How Can AI Scale Innovation 10x Faster in Your Business?

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In this episode of Imperfect Marketing, host Kendra Corman sits down with Amir Elion, founder of Think Big Leaders and a global expert on innovation and AI strategy. Together, they unpack how artificial intelligence is transforming innovation, marketing, and the way businesses think about creativity and efficiency.

Amir shares his journey from leading innovation initiatives at Amazon Web Services to helping organizations around the world use AI to scale ideas faster and smarter.

🚀 The New Face of Innovation

• AI as a Catalyst for Innovation – Why Amir believes AI doesn’t replace creativity—it amplifies it.
• Systematic Innovation – How innovation is more science than magic, with best practices, methodologies, and even ISO standards.
• Scaling Creativity with AI – How AI tools like ChatGPT, Claude, and Gemini can help teams ideate, prototype, and role-play with personas 10x faster.

💡 AI in Marketing and Business

Amir shares real-world examples of how companies are applying AI to streamline marketing processes and drive efficiency:

• Automating SEO and Content Workflows – How a gaming company now processes and localizes thousands of new titles using AI-powered workflows.
• Smarter Marketing Research – Why tools like ChatGPT, Perplexity, and Claude have become Amir’s personal “research assistants.”
• Avoiding the ‘AI Slop’ Trap – The importance of human oversight, editing, and providing examples to teach AI your brand’s tone and standards.

⚠️ The Risks and Responsibilities of AI

• “Human in the Loop” Principle – Why every AI workflow still needs human review, editing, and approval.
• Ethical and Compliance Challenges – How organizations can balance innovation with data privacy and risk management.
• Three-Tier Risk Model – Amir’s framework for identifying low-, medium-, and high-risk AI use cases to guide safe adoption.

🧰 Tools and Tips for Getting Started

• Don’t try every AI tool—pick one or two and learn them deeply.
• Master your tools before scaling: tweak settings, explore model options, and learn how to “squeeze the lemon.”
• Start small, focus on use cases that save time and amplify creativity, not replace human input.

🧭 Lessons from Amazon on Innovation

Drawing from his time at Amazon Web Services, Amir reveals how Amazon’s “Working Backwards” approach fuels innovation:

• Start with the customer, not the product.
• Write an imaginary press release describing your future product before you build it—if it’s not clear and compelling, you’re not ready to launch.
• Always focus on delighting the customer, not just building something new.

🎯 Amir’s Biggest Marketing Lesson

Amir admits his biggest mistake was falling in love with the product instead of the customer. Early in his VR/AR startup days, he focused on selling technology rather than solving customer pain points—a mistake that taught him the importance of clarity, audience insight, and consistency.

🔑 Key Takeaways

Looking to leverage AI? Want better results? Want to think about what you want to leverage?

Check and see how I am using it for FREE on YouTube.

From "Holy cow, it can do that?" to "Wait, how does this work again?" – I've got all your AI curiosities covered. It's the perfect after-podcast snack for your tech-hungry brain.

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Episode Transcript

SPEAKER_01 (00:27):
Hi, I'm Kendrick Corman.
If you're a coach, consultant,or marketer, you know marketing
is far from a perfect science.
And that's why this show iscalled Imperfect Marketing.
Join me and my guests as weexplore how to grow your
business with marketing tipsand, of course, lessons learned
along the way.

(00:52):
Hello and welcome back toanother episode of Imperfect
Marketing.
I'm your host, Kendra Corman,and today I am joined by someone
who has a passion for AI andinnovation similar to me.
He actually might be a lotstronger in that than I am.
Welcome, Amir.
Thank you so much for joining metoday.

SPEAKER_00 (01:11):
Thank you, Kendra.
And I'm very happy to be hereand thanks for having me.

SPEAKER_01 (01:15):
So let's go ahead and talk about how did you get
started with innovation and AIand all this fun stuff?

SPEAKER_00 (01:24):
Yeah, I'd say innovation has always been a
silver line in my career.
So uh I've been in innovationconsulting.
I love innovation, so I I kindof read books and learn about
it.
And even when I wasn't ininnovation roles, I always kind
of did innovation workshopsinternally or with customers if
it even if I was giving otherservices, everybody asked me for

(01:46):
innovation stuff.
So that's kind of uh always beenuh kind of a fire uh that I've
uh kindled.
AI, uh I would say uh a fewyears ago when I was in Amazon
Web Services, I was very curiousabout how does Amazon innovate?
Elements, of course, istechnology, and I got to work
with uh some great customersbecause my role I was helping
other customers think aboutinnovation with the cloud

(02:09):
technology, and in some cases itwas around AI.
And then, of course, when inmore recent years, generative AI
came about, I was veryfascinated about it and I wanted
to connect these two things.
And I started to play aroundwith ChatGPT, with other
technologies, and see how can AIsupport, uh, accelerate, scale

(02:30):
the rate of innovation.

