Romi Gubes is CEO and cofounder of Sensi.AI, the world’s first Agentic Operating System for senior care.
Your inbox is full. Every pitch hits exactly the right note. And the problems they solve are indistinguishable from one another. What is it? AI noise.
In recent conversations with home care agency owners, there’s one thing that’s coming up over and over again. It’s: “We’re drowning in emails from AI companies promising to automate everything. What should we actually invest in?”
Is there too much AI noise?
Technology can save healthcare $360 billion annually, but providers must now separate true AI innovation from mere marketing hype. The inevitability of adding a tool centers the conversation around what, when, and which (tool) to invest in.
“Everyone seems to be tagging AI into everything… trying to sift through all the noise of real solutions, or something where someone retrofitted a neat whiz-bang functionality and calls it AI.”
Juan Tuason, President and CEO of Paragon Home Care in McLean, Virginia, agrees: “Everyone seems to be tagging AI into everything… trying to sift through all the noise of real solutions, or something where someone retrofitted a neat whiz-bang functionality and calls it AI. Twenty twenty-six will bring a lot of noise. The challenge will be, how do we sift through it?”
He’s right. And the stakes for getting it wrong couldn’t be higher. Americans are living longer than ever: life expectancy in the US has reached 79 years. The highest recorded since tracking began in 1900. Yet 75% of Americans want to age in their own homes, not facilities.
Sensi’s own data reveals that 77% of falls happen when no one is nearby to help while demand for caregivers is projected to surge faster than nearly any other profession in the country. With a gap between the number of caregivers and the demand for their services, a solution is crucial. The last thing the senior care industry needs is more noise to manage.
Understanding what AI tool is right for your agency is critical. Here’s how you can decide.
1. How painful is the problem?
Before you use any new technology, consider the problem you’re solving and the business risk.
Consider the drain of off-hours calls. Beyond the expense, the inefficiency and missed updates create a constant “sinking feeling.” This stress doesn’t only hurt an owner’s quality of life; it also hurts the quality of care.
If a vendor’s pitch doesn’t map directly to a problem that keeps you up at night, stop there. Real solutions start by solving a pain point.
2. Will this actually improve outcomes, or just replace a person?
This is the most important question to answer and often the hardest to get a vendor to reply to.
Automation replaces roles; intelligence elevates them. When we built Sally, our growth agent, the bar wasn’t “saving time.” The goal was to deliver fast, consistent, and empathetic responses. We focused on improving lead-to-assessment conversion. It’s the metric we used to judge success. If our aim was to replace a human, we would have failed.
Home care leaders want to use AI for better efficiency, but tech debt and staff training remain major roadblocks. If any AI tool creates more friction than the problem it solves and onboarding is cumbersome, that tool isn’t for you or your team.
Ask vendors what outcome they optimized for? What’s their key performance indicator? If the answer is vague, that’s your answer.
3. Are you building a strategic stack, or a mess?
Your agency has dozens of workflows. To evaluate your tech stack, consider how you’re optimizing for processes like: intakes, scheduling, caregiver communication, compliance, documentation, and family updates. Ask yourself how many tools does your tech stack need? And, do they need to integrate with each other? Be aware of tool creep because we know each tool can solve a discrete problem, but what happens if you end up with 10 tools from just as many companies?
It’s clear agencies want tools that solve day-to-day problems, not technology for technology’s sake. But solving problems in silos creates a different kind of problem. Data that lives in only one system can not aid in efficiencies across an organization. A caregiver recognition program that can’t see scheduling history or point-of-care data is flying blind.
Before adding any new tool, ask what it connects to. A technology strategy built on solutions in silos is a liability.
4. Is this a company you can rely on?
A well-funded trio of engineers can produce a demo in a week. But will the company be around in two years? Do they have real customers, a track record, and a product they’re constantly improving?
