John Atkinson on AxisCare’s Latest Research: Insights for Home Care Agencies
John Atkinson is Chief Technology Officer and Chief Operating Officer at AxisCare, where he leads technology development and operational strategy. With nearly a decade of experience leading software teams, he brings expertise in technology innovation, product development, and operational excellence, with a focus on helping home care agencies work more efficiently and improve care delivery.
Read the Interview:
After reviewing all 400+ survey responses, what result surprised you the most, and why didn't you expect it?
The level of optimism agencies already have around AI surprised me the most. We found that 91% of respondents are either using AI or planning to use it for operations, and 92% are confident it will deliver meaningful value. I expected strong interest, but I was pleasantly surprised that the industry is that far along in its thinking.
Home care has traditionally been careful about adopting new technology, and for good reason. Agencies are managing sensitive data, complex workflows, and deeply personal care relationships. What the research showed is that leaders are no longer asking whether AI belongs in home care, they are asking how to implement it while keeping human touch central, where it can create the most value, and how quickly they can get started.
Was there any trend, correlation, or interesting respondent feedback that didn't make it into the final white paper because of space or relevance?
One theme that came through was that agencies are not simply looking for more AI tools as they are concerned about adding another disconnected system to an already complicated technology environment.
That reinforced something we hear directly from customers: agencies want AI capabilities, but they want them to work together, use the context that already exists in their system of record, and fit naturally into the workflows their teams use every day. The appetite for AI is strong, but so is the desire to avoid further fragmentation.
Another interesting finding was the confidence gap between agencies that had already started adopting AI and those that had not. Agencies with more experience were significantly more confident in AI’s value. That suggests the uncertainty often decreases once teams see a practical use case working inside their business.
Did anything in this research change your AI product roadmap or priorities? If so, how?
The research did not change our overall direction, but it gave us greater confidence in the pace and breadth of our roadmap.
We were already focused on building AI across the core workflows of a home care agency, including growth, scheduling, communication, billing, compliance, and care oversight. The survey confirmed that agencies see opportunities across all of those areas, not within one isolated department.
It also reinforced the importance of adoption. Building a powerful AI capability is not enough. It has to be easy to learn, trustworthy, and embedded into existing workflows. That is why we are prioritizing AI inside AxisCare rather than asking customers to adopt a separate collection of point solutions.
How has your philosophy around building AI for home care evolved over the past two years?
Two years ago, many of the conversations around AI were focused on what the technology could do. Today, our focus is much more specifically on what it should do for a home care agency.
Our philosophy has become increasingly grounded in workflows, context, and measurable outcomes. We are not interested in adding AI simply because a capability is technically possible. We want to understand the challenges our customers face every day and determine whether AI can help them complete that work more accurately, efficiently, or consistently.
We have also become even more convinced that AI works best when it is embedded inside the platform where the work and data already live. AxisCare is already the system of record for our customers. By adding AI directly into those workflows, it can also become a true System of Action that helps teams understand what is happening and take the next step.
What's one misconception you consistently hear from home care leaders about AI that this research either confirmed or challenged?
A common misconception is that using AI requires an agency to take on an entirely new system or completely change the way its team works.
That does not have to be the case. In fact, we think the best AI experience should feel like a natural extension of the tools employees already use. Teams should not have to become AI experts or learn a completely different platform to benefit from it.
The research confirmed that agencies want the value of AI, but they are understandably concerned about complexity, implementation, and disruption. Our job is to remove those barriers by making AI practical, accessible, and integrated into their existing workflows.
The report emphasizes remaining 'human first.' What principles does AxisCare use internally to decide when AI should assist versus when a human should always stay in the loop?
We start by asking whether AI can reduce administrative burden without removing the judgment, empathy, or accountability that the situation requires.
AI is particularly valuable when it is analyzing large amounts of information, identifying patterns, automating repetitive work, or bringing an important issue to someone’s attention. For example, AI can help review care documentation, identify potential concerns, support scheduling decisions, or automate parts of a billing workflow.
But surfacing an insight is different from making a human decision. When something affects a client’s care, a caregiver’s employment, or a high-impact financial or compliance outcome, there should be appropriate human oversight.
The goal is not to remove people from home care. It is to give them better information and more time to focus on the work that requires a person.
What kinds of AI conversations are you having with agencies today that you weren't having even a year ago?
A year ago, many conversations began with, “What is AI, and should we be thinking about it?” Today, the questions are much more operational.
Agencies are asking which workflows they should prioritize, how they should measure ROI, how AI will use their data, and how different capabilities can work together across the organization. They are also thinking beyond basic content generation and looking at areas such as intake, scheduling, care oversight, phone coordination, billing, and compliance.
We are also seeing leaders think more strategically about platform decisions. They recognize the risk of adopting multiple point solutions that do not share data or communicate with one another. They want a connected AI strategy that can scale with the business rather than a series of isolated experiments.
For agencies currently in the 'evaluating' or 'piloting' stage, what's the biggest obstacle preventing them from reaching true transformation?
I think one of the biggest obstacles is expecting AI to transform the entire organization overnight. The agencies seeing the most success are taking an incremental approach.
Start with a workflow that creates a lot of friction today. Find an AI solution that meaningfully improves that process, implement it, measure the results, and then move on to the next opportunity. Over time, those improvements compound into real operational transformation.
It's also important not to let one disappointing experience define your view of AI. The technology is evolving incredibly quickly, and so are the solutions built on top of it. If a particular tool didn't deliver the results you expected a year ago, that doesn't mean AI isn't the right answer. It may simply mean there is now a better approach or a solution that's better integrated into the way your agency operates.
The agencies that will get the most value from AI aren't necessarily the ones trying to adopt everything at once. They're the ones that continuously identify opportunities to improve, implement thoughtfully, and build momentum one workflow at a time.
As AI capabilities continue to improve, what parts of home care do you believe should never be automated?
The human relationships at the center of care should never be automated.
AI can help a caregiver access information, reduce documentation time, or communicate more efficiently, but it cannot replace the trust that develops between a caregiver and a client. It should not replace compassion, personal judgment, or the ability to understand the emotional and situational context of someone’s needs.
The same principle applies to agency leadership. AI can surface information and recommend actions, but leaders remain responsible for decisions that affect clients, caregivers, and the culture of their organization.
The best use of AI is not to make home care less human. It is to remove the repetitive and administrative work that prevents people from spending more time on the human side of care.
5 years from now, what do you think people will look back on and realize we completely misunderstood about AI in home care today?
I think we will look back and realize that we thought about AI too much as a separate tool.
Today, people often discuss AI as something an agency needs to purchase, implement, or add on top of its existing technology. Five years from now, AI will likely be viewed as a foundational part of how modern software works. It will be embedded throughout workflows, helping people complete tasks, understand their data, and make decisions without requiring them to stop and open a separate AI application.
I also think we may realize that the greatest value did not come from replacing large numbers of jobs. It came from increasing the capacity of the people already doing the work. In home care, that means helping agencies grow, supporting caregivers more effectively, strengthening oversight, and allowing teams to spend less time managing administrative complexity.
The organizations that benefit most will not necessarily be the ones that adopted the most AI tools. They will be the ones that integrated AI thoughtfully into their operations, their data, and the way their people work.
