Executive Summary
Direct care workers assist older adults and individuals with disabilities with activities of daily living, as well as provide social and emotional support. With the number of older adults increasing rapidly and a persistent direct care workforce shortage, demand is outpacing the supply of workers. This leads to people going without the long-term care they require, forcing many family caregivers to drop out of the workforce to provide care, risking their own financial security and retirement savings. Clearly defined, managed, and implemented, artificial intelligence (AI) tools can be used to support the paid direct care workforce. These tools may have the potential to reduce caregiver burden, free up caregivers’ time to provide hands-on care, and support independence for individuals with long-term care needs.
While this issue brief focuses on how AI tools can support the paid workforce, these technologies may hold similar potential to support unpaid family caregivers.
While AI tools can support direct care workers, they will not replace the human connection of home care nor solve recruitment and retention challenges without other parallel investments in the direct care workforce. These investments include efforts to improve job quality, create opportunities for advancement, ensure adequate compensation, and provide other essential workforce supports. As the workforce explores adopting AI tools, policymakers should ensure sufficient safeguards to minimize risks to caregivers, older adults, and individuals with disabilities.
Introduction
The direct care workforce is the backbone of long-term care in the United States. Direct care workers assist older adults and people with disabilities with activities of daily living such as bathing, dressing, and eating, and instrumental activities of daily living like medication management. They also provide essential emotional and psychological support, often serving as one of the few consistent points of connection in their care recipients’ lives. This can reduce social isolation and loneliness while positioning them to respond to unaddressed health-related social needs, such as assisting with transportation and food access.
Who Are Direct Care Workers?
The Bureau of Labor Statistics groups direct care workers in three standard occupational classification codes: personal care aides and home health aides; certified nurse aides; and psychiatric aides. Additional segments of the direct care workforce include residential care aides and direct support professionals.
There are currently 5.8 million direct care workers nationwide, and there will be an estimated 14% increase in jobs by 2035 to meet the growing demands of an aging population. Over the next decade, the number of individuals over the age of 65 is projected to increase from 63 million to almost 76 million, a nearly 20% increase. Persistent challenges in recruiting and retaining direct care workers have led to workforce shortages, leaving many Americans unable to access services that enable them to live at home and avoid the higher costs of facility-based care. Despite their outsized role in the nation’s care infrastructure, direct care workers face low wages, limited training, and few opportunities for career advancement, which drive high turnover. The median annual turnover rate for nursing staff in nursing homes was 94% in 2017, while turnover in home care reached 80% in 2024.
AI offers a promising set of tools to help ease some of these pressures. By taking on administrative tasks, supporting patient monitoring, and assisting with treatment care plans, AI has the potential to reduce burnout and free direct care workers to focus on the relational and hands-on work that matters most to their patients. At the same time, sufficient guardrails are required to support thoughtful design and adoption that centers the needs of patients and direct care workers. These safeguards should include transparency in how systems work, human oversight, data privacy, and continuous monitoring of outcomes before, during, and after the rollout of AI tools.
AI and the Direct Care Workforce
As AI use grows, policymakers can begin to understand broad categories of AI applications and the benefits they can provide to the direct care workforce. To date, AI use in long-term care settings remains low compared to other industries. In the most recent Business Trends and Outlook Survey, just one-fifth of respondents in nursing and residential care facilities said they use AI for any type of function. Yet early evidence and examples point toward AI augmenting direct care workers in numerous ways. The survey also asks about anticipated future use of AI. At the end of 2025, less than one-quarter of respondents in the Ambulatory Health Care Services sector (another sector under which select direct care occupations fall) anticipated that they would be using AI within six months. By July 2026, that share climbed three points and was higher than average across all sectors.
Economists and others who study the effects of technology on labor markets often distinguish between automation and augmentation. Automation occurs when an AI tool completely takes over a task formerly performed by a worker. By contrast, augmentation occurs when an AI tool improves how a worker performs a task or enables them to take on new ones.
