How Founder-Led Thinking Creates Faster AI Innovation

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Today on Startup Builders and Backers, I'm joined
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by Fran Loftus, Chief Experience Officer at a
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company called Elise AI. And we're going to be
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talking about a hiring decision that cuts against
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the grain, especially in a market that is obsessed
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with all things AI talent and very little else.
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And while many companies are chasing narrow specialists,
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at Elise they're leaning into founder DNA. Former
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founders, future founders and generalist operators
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who have built something from scratch, shipped
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fast and learned a hard way what customers actually
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care about. And Fran will share her origin story.
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She's lived on both sides of that journey. She
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built a prop tech company, sold it in 2021 and
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then joined Elise as the business scaled and
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expanded deeper into vertical AI across housing
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and healthcare. But today I want to learn more
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about how Elise is structuring teams like micro
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companies and understand why engineers are staying
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close to customers and what happens when you
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stop treating AI as a kind of demo contest and
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start treating it like a product that has to
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survive real world edge cases. So if you are
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building, backing or joining an AI company right
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now, I'm hoping today's conversation will give
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you a very different way to think about teams,
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velocity and what good looks like when the pace
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of shipping can change almost overnight. But
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enough spoilers from me, let me introduce you
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to Fran right now. So thank you for joining me
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on the podcast today. Can you tell everyone listening
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a little about who you are and what you do? Yeah,
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I'm Francesca Loftus. I'm the Chief Experience
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Officer at Elise AI. Background, I was a PropTech
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founder, sold my business in 2021, and was a
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huge fan of Elise AI for a long time. Met Mina
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really early on when she was starting the business,
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and was really excited to join full -time just
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about four years ago. And there's so much I want
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to talk with you about today because you're someone
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that's hiring founders at a time when most companies
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are competing for traditional AI specialists.
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For everyone listening, I always try and give
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people valuable takeaways. What do you see in
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former founders that others are overlooking out
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there? And how has that shaped your culture and
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execution at the company? What are you seeing
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here? I think I didn't expect this when I joined
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Elise four years ago, I thought this is a SaaS
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company. When I joined, I thought it would look
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like another SaaS company. But I think everybody
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who has started on this AI journey around the
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same time has recognized the rate of development
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within an AI company is something completely
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different from what we see in SaaS. release products
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a lot faster. And as you grow the team kind of
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at an exponential rate. And so here, I think
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we need to stay kind of nimble, feel like an
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early stage startup for a lot longer. And so
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we need those entrepreneurs that are going to
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come in, be incredibly flexible, be able to own
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maturing a product really independently. We're
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not really even at this stage of you know being
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a series company we're not really looking like
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a series sass company used to years ago there
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is still quite a lean towards immature products
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and you know developing things zero to one and
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so we need a ton of entrepreneurs in order to
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build you know businesses inside the organization.
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And you have somewhat of a unique vantage point
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here with your experience as a founder and now
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with the coolest job title in the world with
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Chief Experience Officer, Elise. It's a great
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job title, Elise. I'm curious though, very seriously,
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what specific traits separate a generalist founder
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from a strong functional specialist and why do
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those traits translate so well in vertical AI
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businesses? Again, I'm curious what you're seeing
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here. Yeah, so again, because we are constantly
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innovating and we have all these new products
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that are bringing to market all the time. A really
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successful founder that's a little bit more of
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a generalist is able to basically understand
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the really early stage work of developing and
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developing a tool and figuring out what it looks
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like to bring it to market and then able to still
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iterate on operating it at scale as the tool
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starts to roll out to a ton of our customers.
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What's nice here for a founder is we are well
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-funded. We do have a massive customer base.
