges highlighted by prominent carriers such as change management, eradicating enterprise silos, data integrity, legacy systems, and more.

A central theme at the LIMRA Annual Conference was that AI is a tool, not an employee. Strategically, insurance executives are aiming to utilize AI tools to drive efficiency gains, better products, and growth within their organizations. History has taught us that innovation resulting in better products and outcomes for consumers leads to market growth over those who do not adopt new technologies. Hence, AI adoption can be seen as a growth opportunity to capitalize on what David Levenson (pictured), CEO of LIMRA and LOMA, exclaims is a massive unmet demand for products like life insurance in the US. Workplace benefits alone grew ~8% from 2022 to 2023 to a whopping $8.3B.

How to Build Powerful AI Tools​

One way to think about AI is by comparing it to a scalpel. Like a scalpel, AI is highly precise and powerful when used for a specific purpose. A scalpel can deliver intricate solutions and transformative results, but if you try to use it for a task it wasn’t designed for, you risk doing more harm than good. Similarly, AI is not a one-size-fits-all solution; it works best when applied to well-defined, specific challenges in insurance, like automating claims processing or improving underwriting accuracy.

1. Start with Specific Problems, Not with Flashy Technology​

It is no coincidence that the masters and news headliners of GenAI - OpenAI, for one - were simply not present at LIMRA. This is because the problems these large language models are solving are largely generic. However, in the insurance industry, it is important to first focus on the specific problems you are trying to solve for, rather than become lured by flashy technology built for the masses. Ultimately, the goal is to find the AI Tools that help you achieve your business goals and solve your specific problems, and not to pick up the largest tool in the toolbox and try to wield it. These problems include decreasing costs by driving efficiencies. In fact, in workplace benefits there is a 2x to 4x difference in cost between top performing carriers and bottom performing carriers in operations, underwriting, IT, and more.

As was eloquently stated in the workplace breakout session "At the Precipice of Possibilities: Innovating with Purpose" with David Payne, Michael Estep, and Sean O'Donnell: "Technology is enabling innovation, but it does not necessarily drive it." The leaders who wisely use these new technologies to solve their problems and grow their businesses are the ones who drive successful innovation, and that starts with defining your business goals and building the appropriate tools to achieve them.

2. Data Readiness​

Data readiness and compliance was a large open discussion at the conference. During the panel "Unlocking the Promise of AI: Navigating Data Readiness Needs," data readiness was quoted as "one of the biggest challenges to prepare insurance companies for AI." This is so critical because AI depends on data quality in order to become a powerful tool for your organization. Due to the general scattering and sometimes inconsistent data across different systems, carriers must strategize how to organize and collect data in ways that can be made accessible to AI models. Many also pointed out that compliance with data privacy is essential when using personal data to train AI models. AI applications in insurance cannot compromise customer trust, and mishandling sensitive data can lead to breaches or regulatory penalties.

One such way to handle data readiness that was discussed at the conference is to gather and carefully label data in a secure vendor platform designed to solve a specific problem. For example, Uniblox's new AI platform enables carriers to unlock new efficiencies by training AI models on existing group census data that is loaded into the platform and validated from day one. In this way, data quality, accessibility, and compliance remains high without having to start from scratch or extract extraneous data from a carrier's data lake, which may be hard to access and may contain undesirable or conflicting data that would run counter to your business goals.

3. Enterprise Architecture​

Ultimately, a general agreement amongst carriers and vendors at the conference is the desire for enterprise-grade, insurance-specific architecture as everyone continues to weigh the risks between speed and regulation in the world of AI. Working with vendors who can help you design enterprise-grade AI architecture to achieve your business goals is critical for keeping trust with all of your stakeholders and key to avoiding pitfalls from generic models. Those who design powerful AI tools built to solve niche problems with quality data will be able to harness the true power of AI and will become competitive leaders in the industry. Moreover, cybersecurity measures are critical to protect these systems from external threats, ensuring that AI models and the data they rely on are secure from potential breaches.

How to Use AI Tools Successfully​

Once you begin building and piloting new AI tools, using them is the only way to extract the real value from your investment. Step one was echoed often at the conference - change management. One executive wisely pointed out, "what is often understated is how technology that is brought along to aid certain departments does not always directly help the people who first start implementing the change." With this in mind, leadership during these times is critical to keep everyone motivated towards short and long-term business goals that help the entire company move forward and grow.

Additionally, as AI becomes more integrated into business processes, governance cannot be an afterthought. AI governance frameworks should establish clear policies for accountability, transparency, and bias mitigation, ensuring that AI solutions comply with legal requirements and uphold ethical standards. Several carriers at the conference discussed plans to create internal AI governance frameworks to ensure the responsible and compliant use of AI tools within their organizations.

Summary​

At LIMRA Annual 2024, AI emerged as the central theme, not as a threat to jobs but as a powerful tool. The future of AI in insurance isn’t about replacing human workers; it’s about solving specific, niche problems that enhance efficiency and drive growth. However, success lies in precision: those who apply AI like a scalpel—carefully, thoughtfully, and with a clear purpose—will be the ones who lead.

Data readiness, enterprise architecture, and AI governance are critical to building reliable AI tools that scale effectively and responsibly. The leaders who wield these tools with precision and focus will define the next era of innovation in insurance.