From Hype to Habit: How We’re Building an AI-First SaaS Company—While Still Shipping the Roadmap

Channel: aiDotEngineer

Published at: 2025-07-23

YouTube video id: 3YGRcgZJ3yc

Source: https://www.youtube.com/watch?v=3YGRcgZJ3yc

[Music]
[Music]
Hello everyone. Welcome to our talk on
building AI first companies. I'm Rala
and I'm Dibsha. We are so excited to be
here with all of you today. Uh, you've
probably heard the term AI first a
gazillion times already this week. And
if you are in this room, chances are
you're not just wondering what that
means. You're also trying to figure out
how to actually make it real inside your
own company. And that's what today's
talk is about.
We wish we could stand here and give you
the talk, the one with all the answers
and a crystal clearar playbook for
becoming an AI force company. But the
truth is that that talk probably doesn't
exist. AI transformation is really hard.
It's messy. It's full of tradeoffs and
it looks different for every company. At
Sprout Social, we are in the messy
middle of our own AI transformation. So
this talk is a candid realtime look at
what it takes to lead a SAS company into
the eye era without breaking your
business, the team or your values. We'll
share what's working, what's not, and a
practical framework uh to guide you
through your own AI transformation. But
first, let's break down the buzzword.
What does AI first even mean? I feel
like AI first is like teenage sex.
Everyone talks about it. No one really
knows how to do it. Everyone thinks
everyone else is doing it, so everyone
claims they're doing it. Jokes aside,
what does it mean to be AI first? It's
about evolving from AI features
sprinkled into the product to rethinking
how you plan, build, and deliver value
all through an AI lens. More than that,
it's about putting AI at the center of
your strategy. But most importantly,
it's a mindset shift more than anything
else.
Now, if you've been in tech for a while,
you've seen big shifts before. Cloud
first, mobile first, DevOps, and many
more. But here's the difference. Those
shifts disrupted one area at a time. AI
is disrupting all of them at once. Its
product, its architecture, its people,
its process, its ethics, its everything.
And that's what makes it so hard, but
also such a gamecher.
Here's the good news. Being AI first
isn't binary. It's not like flipping a
switch. It's not about throwing
everything out and rebuilding your
company overnight. AI transformation is
a multi-dimensional journey. It's an
evolution.
Where you are on this spectrum will vary
depending on your own company. But the
important thing is to know where you are
and to move with purpose.
AI transformation is complex, but it
doesn't have to feel chaotic. At Sprout
Social, we found that it helps to think
about it through a simple framework. At
its core, becoming an AI first company
means evolving across three key
dimensions. Strategy, what you
prioritize and why. ways of working, how
you build, ship, and adapt, and people,
how your teams evolve, and the skills
that define success.
Whether you're just getting started or
already in the thick of it, these three
areas give a way to make sense of the
work that is ahead of you. And we hope
that this simple framework can help you
navigate your own AI transformation.
So now that we set the bigger picture,
let's start with the first dimension
strategy.
The very first critical shift is about
how to determine what to build about
evolving from an AI enhanced to an AI
first strategy where you are reimagining
what's possible today thanks to AI. In
the past, investing in AI meant asking
where can we add intelligence to an
existing experience. It was about
sprinkling AI across your product to
make workflows better. In contrast, in
an AI first company, the question
becomes, what new experiences can we
deliver that weren't even possible
before?
Being AI first means reimagining what's
possible. It's about solving problems
that were previously unsolvable in ways
that customers may not even imagine.
Now, dreaming about the future is super
fun, but the challenge is that you still
have to ship features to meet your
customer needs today. That's the tension
every company is facing right now. How
do you ship what customer needs today
while also investing in the future you
know is coming? If you overindex on the
present, you risk falling behind and
missing the moment. If you focus only on
the future, you risk disappointing
customers, slowing revenue, and
potentially starving innovation of
resources.
That's the core of the innovator's
dilemma. And AI makes it 10 times
harder. You basically need the
discipline of an enterprise and the
curiosity and nimleness of a startup,
but you need both to be running in
parallel. It's like steering a ship and
launching a rocket at the same time. So,
at some point, you have to figure out
how to build a startup inside your own
company.
I couldn't agree more and that is what
makes this so difficult with this fast
evolving goal for goalpost if you felt
this tension you're not alone when a
three month road map starts to feel
stale in 3 week what is possible is
constantly evolving how do you adjust
that in the road map and because of that