When you choose a company to work with, you’re doing more than just buying a product from them. You’re investing in a partnership because in the care industry, continuity is essential. The standard should be the same standard you have for choosing a caregiver for a client. Make sure the company you work with has the background, references, and a proven record of showing up.
5. Does this company have a real data advantage?
AI without context is weak. Most vendors gloss over this in their pitch.
Healthcare AI is growing at a 36.8% compound annual growth rate, but that tells you nothing about any individual product. A model trained on generic healthcare data behaves very differently from one trained on home care interactions, senior behavioral patterns, and the operational rhythms of non-medical agencies.
Ask the vendor what data was this trained on? How many clients do they have? Of those, how many are seven-day users? How many hours of real-world home care data was it trained on? How long has the product been in production?
“That’s what drew me to Sensi — real data, been around for years, and clients that speak to actual success.”
Data that a care agency can act on and share with families makes for a product home agency owners can use immediately. Juan from Paragon Home Care says, “That’s what drew me to Sensi — real data, been around for years, and clients that speak to actual success.”
Consider that the best AI in this space has been learning and improving for years.
6. Was it built for home care?
The best solutions feel like they’re built for you, by people who understand what it means to manage a no-show at six in the morning. Or, what it takes to reassure a stressed family member who can’t get their elderly parents on the phone.
Many horizontal software platforms, cloud-based applications designed to address common, universal business functions, now claim AI capabilities. Some deliver. But there is a difference between a company that serves home care as equally as it serves dozens of other verticals and one whose entire product strategy is for home care. It’s the difference between a tool your team can immediately see the ROI of and one it abandons, soon after the onboarding call.
According to Gartner’s Hype Cycle for AI, the category is full of inflated expectations. AI is now at the point that it drives experimentation and deployment across all business software. For agency owners, it means the market has tools not built for you, repositioned to look like they are.
Focus on using products built for home care. A product made for home care agencies is what creates reliable outcomes.
Identifying real AI value
AI has the potential to reduce hospitalizations, support caregivers, and give seniors greater independence. Technology-enabled care intelligence is supporting seniors in their homes, across the country. Unfortunately much of that potential is being drowned out by the AI noise from vendors who are relying on a marketing play, not innovation.
Agency owners deserve better. Seniors and their families deserve better. The questions I’ve shared won’t protect you from every bad pitch, but it will protect you from a vendor who has a product in search of a problem. It’s disappointing when all you want is an answer to yours.
Cut through the AI noise. Invest in technology to support the care outcomes you want to see.
FAQs
How do I know if an AI tool is built for home care or just rebranded general software?
Ask the vendor two questions: What vertical was this product originally built for, and what percentage of their customer base is home care agencies? A tool built specifically for home care will reflect the realities of the environment: mobile caregivers, variable visit schedules, unsupervised seniors, and family communication needs. A horizontal platform repositioned for home care will require your team to work around its limitations. The clearest signal is whether the vendor can speak fluently to problems like caregiver no-shows, off-hours family calls, or point-of-care documentation without prompting. If they can’t, the product wasn’t built for you.
Six questions cut through most AI noise in home care: First, what specific problem does this solve, and how painful is that problem for my agency? Second, does this improve outcomes or just replace a person? Third, what does this tool connect to in my existing tech stack? Fourth, how long has this company been in production with real home care customers? Fifth, what data was this AI trained on, and how much of it is specific to home care? Sixth, what is the key performance indicator the vendor optimized for? Vague answers to any of these are a signal to walk away.
Home care AI should be trained on data that reflects the actual environment it operates in: senior behavioral patterns, in-home audio or activity data, non-medical home care workflows, the operational rhythms of agencies managing mobile caregivers and variable visit schedules.
Generic healthcare data, clinical EHR data, or hospital-derived datasets behave very differently from data sourced in a home care context. When evaluating a vendor, ask specifically: how many hours of real-world home care data was the model trained on, how long has the product been in production, and how many of their customers are active seven-day users. Years of continuous, home-specific training data is a meaningful competitive differentiator.
