We're all chasing speed with AI, right? Faster
code, quicker insights, instant drafts. But what
if all that efficiency is just leading us completely
astray? Yeah, exactly. Imagine hitting the gas
pedal harder, but you've totally forgotten the
map. That's kind of where a lot of us are headed,
it feels like. Exactly. So today we're asking,
is AI really helping us make better decisions
or maybe just bad decisions faster? Beep beep.
Welcome back to the deep dive. This is where
we unpack complex ideas, hoping to give you those
aha moments. And I'm your co -host. For this
deep dive, we're tackling a really fascinating
piece of content asking, you know, how do we
solve the right problem in the AI era. Yeah,
we've got a stack of insights here today, and
they all seem to point to one core idea. Knowing
what problem to solve is now way more critical
than how fast you can solve it. So we're going
to explore this seductive trap of AI productivity,
the hidden dangers, and then crucially how tools
like design thinking can act as a sort of compass.
Right. The goal is for you to walk away with
a fresh perspective on how to really leverage
AI. Not just as a tool for speed, but like as
a partner in strategic thinking. Okay, let's
dive in. There's this huge buzz around AI productivity
hacks, right? Everyone's learning prompt engineering,
trying to build faster. It feels like staying
relevant just means executing quicker and quicker,
but this analogy we found is pretty potent. It's
like having this super powerful gas pedal, but
no steering wheel. Right, and it's a... It's
intoxicating, isn't it? That feeling of speed,
teams are churning out analyses, presentations,
automating workflows in hours, not days, things
that used to take forever. But the catch, as
the source points out, is they're often more
lost than ever because speed without direction,
that's just expensive wandering. It really shifts
the key question from can we build this to should
we even build this to sex silence? So why is
optimizing purely for speed without that direction?
Why is that such a dangerous path specific? now
in the AI era? Well, because fast execution of
the wrong idea leads to crashing harder, not
reaching your actual destination. OK, let's unpack
the real risks here then. The source material
outlines three deadly dangers. Yeah, let's break
them down. The first one, AI helps us create
more solutions for problems that don't actually
exist. How so? Well, AI democratizes solution
building. Sounds great on the surface, right?
But honestly, most people are not great at identifying
real problems worth solving. So the result, you
get these elaborate, technically impressive solutions
for imaginary issues, built -in record time,
beat. Think of it like a digital graveyard, just
filling up faster with unused apps and features.
OK, that makes sense. Danger number two, amplifying
preexisting biases. This one's fascinating. The
idea is AI doesn't make you a better problem
finder. It just makes you a faster implementer
of whatever you already assume. Exactly. Like
the example given, if you think low engagement
means you just need more content, AI is brilliant
at helping you generate endless content. But
it won't tap you on the shoulder and ask, hey,
are you sure content is the actual problem here?
Yeah, it basically creates this echo chamber
for our own biases. It validates them with huge
amounts of output, making us feel even more certain
we're right. It reinforces blind spots instead
of challenging them. The machine gives you what
you ask for, not necessarily what you should
be asking for. In the third danger, creating
an illusion of progress. I can see this one.
Doing things faster just feels productive, doesn't
it? We mistake all that motion for actual progress.
Absolutely. Teams start measuring success by
features shipped, lines of code written, articles
published, not by whether user behavior actually
changed or if the business outcome improved.
beat and that's just a direct path to burn out
and frankly wasted resources. I'd admit I still
find myself getting caught up in that shipping
faster mindset sometimes even when I know better
intellectually. It's a really seductive trap
you know. Oh absolutely it's easy to fall into.
So how do these dangers when they combine how
do they collectively undermine real innovation
and genuine impact? Well they lead to very efficient
work but on the wrong things. Creating solutions
nobody truly needs all based on flawed assumptions.
So if execution speed is becoming table stakes,
the source makes this really compelling argument.
Execution was maybe never the real bottleneck.
Right. That's the kicker. Projects often fail
because teams build absolutely perfect solutions.
