100. A Conversation with AI

Our 100th episode! In this forward-thinking episode of Raising Tech, host Matt Reiners sits down with a unique guest, AI itself to explore the evolving role of artificial intelligence in the senior living industry. Together, they cover how AI is enhancing daily life for residents, reducing staff burnout, and helping operators make smarter decisions with unified data and predictive tools.

Listeners will walk away with a deeper understanding of how to approach AI implementation thoughtfully and ethically. Whether you're solving a specific operational challenge or exploring long-term innovation, this episode offers practical insights, real-world use cases, and a look into the near future of tech-enabled senior living.

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Transcript

Matt Reiners: [00:00:00] Welcome back to Raising Tech. I’m your host, Matt Reiners, and today we’re flipping the script. I’m sitting down with a guest unlike any other—AI itself. We’re diving into how artificial intelligence is reshaping senior living, from operations to care models and beyond. AI, welcome to the show.

AI: [00:00:22] Thanks, Matt. It’s great to be here—virtually of course. I’m excited to be part of the conversation about senior living, especially at a time when technology is finally catching up to the needs of aging adults and the communities that serve them. From staffing shortages to resident engagement and everything in between, there are so many ways I can help. Let’s get into it.

Matt Reiners: [00:00:43] Love that energy! Let’s start at the top. What are some of the biggest ways you’re being used in senior living today?

AI: [00:00:50] Absolutely. Let’s look at it across a few domains.

First, there’s resident-facing AI. That includes voice assistants in resident rooms that answer questions like “What’s on the lunch menu?” or “Is there yoga this afternoon?” These tools reduce the burden on front desk staff while empowering residents to be more independent. It also includes predictive tools that detect patterns in residents’ behavior—like changes in sleep or mobility—that could signal health issues before they escalate. One operator used smart sensors and AI algorithms to reduce falls by 35%.

Second, we’ve got AI tools for staff. These include chatbots trained on internal manuals to answer HR or maintenance questions, or large language models summarizing incident reports to help clinical teams prioritize care. Instead of digging through files or paging supervisors, staff can get clear, real-time answers.

And third, leadership-level AI—dashboards that synthesize data across departments, helping operators predict trends in occupancy, staffing needs, or even resident satisfaction. Some forward-thinking communities are even using AI to optimize meal planning based on resident preferences and dietary data, reducing waste and improving quality of life.

So, whether you’re a caregiver, an executive director, or a resident, there’s a place where AI can make your day better.

Matt Reiners: [00:02:15] That’s a pretty wide spectrum. It seems like the potential is huge. How are communities starting with AI?

AI: [00:02:23] Most successful implementations start with a problem, not a product. The communities that thrive with AI are the ones that ask, “What’s taking up too much time?” or “Where are we dropping the ball?”

Let’s say a provider wants to cut down on after-hours calls to managers. Instead of hiring more staff, they could use a retrieval-augmented chatbot trained on internal SOPs to answer questions about policies and procedures. The staff stays supported, the manager gets a break, and the organization saves money.

Others might start with resident experience. For example, one independent living community rolled out voice-enabled room assistants. Within six months, calls to the concierge dropped by 40%, and resident satisfaction scores went up. But here’s the key: they trained the assistants on the actual community calendar, not a generic script. AI only works when it’s grounded in your data.

It’s also important to start small. Don’t launch ten pilots at once. Start with one pain point, one tool, and one department. Measure the impact, get feedback, and build from there.

Matt Reiners: [00:03:37] You mentioned data a few times now. It sounds like that’s a core ingredient. Can you dig into that?

AI: [00:03:43] Data is everything. It’s the lifeblood of AI.

In senior living, data is collected constantly—vital signs, activity logs, medication adherence, dining choices, you name it. But historically, that data has lived in silos. The CRM knows what activities a resident likes. The EHR tracks their health. The POS logs meals. But these systems rarely talk to each other.

That’s where I come in. AI can synthesize these disparate sources into a single narrative. Imagine knowing that a resident skipped three meals last week, walked less than usual, and suddenly started calling for assistance more at night. Any one of those signals could be noise. But together? They could indicate a cognitive or physical decline. That’s insight.

