Fran Loftus, Chief Experience Officer, EliseAI – Interview Series

Fran Loftus, Chief Experience Officer, EliseAI is a dynamic leader known for her creative energy and versatile background, blending entrepreneurial drive with deep operational expertise. Before joining EliseAI, she co-founded HOM, a tech-enabled amenity and engagement platform acquired by Alfred, where she later served in multiple executive roles shaping resident experiences across hundreds of thousands of units. At EliseAI, she has held senior operational leadership positions reporting to the CEO and Board, driving cross-company efficiency, overseeing client outcomes, and partnering closely with engineering and sales on product strategy and rollout. Recognized as a founder and industry thought leader, she has been nominated for Top Founder at MIPIM and repeatedly named among CRETech’s top female influencers.

EliseAI is a New York-based AI software company transforming how housing and healthcare organizations manage high-volume communication and operational workflows through advanced conversational and agentic artificial intelligence. Its platform automates tasks such as tour scheduling, leasing communication, maintenance requests, renewals, patient intake, and administrative support across text, voice, email, and chat, enabling teams to operate more efficiently while improving responsiveness and customer experience. By centralizing these interactions into a unified AI system, EliseAI helps organizations scale operations, reduce manual workload, and deliver measurable performance improvements across mission-critical functions.

As Chief Experience Officer at EliseAI, you bring a rare mix of experience across community, operations, and technology. You founded HOM, a platform that helped multifamily buildings deliver shared amenities and a sense of community at scale, then led experience and service delivery at Alfred, a residential services company embedded inside thousands of apartment buildings. How do those deeply hands-on, people-first roles influence how you design and operationalize AI experiences at EliseAI today?

My background in physical property management taught me that technology is only as good as the service it actually delivers on the ground. At HOM and Alfred, I learned that a resident doesn’t care about a sleek interface if their maintenance request goes into a black hole or their move-in is delayed. This has made me obsessive about closing the loop. I don’t design AI solely to handle conversations,I design it to manage the entire lifecycle of a task. I prioritize the resident in a crisis or the staff member who is stretched too thin, ensuring that the AI isn’t just another thing for them to monitor, but a tool that takes the weight off their shoulders.

From a CX perspective, what does effective AI actually look like in housing and healthcare environments where trust, clarity, and follow-through matter as much as speed or automation?

In housing and healthcare, being fast is great, but actually following through is what builds trust. Good AI shouldn’t just give you a quick answer; it needs to make sure something actually happens. If a patient asks about a bill or a renter wants to renew their lease, the AI needs to update the records, tell the right manager, and let that person know the wheels are in motion. It’s about being clear and proactive so people don’t feel like they have to call back three times just to check the status.

EliseAI has taken a vertical-first approach rather than offering a general-purpose AI layer. How does that decision change the day-to-day experience for property teams, residents, and healthcare staff compared with horizontal AI tools?

General AI tools require the customer to do the heavy lifting of teaching the software the industry rules. Because we’re a vertical solution focused exclusively on housing and healthcare, our system arrives with a deep understanding of the specific workflows, like security deposit regulations  or patient scheduling nuances. For the staff on the ground, this means they aren’t managing a generic chatbot that might hallucinate a policy. They are working with a system that is already integrated into their existing software and speaks the language of their daily operations from day one.

Can you explain how shadowing frontline workers to capture end-to-end workflows improves model behavior and the ultimate customer experience, compared to training on abstract data alone?

Abstract data shows you the “what,” but being on the ground with customers shows you the “how”. When my team spends time in the actual environment where the software is deployed, they see the friction points that a data set would miss. For example, during discovery sessions with a military housing operator, we identified complex approval flows and strict daily constraints that weren’t in any manual. By capturing those messy, real-world details and translating them into the product, we can build AI that doesn’t just work in a demo, but survives the chaos of a busy office.

When AI systems move beyond answering questions to executing tasks, what new experience risks emerge, and how do you ensure automation feels supportive and reliable rather than intrusive or opaque?

The primary risk shifts from the AI saying something incorrect to the AI doing something incorrect. To mitigate this, I’ve established a product solutions model where we have experts—many of whom are former founders—who own specific domains of the product. They set the specific rules and guardrails for how the AI executes a task, ensuring it never takes an action that would surprise or confuse a human staff member.

In housing and healthcare, even small breakdowns can quickly erode trust. How do you design agentic AI systems that remain transparent, predictable, and easy for humans to step in and correct when needed?

Designing for trust starts with moving away from a traditional reactive mindset where you simply wait for things to break. Instead, I’ve built a team that acts as a bridge between real-world operational messiness and the AI’s logic. These aren’t support agents –they are strategists and former founders who own specific domains of the product from end to end.

They spend their time shadowing workers to understand the non-negotiable rules of an industry such as complex military housing approval flows or strict medical response times, and they bake those directly into how the AI behaves. Transparency comes from the fact that our systems are designed to flag potential issues proactively before they become resident or patient complaints. When the AI senses a high-emotion situation or a complex request it isn’t trained for, it doesn’t guess. It triggers a handoff that provides the human staff member with the full history and context of the interaction, allowing them to step in as an informed partner rather than starting from zero.

From your vantage point overseeing implementation and client success, what are the most common friction points customers face when adopting AI into operational workflows, and how does EliseAI reduce that friction early in deployment?

The biggest friction is rarely the technology itself but rather the anxiety that the technology will disrupt the team’s flow or replace their roles. We address this by focusing on immediate, practical relief. In the first week, we use the AI to take over the repetitive, high-volume tasks that typically cause the most burnout, like initial lead follow-up. We also use our own AI to manage our internal company operations. This helps us understand the exact stress points of changing a workflow, so we can be better partners when our customers go through that same transition.

Many organizations struggle to move beyond pilot phases. How do you help customers evolve their processes and expectations so AI becomes embedded in daily operations rather than remaining a side experiment?

AI only sticks when it stops being a side project and becomes a normal part of the workday; we help leaders reorganize their teams so that once AI handles the busywork, people can focus on the tasks that actually require a human touch.

EliseAI now supports roughly one in six U.S. apartments. How do customer expectations shift at that level of scale, and what new experience challenges emerge as AI becomes part of standard operations?

At this scale, AI is no longer a cool innovation. It is a utility, just like electricity. The expectation shifts from it being impressive when it works to it being unacceptable when it fails. We have to manage that variance while maintaining 100% reliability.

Looking ahead 2 to 3 years, how do you see the role of the Chief Experience Officer evolving as AI systems take on more responsibility inside essential services like housing and healthcare?

The role will evolve from managing customer service to orchestrating the partnership between humans and AI. As AI takes over the administrative and repetitive parts of the job, the CXO’s focus will shift entirely to the strategic and emotional touchpoints. Our responsibility will be to ensure that as the world becomes more automated, the human interactions in housing and healthcare become more intentional and more impactful.

Thank you for the great interview, readers who wish to learn more should visit EliseAI.