SPEAKER_01 (02:31):
Very interesting because I have a tendency to
hear that innovation is the onething that AI doesn't do, right?
And that's a lot of what I shareis, you know, hey, you know, AI
is going to flip the workpyramid, right?
Right now, 60% of our day isspent on work about work.
And like less than 10% that topof that pyramid is spent on us

(02:54):
thinking, innovating, coming upwith new ideas.
And when I talk to people, Isay, you know, I've been to
several presentations and theytalk about flipping the work
pyramid.
And when they do, we're gonna behopefully spending 60% of our
day on innovation and coming upwith new ideas and the things
that AI can't do.
But you're saying that AI ishelping speed up innovation.

(03:17):
How is that happening?

SPEAKER_00 (03:19):
Speed up and scale.
I mean, it comes back to thewhat I believe about innovation.
And again, uh, and here it maybe also a surprise to some
people.
I think that innovation isactually a systematic endeavor,
right?
And and there are there's a wayto do innovation in a right way.
Of course, there is a creativespark there.
There are things that are uhspecial, but if you really want

(03:40):
to do innovation for the longterm and at large scale, it
takes a systematic approach andit takes methodologies.
Basically, it's a profession.
I'm uh here in, I mean, I'mbased in Stockholm in Sweden,
and I'm a member of theInnovation Leaders Association.
And you know, last year we evenwere supporting a new ISO
standard for innovation.
And you think, how can that belike ISO standard, right?

(04:01):
And you know, and innovation,how does that work?
Yes, because innovation is asystem, it's a methodology,
there's best practices, andconnecting it back to AI.
If you teach AI to be that superinnovator accord, and you give
it the context, uh, you make itpart of the team.
Then it can help you scale andinnovate and play roles.

(04:23):
There's so many things you cando with AI.
Again, you have to understandwhat the capabilities are, you
have to understand what it'sgood at and what it might not be
good at.
But I find it that I caninnovate now 10 times as much,
not 10% more, like 10 times muchfaster and bigger with uh
innovation.
I can prototype, I can ideate, Ican roleplay with imaginary

(04:46):
customers and personas.
So, so many things you can dowith AI if you know how to guide
it.

SPEAKER_01 (04:52):
Yeah, I mean, it's amazing how quickly you can test
and try and do things with AI,right?
I mean, things that might havebeen hard for somebody to do and
they would have had to put in aproject to IT to get things
coded.
You don't need to do thatanymore.
It's just amazing.
So you covered a couple ofthings that AI is doing, but how

(05:13):
do you see AI being used inmarketing or in general in
business?
Is it beyond innovation?

SPEAKER_00 (05:19):
Yeah, I'll focus on marketing because this is this
is what the podcast is about.
And I have I have quite a numberof use cases that I've done with
customers.
Uh, just to give you a fewexamples.
So I was working with uh agaming company, and uh they have
hundreds of games, if notthousands, every year coming in
from studios, and they need toyou know put those out on their

(05:41):
uh portfolio that the gamers canplay.
And they come from differentsources, very unstructured data
from PDFs from differentstudios, and just processing
that data and making you know,creating a web page for that
game with all the tagging andall the categorization used to
take a lot of time from thehumans, and now you know we've

(06:01):
we've built some uh AI-poweredworkflow for them.
There is a human in the loopjust checking things, but now on
not only can they do that likeuh a hundredfold uh uh faster,
but they can also almostautomatically translate to more
markets and more languages, sothat's like SEO-focused uh AI

(06:22):
workflow.
Another example is is marketingresearch, especially in recent
months where we had like morepowerful uh large language
models that are capable of doinglong-term research.
So even I, when I want to lookat my competitors at uh uh at
some of the ideas that I havefor products, right?
Because I want to build them, uhI start by doing some some

(06:44):
marketing research, and I don'thave a marketing research team
or analysts uh by my side, Ijust have my friends, my uh
perplexity, and I have Claudeand I have uh Gemini and and
Chat GPT, and I can you knowsend them off to do good
research on my behalf.
So that's like marketingresearch example, and then
there's you know contentgeneration.