Due to limited available data and rapid emergence of new AI tools, it is unclear how AI will affect delivery and management of care services. However, early research shows that home health agencies are using AI to assign shifts, assist with hiring, process claims, and coordinate communication across care teams. Research also shows that some direct care workers rely on AI-enabled sensors, fall detectors, and predictive analytic tools in their daily work. Early evidence suggests AI will likely augment home care jobs rather than replace them outright largely due to the physical and interpersonal nature of these jobs. Instead, AI stands to take on the burden of many administrative functions while allowing direct care workers to spend more time with their patients. Direct care work requires human skills such as communication and time management, as well as physical ability to assist with mobility support and personal care—things AI cannot presently do. AI’s growing presence raises important questions for both the workers who use these tools and older adults and people with disabilities who receive care.
The effects of AI tools that augment direct care fall into three broad categories: improved quality of care, enhanced communication and coordination, and workforce alleviation. Together, these AI uses could help improve outcomes for patients and increase job satisfaction for workers.
- Quality of care: AI tools allow direct care workers and other providers to improve care through better collection and monitoring of health data.
- Coordination: AI can strengthen communication and coordination among health care providers, systems, beneficiaries, and their caregivers.
- Alleviation: AI use allows workers to spend more time on hands-on caregiving and less on administrative tasks.
Quality of Care: Better Health Monitoring, Data Collection, and Care Delivery
AI tools can improve the quality of care by streamlining and enhancing patient health data collection, providing consistent monitoring and real-time alerts. Many AI-powered wearables, such as smartwatches or other accessories, are designed to detect, monitor, and track vitals over time more easily and more consistently, which is especially important as certain vitals like temperature and blood oxygen saturation can vary greatly across time. This can save time for workers, ensure standardization across vital sign data, and augment care by tracking otherwise difficult-to-measure indicators that can inform care delivery decisions. For example, enhanced data collection can reveal hidden health risks associated with heart attacks such as blood flow, vascular stiffness, and sleep patterns, which can help to inform a patient’s care plan. AI-enabled smart monitoring tools can learn a patient’s movement patterns, detect variations in movement, identify fall risks, and alert caregivers.
Coordination: Improving Communication Across the Spectrum
Collaboration between providers, individuals requiring long-term care, and their families is often fragmented, leading to lapses in care, communication, or coverage. Individuals with long-term care needs often require complex care, and AI shows promise in improving care delivery and outcomes between providers and patients. Communication breakdowns between patients and providers, for example, can lead to adverse outcomes. AI tools help by using real-time captions, virtual assistants, and speech recognition to improve accessibility and reduce paternalistic decisions by caregivers, especially for clients with disabilities. Other AI tools can help develop emergency safety plans and direct communication with emergency responders. Voice dictation and ambient listening can further streamline care coordination by providing more detailed accounts of visits as care plans move across care teams and providers.
AI tools can also address siloed care and improve health outcomes by supporting communication across providers through a coordinated, team-based approach to health care delivery. Too often, communication and care among the care team is fragmented, to the detriment of patients. AI-powered wearables can help by enabling bidirectional communication between clinicians and direct care workers, including when data signals a change in a patient’s health status. In an emergency, for example, this data can support timely detection and seamless care between emergency care teams, the patient’s primary care provider, and the direct care worker.
AI-enabled platforms can also manage complex scheduling needs and connect families with caregivers without using traditional, manual matching processes, improving worker retention and client satisfaction. AI also has the potential to integrate with assistive technologies that already support the workforce. For example, AI-powered assistive robots can deliver cognitive impairment screening and early warning assessments, promoting timely intervention and reducing the burden on caregivers.
Alleviation: Allowing Workers to Focus More on Care, Less on Administrative Tasks
Direct care workers often shoulder extremely burdensome administrative tasks in addition to managing complex care needs. Caregiving for older adults and those with disabilities is multifaceted. Each patient may have slightly different needs and preferences, requiring direct care workers to implement individualized plans—for nutrition, for example—while attending to daily living activities, immediate needs, and occasionally emergencies. Direct care workers must also comply with administrative requirements for insurance companies and government programs. Here, too, AI platforms can help manage documentation, record keeping, and regulatory mandates. This can help direct care workers fulfill administrative responsibilities more easily while freeing up more time for patient care.
