The senior living and aging services sector is in the midst of a profound transformation. Providers are being asked to do more with fewer resources, while at the same time responding to growing resident expectations for quality, personalization, and responsiveness. In this environment, technology is no longer “nice to have.” It has become a core enabler of operational efficiency and resident well-being1.
Yet as organizations invest in new platforms for electronic health records, care coordination, communication, resident engagement, building management, and workforce optimization, they often discover a new challenge: data silos. Each application generates valuable information, but without integration, providers face a fragmented picture of residents, staff, and operations2. This makes it nearly impossible to unlock the full potential of analytics and artificial intelligence (AI).
The industry is on the verge of widespread adoption of AI tools, both generative (text, images, predictive analytics) and agentic (AI systems that can take action, such as scheduling or resource allocation). But AI cannot deliver meaningful outcomes without well-structured, centralized, and accessible data. The bridge between the promise of AI and the reality of care delivery in assisted living is data integration3.
The Data Problem in Aging Services
Most senior living providers today rely on a patchwork of technology systems. A typical organization may use:
- An electronic health record (EHR) for resident care documentation
- A customer relationship management (CRM) system for family engagement
- A workforce management tool for scheduling and payroll
- Smart building technologies to monitor safety, energy usage, and security
- Resident engagement apps for activities and wellness tracking
- Culinary software applications for menu management and point of sale
Each of these tools may perform well in isolation, but collectively they produce a deluge of uncoordinated data. Information lives in separate databases, formatted differently, and accessed through proprietary interfaces. This makes even basic reporting, such as linking staff scheduling to resident falls, painstakingly difficult1.
When data lives in silos, it is underutilized. The organization cannot easily aggregate or analyze across systems, and decision-makers lack the insights they need to adapt operations in real time. Worse, AI tools cannot be effectively applied to this fragmented environment4.
Why Centralized Data Is Essential for AI
Artificial intelligence depends on context-rich, well-organized data. A large language model (LLM) like those behind popular generative AI applications is powerful, but it must be able to access relevant information in structured ways to produce meaningful insights for senior living providers.
Centralizing data into a data lake house, a modern architecture that combines the flexibility of a data lake with the structure of a data warehouse, provides that foundation5. In a lake house model:
- Integration through APIs allows disparate enterprise applications to feed data into a common environment.
- Data harmonization ensures consistency in how information is labeled, stored, and accessed.
- Central governance provides a single point of control for privacy, security, and compliance6.
Once data is centralized, it becomes much easier to run analytics that inform staffing models, predict risks (such as falls or hospitalizations), and identify trends in resident well-being. More importantly, the same architecture positions the organization to adopt AI safely and effectively7.
Generative and Agentic AI in Assisted Living
The industry conversation around AI often emphasizes “generative” applications, tools that can summarize documentation, draft communication to families, or analyze resident trends in natural language. These capabilities are undeniably powerful, especially for overburdened staff.
But equally important is the rise of “agentic” AI. Unlike generative models that simply produce outputs, agentic AI systems can execute actions on behalf of users. In assisted living, this might include:
- Automatically adjusting staff schedules based on predicted acuity changes
- Triggering a maintenance work order when building sensor data signals a risk
- Recommending activity programming based on aggregated resident preferences and participation patterns
Agentic AI can serve as a digital assistant for administrators and frontline workers, freeing time for the human interactions that matter most. However, both generative and agentic AI require trustworthy, unified data to function responsibly2.
The Role of Model Context Protocol
A recent development in the AI ecosystem, known as the Model Context Protocol (MCP), offers a promising way to connect large language models to enterprise data sources. MCP enables LLMs to securely “read into” structured environments such as data lake houses without needing to replicate or expose sensitive information8.
In practice, this means a provider could ask an AI assistant a question such as: “What are the top three factors correlated with resident falls in our communities this quarter?” The AI does not rely on generic training data to guess the answer. Instead, through MCP, it securely queries the provider’s actual integrated datasets, producing an output grounded in real operational and resident information9 10.
This approach not only increases accuracy, but also helps ensure data privacy and compliance, two critical considerations in healthcare and senior living11.
Preparing the Industry for AI Adoption
For aging services organizations, the immediate priority is not to deploy the latest AI tool, but to ensure that their data is integration-ready. The steps include:
- Audit existing systems to identify major sources of data and current silos.
- Invest in API-driven integration that feeds a central repository, rather than relying on manual exports or custom one-off connections.
- Adopt a data lake house architecture to balance flexibility with governance12.
- Establish data governance policies covering privacy, access rights, and compliance.
- Experiment with AI pilots that demonstrate how integrated data can support decision-making, while building internal capacity to manage these tools13.
Organizations that follow this path will not only gain immediate value from improved analytics but will also be prepared to safely and effectively adopt AI tools as they mature.
Conclusion
Artificial intelligence holds great promise for assisted living, from reducing administrative burden to improving resident outcomes. But without integrated, centralized, and well-governed data, AI cannot fulfill that promise.
The sector’s real opportunity lies in preparing its digital foundations today. By moving beyond data silos, adopting a lakehouse approach, and embracing protocols like MCP, assisted living providers can ensure that when AI tools are ready to act, they will be acting on the right information.
This is not simply a technology challenge. It is a strategic imperative. The organizations that invest in integration now will be the ones best positioned to deliver personalized, efficient, and compassionate care in the AI-enabled future of aging services.
