People + AI: Empowering the home‑based care workforce of the future [webinar recap]

AI isn’t coming for caregivers’ jobs. It’s coming for the friction that keeps them from doing their best work.

That was the central message of AlayaCare’s recent roundtable, “People + AI: Empowering the Home-Based Care Workforce of the Future,” moderated by Bhavesh Mistry, Senior Director of Product Marketing at AlayaCare. Bhavesh was joined by:

Tim Hanold – Healthcare growth and operations executive and former CEO of Care Advantage

Robert Kolodenko – SVP of Data and Technology, PurposeCare

Chase Potter – VP at AlayaCare, leading the delivery of agentic workflows across multiple EHRs and EMRs

Together, they unpacked what AI really looks like in home-based care today: where it’s already proving its value, how to roll it out responsibly, and what a truly AI‑enabled workforce will look like over the next 3–5 years.

In a dynamic roundtable discussion, AlayaCare brought together industry leaders to explore how artificial intelligence (AI) is reshaping the home-based care workforce. The conversation moved beyond the hype to examine practical applications, real-world challenges, and the future of AI-augmented caregiving.

---

The workforce reality: Demand outpacing supply

The panel opened with a stark assessment of today’s home care landscape. Tim Hanold framed AI not as a luxury but as an imperative for sustainability in a tight-margin business facing relentless pressure.

The numbers tell a compelling story: By 2030, one in five Americans will be over 65, with 80% managing multiple chronic conditions. Meanwhile, immigration headwinds, pipeline constraints, and competition from retail and hospitality sectors are squeezing the caregiver supply.

“This is definitely not a fad,” Hanold emphasized. “It’s really a must-have to unlock future sustainability and profitability.”

Robert Kolodenko reinforced this perspective from the provider side. At PurposeCare, the challenge isn’t just financial— it’s operational. “We have a lot more demand than we have supply,” he noted. “How can we get caregivers in the right place at the right time, taking care of very, very sick clients?”

Core philosophy: Practicing at the top of license

A central theme emerged throughout the discussion: AI should enable people to practice at the top of their license, not replace the human element that makes home care unique.

Kolodenko articulated PurposeCare’s guiding principle: “We want people practicing at the top of their license. We don’t want them filling out paperwork. We have clinicians that are doing paperwork in their car. How can we make their life easier?”

Chase Potter echoed this sentiment, noting that the workforce challenge has evolved: “More and more, we’re having these conversations with providers now. The workforce challenge isn’t just about finding more people, it’s about removing the friction that keeps the people you already have from doing their best work.”

Audience priorities: Where AI can make an impact

Two live polls during the webinar revealed clear priorities among attendees. When asked about their top AI priority:

• Reducing back office workload emerged as the leading concern, with many organizations drowning in administrative burden
• Making life easier for caregivers in the field followed closely behind
• A significant portion acknowledged they’re still figuring out where to start

A second poll on caregiver friction points showed documentation and visit notes as the primary pain point, with finding information and scheduling challenges also ranking highly.

Practical applications: Reducing back office friction

The conversation turned concrete when discussing where AI agents are already delivering value. Chase Potter identified three high-impact areas:

1. Vacant visit management and schedule changes
   Every agency has a vacant visit list that requires constant attention. AI agents can now understand the rules around visits, match them to appropriate caregivers based on skills and availability, and autonomously propose or execute reassignments, eliminating hours of manual scheduler work.
2. Visit verification
   Coordinators traditionally spend hours chasing missing timesheets and resolving location mismatches. AI agents can flag anomalies, auto-resolve straightforward cases based on configured rules, and surface only the true exceptions requiring human judgment. This dramatically reduces time spent on non-value-adding work.
3. Employee engagement
   Pre-visit engagement to confirm employees are on track, connecting with caregivers about availability, and managing compliance expirations are all high-touch activities. Agents can handle the heavy lifting of outreach, ensuring critical touchpoints happen without overwhelming office staff.

Real-world implementation: PurposeCare’s journey

Robert Kolodenko shared PurposeCare’s approach to AI adoption, which centers on tangible ROI measured across multiple dimensions, not just financial.

Visit verification as first use case

With nearly 3,000 clients in Indiana and 300 in Ohio, PurposeCare generates massive amounts of data. They identified visit verification as their initial AI pilot. By analyzing historical patterns, discovering that the same error appeared 32,000 times in one year with staff clicking the same resolution 31,900 times — they built a strong business case for automation.

