Think Fast Talk Smart: Communication Techniques

“Most teams leave the vast majority of their innovation potential on the table.”
If you treat artificial intelligence like an oracle, you’ll likely be disappointed. But treat it like a teammate, and Jeremy Utley and Kian Gohar say you may be surprised by how useful a collaborator it can be. Utley, an adjunct professor at the Stanford d.school, and Gohar, a bestselling author and futurist, have studied how teams can use AI to generate more creative ideas and solve problems. Their research shows that tools like ChatGPT can unlock new possibilities, but only when teams know how to work with them. In this episode of Think Fast, Talk Smart, Utley and Gohar join Matt Abrahams to discuss how to move beyond treating AI like a magic 8-ball and start using it as a creative partner for brainstorming and innovation.

Takeaways:
  • Treat AI like a teammate, not an oracle. AI becomes more useful when you actively collaborate with it, asking questions, building on responses, and using it as a partner rather than expecting one perfect answer.
  • Better AI collaboration can unlock better ideas. Integrating AI into brainstorming and problem-solving can help teams expand their thinking, generate more possibilities, and tap into creative potential they might otherwise leave unexplored.

Activity:
  • Give AI a seat at the table. During your next brainstorm, treat AI as another member of the team. Ask it to contribute ideas, challenge the group’s assumptions, or suggest a direction no one has considered yet.

Episode Reference Links:
Connect:

 Chapters:

  • (00:00) - Introduction
  • (01:43) - AI and Problem-Solving
  • (04:13) - What the Research Revealed
  • (05:18) - Why Better AI Takes More Work
  • (08:40) - The FIXIT Method
  • (13:10) - Better Conversations With AI
  • (16:07) - Making AI Critique Itself
  • (18:45) - The Final Three Questions
  • (24:35) - Conclusion

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Creators and Guests

Host
Matt Abrahams
Lecturer Stanford University Graduate School of Business | Think Fast Talk Smart podcast host
Guest
Jeremy Utley
Stanford Adjunct, Venture Investor, Co-Author
Guest
Kian Gohar
AI Researcher, Author, and Keynote Speaker

What is Think Fast Talk Smart: Communication Techniques?

One of the most essential ingredients to success in business and life is effective communication.

Join Matt Abrahams, best-selling author and Strategic Communication lecturer at Stanford Graduate School of Business, as he interviews experts to provide actionable insights that help you communicate with clarity, confidence, and impact. From handling impromptu questions to crafting compelling messages, Matt explores practical strategies for real-world communication challenges.

Whether you’re navigating a high-stakes presentation, perfecting your email tone, or speaking off the cuff, Think Fast, Talk Smart equips you with the tools, techniques, and best practices to express yourself effectively in any situation. Enhance your communication skills to elevate your career and build stronger professional relationships.

Tune in every Tuesday for new episodes. Subscribe now to unlock your potential as a thoughtful, impactful communicator. Learn more and sign up for our eNewsletter at fastersmarter.io.

Matt Abrahams: AI can generate
answers in seconds, but the quality

of those answers depends on the
quality of the conversation.

I'm Matt Abrahams, and I teach
Strategic Communication at Stanford

Graduate School of Business.

Welcome to this Rethinks episode of
Think Fast, Talk Smart, the podcast.

We're revisiting my conversation with
Jeremy Utley and Kian Gohar to explore

how conversational AI can become a true
thinking partner, helping individuals

and teams solve problems more creatively
and communicate more effectively.

Enjoy this journey into
our communication vault.

The promise of AI is tremendous.

Yet most of us, if we're using it
at all, are using it incorrectly.

It's not about a transaction.

It's about a conversation and interaction.

I'm Matt Abrahams, and I teach
Strategic Communication at Stanford

Graduate School of Business.

Welcome to Think Fast,
Talk Smart, the podcast.

Today, I am really excited to chat
with Jeremy Utley and Kian Gohar.

Jeremy is a repeat guest
to Think Fast, Talk Smart.

He's an adjunct professor specializing
in creativity and entrepreneurship at

Stanford, and the author of Idea Flow:
The Only Business Metric That Matters.