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So you get all the benefits of being able to
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own something from the very beginning. as well
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as all the benefits of being able to scale it
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out with processes that already exist for some
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of our products that have been live in market
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for a long time and the massive audience that
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you get here. So for us, these generalist founders
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that can take the product quickly from really
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early stage to rolled out to a huge portion of
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our market are really valuable to us. When doing
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a little research on you, one of the things I
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love about what you're doing at Elise AI is how
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you're structuring teams almost like micro companies
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with engineers speaking directly to customers
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and owning products end to end. Incredibly cool,
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but to bring that to life and the kind of difference
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that's making, how does this model change things
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like accountability, speed, and the quality of
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AI workflows compared to the more layered organizations
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that are out there? getting closer to the customer
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has been seen in the past as clunkier. It means
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that there's less time hands on keyboard. But
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I think with the speed of development now possible
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through AI, you can go in the wrong direction
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really quickly if you're not close enough to
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the actual problem, not the philosophical problem,
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but the on the ground constraints of the end
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user, if you don't truly understand how it impacts
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the business, what the actual experience of the
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product is going to be once it's released for
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these folks. You can go, you can go really far
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in the wrong direction and then lose a lot of
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time. So for us, it's about because of the speed
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that is possible. It's about making sure that
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you're going in the right direction pretty early
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on in the process of developing a product. And
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that's why we want to be incredibly close to
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our customers on the engineering side. So when
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engineers are closer to the customer and responsible
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for outcomes, how does this affect the way AI
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systems are designed, tested, and iterated in,
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let's say, high -stake environments from housing
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and healthcare, et cetera? What do you see there?
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Yeah, I mean, we do our best to skip building
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demos and go straight to building the product,
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and then... working with some beta customers
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really quickly, really early on in the process.
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We're like this across the board, even in our
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operational processes, you know, get a V1 live
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and iterate off of that. There's just no point
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now in kind of building a mock -up. You can really
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get to the first version of the product much
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faster now, so why not? And then see how it interacts
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in real life. We're always going to find edge
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cases. we're always going to identify that someone
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didn't anticipate that the product was going
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to change their business in the same way that
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we set out to at the very beginning. So it's
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better to get those insights really early on
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in the process and make that part of the iteration
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of the product itself. And there'll be many people
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listening, and it'll be from organizations that
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think this is great, but they're from an organization
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that struggle with long feedback loops between
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product operations and customer experience, et
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cetera. So I'd love to drill down on that. How
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do founder -minded highs, how do they compress
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those loops, and what is the measurable impact
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at the end that it's had on customer outcomes?
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Yeah, there's just less context, I guess. Yeah.
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This is less context loss leakage. Not an attractive
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word. You know how telephone works, right? It's
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inevitable when you have multiple stakeholders
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involved in a process. For an engineer to speak
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directly to the customer or to watch the product
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live during beta. in function with the actual
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end users just cuts down on all that context
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loss. Obviously, we have to prioritize the right
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issues within businesses or problems that we
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wanna solve for our customers. That inevitably
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comes from client -facing teams that are spending
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100 % of their time with customers. So the CSM
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or the engagement manager is going to escalate
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kind of key issues, but... Once escalated and
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we recognize this as a priority, we absolutely
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want the engineer directly integrated with the
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end user for building the product. And yeah,
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the benefit is the context is all retained. And
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again, when doing a little research on you guys,
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I was reading how you've argued that org charts
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will flatten as AI automates various layers of
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management. So what does leadership look like
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in that kind of structure? And how do you maintain
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clarity and cohesion without that traditional
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hierarchy, which arguably slowed things down
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anyway? But what do you see there? Yeah, it's
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about in order to have a flatter org, where there's
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more independence and kind of more autonomy within
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each person's role, we really have to develop
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that muscle around prioritization and make sure
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that we trust that everybody who's coming into
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the organization as part of their ramp gets a
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really good sense of how we prioritize in the
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business. We're always going to prioritize the
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end user's experience, the impact to the customer
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above any kind of efficiencies internally, right,
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we got to always make sure that we can see the
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path to the impact directly to the customer end
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user. And that's probably the thing that takes
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the takes the time up front during ramp is making
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sure that people are incredibly clear how to
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prioritize so that they can own the entire project
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themselves. I came from an organization that
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had a massive C -suite before this. And there
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was definitely that bifurcation of people who
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knew what was happening tactically and the folks
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that were doing more strategic work. And I think
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now strategy and the actual execution or the
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tactical work can all be owned by the same person.
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And again, it's just about not losing context.