Individuals with long-term care needs often take multiple medications, and direct care workers help by providing reminders or assisting with self-administered medications. Managing complex medication regimens is also a persistent challenge for patients and direct care workers. Pill management systems are beginning to integrate AI to improve medication tracking for patients, reminding them when different pills need to be taken at distinct time intervals throughout the day. This is especially helpful as older adults, particularly those with multiple or chronic conditions or cognition issues, struggle with medication management and direct care workers must juggle many tasks at once.
AI platforms can help caregivers create personalized meals tailored to patient needs. By incorporating dietary restrictions, allergies, and health goals into meal plans, AI can help workers redirect their time to tasks that may be more difficult for AI to perform such as understanding cultural food preferences, building trust with patients, and seeing subtle changes in appetite or eating patterns. AI platforms such as CareSmartz360 can help alleviate the administrative burden as well by allowing direct care workers to focus more on people than on paperwork.
Risks and Obstacles
Alongside the current and future benefits of AI tools, there are risks. These include threats to privacy and consent, algorithmic bias, and compliance and liability concerns, particularly for a workforce made up disproportionately of women, people of color, noncitizens, and immigrants, many of whom rely on public assistance themselves due to limited economic mobility. While such risks are not unique to direct care workers, three are worth highlighting:
- Privacy and consent: Weak safeguards, particularly around AI tools that operate outside HIPAA, can result in exposure or misuse of sensitive client and worker data.
- Output errors: AI tools, particularly those in early stages of use, are vulnerable to false positives and negatives. Incorrect or even incomplete outputs can trigger conflicts with regulations and programs like HIPAA, Medicare and Medicaid, and labor or contractual agreements.
- Output bias: AI outputs are only as reliable as the data used to train them. Older adults, people with disabilities, and rural populations are generally underrepresented in training datasets, so tools built on unrepresentative data may perform worse for these groups.
Additionally, while they cannot replace human connection and care, AI tools could contribute to the erosion of human judgment, autonomy, and relationship-based care if not used as a support tool.
Such risks can be an obstacle to AI adoption, even if the benefits are clear. Nearly nine out of 10 direct care workers lack trust that their employers will support them in adopting AI. Clear demonstration of benefits—and commitment to use of AI tools for patients and workers—can help increase that trust. Cost, too, may prove to be a barrier to adoption. Innovations such as AI-enabled pill management systems remain financially out of reach for many people, and clinicians can face reimbursement hurdles when using these tools.
Despite these risks and barriers, experts across sectors see the potential for useful and positive applications of AI for both patients and direct care providers. Many older adults already use technology daily, and roughly a third believe AI could help them manage their homes. Yet a majority remain concerned that AI may erode human interaction and worry that ethical safeguards are not keeping pace with the technology’s rapid advancement. Direct care workers, meanwhile, express optimism about AI’s potential, though most know little about how the tools they already encounter on the job work or how their data is used. Expanded education and literacy training could help direct care workers more effectively use AI for administrative and other tasks.
As AI use expands, the direct care workforce will need parallel investment: stronger AI literacy training, clear career pathways for advancement, transparent processes to contest AI-driven decisions, and meaningful participation in governance structures. This builds on BPC’s prior work, which highlights that stagnant wages, limited training, and a lack of career advancement pathways are driving recruitment and retention problems that AI adoption alone will not solve.
What Policymakers Should Know
To date, states have taken the lead on regulating AI, although momentum may be gathering on Capitol Hill for the federal government to take a more active role, including in areas pertaining to the use of AI in direct care.
Congress has also taken steps to provide guidance for use of AI among caregivers. On general AI oversight, Sen. Mark R. Warner (D-VA) released “A Framework for America’s AI Future,” which outlines a legislative agenda to establish guidelines for AI and ensure the U.S. leads the world in responsible AI development. Included in Sen. Warner’s framework is the bipartisan Aging with Artificial Intelligence Act of 2026 (S.4916), which was introduced in June 2026 and works to study and address how AI systems and chatbots impact older adults, giving new insight into how the use of AI by caregivers will affect their patients.