Kian is founder of Geolab
and former executive director

at Singularity University.

He's the bestselling author of
Competing in the New World of Work.

Welcome, Jeremy and Kian.

Thanks for being here.

Jeremy Utley: Thanks for having me back.

Kian Gohar: Such a joy
to be here with you.

Matt Abrahams: All right.

Shall we get going?

Jeremy Utley: Let's do it.

Kian Gohar: Sounds good.

Matt Abrahams: To get us started,
Kian, I'm curious, what motivated you

and Jeremy to look at the impact of
AI on creativity and problem-solving?

Kian Gohar: So in my business, we coach
teams to achieve complex business goals

through team transformation practices.

Sometimes this involves tackling
a difficult innovation project,

sometimes it's struggling with
change management, but it always

boils down to this one issue, which
is: How can we solve this problem?

And that usually entails human
ideation and prioritization.

And I wanted to see if we can
bring a new technology tool into

the mix to get better ideas.

And given that I've been teaching AI
in Silicon Valley to executives for a

long time, that was always my hunch.

But it wasn't until ChatGPT became
publicly available that I had the ah-ha,

and I didn't quite know how it could be
useful to the issue of problem-solving

on teams, but I had a hunch.

And so we designed the study together,
uh, to learn and from real world

practice of how teams might use AI to
get better ideas to solving problems.

And what we did was we recruited
hundreds of participants from many

companies in Europe and the US, and
we asked them to identify a particular

pain point within their organization
or a problem that they needed to

solve for, something that was real.

And then we actually gave half of
the participants in each of these

problem-solving ideation workshops
had access to ChatGPT, and the

other half didn't have access to it.

So they were just thinking
on their own as humans.

And then the other half
had humans plus AI.

And we went through a problem-solving
ideation exercise, and at the end

of it, we asked the problem owners
or the executives who shared with

us the particular pain point to
grade all the various ideas from

A, being spectacular, all the way
to D, not worth pursuing further.

And so we ran this blind study to
understand how generative AI can

facilitate problem-solving, ideation, and
collaboration with real world examples.

Jeremy Utley: The other thing that we
did was we asked participants before

and after the session how they felt
about collaboration and problem-solving

and ideation because we wanted to see
what was the impact of just a standard

brainstorming session on someone's
attitude towards problem-solving

and collaboration and innovation.

And what was the impact of a
session that included generative AI?

Did it have a differential
impact on participant sentiment?

Matt Abrahams: And the
research to me is fascinating.

Jeremy, can you summarize the results
of the research you and Kian did

and relate it to the quality of idea
generation and the feelings people

have about the ideas that were created?

Jeremy Utley: Very broadly speaking,
we found that teams, if they want

to outperform using AI, they need
to adhere to certain practices.

And sadly, most teams do not.

So despite the potential to dramatically
outperform, most teams leave the

vast majority of their innovation
potential on the table when they use AI.

When you approach AI like an oracle or
like a search engine just looking for the

answer, it may feel like magic, but the
data shows you end up underperforming.

If you want to get world-class
results and outperform, you

can't approach it like an oracle.

You need to instead approach AI
like a conversation partner, and

that doesn't feel nearly as magical.

It feels more like work.

And for that reason, the teams
that outperform actually feel worse

than the teams that underperform.

It's very counterintuitive.

Matt Abrahams: I find that absolutely
fascinating, and I also find that

interestingly, the ability to have
a good conversation with AI is what

makes the difference for creating
valuable ideas and solving problems.

Kian, can you go into a little more
depth with the counterintuitive

findings that Jeremy just shared?

Why do you think it is that AI-assisted
teams that delivered worse solutions

actually felt better about their work, and
the AI-assisted teams who delivered better

outcomes actually felt worse related
to their non-AI-assisted counterparts?

Kian Gohar: Because rewiring our brains
to act differently takes practice.

And we professionals have decades
of experience or certain ways of

doing things, and we're asking
them to work differently now with a

different workflow, and that's hard.

It's like going to the gym after not
having exercised for a long time.

It really sucks, and you don't
particularly enjoy it at first,

but it has long-term benefits.

