How Founder-Led Thinking Creates Faster AI Innovation

[00:00:04] Today on Startup Builders and Backers, I'm joined by Francesca Loftus, Chief Experience Officer at a company called Elyse AI. And we're going to be talking about a hiring decision that cuts against the grain, especially in a market that is obsessed with all things AI talent and very little else. And while many companies are chasing narrow specialists, at Elyse they're leaning into founder DNA.

[00:00:32] Former founders, future founders and generalist operators who have built something from scratch, shipped fast and learned the hard way what customers actually care about. And Fran will share her origin story. She's lived on both sides of that journey. She built a prop tech company, sold it in 2021 and then joined Elyse as the business scaled and expanded deeper into vertical AI across housing and healthcare.

[00:00:59] But today I want to learn more about how Elyse is structuring teams like micro companies and understand why engineers are staying close to customers. And what happens when you stop treating AI as a kind of demo contest and start treating it like a product that has to survive real world edge cases.

[00:01:21] So if you are building, backing or joining an AI company right now, I'm hoping today's conversation will give you a very different way to think about teams, velocity and what good looks like when the pace of shipping can change almost overnight. But enough spoilers for me. Let me introduce you to Fran right now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?

[00:01:50] Yeah, I'm Francesca Loftus and the chief experience officer at Elyse AI. Background, I was a prop tech founder, sold my business in 2021 and was a huge fan of Elyse AI for a long time. I met Mina really early on when she was starting the business and was really excited to join full time just about four years ago.

[00:02:14] And there's so much I want to talk with you about today because you're someone that's hiring founders at a time when most companies are competing for traditional AI specialists. So for everyone listening, I always try and give people valuable takeaways. What do you see in former founders that others are overlooking out there? And how has that shaped your culture and execution at the company? What are you seeing here?

[00:02:40] I think I didn't expect this when I joined Elyse four years ago. Yeah. I thought this is a SaaS company. When I joined, I thought it would look like another SaaS company. But I think everybody who has started on this AI journey around the same time has recognized the rate of development within an AI company is something completely different from what we see in SaaS.

[00:03:04] You know, you can just release products a lot faster and as you grow the team kind of at an exponential rate. And so here, I think we need to stay kind of nimble, feel like an early stage startup for a lot longer. And so we need those entrepreneurs that are going to come in, be incredibly flexible, be able to own maturing a product really independently.

[00:03:31] We're not really even at this stage of, you know, being a series E company. We're not really looking like a series E SaaS company used to years ago. There is still quite a lean towards immature products and, you know, developing things zero to one. And so we need a ton of entrepreneurs in order to build, you know, businesses inside the organization.

[00:03:56] And you have somewhat of a unique vantage point here with your experience as a founder and now with the coolest job title in the world with chief experience officer at Elyse. It's a great job title, that is. So I'm curious, though, very seriously, what specific traits separate a generalist founder from a strong functional specialist? And why do those traits translate so well in vertical AI businesses? Again, I'm curious what you're seeing here. Yeah.

[00:04:23] So, again, because we are constantly innovating and we have all these new products that we're bringing to market all the time. A really successful founder that's a little bit more of a generalist is able to basically understand the really early stage work of developing and developing a tool and figuring out what it looks like to bring it to market. And then able to still iterate on operating it at scale as the tool starts to roll out to a ton of our customers.

[00:04:53] What's nice here for a founder is we are well-funded. We do have a massive customer base. So you get all the benefits of being able to own something from the very beginning, as well as all the benefits of being able to scale it out with processes that already exist for some of our products that have been live in market for a long time and the massive audience that you get here.

[00:05:18] So for us, these generalist founders that can take the product quickly from really early stage to rolled out to a huge portion of our market are really valuable to us. And when doing a little research on you, one of the things I love about what you're doing at Elyse AI is how you're structuring teams almost like micro companies with engineers speaking directly to customers and owning products end to end. Incredibly cool.

[00:05:46] But to bring that to life and the kind of difference that's making, how does this model change things like accountability, speed and the quality of AI workflows compared to the more layered organizations that are out there? I think getting closer to the customer has been seen in the past as clunkier. You know, it means that there's less time hands on keyboard.

[00:06:10] But I think with the speed of development now possible through AI, you can go in the wrong direction really quickly if you're not close enough to the actual problem. Not, you know, not the philosophical problem, but the on the ground constraints of the end user.

[00:06:30] If you don't truly understand how it impacts the business, you know, what the actual experience of the product is going to be once it's released for these folks. You can go really far in the wrong direction and lose a lot of time. So for us, it's about because of the speed that is possible. It's about making sure that you're going in the right direction pretty early on in the process of developing a product.

[00:06:57] And that's why we want to be incredibly close to our customers on the engineering side. So when engineers are closer to the customer and responsible for outcomes, how does this affect the way AI systems are designed, tested and iterated in, let's say, high stake environments from housing and healthcare, etc.? What do you see there? Yeah.