we're realizing that we're moving
farther from deterministic road maps
where we generally knew what we were
building for months in advance we've had
to shift our mindset that embraces
ambiguity where learning and discovery
are what shaped the path forward. Now
the destination itself can evolve as we
learn more about what's possible
and that's how we plan. It's also
evolved how we design when earlier we
could build AI into features in an AI
first interaction. Customers expect
seamless intelligent systems that
stretch across workflows and even roles.
Let's think through that with an
example. Let's talk about Wufill. It's a
health tracking app for puppies. And
here our customer is Pineapples.
Pineapples uses Wufill to track meals uh
activities, digestive insights. Yes,
that's a pool blog. And also track
supplement recommendations.
Each of these features in our company is
owned by different teams. But in an AI
first world, when Pineapples is engaging
with that app in a natural language,
he's not thinking in features. He's
expecting a unified value. So when
Pineapple says, "I'm feeling kind of
blah." A meal-based response could say,
"Hey, maybe because you skipped
breakfast," a digestive based response
could say, "Yeah, your poop seemed a
little off today." But both both answers
are useful, but very narrow. Now imagine
a unified experience for pineapples. The
answer could be it might be the new
kibble. Your activity seems to be going
down over the days and your poop also
seemed to be off today. Maybe this new
supplement that you're trying. Pause it
and see how you feel tomorrow. That's
what AI first really means. Not just
smarter features with AI sprinkled in
them, but the unification to generate a
broader value that you unlock
incrementally through your road map.
But that has also evolved how we build,
which means how we work together, move
fast, learn fast, and stay aligned
through this uncertainty. Here's what
those shifts look like.
Historically, innovation often happened
in a reactive way. Someone had an idea,
we test it in isolation, maybe do a
spike to see if something worked, and
then move on to the next big idea. It
was ad hoc. And while it led to moments
of inspiration, it rarely became
sustained strategic driver. In an AI
first world, that kind of fragmented
discovery doesn't hold up. The landscape
is moving so fast with the stakes so
high that we need to treat discovery as
repeatable deliberate process. And
that's what ritualized discovery means.
It means building time into our planning
cycles for experimentation, hackathons,
and learning in various forums and
formats that are visible and actionable
across the company strategy. As part of
virtualized discovery, we've also
embraced a mindset of MVPs for learning,
not just to launch quickly, but to
validate direction. We assume that not
every bet will land. Some features won't
work the way that we're expecting them
to. But in this case, failure is a
feature, not a bug. It's what drives
clarity through ambiguity.
We've also had to rethink what processes
are for. Many of the processes we've
built in the past were designed for
predictable linear road maps with
deterministic risks. Uh it was a world
where we generally knew what we were
building months in advance. But in an AI
first world where timelines shift,
capabilities are evolving monthly, those
old processes don't always hold up. At
the same time, if you introduce too many
new processes all at once, it can
backfire. Instead of helping, they slow
us down. They become overhead. They
create drag. And a few folks here from
Sprout will confirm that in Sprout,
we've learned that the hard way. That's
why we've started treating process as a
product. We evaluate it against
outcomes. Does it create clarity of
direction? Does it unblock teams? Does
it help us make better decisions faster?
And if the answer is no, we have to
reiterate or cut it entirely. Process
isn't a dirty word. When it's
purposeful, it gives teams the clarity
and flow and becomes a process that
accelerates.
Now, we heard in the amazing keynotes
yesterday that execution is the mode.
Speed is paramount to delivering against
the evolving landscape of tech and the
customer expectations. But if you move
fast without clarity of direction, you
end up with chaos. And if you have
clarity of direction but no momentum,
you stagnate. How do you find that
balance? Because in AI world, speed
matters, but only when it's paired with
direction. We've had to move from speed
to smart velocity, which means building
the muscle to move fast with purpose.
When we talk about prototyping quickly,
building MVPs and speed, we're not just
saying ship it for the sake of shipping
it. We're talking about working with
clarity, momentum, and adaptability.
Smart velocity is what keeps us grounded
and moving all at the same time.
And now let's talk about the most
important piece of it all, the people.
If strategy sets the direction and ways
of working determines how we execute,
the people are what makes the whole
thing real. After all, culture eat
strategy for breakfast, right? And
here's the truth. Becoming an AI first
company isn't just tech transformation.
It's mainly a cultural transformation.