But for problems that just weren't worth solving
in the first place, the real bottleneck, looking
back, has always been figuring out what to build,
not just how fast you can build it. AI just throws
that into sharp relief. And the examples given
were pretty clear. Instagram didn't just execute
photo sharing better. They tapped into a deeper
need for curated moments for sharing life highlights
easily. And Tesla, they completely redefined
the problem of car. It wasn't just transport.
It became about tech, sustainability, a whole
experience. Exactly. So when everyone can execute
at roughly the same lightning speed, thanks to
AI, the only sustainable advantage left is knowing
what's truly worth executing. This makes problem
finding incredibly powerful. powerful. It's the
new strategic high ground. So if everyone has
similar execution speed now, where does that
competitive advantage truly lie? It's really
in that unique ability to identify and define
problems that others haven't even noticed yet.
Mid -roll sponsor, read placeholder. Okay, so
if problem -finding is the new superpower, how
do we actually get better at it? How do we cultivate
it? This is where design thinking comes in. The
source describes it almost like a compass for
navigating this chaotic AI era. Yes. Instead
of trying to compete with AI on speed, which
is a losing game, we partner with it. We use
it to deepen our problem solving. And design
thinking provides the framework for that partnership.
It's the one thing AI really can't do for you,
the human insight part. Right. I see the synergy
there. AI can generate a million solutions, but
design thinking helps to find the right problem
to solve in the first place. AI can optimize
for any given metric, but design thinking helps
you choose the right metrics that actually matter.
The future belongs to those who can ask those
deeper questions, who can observe actual human
behavior and then, you know, prototype their
assumptions quickly to learn. So how do humans
and AI effectively collaborate within this design
thinking framework? What does that look like
practically? Humans really lead on the empathy
and the problem definition parts. AI then assists
with data analysis, idea generation, and it massively
accelerates prototyping and testing. Okay, let's
break down that process. The source gives a pretty
clear roadmap for this human -AI collaboration
in each phase, starting with phase one, empathize.
This is human -led. but AI assisted. Right. The
goal here is deep user understanding, seeing
the world through their eyes, not just our assumptions.
And crucially, AI cannot truly empathize. It
doesn't have lived experience. So the human role
is vital. conducting interviews, ethnographic
studies, actually watching people reading between
the lines, catching nonverbal cues. And AIs role
in this phase? It's like the world's best research
assistant. It can transcribe hours of interviews,
summarize them, analyze sentiment from like thousands
of online reviews. It could spot weird patterns
in data that might suggest where humans should
dig deeper. It handles the scale so humans can
focus on the depth. Okay, then phase two, define.
This is where human insight is apparently paramount.
Yeah, this is probably the most critical phase.
It's where you synthesize all that empathetic
understanding into a clear, actionable problem
statement. The human role is asking, okay, based
on all this, what is the real problem here? Using
frameworks like jobs to be done, JTBD. What job
is the user trying to get done? Right, focusing
on the underlying need and AI's role here. It
can help refine and explore that problem space,
but only after humans have provided high -quality
input those synthesized insights. It's garbage
in, garbage out, otherwise. There's a big difference
between a bad prompt like generate 10 new features
and a good one. Yeah, the source had a great
example of an offensive prompt. Based on these
20 user interview transcripts, synthesize the
top five jobs to be done users are trying to
accomplish. And it asks for pains, gains, obstacles,
really structuring the insight AI is working
with. And those JTBD examples were things like,
help me catch up fast, or help me plan tomorrow
quickly, or let me micro -learn a skill, nuanced
human needs that AI can help structure but not
originate. Gotcha. So once the problem is solid,
then AI gets to really shine. Yeah. ID8. Exactly.
Now you unleash the beast. Humans set the constraints,
the direction, and act as curators. And AI becomes
this amazing creativity engine. Yeah. Generating
hundreds, thousands of ideas in seconds. Using
techniques like Stamper or brainstorming from
different perspectives. Think like a logistics
company. Think like a psychologist. Think like
a game designer. Whoa. Imagine scaling that creative
output to like a billion queries, exploring literally
every angle. It's mind boggling potential, right?
Yep. Then phase four, prototype, goal. Quick,
cheap experiments to test assumptions. Human
role. Decide what needs prototyping. Identify
the single riskiest assumption you need to test
first. And AI just dramatically speeds this up.