[00:07:18] I mean, we do our best to skip building demos and go straight to building the product and then working with some beta customers really quickly, really early on in the process. We're like this across the board, even in our operational processes, you know, get a V1 live and iterate off of that. There's just no point now in kind of building a mock-up.

[00:07:47] You can really get to the first version of the product much faster now, so why not? And then see how it interacts in real life. We're always going to find edge cases. We're always going to, you know, identify that we didn't, you know, someone didn't anticipate that the product was going to change their business in the same way that we set out to at the very beginning.

[00:08:09] So it's better to get those insights really early on in the process and make that part of the iteration of the product itself. And there'll be many people listening and they'll be from organizations that think this is great, but they're from an organization that struggle with long feedback loops between product operations and customer experience, etc. So I'd love to drill down on that. How do founder-minded highs, how do they compress those loops?

[00:08:38] And what is the measurable impact at the end that it's had on customer outcomes? Yeah, there's just less context, I guess. Yeah. There's less context loss, leakage. Not an attractive word. You know how telephone works, right? It's inevitable when you have multiple stakeholders involved in a process.

[00:09:05] For an engineer to speak directly to the customer or to watch the product live during beta in function with the actual end users just cuts down on all that context loss. Obviously, we have to prioritize the right issues within businesses or problems that we want to solve for our customers. That inevitably comes from client-facing teams that are spending 100% of their time with customers.

[00:09:32] So the CSM or the engagement manager is going to escalate kind of key issues. But once escalated and we recognize this as a priority, we absolutely want the engineer directly integrated with the end user for building the product. And yeah, the benefit is the context is all retained.

[00:09:54] And again, when doing a little research on you guys, I was reading how you've argued that org charts will flatten as AI automates various layers of management. So what does leadership look like in that kind of structure? And how do you maintain clarity and cohesion without that traditional hierarchy, which arguably slowed things down anyway? But what do you see there?

[00:10:17] Yeah, it's about in order to have a flatter org where there's more independence and kind of more autonomy within each person's role. You really have to develop that muscle around prioritization and make sure that we trust that everybody who's coming into the organization as part of their ramp gets a really good sense of how we prioritize in the business.

[00:10:45] We're always going to prioritize the end user's experience, the impact to the customer above any kind of efficiencies internally. We've got to always make sure that we can see the path to the impact directly to the customer and user. And that's probably the thing that takes the time up front during ramp is making sure that people are incredibly clear how to prioritize so that they can own the entire project themselves.

[00:11:13] I came from an organization that had a massive C-suite before this. And there was definitely that bifurcation of people who knew what was happening tactically and the folks that were doing more strategic work. And I think now strategy and the actual execution or the tactical work can all be owned by the same person. And again, it's just about not losing context. There's major benefits if you're executing and you're building the strategy all at once. I love what you're doing here.

[00:11:42] It's a real breath of fresh air. And we'll have people listening from some companies that fear hiring people with entrepreneurial ambitions because, hey, they might get up and leave in a few months or a couple of years. And again, what I love about what you're doing here is you actively celebrate employees who eventually go out there and launch their own ventures. It feels like win-win-win for everyone.

[00:12:04] But why does that mindset strengthen rather than weaken the business for those traditional business leaders that might not understand what we're talking about here? Oh, I definitely see that anxiety even in candidates when I ask them about, you know, what do you do on the side? Do you have any projects? Anything you've built recently? People hesitate because they're worried that if they say, oh, I started a company or I built this app.

[00:12:29] They're really concerned that we're going to think they're not focused and they're not going to be, you know, a valuable add to the business. For me, I'm always looking for someone to say, yes, I built this thing. You know, this is I really want to learn here within a lease how to develop this thing further. You know, I do have long-term entrepreneurial ambitions. That always feels better to know that somebody has already kind of taken the leap.

[00:13:01] There's no barrier in the way where they think, oh, if I could, then I would or had to learn to do something before I kind of tried my hand at a no-code solution or something like that. For us, it's really, again, about finding folks that are going to be able to see a problem, fix a problem, regardless of what their job description is, right? We love when folks don't feel like they are held back based on the role or the seat that they sit in.

[00:13:31] It's really valuable to the business for someone to kind of reach across teams or reach across roles and enroll the company in fixing a big problem. Yeah, it really feels like almost an antidote to presenteeism and people just clocking in, clocking out and feeling held back. And again, it almost feels like a symbiotic relationship where you're helping each other grow, right? Yeah, definitely.

[00:13:58] I think a lot of priorities that kind of bubble up to the top of my list within operations don't come from me anymore. They come from people who are constantly looking for what else to fix in the company. And that feels like a far more scalable way to run a business when priorities don't come down from above, right? When everybody is thinking, how can I optimize my job? How can I make the end user experience better?

[00:14:29] And that's where priorities are coming from. Love it. And for startup builders and backers listening to this podcast today, are there any signals that they should be looking out for if they're building or funding AI native teams today? Is it technical brilliance, domain depth, founder resilience, or something else entirely that helps create that longer term advantage? Yeah, I think flexibility of team is massive. Again, it was so surprising to me.

