This article is sponsored by ActiveDEMAND. It is based on a discussion with Sean Leonard, CEO and Founder of ActiveDEMAND. This discussion took place on February 18th, 2025 at the SHN Sales and Marketing Conference.

Sean Leonard: I want to focus on helping everyone understand the key differences between machine learning and generative AI, and more importantly, the critical role of first-party data strategies in leveraging AI for better marketing outcomes. AI is everywhere—everyone is talking about it, using it, and figuring out how to make it work for their business. But in senior living, the reality is we all face the same challenges: staff shortages, the need to scale marketing and sales activities, building trust at scale, and making the most of limited budgets. I have yet to meet someone in marketing and sales with an unlimited budget, so we are all trying to achieve more with less.

Forward-thinking companies, or pacesetters, are already using AI to solve these challenges. There are many myths about AI, but the truth is that it can be leveraged to drive better marketing outcomes. A key part of this discussion is understanding the differentiation between machine learning—how to use it, where it fits—and generative AI, and how we can leverage both effectively.

Let’s start with generative AI. This is something everyone is now familiar with, as it’s integrated into many of the tools we use daily. Phones and computers are all marketed as “AI-enabled,” but what they are really referring to is generative AI. This technology is built on trained models that process content—whether it’s for understanding, summarizing, or creating new material. The power of generative AI is in its accessibility; people can use it today and see immediate benefits. For example, my VP of Sales and Marketing often uses AI-generated summaries to refine her emails, ensuring they are clear and effective. At its core, generative AI is about communication.

Some of the most well-known generative AI models include OpenAI’s GPT, DeepSeek from China, and Meta’s Llama. In marketing, generative AI is particularly valuable for content processing, a crucial factor in senior living. The process of searching for a better care environment for a loved one is both emotional and overwhelming. My wife is a physician in senior living, and my company, ActiveDEMAND, focuses on marketing automation for the industry. But when it came time to find care for my mother-in-law, I realized firsthand how complex and difficult it can be. In this kind of decision-making process, content plays a crucial role, making it an essential part of any marketing strategy.

If you are currently investing in marketing through paid leads or aggregators, you are relying on a transactional model—one that stops delivering results as soon as you stop paying. In contrast, content marketing is what I call “equity-based marketing.” It’s an investment that continues to pay off over time. Generative AI can significantly enhance content creation by helping marketers generate, refine, and personalize material at scale. Many chatbot providers, for example, use generative AI to create interactive experiences that answer questions in real time. The real magic of generative AI lies in its ability to personalize content at scale, making marketing efforts more efficient.

When considering generative AI in marketing, it’s important to remember that it is focused on improving outcomes, not predicting them. These models are pre-built and require training to deliver the best results. By incorporating an organization’s unique content, AI can become more effective at producing relevant materials. While generative AI enhances creativity and efficiency, it should not be seen as a replacement for human content creation. It is a tool that complements human efforts, not a substitute. One major pitfall of relying too heavily on AI-generated content is the risk of producing generic, uninspiring material. Additionally, if a model is not properly trained, the results can be inaccurate. AI tools must also be used ethically and legally, as they often pull from existing content unless trained with proprietary data.

Now, let’s move on to machine learning, which is what truly excites me. Machine learning is a more advanced form of AI that consumes large data sets, processes them, and identifies patterns that humans may not be able to see. People do not process data at scale, but computers do. Machine learning works by analyzing massive amounts of information, learning from it, and generating predictive models. Once a model is trained, it can be used to anticipate outcomes and optimize decision-making.

This technology is particularly valuable in senior living because pacesetters in the industry are collecting enormous amounts of data, even if they don’t realize it. With a solid first-party data strategy, organizations may already have years’ worth of valuable data that remains untapped. By leveraging historical data, companies can build predictive models that improve efficiency in helping families navigate the senior living decision-making process. The ability to anticipate needs and optimize marketing efforts is critical for better engagement and improved occupancy rates.

The key difference between generative AI and machine learning is that while generative AI focuses on content, machine learning is about data-driven decision-making. Machine learning allows marketers to identify trends, predict customer behavior, and determine the probability of prospects moving forward in their journey. Many organizations use machine learning to optimize sales engagement by analyzing customer interactions and identifying patterns that drive success. Unlike generative AI, machine learning improves over time, continuously refining its models to increase accuracy.

However, machine learning comes with challenges. Without a first-party data strategy, implementing machine learning can be time-consuming and complex. It requires data infrastructure, technical expertise, and skilled data scientists. Moreover, machine learning lacks creativity. It can analyze trends and patterns but cannot create compelling, human-centered content. Another challenge is transparency—machine learning operates as a “black box,” meaning that while it provides predictions, it can be difficult to explain exactly how it arrived at those conclusions.

So, which AI approach is right for your organization? The answer is both. Generative AI enhances efficiency in content creation and personalization, while machine learning drives predictive decision-making. Regardless of which AI tools you use, the most critical factor for success is data. Without structured data collection and analysis, AI won’t be as effective.

Senior living marketing is unique in that it involves multiple decision-makers within a family. Traditional marketing AI models focus on tracking individuals, but senior living decisions are often made collectively. A strong first-party data strategy must capture the full journey, tracking engagement across multiple family members. This means collecting data from website visits, ad clicks, phone calls, live chats, and more. Many AI presentations focus only on sales interactions, but without tracking the entire journey, valuable insights are lost.

To put this into real-world use, my company has developed an AI-driven approach that combines machine learning with generative AI to improve marketing outcomes. By analyzing content consumption patterns, we can determine which types of content drive the most engagement and move-ins. Our system ranks content based on its impact, allowing us to serve the right material to the right person at the right time. This method personalizes the customer journey, improving overall marketing effectiveness.

Ultimately, pacesetters in the industry are already using AI, whether through generative AI for content creation or machine learning for predictive analytics. If you don’t have a first-party data strategy in place, now is the time to develop one. AI is a powerful tool, but its true value comes from how well you use your data.

One question I often hear is about whether companies should prioritize generative AI or machine learning. The answer is that generative AI offers instant value, particularly in streamlining content creation, while machine learning requires a more structured data approach but delivers long-term predictive insights. Another common question is whether AI can personalize content for different members of a family navigating a senior living decision. The answer is yes—our machine learning model analyzes relationships and behaviors within the decision-making process to serve personalized content to each individual based on their needs.

Senior living is a unique industry, and the data systems used in traditional marketing may not always be a perfect fit. However, AI offers incredible opportunities to enhance marketing efficiency and effectiveness. If you take one thing away from this presentation, let it be this: AI is not just the future—it is happening now, and the companies that embrace it with a solid data strategy will see the greatest success. Thanks for your time, and I hope this discussion has been valuable.

ActiveDEMAND is specifically built for senior living, combining powerful marketing and sales automation to quickly increase occupancy. It tracks every interaction, including caregivers and family members, using integrated marketing tracking software to provide insights critical to move-ins. With unmatched CRM integration and a friendly support team of senior living experts, you’ll have everything you need to reach your goals. To learn more, visit: https://www.activedemand.com/.