The approach maintains transparency: rather than automatically resolving issues in a black box, the AI presents its recommended resolution with clear reasoning, giving staff the ability to review and approve.

Care plan generation

PurposeCare is partnering with AlayaCare on an AI agent that pre-populates care plans based on physician orders and OASIS assessments. Instead of clinicians manually searching through care plan libraries and clicking through 60-70 clicks per plan, the AI leverages existing data to generate an 80-90% complete care plan.

Clinicians retain full decision-making authority, reviewing and modifying the AI-generated plan as needed. This reduces clicks dramatically while allowing clinicians to care for more clients.

Early warning system: Canary

PurposeCare built an AI-powered early warning system called Canary that analyzes 24 hours of clinical context and integrates with state health information exchanges. Instead of clinicians manually reviewing every data ping from HIEs, the AI prioritizes alerts based on risk, allowing care management teams to focus on clients who truly need immediate attention.

The model was internally nicknamed “Nurse Emily” after PurposeCare’s VP of Quality and Compliance, who validated the AI’s clinical reasoning before rollout, a powerful change management strategy that built trust by demonstrating that the organization’s highest-level clinical authority had vetted the technology.

At the point of care: Supporting caregivers and clinicians

The discussion then turned to how AI can support frontline caregivers and clinicians during and around visits.

Short-Term: Co-pilots and better guidance

Chase Potter described current capabilities like AlayaCare’s Layla co-pilot, which summarizes complex client histories into digestible information before visits, provides language translation for caregivers, and clarifies documentation requirements so caregivers aren’t guessing about what needs to be captured.

These tools minimize the frustrating scramble before, after, and during visits, allowing clinicians to focus on delivering strong clinical care rather than navigating systems.

Medium-Term: Agents handling information flow

The next evolution involves agents managing routine follow-ups, reminders, and even generating care plans from already-captured documentation. This reduces the burden on clinicians and caregivers to act as routers between the home and the office for every minor update.

Potter emphasized that clinical judgment must remain paramount: “AI is there to remove friction, not to turn care into a set of automated checkboxes. If we do that right, AI becomes almost invisible at the point of care, it’s just making it easier to be prepared, present, and connected with your client.”

Building trust: Governance, safety, and transparency

A recurring theme throughout the discussion was that technology is only half the battle — trust, transparency, and effective change management are equally critical.

Operational and cultural guardrails

Tim Hanold stressed the importance of involving frontline staff from the beginning: “The end user, the front line — from caregivers to the care coordination team to clinicians — they need to be involved from the development of what we want to accomplish and why, through the integration, and then those QA loops.”

He recommended identifying strong peer leaders who can validate whether AI implementations work in practice. When respected frontline leaders buy in, their peers follow.

Hanold also emphasized clearly defining which tasks get fully automated versus escalated to humans. Clerical data handling and simple communications can be automated, while high-stakes clinical judgments, care plan decisions, and complex client or family issues must remain human-led.

No black boxes: The importance of observability

Robert Kolodenko returned repeatedly to a core principle: “There is no trust us at PurposeCare. If I were to go to a nurse and say, ‘This model’s perfect, trust us,’ I think we know what every nurse would say.”

PurposeCare established an AI steering committee including the CEO, COO, and VP of Quality and Compliance to prioritize projects with clear, measurable outcomes. They avoid pitching every possible AI idea to staff, instead focusing on high-impact, well-vetted initiatives.

Chase Potter outlined AlayaCare’s responsible AI framework, which includes:

• Guardrails: Human-in-the-loop reviews for higher-risk decisions, role-based permissions, and full audit trails
• Control: Configurable thresholds so agencies can define what agents can do autonomously versus what requires approval
• Clarity: Transparent reporting on what agents are doing and why, including Agent Insights to make behavior understandable and predictable for operations leaders and frontline staff

Providers should ask vendors direct questions about data residency, PHI protection, whether data is used to train models, and how agents are monitored over time.

Change management: Small wins build momentum

Tim Hanold cautioned against over-promising: “We’re not building a rocket ship to Mars today. Start with easy wins that create internal momentum.”

He emphasized that AI implementations must demonstrate tangible improvements — not just for the organization, but for caregivers and clients. Visible, consistent smaller wins build on each other and create the foundation for more ambitious initiatives.

Hanold also recommended conducting time studies with frontline staff to understand how they spend their time and energy, categorizing activities as low-skill/low-impact versus high-skill/high-impact. This data-driven approach identifies which tasks are prime candidates for automation and which require the human touch.