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There's major benefits if you're executing and
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you're building the strategy all at once. I love
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what you're doing here. It's a real breath of
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fresh air. And we'll have people listening from
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some companies that fear hiring people with entrepreneurial
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ambitions because, hey, they might get up and
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leave in a few months or a couple of years. And
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again, what I love about what you're doing here
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is you actively celebrate employees who eventually
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go out there and launch their own ventures. It
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feels like win, win, win for everyone. But why
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does that mindset strengthen rather than weaken
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the business for those traditional business leaders
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that might not understand what we're talking
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about here. Oh, I definitely see that anxiety
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even in candidates when I asked them about, you
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know, what do you do on the side? Do you have
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any projects, anything you've built recently?
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People hesitate because they're worried that
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if they say, oh, I started a company or I built
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this app, they're really concerned that we're
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going to think they're not focused. and they're
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not going to be a valuable add to the business.
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For me, I'm always looking for someone to say,
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yes, I built this thing. I really want to learn
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here within Elise how to develop this thing further.
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I do have long -term entrepreneurial ambitions.
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That always feels better to know that somebody
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has already taken the leap. there's no barrier
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in the way where they think oh if I could then
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I would or had to learn to do something before
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I kind of tried my hand at a no -code solution
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or something like that. For us it's really again
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about finding folks that are going to be able
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to see a problem fix a problem regardless of
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what their job description is right. love when
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folks don't feel like they are held back based
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on the role or the seat that they sit in. It's
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really valuable to the business for someone to
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kind of reach across teams or reach across roles
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and enroll the company in fixing a big problem.
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Yeah, it really feels like almost an antidote
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to presentism and people just clocking in, clocking
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out and feeling held back. And again, almost
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feels like a symbiotic relationship where you're
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helping each other grow, right? Yeah, definitely.
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I think a lot of priorities that kind of bubble
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up to the top of my list within operations don't
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come from me anymore. They come from people who
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are Constantly looking for what else to fix in
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the company and that feels like a far more scalable
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way to run a business when priorities don't come
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down from above right when everybody is thinking
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how can i optimize my job how can i make the
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end user experience better and that's where priorities
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are coming from. Love it. And for startup builders
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and backers listening to this podcast today,
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are there any signals that they should be looking
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out for if they're building or funding AI native
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teams today? Is it technical brilliance, domain
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depth, founder resilience, or something else
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entirely that helps create that longer -term
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advantage? Yeah, I think flexibility of team
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is massive. Again, just it was so surprising
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to me. It still is every day. seeing how different
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innovation or the rate of innovation is in an
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AI company versus SaaS businesses, where I started
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before. You really need a team that's going to
00:15:12.330 --> 00:15:18.370
be able to flex their skills, be able to jump
00:15:18.370 --> 00:15:22.889
into new products, be able to jump into new responsibilities
00:15:22.889 --> 00:15:25.929
as the products look incredibly different from
00:15:25.929 --> 00:15:28.370
the set of products that they started the company
00:15:28.370 --> 00:15:32.360
with. I think, yeah, the flexibility is massive
00:15:32.360 --> 00:15:34.639
and you really only get that from somebody who
00:15:34.639 --> 00:15:39.360
has navigated these, you know, pivots that entrepreneurs
00:15:39.360 --> 00:15:44.659
have navigated previously. That and I do think
00:15:44.659 --> 00:15:48.159
we talk a lot about vertical AI companies and
00:15:48.159 --> 00:15:51.059
the success of vertical AI companies being that
00:15:51.059 --> 00:15:54.500
you can go incredibly deep with the end users.
00:15:54.980 --> 00:15:57.759
So you want to look for an organization that
00:15:57.759 --> 00:16:01.639
is obsessed with making sure that they understand
00:16:01.639 --> 00:16:05.059
the day -to -day, all the tactical, all of the
00:16:05.059 --> 00:16:08.039
nuances of the vertical that they're in because
00:16:08.039 --> 00:16:11.679
that's really what builds a pretty stable company
00:16:11.679 --> 00:16:15.860
and a pretty stable product. And it's now time
00:16:15.860 --> 00:16:18.460
for me to pull out my virtual soapbox here I
00:16:18.460 --> 00:16:21.559
always ask my guests for I give them an opportunity
00:16:21.559 --> 00:16:24.279
if they've got any myths and misconceptions They'd
00:16:24.279 --> 00:16:26.600
like to lay to rest that they see on their social
00:16:26.600 --> 00:16:29.179
feeds And as I said with the work you're doing
00:16:29.179 --> 00:16:31.899
at least it's incredibly forward -thinking a
00:16:31.899 --> 00:16:34.320
real breath of fresh air I do suspect you come
00:16:34.320 --> 00:16:37.139
across things in your newsfeed or in conversations
00:16:37.139 --> 00:16:39.659
with Traditional businesses that might just not
00:16:39.659 --> 00:16:44.039
get your message because they have certain preconceived
00:16:43.659 --> 00:16:47.200
But are there any misconceptions, myths, or anything
00:16:47.200 --> 00:16:50.039
that we can just lay to rest today before we
00:16:50.039 --> 00:16:52.720
let you go? The floor is yours. You can get on
00:16:52.720 --> 00:16:55.059
the soapbox. Anything you'd like to retire today?
