The Business of Aging: How AI Is Reshaping Long-Term Care Economics

Long-term care in the United States isn’t just for grandma and grandpa anymore.

It’s true that when most people hear the words long-term care (LTC), they still think of nursing homes and folks in rocking chairs. The reality is that LTC is a sprawling industry. Estimates put its market size north of $1 trillion.

It touches everything from assisted living to at-home health aides, but it comes with a hefty price tag. Long-term care is also commonly much more of a personal expense than many people realize, meaning that insurance or Medicaid might not cover much.

As global populations age, the pressures on families, governments and the insurance industry to address the rising costs and complexities of long-term care have reached unprecedented levels.

“People are living longer, and family structures have dynamically changed,” Waterlily founder and CEO Lily Vittayarukskul told PYMNTS’ Karen Webster. “It’s the first time ever that we’ve had predominantly single generations under one roof. Families already take on 75% of care hours for their aging loved ones, but what happens when they can’t anymore?”

LTC is ultimately one of those problems everyone knows is likely broken, but no one really has a solution for. It’s a costly, complicated and emotional challenge that can frequently leave many families scrambling. It’s also one that Waterlily, which announced the closing of a $7 million seed round Wednesday (Jan. 29), is set on solving by using artificial intelligence to predict LTC needs up to decades before they happen.

Shifting From Reactive to Proactive Planning Can Transform LTC Industry

One of the biggest challenges in LTC planning is overcoming the reactive mindset.

“People typically start thinking about long-term care at 60 or 65, often after a triggering event like a parent’s health crisis,” Vittayarukskul said.

However, this is starting to change.

“Gen X, as the sandwich generation, is feeling the strain of caregiving and is beginning to plan earlier,” she said. “We even see some in their 30s who, after navigating a parent’s dementia or cancer, start to understand the financial and emotional toll and want to protect their wealth and their families.”

“The U.S. pension system can’t take this on,” Vittayarukskul added. “Insurance and financial services see this as both a challenge and an opportunity, particularly in markets like Asia where savings are high, but the concept of family protection is underdeveloped.”

One of Waterlily’s key innovations is its predictive analytics engine, which models individual care needs based on activities of daily living (ADLs) such as eating, bathing and dressing.

“We predict when someone will start needing help with these activities and how much care will come from family versus professionals,” Vittayarukskul said, adding that the platform’s ability to model these scenarios empowers users to make informed decisions.

“Families often decide to zero out the hours for spouses or children and rely more on professional care,” she said. “That’s when they see how much it will cost to protect their loved ones and their own well-being.”

By providing a granular breakdown of costs and care needs, Waterlily helps transform abstract financial planning into actionable insights.

“We’ve moved beyond averages,” Vittayarukskul said. “Our platform provides tailored predictions based on an individual’s unique circumstances.”

A Data-Driven Approach to Changing Aging’s Conversation

At its core, Waterlily’s platform is designed to shift how people think about aging.

“This isn’t just about cost,” Vittayarukskul said. “It’s about preserving dignity, ensuring legacy and fostering healthier family dynamics. Every household will face this challenge. We’re here to make sure they’re prepared.”

The key is in the platform’s data-driven methodology. The platform aggregates data from diverse sources, including government databases, academic research and private care provider records.

“We intentionally avoided starting with insurance carrier data, which tends to skew toward wealthier individuals,” Vittayarukskul said. “Our goal was to ensure equal demographic representation.”

This robust dataset allows Waterlily to provide highly personalized predictions.

“Instead of telling users they have a 70% likelihood of needing care for three and a half years, we can say, ‘You have a 52% likelihood of needing care starting at age 89 for 4.3 years,’” Vittayarukskul said.

These nuanced insights help families move past “unrealistic averages” and plan effectively, she said.

Looking ahead, Vittayarukskul said she sees potential for Waterlily to grow beyond LTC.

“Carriers are asking us to expand into life insurance, disability and critical illness products,” she said. “We’ve proven that we can drive ROI in a space that has been misunderstood and underserved.”

The company also aims to integrate more data sources, including electronic medical records and input from care physicians, to further personalize its predictions.

“Our goal is to make the user experience seamless and intuitive,” Vittayarukskul said. “We want users to feel like they’re making the smartest financial decisions possible.”

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