On training and adoption, he stressed the importance of methodical execution: “You can have speed to execution but also do it in a really intentional and deliberate way. For this to have real durability and legs to it, especially in larger organizations, it really has to permeate throughout the organization.”

Getting it right upfront is an investment, not a tax. Rushing implementation risks burning out staff, losing champions, and undermining the long-term potential of AI.

Looking ahead: The AI-enabled workforce of the future

As the discussion turned to the next three to five years, the panel painted a picture of an industry where AI becomes invisible infrastructure — essential but unremarkable.

Financial and operational success markers

Tim Hanold described success as the ability to increase volume of care delivered while maintaining or improving gross margins and operational efficiency as a percentage of revenue. This creates a virtuous cycle where capital can be reinvested into the business and frontline support.

“Three to five years from now, I don’t think we’ll necessarily be talking about AI per se, because it will just be,” Hanold noted. “Someone asked me what AI will look like in the future. I said air and water. It just is. It’ll be part of our function, part of our ecosystem of how we do things more efficiently.”

A realistic roadmap for organizations just starting

Chase Potter offered practical guidance for organizations that haven’t yet built a cohesive AI strategy:

• Don’t panic if you haven’t landed on something yet — only about 5% of AI projects make it past pilot, so you’re not alone
• Pick one or two high-ROI workflows and commit to them rather than trying to boil the ocean
• Start with well-understood, high-volume processes like visit verification, scheduling, or care plan creation
• Measure relentlessly — baseline current performance before turning on the agent, then track time saved, error reduction, and staff satisfaction

That evidence of clear ROI unlocks buy-in from leadership and frontline teams alike.

Advice for those feeling behind

Robert Kolodenko’s final counsel was reassuring: “This feels a lot like big data 10 years ago. Everybody was talking about it, nobody had any idea what it was. Dip your toes in and learn.”

He stressed finding a narrow problem with potential for real impact, taking a run at it, and iterating. “If you fail the first time, keep going. I can’t even name the amount of projects that I failed on that are now successfully in production.”

The key is to partner with vendors willing to iterate alongside you, building solutions that genuinely solve your organization’s problems rather than chasing AI for AI’s sake.

Key takeaways

1. AI is an imperative, not a fad. With demographic pressures intensifying and labor supply constrained, AI and automation are essential for the sustainability of home-based care business models.
2. The goal is empowerment, not replacement. AI should enable people to practice at the top of their license by removing administrative friction, not by replacing the human relationships that define quality care.
3. Start with high-volume, rule-based workflows. Visit verification, vacant visit management, and employee engagement are proven starting points with clear, measurable ROI.
4. Transparency builds trust. Black box AI won’t gain adoption. Staff need to understand what the AI is doing and why, with the ability to review and override decisions.
5. Involve frontline staff early and often. Successful implementations engage end users from development through integration, leveraging peer leaders to validate that AI works in practice.
6. Measure what matters beyond dollars. ROI includes employee satisfaction, error reduction, time saved, and the ability to serve more clients, not just financial metrics.
7. Small wins build momentum. Over-promising and under-delivering kills AI initiatives. Start with achievable pilots that demonstrate tangible value, then scale from there.
8. Iteration is expected. Most AI projects don’t make it past pilot on the first try. Success comes from learning, adjusting, and persisting through early failures.

The home-based care industry stands at an inflection point. Demographic trends and workforce constraints are undeniable, but so is the promise of AI to help providers do more with existing resources while preserving the human relationships at the heart of quality care.

The organizations that will thrive are those that approach AI thoughtfully, starting with high-impact workflows, building trust through transparency, involving frontline staff in the journey, and measuring success across multiple dimensions. As Tim Hanold suggested, the real victory will be when AI becomes invisible infrastructure, quietly enabling caregivers and clinicians to do what they do best: serve vulnerable individuals with skill, compassion, and dignity.

The conversation is just beginning, and the path forward requires partnership between providers, technology vendors, and frontline staff. But the direction is clear: people plus AI, not people versus AI.

Additional resources

For those interested in exploring these topics further, AlayaCare offers:

• AI Resource Hub: A collection of webinars, blogs, and practical guides on AI in home-based care

• AI in Home-Based Care Industry Report: Insights from over 100 leaders on planning, investing, and rolling out AI

The full webinar recording is available to everyone, offering an opportunity to revisit the detailed discussions and expert insights shared during this engaging roundtable.