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AI can help a home care agency use its workforce more effectively: it can support recruiting, training, scheduling, client-worker matching, and timely supervisor outreach. It cannot create more caregivers or fix low pay, unstable hours, burnout, or poor supervision. Its value depends on whether it removes operational friction while leaving consequential decisions and care relationships in human hands.
Why agencies are looking to AI
Caregiver shortages affect whether agencies can accept new clients, cover visits reliably, and give workers manageable schedules. The scale and causes vary by country and local labor market, so a UK provider survey should not be treated as a global measure. It does, however, illustrate the pressure agencies may face: in a Homecare Association survey of 307 UK providers conducted from 13 March to 12 April 2024, 48% said they could not meet current homecare demand. Among those providers, 84% cited recruitment difficulties as the primary reason. Respondents served 68,000 clients and employed more than 38,000 careworkers.
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AI is best understood as an operational support layer, not a workforce solution by itself. It may reduce time spent on repetitive coordination and help staff spot where action is needed. Whether that makes a particular agency more capable or improves retention depends on its processes, data, management, and working conditions—not simply on whether it has adopted AI.
Which home care workflows can AI support?
A government-hosted report, A New Era of Care: Reimagining Home Care Work with Artificial Intelligence, from the National Council on Aging’s Direct Care Workforce Strategies Center report series, describes examples across the employment and care-planning cycle. It presents these as possible uses, not proof that every tool produces better outcomes.
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| Workflow | Possible AI support | What still needs human judgment |
|---|---|---|
| Recruiting | Sort applications against stated requirements, coordinate interviews, handle routine applicant questions, or support preliminary assessments. | Assess motivation, interpersonal skills, values, role fit, and whether an applicant understands the work. Automated screening should not be treated as a reliable forecast of future performance or retention unless validated for that purpose. |
| Onboarding and training | Assign learning tied to a worker’s role, deliver or tailor lessons, track completion, and make information available on demand. | Check that training matches current local requirements and the worker’s actual duties. Requirements differ by jurisdiction and role. |
| Staffing and scheduling | Coordinate worker availability, qualifications, preferences, client needs, travel, visit continuity, and schedule changes. | Resolve trade-offs, respond to worker or client concerns, and decide when continuity or a person’s circumstances matter more than an optimization target. |
| Retention support | Analyze attendance, performance, survey responses, or scheduling patterns to suggest that a supervisor check in. | Find out what is happening through a respectful conversation. A data pattern is a prompt to ask, not a diagnosis or a definitive label about a worker’s intentions. |
Recruiting and applicant communication
Initial screening and interview logistics can consume staff time before an agency has spoken with a candidate. AI-assisted tools may handle routine steps and questions, allowing recruiters to spend more time discussing the work and understanding a candidate’s goals. The report names HireVue as an example of a platform used in hiring nurses, nursing assistants, and home health aides; that example is illustrative, not an endorsement or evidence that a particular tool selects effective caregivers.
Training and onboarding
Agencies can use technology to keep required learning organized and make reference information easier to find. The report names CareAcademy as an example in home-care training and support. The practical test is not just whether a system tracks course completion: managers need to verify that assigned content fits the position and applicable local rules, and that workers can get help when a lesson does not answer a real care question.
Scheduling, staffing, and matching
Home care scheduling involves more than filling open shifts. A useful system must work with availability, worker preferences and skills, client needs, travel distances, continuity, and changes to visits. The report describes Honor’s use of AI to schedule workers with clients. Honor has reported retention and satisfaction improvements, but those outcomes have not been independently verified and published in the reviewed report. Treat the example as a description of a use case, not proof of effectiveness or a product ranking.
For workers, scheduling is a visible part of the job experience. HHAeXchange’s 2025 survey of more than 8,200 caregivers found that 49.3% said technology helped most with scheduling and shift management. That is a vendor survey result, not an independent test showing that AI scheduling causes better retention.
Can AI improve caregiver retention?
It may help agencies notice and respond to problems earlier. For example, a pattern of canceled visits, difficult travel, or missed training could give a supervisor a reason to ask whether a worker needs a schedule adjustment, coaching, or another form of support. The useful outcome is the support that follows—not the existence of a risk score.
Retention analytics can also get the situation wrong. Incomplete records, biased historical data, or an agency’s misunderstanding of what a metric means can make an output look more certain than it is. Constant monitoring can damage trust. Workers should know what information is collected and how it is used; consequential inferences should be reviewable, correctable, and discussed with a person rather than treated as fact.
