Lily Vittayarukskul shares her remarkable journey from working at NASA in her teens to founding a company that innovates with AI to transform long-term care planning. We explore why long-term care remains one of the most misunderstood and underserved areas in wealth management, despite being one of the biggest retirement risks. We break down how long-term care works, who needs it most, the pros and cons of self-funding versus insurance products, and why many families fail to plan until it’s too late. Today we discuss...

A New Approach to Prepare for Long-Term Care Planning 

Long-term care (LTC) planning is the financial planning equivalent of an elephant in the room. Data from LIMRA reveals that only 3% to 4% of Americans aged 50 and older have dedicated long-term care insurance. We know the risk is real, the costs are staggering, and the need for planning is acute, but families and even their professional advisors often freeze, procrastinate, or ignore.

LTC planning is uniquely burdened by three things that derail rational financial decision-making: emotional heaviness, overwhelming complexity, and a profound lack of personalization

To most families, the question of LTC feels like a vague, terrifying "what-if" about a future no one wants to imagine. Unlike retirement savings, where the goal is a desirable outcome, LTC planning forces a confrontation with dependency and mortality.

I saw this emotional and financial devastation firsthand when my aunt, who was instrumental in raising me, became ill with terminal cancer. Beyond the shock of my aunt's diagnosis, the show and realization that the U.S. healthcare system was abandoning my family at our most vulnerable. Health insurance doesn’t cover custodial care. My family was forced to leave work and school to take on the physically and emotionally demanding role of full-time caregivers. The financial and emotional toll left us bankrupt and with deep, unhealed familial rifts.

That experience was the genesis of my mission to build Waterlily. I became obsessed with moving beyond generalized national statistics and into personalized education and planning that makes the process of LTC planning feel urgent, relevant, and actionable for every family.

The typical advice for LTC planning is surface-level, built on generalized averages:

• Rough timeline: Estimate when you might need care based on vague family history.
• Future cost considerations: Call a few local facilities and apply a blanket national inflation rate.
• Weigh available options: See if Social Security, government programs, personal savings, or a policy will sufficiently cover LTC costs.

This generalized approach is why so many families do nothing. They look at the national average of, say, three years of care, then see the average cost of a private room in a nursing home is $127,750 per year, and the $383,250 figure feels both distant and hypothetical. Without a concrete plan, inaction becomes the default, until a crisis occurs. As a result, families are left with extremely limited options, massive financial strain, and the desperate scramble to find support.

The biggest flaw in traditional LTC planning is its reliance on the average person. But the "average person" doesn't exist.

Your long-term care journey isn't a national statistic; it's a deeply personalized outcome driven by your specific health history, lifestyle, available family support, geographic location, and local care costs. Planning based on general data is like planning your retirement as a resident of New York City using the median home price in rural Iowa. It misses the mark entirely.

This is where the technological advancements of Artificial Intelligence (AI) are fundamentally changing the conversation.

The core problem to solve is the prediction. Instead of relying on guesswork, we can now leverage data science. My company, Waterlily, for example, built a platform that uses machine learning algorithms powered by over 500 million data points, including detailed health histories, family caregiving availability, hyper-local cost of care data, and inflation trends, in order to generate a personalized LTC care plan. Because LTC isn't a single, fixed cost, we model the progression of care across three graduating phases: early, moderate, and full care. This is important when it comes to a financial care plan because a person's need for basic assistance in the "early" phase and their need for extensive support in the "full care" phase, which often occurs years later, will have significantly different expenditure levels. This fluctuation in cost, dependent on the phase of care the individual is in, significantly impacts total lifetime projections and, ultimately, the optimal funding strategy.

This blueprint transforms a hypothetical conversation into a data-driven planning session. It predicts:

1. The Timing: When an individual is most likely to need care.
2. The Level: What kind of support will be required (home health, assisted living, skilled nursing).
3. The Cost: The specific, localized cost estimates for that level of care.
4. The Impact: The potential stress and financial burden on family members.

With this level of precision, the vague "what-if" about a $383,250 national cost is replaced with a tangible, specific projection, for example: "Based on your data, you have an 44% chance of needing 3.9 years of care, most likely divided into 19 months of early care, 16 months of moderate care, and 12 months of full care, which, based on today’s costs in your ZIP code and adjusting for healthcare inflation over the next 42 years, is projected to cost $3,381 per month in early care, $5,355 per month in moderate care, and $11,136 per month in full care, starting from age 83.”

Once a family has a clear, data-driven care plan, the rest of the planning process snaps into focus. This data allows for the kind of mathematically complete analysis that was previously impossible.

Historically, families and advisors have had to rely on cumbersome illustrations and manual comparisons when evaluating insurance products. There are hundreds of thousands of different policy configurations, variations in benefit periods, daily maximums, inflation riders, and elimination periods. Analyzing these manually for the optimal fit is a process that would literally take years to optimize, so most planners take a stab at a small handful and call it good.

Our AI technology can cut through this complexity and solve for the mathematically optimal options. Waterlily systems can instantly analyze hundreds of thousands of policy permutations against a client’s unique, AI-predicted care needs to find the mathematically optimal solutions of the policies with the highest expected Return on Investment (ROI) for the care scenarios that matter most to that individual. This allows a family to see precisely how a policy would perform against their projected cost, rather than a generalized carrier illustration.

Waterlily AI insights can help individuals and their advisors:

• Optimize funding: Evaluate options outside of the default path of self-funding, which hold the risk of derailing retirement plans and depleting legacies. Instead, they can explore an optimized mix of strategies, whether that’s traditional LTC insurance, hybrid life policies, annuities, or short-term care options combined with a source of self-funding for a well-rounded solution.
• Streamline execution: Once a solution is identified, technology can be used to pre-fill lengthy, complex carrier applications and even flag potential underwriting hurdles in real-time, compressing a months-long process of guesswork and back-and-forth into a single, confident planning session.
• Preserve relationships: The most important benefit of early planning is protecting family relationships. Having a financial and logistical plan removes the pressure of caregiving decisions during a crisis, allowing adult children to focus on being a supportive family member, rather than a full-time caretaker.

And perhaps most importantly, it helps overcome the emotional resistance that has long stalled LTC planning that we all should be incorporating into our long term financial outlook. Even with the most powerful AI tools, LTC is still a difficult conversation, but this technology is assisting to make those conversations more impactful, early, and effective.

Lily Vittayarukskul is the CEO and Co-Founder of Waterlily, a company that uses AI to project an individual’s future long-term care needs and helps build a personalized plan.