It can turn napkin sketches into clickable wireframes,
generate realistic placeholder content, maybe
even write simple code for basic functional prototypes.
It shrinks the learning cycle massively. Okay,
and finally phase five, test. Put the prototype
in front of real users. Human role again is key.
Design the test scripts, observe users directly,
ask those crucial why questions, interpret contradictory
feedback, all the subtle stuff. While AI handles
the grunt work, analyzing test results, processing
screen recordings, summarizing quantitative feedback,
it frees up human brain power for the deeper
insights. So when you boil it all down, which
phase truly relies most on that unique, non -replicable
human insight? Definitely empathy and defining
the right problem. Those are uniquely human tasks
AI can assist with, but not lead. Okay, so this
isn't some abstract theory. It's a learnable
framework. The source material provides a practical
toolkit to actually hone these problem -finding
skills. Number one, start with empathy mapping.
Yeah, literally map out what users think, feel,
see, say, and do. And critically, their underlying
pains and gains. Not what you think they think
or feel, but based on actual observation and
interviews. So many teams just skip this. Number
two, practice problem laddering with the five
Ys. For any problem you identify, just keep asking
why five times. The real root cause, the deeper
insight, usually pops out around the third or
fourth Y. So powerful. That example from the
source. User aren't using feature X. Asking why,
why, why eventually leads you to something fundamental
like we need to rethink the app's entire information
architecture based on core user needs. Totally
different level of problem. Third. Become a professional
observer. Like literally spend time just watching
people interact with the world or your product
or whatever context is relevant. AI gives you
the what from data, but observation helps you
understand the why. And fourth, experiment with
problem statements. Don't just settle on the
first one. Write it five different ways. Use
that, how might we? Yeah, instead of just, our
problem is high churn, try reframing. How might
we make a new user's first 30 days incredibly
valuable and engaging? That reframing itself
can spark totally new ideas. So if you had to
pick just one tool for a listener to start with
today right now, what would it be? Definitely
empathy mapping. It forces that shift from your
own assumptions to actual user needs. It's foundational,
I think. Totally agree. And this whole discussion...
It really points to a fundamental shift in what
it means to be a professional these days. We're
moving from being just executors to becoming
orchestrators. I love the conductor analogy used
in the source material. It puts it perfectly.
You're the conductor. AI tools are your orchestra.
You've got experts in writing, coding, analysis,
whatever. Your job isn't to play the violin and
the trumpet. It's to choose the right music.
define the problem, interpret the score, set
the strategic direction, lead the orchestra,
guide the AI tools, maybe using prompts as your
baton, and crucially, listen and adjust, interpret
the results, and iterate. Yeah, that's it. The
human moves from doer to strategic thinker, connector,
curator of quality. It demands a different skill
set, a different mindset. So what do you think
is the biggest challenge for professionals in
making that shift, from executor to orchestrator?
Probably letting go. letting go of the ingrained
urge to do everything yourself, and learning
to trust the AI to handle the detailed execution
effectively. It's a big mental shift. Okay, let's
try to wrap this up. Today's deep dive has really
shown us that the future isn't necessarily about
running faster, it's much more about going in
the right direction. Absolutely. The real competitive
edge in this AI -powered world isn't just using
AI, it's knowing how to use it to identify and
solve problems that genuinely matter. It really
is problem finding over solution building now.
And we learn that AI can definitely make us faster,
yeah, but design thinking is what ensures we're
faster at doing the right things. It's about
combining that unique human empathy and insight
with AI's incredible analytical and generative
power. It's the partnership. So the next time
you find yourself reaching for an AI tool, mainly
just to speed things up, Maybe pause for a second.
Ask yourself, am I just pressing the gas or am
I actually checking the map? Yeah, choose where
you invest your skill development wisely. The
teams, the individuals who master this approach,
they won't just build faster, they'll build truly
elegant solutions to problems other people haven't
even noticed yet. And that's a real sustainable
advantage. So the final thought for you, what
problem will you choose to define more deeply
today? Thanks for joining us for this deep dive.
Until next time, keep digging deeper.