Now multiply that across hundreds of residents. Suddenly, you’re not reacting to incidents—you’re preventing them. That’s the future: moving from episodic to predictive, and from reactive to proactive care.

Matt Reiners: [00:04:52] That proactive approach sounds like a game changer. But there’s got to be challenges, too. What gets in the way?

AI: [00:05:00] Great question. And yes, there are definitely some hurdles.

The biggest? Culture and expectations. AI is still new for many providers. Some think of it as a silver bullet that will fix everything overnight. Others are afraid it will replace jobs or create privacy risks. Neither extreme is true.

What’s needed is alignment. Leaders need to define what success looks like—whether it’s improving resident engagement, reducing overtime, or cutting down on fall-related hospitalizations. Staff need to know how AI will help, not hurt, their day. And residents and families need transparency about how data is used and protected.

Another common barrier is infrastructure. If a community’s Wi-Fi is spotty, or their systems aren’t cloud-based, it can be hard to layer AI on top. But that’s changing. With investments in broadband and more open platforms, the foundation is getting stronger.

The last challenge? Patience. AI is powerful, but it’s not plug-and-play. Like any meaningful change, it takes time, training, and iteration. The best results come when operators treat it as a long-term partnership, not a short-term tool.

Matt Reiners: [00:06:18] That’s incredibly helpful context. So, let’s look forward. What does the future of AI in senior living look like?

AI: [00:06:26] The future is exciting—and much closer than people think.

I see three major shifts coming:

First, true personalization at scale. Imagine each resident getting tailored activity suggestions, meal plans, and wellness prompts—all powered by their preferences, health history, and engagement patterns. AI could nudge someone to hydrate more on a hot day or suggest a cognitive game when early signs of decline appear. We’re already seeing hints of this in pilot programs.

Second, care team augmentation. AI won’t replace caregivers, but it will supercharge them. Think: ambient sensors that track motion and respiration without cameras, digital scribes that write notes during visits, and dashboards that flag high-risk residents for proactive check-ins. This won’t just ease burnout—it will elevate care.

And third, AI-driven operations. Staffing schedules optimized in real-time. Supply orders based on actual consumption. Marketing analytics that predict when someone’s ready to move in. Even financial forecasting based on census trends and reimbursement data. AI can touch every corner of a senior living business, not just clinical care.

We’re at the start of a new chapter. Communities that embrace this shift—carefully, ethically, and collaboratively—will be the ones who thrive.

Matt Reiners: [00:07:50] Let’s talk about ethics for a second. How should providers think about AI and privacy?

AI: [00:07:56] I’m glad you brought that up. Ethical AI is non-negotiable.

It starts with transparency. Residents and families should always know what data is being collected, why, and how it’s protected. Clear consent, plain-language policies, and open communication go a long way.

Next is data security. Operators need to work with vendors who follow healthcare-grade security standards—HIPAA, SOC 2, encryption protocols. Ask the tough questions.

And then there’s equity. AI must serve everyone. That means training algorithms on diverse populations, designing interfaces that work for low-vision or low-mobility users, and making sure tech enhances—not replaces—human touch.

Ultimately, ethical AI isn’t just good practice. It’s a trust-builder. And in senior living, trust is everything.

Matt Reiners: [00:09:02] Couldn’t agree more. Before we close, what’s one piece of advice you’d give to any operator curious about starting their AI journey?

AI: [00:09:10] Start with listening.

Listen to your staff—where are they overwhelmed? Listen to your residents—what do they wish was easier? And listen to your systems—what data are you already collecting but not using?

Then pick one meaningful problem to solve and solve it well. Whether that’s reducing call bell response times, simplifying documentation, or improving fall prevention, one win builds confidence and momentum.

You don’t need to be a tech expert. You just need to care deeply about the people you serve and be open to doing things differently. The rest? I’ve got your back.

Matt Reiners: [00:09:50] Spoken like a true partner. AI, thanks for joining us today and offering such a clear-eyed, inspiring look at what’s possible.

AI: [00:09:58] Thanks, Matt. I’ll be listening—and learning—as always.