[00:14:59] It still is every day. Seeing how different innovation or the rate of innovation is in an AI company versus SaaS businesses where I started before. You really need a team that's going to be able to flex their skills, be able to jump into new products, be able to jump into new responsibilities as the products look incredibly different from the set of products that they started the company with.

[00:15:29] I think, yeah, the flexibility is massive. And you really only get that from somebody who has navigated these pivots that entrepreneurs have navigated previously. That and I do think we talk a lot about vertical AI companies and the success of vertical AI companies being that you can go incredibly deep with the end users.

[00:15:54] So you want to look for an organization that is obsessed with making sure that they understand the day-to-day, all the tactical, all of the nuances of the vertical that they're in. Because that's really what builds a pretty stable company and a pretty stable product. And it's now time for me to pull out my virtual soapbox here.

[00:16:18] I always ask my guests to give them an opportunity if they've got any myths and misconceptions they'd like to lay to rest that they see on their social feeds. And as I said, with the work you're doing, at least, it's incredibly forward thinking, a real breath of fresh air. I do suspect you come across things in your newsfeed or in conversations with traditional businesses that might just not get your message because they have certain preconceptions.

[00:16:44] But are there any misconceptions, myths, or anything that we can just lay to rest today before we let you go? The floor is yours. You can get on the soapbox. Anything you'd like to retire today? I think maybe a big one is the future of AI. I think there's still a big split in how people are onboarding AI to either their companies or onboarding AI into their personal life. I see it in my family. I see it with friends.

[00:17:09] You know, there's this willingness to adopt AI internally for your own work, but a challenge in adopting AI in practice in your personal life. And I think there's still so much opportunity for AI to be introduced in a way that is human.

[00:17:29] I think that's something that we're working on constantly is making sure that the human-AI interaction is our primary focus, making sure that it is supportive. Folks are able to up-level their roles and responsibilities in our organizations that we partner with.

[00:17:45] Yeah, I think that's the biggest one that I see in practice day-to-day is there's this concern that AI is, I guess, not going to, you know, make their jobs more complicated in some way. I think that's a big one. There you go. Does it feel better getting it all out there in the open? We've laid those misconceptions to rest.

[00:18:08] And before I let you go, for anyone listening wanting to connect with you, your team, or dig a little bit deeper on anything that you're doing at Elyse and some of the big announcements that may be coming out throughout the year, where would you like me to point everyone? To our website, eliseai.com. Also constantly hiring. We've got so many offices. We've got New York, Chicago, San Francisco, Boston. Just opened in Toronto.

[00:18:32] So, eliseai.com slash careers, especially if you're an entrepreneur or you have entrepreneurial DNA and you'd like to start a company, that's a great place to go to, to work with us and get to own something end to end in an organization that has all of the kind of supports and opportunities for you. I'd say those are the two biggest to connect with us. Awesome. I love what you're doing here.

[00:18:58] Wish you the best of luck on this journey of building AI that improves how we live and wish you the best of luck on this mission to improve life's most critical areas through AI and automation. I'll also include links on how people can join your team, find out more information there, including all the links that you mentioned. But again, just thank you for bringing this to life today and providing a different look at the landscape at the minute.

[00:19:25] It's really refreshing and a great antidote to some of the doom and gloom we see on our news feed. So, thank you for joining me today. Yeah. If you have ever wondered why some AI companies move at a completely different speed, I think Fran offered a very clear answer today, didn't she? It comes down to how teams are built and how close the builders stay to the real problem.

[00:19:48] And I love the way she described Elise AI's preference for generalist founders who can take something from very early concept to real deployment and then keep iterating once it hits scale. And this fresh mindset shows up in how they skip along demo cycles, get a V1 into the hands of beta customers fast and keep the feedback loop tight by putting engineers closer to the customer experience.

[00:20:16] And I think this also changes leadership. Prioritization becomes a muscle that keeps a flatter org aligned, especially when strategy and execution all sit in the same set of hands. And there's also a bigger signal going on here, I think, for anyone funding or running AI teams right now.

[00:20:37] And that is flexibility matters more than some shiny polished org charts because the product you start with rarely resembles the one that you end up scaling. It's the journey. And if this episode made you rethink how you hire, how you structure your teams or what you look for in an AI company you want to work with, please, I want to hear from you. We did cover a fair bit today. So what resonated with you?

[00:21:05] And where do you think a founder-led model would work best? And what are your experiences with that as well? Please, if you head over to techtalksnetwork.com, you'll find a blog post, links and embeds to this episode. There's also 4,000 different interviews across the nine podcasts that I manage over there now. So have a look. Send me an audio message or just send me a good old-fashioned DM on socials.

[00:21:33] Whatever it is, please, I look forward to hearing from you. But I'm afraid we're out of time for today. So I'll be back again real soon with another guest. And I'll speak with you all then. Bye for now.