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The underlying conditions matter. The ACL-hosted report connects turnover with job quality, wages, supervision, burnout, and emotional stress. In its 2025 survey of 450 UK providers, conducted from 17 June to 22 July and covering providers serving more than 186,000 clients and employing over 135,000 careworkers, the Homecare Association argued that fragmented, lowest-price commissioning can contribute to travel time, irregular schedules, and job insecurity. It said the public sector funds 80% of UK homecare. These are the association’s UK policy analysis and survey context, not universal findings. Software cannot substitute for fair conditions or stable funding.
What do caregiver and agency surveys say about technology?
Survey results can indicate interest and perceived usefulness, but they do not establish that a specific AI product caused a change in staffing or retention.
Best Value
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- Doctor Visit & Lab Logs – Pre-fill your questions before appointments and record answers instantly. Keep all lab test results organized to monitor trends and share with your care team.
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- In the same HHAeXchange 2025 caregiver survey, 28.2% identified flexible hours as a top need, compared with 23.8% in 2024; 21.7% sought more training, compared with 14.5% in 2024. These vendor-reported responses point to needs that agencies can consider when designing schedules and support, not proof that a software feature meets them.
- The Homecare Association’s 2024 UK provider survey found that 44% of respondents reported lower careworker turnover than in the previous year. The survey does not attribute that change to AI.
- An AxisCare-commissioned survey, reported by CEO Todd Allen in 2026, found that 91% of respondents said their agency was already using or planned to use AI in home care operations management, and 94% of agencies already using AI reported tangible benefits. These are vendor-commissioned findings; the reported figures do not establish independent verification or a causal effect on retention.
HHAeXchange President Stephen Vaccaro described the company’s 2025 survey as showing that caregivers see technology “not as a barrier, but as a bridge to more compassionate care.” That is a vendor statement accompanying its survey, rather than an independent finding about every caregiver’s experience.
Will AI replace caregivers?
The use cases described in the ACL-hosted report are about supporting agency operations and workers, not replacing hands-on care or human supervision. Software can help coordinate a visit or surface information; it cannot supply the presence, judgment, communication, and relationship involved in caring for a person. Agencies should assess a tool by whether it gives coordinators and supervisors more room for meaningful conversations, conflict resolution, and relationship-building—not by assuming automation itself improves care.
Sayard Evans, PhD, Chief Executive Officer of Arkansas Support Network, cautions in the report: “What keeps me up at night is this false expectation that there’s some all-knowing computer system that’s always right. The more success we have with AI, the more I find myself reminding people: This is just a calculator—and you have to check the math. AI can only work with the information you give it. If the data are incomplete, biased, or poorly understood, the output will be wrong, even if it looks polished and convincing.”
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Start with a specific bottleneck and assess whether a tool addresses it without creating new burdens for workers, clients, or coordinators. The reviewed sources do not provide a comparative product test, so agencies should validate functionality and fit for their own operating model rather than infer that one named example is best.
- Name the workflow. Decide whether the problem is recruiting administration, training coordination, visit scheduling, worker-client matching, or supervisor follow-up. A broad promise to “use AI” is not a measurable operational need.
- Check the information and integrations. Confirm that the tool can use the agency’s relevant systems and data, and identify missing, outdated, or inconsistent records that could undermine its output.
- Include workers and clients in design decisions. Ask whether schedules reflect worker preferences and client needs, and explain what information the system collects, who can access it, and how it is used.
- Set rules for review and correction. For consequential recommendations, define who reviews the output, how a worker or client can challenge it, and how inaccurate information can be corrected. Test for errors and bias rather than relying on a polished interface.
- Measure operational effects and burdens. Track whether the tool reduces coordination work or improves a defined process, while also checking whether it adds monitoring, administrative steps, or schedule problems. Do not treat adoption or favorable vendor survey results as evidence of retention gains.
- Keep human support available. Decide how staff will follow up when a system flags a concern or cannot account for a person’s circumstances. An alert should lead to an appropriate conversation, not an automatic penalty or decision.
The right implementation makes technology accountable to the people doing and receiving care. If a tool saves time but undermines privacy, trust, or a worker’s ability to explain their circumstances, it is not solving the agency’s workforce problem.
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