Similarly, the administration has taken steps toward a comprehensive national policy on AI. In July 2025, the Trump administration released a three-pillar AI Action Plan. The plan calls for strengthening AI literacy, better understanding impacts on the labor market, rapid retraining and reskilling, and developing a skilled workforce. The federal government’s most prominent step toward addressing AI usage among the direct care workforce is the Caregiver AI Prize Challenge. The $2 million initiative backed by the Department of Health and Human Services funds and recognizes innovators developing AI tools to support family caregivers and employers in the delivery of direct care and builds on the efforts of the National Institute on Aging’s Artificial Intelligence Technology Collaboratories for Aging Research and Small Business Innovation Research portfolios. The program addresses AI risk and barriers by emphasizing affordable, AI-enabled solutions that meet care demands and highlights key principles such as data protection, accountability safeguards, and personalized care. The Administration for Community Living’s Direct Care Workforce Strategies Center is providing technical assistance and research to support responsible adoption of AI in direct care.
Based on BPC’s work on long-term care, the direct care workforce, AI, and the workforce more broadly, federal policymakers who want to support direct care workers’ safe and effective use of AI tools should consider the following opportunities:
- Oversight: Continued federal oversight can assist with development and adoption of high-value AI tools that are consistently monitored and evaluated, and that protect patient and caregiver data and privacy. Regulations can also support continuous and independent monitoring, which can reduce biases, limit errors, and ensure the technology works for all Americans.
- Narratives: Through their public remarks and engagement with institutions, policymakers can frame AI as a tool to support workers, fill in gaps in workforce shortages, and address barriers to accessing care. They can help ensure that AI use and deployment are directed toward augmenting workers, easing their burdens, and improving patient outcomes. Policymakers can also emphasize the need for co-designing AI to ensure the tools work for direct care workers and the patients they serve.
- AI literacy: As noted, direct care workers are generally optimistic about how AI tools can shape their jobs but lack sufficient knowledge about the tools, their use, their potential benefits, and the risks they carry. Policymakers can help public and private institutions raise overall AI literacy by closing gaps in training and digital access and supporting shared foundational knowledge.
- Economic mobility: AI alone cannot relieve the compounding pressures on the direct care workforce. To address persistent workforce shortages, policymakers will need to consider establishing tax-advantaged skills savings accounts and portable Learning and Employment Records, and expanding access to short-term credentials, which can help direct care workers promote their “human” skills and build their technical ones.
- Labor shortage: Direct care workers are often caregivers themselves in addition to their full-time jobs. Ensuring direct care workers have access to paid leave and child care can help increase and retain the workforce.
Conclusion
AI has the potential to address persistent challenges facing the direct care workforce by reducing administrative burdens, improving care quality, and creating more efficient coordination among caregivers, care recipients, and their families. As workforce shortages intensify alongside growing demand for care, technology can help providers make better use of limited resources while improving the day-to-day experience of direct care workers. However, AI should be viewed as a tool that supports, not replaces, the human workforce. Realizing this goal will require thoughtful implementation, appropriate safeguards, and meaningful involvement from workers, consumers, providers, and policymakers. Policymakers also have a key role in establishing standards around privacy, transparency, bias, and accountability while ensuring that smaller providers and underserved communities are not left behind as these technologies advance. Used responsibly, AI can become a critical component of a broader strategy to build a stronger, more sustainable workforce that is ready to meet the growing demand for care while preserving the dignity, independence, and preferences of the people receiving services.
Acknowledgments
BPC would like to thank our partners, including The John A. Hartford Foundation, for their generous support. BPC maintains strict intellectual independence across all projects and publications. The findings and recommendations expressed herein are solely those of the authors and do not necessarily represent the views or opinions of BPC’s founders or its board of directors.