And culture lives in people. How they
think, how they work, how they lead, and
how they feel. That means that we need
to rethink what great talent looks like
in the AI era. Not just in your AI team
but across the entire company. As social
we are seeing two major shifts. How
talent is evolving in this AI area and
how to scale AI fluency across organiza
excuse me the organization. So let's
start with the first one.
As the world changes, so do the
capabilities that matter most. To be
clear, the talent that we need today
isn't replacing what we had yesterday.
It's just building on it. Until now,
what made an AI practitioner great was
deep specialization.
Being a generalist or a visionary
builder gave you an edge, but for most
roles, those skills weren't critical.
Today, that is changing. AI depth is
still essential, but now being a
versatile visionary builder has become
critical to success.
That's why we are investing in T-shaped
talent. People with deep expertise who
can also stretch wide, prototype
quickly, collaborate fluidly across
silos, and bring end to end systems to
life. It's a bit like Indiana Jones.
We're not choosing the professor and the
adventurer. We are combining them.
Bringing the deep specialization of the
scholar into the jungle to uncover
unimaginable possibilities.
And that's why more than ever we need
people who can navigate ambiguity.
There's no playbook. The technology is
evolving fast. The road map keeps
shifting. That means we need
pathfinders.
people who can hold to their deep
expertise while forging new ways through
shifting terrain. And in most cases, the
path doesn't even exist. So they have to
imagine it first. And that's where
visionary thinking becomes a core skill.
So this shift from shipping what's known
to charting what's newly possible is
what we believe defines the next
generation of AI builders.
But evolving our builders is only part
of the story. Becoming AI first isn't
just about the few who train models or
build agents. It's about building
fluency across the whole organization.
You can't go AI first if the rest of the
company is still AI last.
So in an AI first company, everyone
touches AI, even if they're not training
models or building agents, from
marketers and designers to PM and care
agents. We want every team to feel
empowered to understand AI and confident
enough to build with it. And that's why
we're investing in orwide AI fluency to
inspire people of what's possible and to
make AI feel usable, safe, and real in
the context of their day-to-day work.
We're supporting this enablement through
things like AI newsletters, podcast,
crossunctional AI show and tell. And by
empowering teams to use whatever AI
tools help them work smarter. But
fluency isn't enough. It has to scale.
To do that, we aim to build souls
service platform to enable product and
engineering teams across the
organization to prototype and ship AI
powered features without needing deep
involvement from the AI team. The goal
is not to turn everyone into an AI
expert. It is to create a company where
AI thinking and exploration is the
default, not the exception.
Sorry.
Sorry.
All right. Now, we've talked about a lot
of the things that have changed, and I
know that's very overwhelming, but there
are also some things that haven't
changed, the fundamentals.
Our best AI features still solve
customer problems. They're rooted in
needs, not the novelty of AI, as long as
they're solving customer problems. User
experience, performance, reliability,
trust, these are still non-negotiable in
the AI force world.
Human creativity, human judgment, and
human care remain central to how we
lead, how we make decisions, and how we
show up for our teams and customers.
So, if you're leading AI transformation
in your company, get honest about the
trade-offs. Invest in people, not just
models and agents. Kill the road map if
needed. Be bold, learn out loud, and
ship that weird idea.
We hope this talk served as the
flashlight in the maze and you leave
knowing that becoming AI first won't be
a linear path and it won't be perfect.
But the good news is you don't need all
the answers to get started. You just
need the right questions and the
conviction to evolve.
We're going to leave you with this one
with this last reflection. The most
transformative inventions in human
history only became truly revolutionary
when they became part of our day-to-day
life. And we're now standing at the cusp
of the next greatest revolution. This is
a profound privilege and an honor. Not
only are we witnessing this moment, but
we have the opportunity and
responsibility to shape it, to guide our
teams, our companies, and our
communities through a transformation
that will redefine how we live, how we
work, and how we connect. This isn't
just a technological revolution. It's a
human one. This transformation isn't
easy. It takes time, grit, and the
patience to navigate setbacks. doubts
and growing backs.
In fact, this is how we look like when
we started the journey.
And now, well, let's just say we learned
a lot.
So, if if it feels like a long road
ahead, you're not alone, but we promise
it's totally worth it. Thank you so much
for spending time with us today.
[Music]