When people ask Siva Juturi how he became Chief Customer Officer at AutomationEdge, he often smiles and tells them it was a happy accident.
Juturi’s path began with a tech-focused foundation. After earning a master's degree in computer science from Grand Valley State University outside Grand Rapids, Michigan, he dove into the consulting world, where his passion for technology and innovation found a home. During those early years, he worked with various organizations, eventually finding his way to product companies like BMC Software, known for their IT help desk solutions.
He then transitioned from consulting to product development, discovering how to blend his consulting experience with product strategy, leading to his current role at AutomationEdge. But the real transformation came about 5 years ago, for himself and the company, when AutomationEdge decided to focus heavily on the at-home care space, an industry with “immense potential for innovation,” he said.
“Today, you might call me the Chief Home Care Officer at AutomationEdge (though my official title is Chief Customer Officer),” he said. “It's a role that brings together everything I'm passionate about: technology, innovation, and making a real difference in people's lives.
“The journey from IT consulting to healthcare innovation might seem unexpected, but it's been incredibly fulfilling. Every day, I get to see how our AI and automation solutions are transforming home care delivery and improving patient outcomes. That's what makes this accidental career path feel so intentional after all.”
We spoke with Juturi, a featured speaker at the Home Care Innovation Forum, about how AI will be leveraged in the future in at-home care, where providers can best utilize this technology in their operations, and why those on the fence about AI’s adoption need to embrace it with open arms.
This transcript has been edited for length and clarity.
While return on investment has been a core industry metric for decades, at AutomationEdge, we've moved beyond the traditional focus on just cost savings.
Instead, we look at ROI through a broader lens that includes patient experience, clinician satisfaction, and what we call 'mean time to respond,' or MTR. Our prescriptive AI helps us measure how we're transforming these experiences.
Let me give you some concrete examples of our AI ROI metrics. In referral intake, we're looking at whether our AI solution can boost patient acceptance rates from 10% to 30-40% year-over-year. With revenue cycle management, we aim to reduce revenue leakage and write-offs by 70-80%.
Another big focus is clinician and caregiver productivity. They're spending countless hours on documentation to meet regulatory requirements and payer terms. Our goal is to improve their productivity by 30-50%. It's not about cutting staff or reducing FTE costs anymore. We're measuring ROI based on meaningful outcomes that benefit everyone in the care ecosystem.
As Chief Customer Officer, I need to think like a provider CEO. I have to put myself in their shoes and understand their challenges. And right now, for CEOs in home care and home health, survival is the biggest concern. They're facing existential threats - squeezed reimbursement rates, skyrocketing compliance costs, and razor-thin margins.
That's why I position AI as a financial survival toolkit. Here's an analogy I like to use: Do you want to be just a regular person, or do you want to be Iron Man? Like the Marvel character who combines genius, scientific knowledge, and advanced technology, AI can provide CEOs with similar powerful capabilities.
When CEOs adopt AI, they can focus on what I call the 'Triple E': Expansion, Efficiency, and Experience. They can expand operations by adding more locations and service lines. They can gain efficiency in operations to reduce costs. And they can improve both clinician and back-office experience.
My message to CEOs is simple: Trust your AI, but do it responsibly. Have a solid plan for transitioning from manual to AI-based operations. Just don't make the mistake of adopting too many different AI tools at once - that only leads to confusion about priorities and implementation. Focus on a strategic, well-planned approach.
Prescriptive AI is different from predictive AI. The beauty of prescriptive AI solutions is that they're incredibly seamless and frictionless to deploy. Sometimes, this is so smooth that industry leaders actually miss just how powerful the potential impact can be.
Let me paint a picture of how this works across the care journey. Think about a patient who starts with personal, more non-medical care that might last months or years. Then they may transition to home health services, or maybe a combination of home health and personal care as they age. Eventually, they might need palliative care and then hospice care.
Prescriptive AI is particularly powerful in those first two service lines - personal care and home health. This allows us to focus more direct, hands-on medical attention where it's needed most, like in hospice care where patients face greater challenges.
At Automation Edge, we're seeing remarkable results with our top five care flows - from referral intake to caregiver onboarding and retention, to our digital assistant tools. These solutions are delivering significant impact, both in terms of bottom-line efficiency and top-line growth through higher census numbers. The key is for leaders to recognize this upside potential and leverage it effectively.
In technology, even two years can bring dramatic changes, so trying to predict 5-10 years out is truly revolutionary.
First, AI has already solved what I call the interoperability problem. It's created transparency in operations by seamlessly connecting different systems and transforming unstructured data from spreadsheets into a unified interface. This gives real-time data access to clinicians, caregivers, and back-office staff through apps and portals.
Looking ahead 3-5 years, we'll see something even more exciting as prescriptive and predictive AI models evolve into what I call Multimodel AI. Instead of just text-based responses, we'll be combining voice and video content to enhance both patient and clinician experiences. We'll see digital assistants evolving into digital twins.
What's the difference? While digital assistants operate on a question-and-response basis, digital twins make predictions. For example, they'll analyze video content and voice conversations between clinicians and patients to predict and prevent falls at home. This prevents hospitalizations and readmissions - exactly what we want to achieve.
When prescriptive and predictive AI start working together like this, the next five years will bring huge transformations for patients in home care settings. The convergence of these technologies will revolutionize how we deliver care.
Right now, we're not talking about hundreds of AI tools - we're talking thousands, just in healthcare alone. Take the FDA's website as an example. They've listed around a thousand AI tools that are either approved or going through approval. Here's an interesting data point: the largest category, both in quality and quantity, is radiology tools.
What does this tell us? It shows how AI is already transforming how we handle patient vitals and clinical information. Think about the journey: from a device collecting patient data, to translating that into clinical findings, to flowing that information to caregivers and clinicians, and finally incorporating it into care plans.
This is where digital assistants and twins become game-changers. Instead of relying on faxed patient documentation - yes, everything still comes by fax today - AI tools will pull data directly from diagnostic devices. These integrated systems will run models in real-time to create more efficient care plans.
The impact on patient care longevity could be huge. This intelligent health monitoring approach will transform home care in significant ways. We're moving from fragmented, delayed information to real-time, integrated care decisions. That's the future we're building toward.