And so we found that teams that were
able to use AI effectively, they

actually had to rewire their workflow
and how they went about to try to

incorporate AI as a co-pilot on the team.

And that's different, and it was
exhausting, and they felt like

it was just a lot of work, but
ultimately, they had better responses.

Matt Abrahams: Would you add
something to that, Jeremy?

Jeremy Utley: Part of the challenge
is, I almost picture a cartoon strip

with a thought bubble that's like,
"But I was promised superpowers." The

premise and the promise of AI, I think
it's intimidating, and it maybe strikes

fear into a lot of people's hearts.

That's one thing.

I don't think there's
a reason to be afraid.

I think there's a lot of reason
for enthusiasm and fun and

seeking fluency, obviously.

But I think there's a premise
of this is gonna be like magic.

But when you start there and you think,
"All I have to do is ask a question, and

then I get answers," well, guess what?

You do.

You ask a question, and you get three
pages of, call it B+ documentation.

And teams go, "It is kind of
magical. Well, let's go get coffee."

Like, we're kind of done, right?

If you're expecting magic, you will
get it, but you won't do that well.

If you've gotten practice being a
conversation partner to an AI copilot,

what you realize is the first stuff that
you get out isn't great unless you're

really thoughtful about framing and
providing context and digging deeper and

pushing back and asking questions and
treating it much more like a conversation.

And then the more you treat it like
a conversation, the less magical it

feels, and the more it feels like AI
is actually getting you to work and

pulling your best thinking out of you.

And so I think part of it is just
the mindset people have when they

hear, "Oh, we get ChatGPT." The
people who go, "Oh, magic," are

almost always the underperformers.

The people who roll up their sleeves
and go, "Oh, now we have work to do.

It's maybe a different kind of work
to do, but I'm excited to do it,"

those are the people who outperform.

Kian Gohar: I would also add that
there is a reason why ChatGPT gives

you generic answers, and it's designed
mathematically to give you the most likely

answer that it thinks you want to hear.

And so as a result, the first
few suggestions it offers are

supposed to be generic and average.

And so unless you have this conversational
back and forth with it like you would

with a colleague or a friend to push it
and to give it context, you're just gonna

get average answers and average ideas,
and that's really not good enough if

you're trying to solve complex problems.

Matt Abrahams: It comes down
to mindset shift and work.

There is no magic.

It's not for free.

If you really want to solve problems
well and get creative, you actually

have to have a conversation.

So Jeremy, can you share your FIXIT
methodology and describe the different

components and how they work?

Jeremy Utley: Absolutely.

So FIXIT is a five-step methodology
to turbocharge your collaboration.

F stands for you have a
focused problem, right?

So you don't want to boil the
ocean, but you want to be very

focused in the kind of challenge
that you are bringing to ChatGPT.

So in this example, instead of saying what
recommendations would you make for a human

interfacing, I would say, "I'm joining a
podcast, and I'd like to give concrete,

tangible suggestions to professionals who
haven't had much experience with ChatGPT.

If you had three or four, or maybe I'd
start with ten suggestions for someone

who's unfamiliar but wants to be more
familiar, start by asking me three

questions to better understand the kind
of professional I'm describing." Right?

That's what we mean by F, kind of focused.

So that's a very focused prompt, right?

I is ideate individually.

So before coming to the team, 'cause
remember our study was conducted in the

context of teams trying to solve problems.

Before coming to your team and even
before coming to ChatGPT, think

for yourself about what do you
know and what's your point of view.

Again, this kind of hones the context
that you bring to the conversation, right?

It's critical.

Research has shown that the best way
to brainstorm is to alternate kind of

individual ideation and group ideation.

Well, the same is true
actually in the context of

collaborating with generative AI.

You want to alternate between individual
personal thinking and thinking

assisted and amplified by AI, right?

So that's the I. X is context, so
providing sufficient background context.

A lot of times we recommend that
folks upload documents, right?

When we were conducting our study, we
would have a problem owner actually

do a dossier that we would upload
to ChatGPT to provide context.

If you don't know what your
context is, here's an amazing hack.

Ask ChatGPT to ask you for the context.