00:16:55.440 --> 00:16:58.399
I think maybe a big one is the future of AI.
00:16:58.840 --> 00:17:02.179
I think there's still a big split in how people
00:17:02.179 --> 00:17:05.380
are onboarding AI to either their companies or
00:17:05.380 --> 00:17:07.759
onboarding AI into their personal life. I see
00:17:07.759 --> 00:17:10.319
it in my family. I see it with friends. You know,
00:17:10.319 --> 00:17:14.579
there's this... This willingness to adopt a internally
00:17:14.579 --> 00:17:18.400
for your own work but a challenge in adopting
00:17:18.400 --> 00:17:22.299
practice in your personal life and i think. There's
00:17:22.299 --> 00:17:25.500
still so much opportunity for it to be introduced
00:17:25.500 --> 00:17:30.980
in a way that is is human i think that's something
00:17:30.980 --> 00:17:33.799
that we're working on constantly is making sure
00:17:33.799 --> 00:17:37.400
that. the human AI interaction is our primary
00:17:37.400 --> 00:17:40.640
focus, making sure that it is supportive. Folks
00:17:40.640 --> 00:17:43.779
are able to up -level their roles and responsibilities
00:17:43.779 --> 00:17:46.380
in our organizations that we partner with. Yeah,
00:17:46.420 --> 00:17:50.859
I think that's the biggest one that I see in
00:17:50.859 --> 00:17:54.519
practice day to day is there's this concern that
00:17:54.519 --> 00:17:58.720
AI is, I guess, not going to make their jobs
00:17:58.720 --> 00:18:00.960
more complicated in some way. I think that's
00:18:00.960 --> 00:18:03.920
a big one. Does it feel better getting it all
00:18:03.920 --> 00:18:07.400
out there in the open? We've laid those misconceptions
00:18:07.400 --> 00:18:11.299
to rest and before I let you go for anyone listening,
00:18:11.480 --> 00:18:13.640
wanting to connect with you, your team or dig
00:18:13.640 --> 00:18:16.220
a little bit deeper on anything that you're doing
00:18:16.220 --> 00:18:18.380
at Elise and some of the big announcements that
00:18:18.380 --> 00:18:20.019
may be coming out throughout the year, well do
00:18:20.019 --> 00:18:22.089
you like me to point everyone? to our website,
00:18:22.609 --> 00:18:25.950
alisei .com. We're also constantly hiring. We've
00:18:25.950 --> 00:18:28.730
got so many offices. We've got New York, Chicago,
00:18:28.970 --> 00:18:31.910
San Francisco, Boston, just opened in Toronto.
00:18:32.750 --> 00:18:35.890
So alisei .com slash careers, especially if you're
00:18:35.890 --> 00:18:39.299
an entrepreneur. or you have entrepreneurial
00:18:39.299 --> 00:18:42.380
DNA and you'd like to start a company, that's
00:18:42.380 --> 00:18:46.000
a great place to go to to work with us and get
00:18:46.000 --> 00:18:49.039
to own something end to end in an organization
00:18:49.039 --> 00:18:52.140
that has all of the kind of supports and opportunities
00:18:52.140 --> 00:18:55.319
for you. I'd say those are the two biggest to
00:18:55.319 --> 00:18:58.210
connect with us. Awesome. I love what you're
00:18:58.210 --> 00:19:00.130
doing here. Wish you the best of luck on this
00:19:00.130 --> 00:19:03.970
journey of building AI that improves how we live
00:19:03.970 --> 00:19:06.829
and wish you the best look on this mission to
00:19:06.829 --> 00:19:10.230
improve life's most critical areas through AI
00:19:10.230 --> 00:19:13.109
and automation. I'll also include links on how
00:19:13.109 --> 00:19:15.849
people can join your team, find out more information
00:19:15.849 --> 00:19:18.950
there, including all the links that you mentioned.
00:19:19.549 --> 00:19:21.329
But again, just thank you for bringing this to
00:19:21.329 --> 00:19:24.950
life today. providing a different look at the
00:19:24.950 --> 00:19:26.630
landscape at the minute. It's really refreshing
00:19:26.630 --> 00:19:29.230
and a great antidote to some of the doom and
00:19:29.230 --> 00:19:31.109
gloom we see on our newsfeed. So thank you for
00:19:31.109 --> 00:19:34.130
joining me today. Yeah. If you have ever wondered
00:19:34.130 --> 00:19:37.490
why some AI companies move at a completely different
00:19:37.490 --> 00:19:40.089
speed, I think Fran offered a very clear answer
00:19:40.089 --> 00:19:43.430
today, didn't she? It comes down to how teams
00:19:43.430 --> 00:19:47.190
are built and how close the builders stay to
00:19:47.190 --> 00:19:50.650
the real problem. And I love the way she described
00:19:50.650 --> 00:19:53.369
Elise AI's preference for generalist founders
00:19:53.369 --> 00:19:56.329
who can take something from very early concept
00:19:56.329 --> 00:20:00.470
to real deployment and then keep iterating once
00:20:00.470 --> 00:20:04.690
it hits scale. And this fresh mindset shows up
00:20:04.690 --> 00:20:08.150
in how they skip along demo cycles, get a V1
00:20:08.150 --> 00:20:11.630
into the hands of beta customers fast and keep
00:20:11.630 --> 00:20:14.710
the feedback loop tight. by putting engineers
00:20:14.710 --> 00:20:17.670
closer to the customer experience. And I think
00:20:17.670 --> 00:20:21.029
this also changes leadership. Prioritisation
00:20:21.029 --> 00:20:24.130
becomes a muscle that keeps a flatter org aligned,
00:20:24.289 --> 00:20:27.710
especially when strategy and execution all sit
00:20:27.710 --> 00:20:31.569
in the same set of hands. And there's also a
00:20:31.569 --> 00:20:33.990
bigger signal going on here I think for anyone
00:20:33.990 --> 00:20:38.470
funding or running AI teams right now. And that
00:20:38.470 --> 00:20:42.589
is flexibility matters more than some shiny polished
00:20:42.589 --> 00:20:46.450
org chart, because the product you start with
00:20:46.450 --> 00:20:49.130
rarely resembles the one that you end up scaling.
00:20:49.369 --> 00:20:53.250
It's the journey. And if this episode made you
00:20:53.250 --> 00:20:56.029
rethink how you hire, how you structure your
00:20:56.029 --> 00:20:59.650
teams, or what you look for in an AI company
00:20:59.650 --> 00:21:01.529
you want to work with, please, I want to hear
00:21:01.529 --> 00:21:04.390
from you. We did cover a fair bit today, so what
00:21:04.390 --> 00:21:07.960
resonated with you? And where do you think a
00:21:07.960 --> 00:21:10.700
founder led model would work best? And what are
00:21:10.700 --> 00:21:14.059
your experiences with that as well? Please, if
00:21:14.059 --> 00:21:17.240
you head over to techtalksnetwork .com, you'll
00:21:17.240 --> 00:21:21.460
find a blog post, links and embeds to this episode.
00:21:21.619 --> 00:21:24.900
There's also 4 ,000 different interviews across
00:21:24.900 --> 00:21:27.599
the nine podcasts that I manage over there now.
00:21:28.079 --> 00:21:31.269
So have a look, send me an audio message. Or
00:21:31.269 --> 00:21:33.670
just send me a good old -fashioned DM on socials.
00:21:33.849 --> 00:21:35.789
Whatever it is, please, I look forward to hearing
00:21:35.789 --> 00:21:38.289
from you. But I'm afraid we're out of time for
00:21:38.289 --> 00:21:40.869
today, so I'll be back again real soon with another
00:21:40.869 --> 00:21:43.609
guest. And I'll speak with you all then. Bye
00:21:43.609 --> 00:21:44.049
for now.