So as an example, I've got a friend who's
negotiating a lease trying to build a gym,

and he's trying to come to an agreement
with the property owner, and there's

a gap between where they need to be.

And I said, "Hey, why don't you
ask ChatGPT for help?" He said,

"Well, how would I do that?"

I said, "Well, have ChatGPT interview you
about your objectives and then interview

you about the counterparty's objectives
and then make some recommendations."

Well, that interview effectively becomes
a means by which you can provide context.

So the X is make sure that you're
giving ChatGPT context on the problem.

The second I is interactive
iterative conversation, right?

So you're never just taking the first
response that ChatGPT gives you.

You wanna be having a back
and forth and a dialogue.

So for example, if Matt and Kian and I
are going to try to title this episode,

we might say to ChatGPT, "Hey, we'd
love ten titles for an episode." And

then ChatGPT comes back with ten.

Almost everyone's default is to
think, "Which of these ten do I like

best?" What we'd recommend is just
immediately say, "I'd like another

ten." And then read all twenty and
say, "Here are the ones that I like.

Would you give me ten more like
these?" Chances are you're not gonna

get as good. And then if you think,
"Wait, why did I like number two?

Oh, there was a funny alliteration,
and number seven had a funny pun,

and number nine made reference
to…" Okay, use those as design

principles for the next ten, right?

That's an iterative back
and forth that yields.

We'd probably find that the fourth
tranche of ten title suggestions

would be radically exponentially
better than the first ten, right?

But it's by that going back and forth.

F-I-X-I-T.

T is for team incubation.

So it's important to then bring the
ideas that you've generated individually

and with ChatGPT or whatever LLM you're
using back to your team, and then

importantly, commission some experiments.

So this is where this work dovetails
with traditional innovation

methodology, but you never just wanna
select one idea and move forward.

It's impossible for an LLM or for
a human to a priori know which

solution is gonna be the best fit.

So as a team, you wanna have a practice
and a process around incubating or

low-resolution prototyping a handful of
the high-potential solutions that you've

generated in order to determine which
one actually solves the problem the best.

Matt Abrahams: This methodology,
upon hearing it, makes a lot

of intuitive sense, but I can
definitely see the effort involved.

I like that it involves individual
work and collaborative work, not

just with the ChatGPT LLM, but
also with others on the team.

Kian, not surprisingly, I'd like
to dive deeper into the fourth

step that Jeremy introduced us
to: interactive conversations.

We've spent a lot of time on
this podcast talking about how

to have better conversations, but
of course, focusing on humans.

What specific advice can you provide
to us for improving our conversations

and our communication with LLMs to
make sure we maximize the potential

goodness that can come from
collaborating with a tool like ChatGPT?

Kian Gohar: Yeah.

So the first thing I'd say is to make sure
you've downloaded a LLM app on your phone

and interact with an LLM on the phone
instead of doing it on the web browser.

If you don't see a app on your
phone, it's very unlikely that

you'll use it on a consistent basis.

And the more you use it, the more
familiar you become with it, the

more likely it'll actually be part
of your workflow, whether it's

individually or whether it's as a team.

So that's really the first step.

Download one of these apps on your phone.

And part of that is because we have
historically been, for the last

twenty-plus years in the internet era
and the browser era, we see a text box,

and we know exactly what to do with it.

We type in a particular
word or particular question.

And now that we have, uh, large
language models, the user interfaces

look pretty much exactly the same,
like the Google search engine box.

And it's oftentimes difficult to figure
out the right kind of question to ask

it or to word it in the right way to
get the right answer to suggestions.

And so we actually think it's a lot
better if you start interacting with

these large language models through
conversation, through spoken audio,

rather than just trying to type it
into a search engine box and trying

to figure out the exact right prompt.

The second thing is that you should
be uploading your prompts with audio

messages, voice messages, and talk to
it just like you would talk to a friend.

So whether you're talking to a friend
about a particular problem or talking to

a colleague about a particular problem,
just record it literally on the phone

on audio to text, and then the large
language model will transcribe that

and then offer out some suggestions.

And then the third thing I'd say is
think about using different models.

There are several different kinds of large
language models that you could use, from

ChatGPT to Claude to Bing and to others,
and they all have their different flavors

and different kinds of personalities.

And you might want to use one of those
models for a particular exercise or

activity, and then maybe you should go
talk to another model, just like you

would talk to two or three different
friends and get their opinions or

two or three different colleagues and
get their opinions, because they're

gonna give you different kinds of
nuance and different kinds of answers.

Matt Abrahams: I find that
really helpful advice.

I mean, we approach the communication
with technology based on the

interface that we have with it.

And if we move to our phones,
which we're used to communicating

with very differently than we
are, let's say, a browser and a

text box, it can really change it.

And it strikes me that it's not just
the actual interaction interface, but

it's also the curiosity we bring to it.

When I go to enter information
into a search engine, I

just want to get the answer.

I don't necessarily see it as
a conversation and a dialogue.

So that curiosity that I bring with
the follow-up questions or the doubting

and the exploration and expansion,
I think, is really important.

And I like the advice to check
with different LLMs, just like you

would check with different friends.

It gives you different ideas and inputs

Jeremy Utley: You can even feed different
LLMs answers into one another, right?

Matt Abrahams: Oh my goodness.

We can facilitate and broker
a conversation amongst LLMs.

Jeremy Utley: I, absolutely.

I mean, I plug in ChatGPT's answer into
Claude and say, "What do you think of

this?" all the time, and vice versa.

Take Claude's answer and
plug it into ChatGPT.

Ask ChatGPT what it thinks of it's own.

So this is, this is why Googling is
such a bad metaphor for what we're

talking about here, because you'd
never query Google about a Google query

to, to get meta for a second, right?

But after a sales call, I hop
on my ChatGPT voice app while

I'm stretching for a run.

Instead of sitting at the
screen, I'm stretching for a run.

I say, "Hey, I just talked
with Matt and Kion about our

research, and a couple follow-ups.

Would you craft a quick memo that I
could send to the team just thanking

them for their time today and how much
I enjoyed the conversation," right?

Well, it's gonna do it.

Well, then my next thing is I just look
at it and I say, "If I were to upload this

memo to you, and I were to ask you for
advice on how to make sure that they read

it and respond, what three changes would
you recommend?" And then immediately,

I had this happen the other day.

I was doing a voice vomit on a
post-sales call, and I had ChatGPT

write a memo, and then I asked ChatGPT,
"How would you criticize this memo?"

And ChatGPT said, "It's far too
long for today's busy professionals.

No one's gonna read this memo." And I
said, "Would you please go ahead and

shorten it to a point where you think
that people will actually read it?" You

can ask ChatGPT to evaluate its own work,
and it will do so dispassionately, right?

And that's the fun, but that requires
iteration and a back and forth.

Matt Abrahams: In many ways, the
advice that you all are giving are

advice that we give to people when
they are engaged in empathetic

dialogue in human-to-human interaction.

It's about curiosity, questioning,
about challenging in certain

ways, and it changes the metaphor
completely, as you mentioned, Jeremy.

So before we end, I like to ask
my guests a series of questions.

Two are similar to everybody,
and one that's very unique.

So Jeremy, for those who haven't
started with ChatGPT and large language

models, where should they start?

Jeremy Utley: I think for a lot of people,
if, if they say, "Where should I start?"

We've talked to so many professionals who
say, "I've been meaning to try ChatGPT.

I've been meaning to try generative
AI." And that to us is a shame.

Every single listener to this
podcast could have at least ten

hours of ChatGPT under their belt.

And if you find yourself going, "Oh
man, I'm behind," well, don't worry.

You can get up to speed quickly.

Here's a simple place to start.

This is something that every
single listener can do right now.

Think of an emotional human decision
you're trying to make in your life,

something that you would ordinarily
ask a partner or a friend or a

spouse or a colleague about, okay?

Think about what that is.

Go to ChatGPT and say, "Hey, I'm trying
to make this decision. Will you please

ask me three or four questions before
giving me your recommendation?" If

every single listener will do that
one simple activity, they're gonna

have what we call a personal epiphany.

That's gonna have cascading impact.

All of a sudden they're gonna start
thinking, "Could ChatGPT do this?

Could I ask ChatGPT about that?" Right?

But it must be emotional, it must be
deeply personal, it must be the kind

of thing that you would ordinarily
ask another human being about.

Get ChatGPT to ask you three or
four questions about it before

giving you advice, and start
the snowball rolling that way.

Matt Abrahams: Question
number two and three.

Kian, I'm gonna ask you because Jeremy
has previously answered these on an

earlier episode, and I encourage everybody
to listen to that episode where Jeremy

and I talk about his book, Idea Flow.

So Kian, who's a communicator
that you admire and why?

And you can't say ChatGPT.

Kian Gohar: I'm gonna
give you two answers.

One is Peggy Noonan, who is a columnist
for The Wall Street Journal, and I

so deeply admire her elegant prose
and her personal anecdotes that make

complex topics readily understandable.

She was a speechwriter for George Bush
Senior, and she developed the phrases

that we became very familiar with during
his presidency, like "Kindler, gentler

nation," or "Read my lips, no new taxes."

And the politics and the policy aside,
the ability to translate big ideas

into a few short words is just so
masterful that I always enjoy reading

her articles in The Wall Street Journal,
regardless of the politics or the policy.

And the second person I'd
recommend is Sam Horn.

She is an author of a book called
Tongue Fu, and she is a complete

master at teaching which words to
lose and which words to use to convey

meaning without tripping over yourself
and creating unintended arguments.

And these two are role models of
how I think about communication.

Matt Abrahams: Thank you.

Both of those recommendations, I
hear you talking about words and the

ideas that those words bring about.

Both of those are individuals
who, who have mastered that craft.

Kian Gohar: Because words are
really conversations that are the

seeds of innovation and how we
think about solving big problems.

And if we don't get the words,
then we won't be able to actually

get to the meaning and connection
of what we're trying to solve for.

Matt Abrahams: Final
question for you, Kian.

What are the first three ingredients that
go into a successful communication recipe?

Kian Gohar: The first ingredient is
something that might seem counterfactual

when we're talking about a communication
recipe, and that is listening.

Listening to the environment, listening
to the context, and listening to what

your counterparty might be thinking.

And so trying to understand the context
by listening is super important.

The second one is to know your audience.

Who are you speaking to?

Who's on the other side of the table?

Who's in the room?

What are their priorities,
and what are their goals?

And so that you can think about
crafting your message in a

way that resonates with them.

And the third thing is
to know your end goal.

How do you want the audience of
your intended communication to feel

after the communication is over?

Whether that's in person, whether that's
in a meeting or whether that's online.

How do you want the audience
receiving the communication to feel?

And then you work backwards from
that to structure the flow and the

necessary ingredients to make your
communication easily relatable,

understandable, and to land.

Matt Abrahams: We have certainly
heard the notion of knowing your

audience and being critical.

I really appreciate and like
this idea of backward mapping.

Start from the outcome, and then
we have to build and do and say

and emote to get to that outcome.

Thank you for that.

And thank you both, Kian and
Jeremy, for the conversation.

I find your work fascinating and
super helpful as generative AI

becomes more and more commonplace.

Further, I love how the concepts
of effective communication and

conversation and critical thinking
can help our partnering with AI to be

better, more creative problem solvers.

To learn more about the results
from Kian and Jeremy's work, go to

howtofixit.ai and check
out their article in HBR.

Thank you so much.

Jeremy Utley: Thanks for having us.

Matt Abrahams: Thank you for joining
us for another episode of Think

Fast, Talk Smart, the podcast.

It was great learning
from Jeremy and Kian.

Among the many things they shared, the
one that stands out most to me is that

AI is most powerful when we treat it as
a collaborative thinking partner rather

than simply a tool for finding answers.

To learn more, please be sure to dive
into our library of past episodes

wherever you get your podcasts.

Our back catalog is full of helpful
communication tips and tools to

help you be a better communicator.

This episode was produced by Katherine
Reed, Ryan Campos, and me, Matt Abrahams.

Our music is from Floyd Wonder, with
special thanks to Podium Podcast Company.

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