(07:06):
That's uh uh so much to be donethere again.
If you but we don't also havethe the AI slop, right?
So we have to be careful uh whenwe do content generation, um, to
do it in a smart way.
Uh so yeah, that these are justkind of uh selected use cases.

SPEAKER_01 (07:25):
So, in talking about work slop, because I've been
hearing a lot about it, I'mgonna have a friend of mine on
on the show in a couple ofweeks, hopefully, um, where
she's gonna be talking aboutwork slop.
But some companies are actuallyinvesting a ton of money in AI
and they're finding that it'sactually not saving them money
because too many people arephoning things in using AI, not

(07:45):
correctly, right?
Because I think that's the bigthing, right?
They're not using AI the rightway.
They're not reviewing andediting the content they're
getting.
They're phoning stuff in andthey're just sending it out.
And when it gets down to theperson or up to the person that
needs to put it all together,they're having to start from
scratch because they don't haveany good content to work with.

(08:05):
How do we fix that?
Any ideas?

SPEAKER_00 (08:08):
Yeah, so a couple of thoughts there or suggestions.
Uh, first, uh, for the peoplewho are creating the content,
producing the content, there areways that you can guide the AI
to come up with something whichis human-level or you know, even
high quality, right?
You need to give it examples ofprevious or you know,

(08:28):
high-performing content, right?
So it learns the style and itlearns what is relevant to this
audience.
So think of it as you know, thisis like a really talented um uh
marketing professional that youjust hire to your team, but they
don't know anything about yourproduct, about your brand, about
your tone, about your audience.

(08:50):
Would you give them, you know,go out and create a full uh
campaign for me?
No, you need to train them.
So you need to know how to trainthose those uh models and how to
work with those tools.
That's the the first thing Iwould do.
And second, you do need to thinkwhere does the human need to be
in the loop?
Okay, when do we need reviews?
So maybe we'll start with justthree concepts and then the

(09:12):
human will review and give somefeedback, and then we'll the
second step will be from thesethree concepts, generate ideas
for copy, and then okay, nowwith this copy, a human is going
to check this.
So don't just you know give itone prompt, create a full
campaign for me, and just waitto get the best on the other
side.
You need to see where do thehumans come in the loop in order

(09:33):
to work together with AI as ateam member.

SPEAKER_01 (09:37):
Well, and AI can save so much time, but humans do
need to be in the loop.
And I think people, I don't knowif it's like, oh, this is so
cool and so great.
I don't need to do anythinganymore, or if they don't care,
or what that attitude is.
But it's like, yes, it's savingyou time, it's allowing you to

(09:57):
do other things.
That doesn't mean that you likejust wash your hands of
everything that it creates,right?
You need to really create thingsthat are going to be of use to
other people.
And I love what you said wasgive it examples, right?
That is so important.
You always AI thrives onexamples, so give it examples,

(10:19):
and it still doesn't alwaysfollow those examples 100%,
right?
But you can edit it to getthere, it'll get you like 90%
there, which is just huge.

SPEAKER_00 (10:30):
In order to do this, you did you do need to play with
the tools, you need tounderstand what are their
limitations.
Uh, by the way, there's alsorisks involved.
If if you in your marketingcampaign promise, give some sort
of promise, and you can't, youknow, you can't really deliver
on that promise, you are liableto it.
Nobody's gonna sue the AI,right?
So that's another reason to haveuh the human in the loop as the

(10:52):
the responsible manager whoapproves the campaign, approves
the content, approves the thelanguage that we're sending out
and the the marketing promise,right?

SPEAKER_01 (11:01):
AI adds things into what I ask it to do, even after
I tell it not to add anything inall of the time, right?
So you're I'm constantly going,no, that's not what I'm talking
about in this presentation whenit helped me create my my talk
description or whatever it is.
I'm like, no, that's not whatwe're doing.
Um, and have to edit it.
Again, the human in the loop isso important.

(11:23):
And making sure you deliver onwhat you promise is also so, so
important.
That's just just crazy.
So let's talk about these risksthat you were mentioning, right?
We talked about tools.
Actually, let's start with thetools, right?
So you're using GeminiPerplexity, Chat GPT, and
Claude.
I don't use Gemini.

(11:44):
Um, I probably should a littlebit, but I use Chat GPT Copilot,
which is basically ChatGPTPerplexity and Claude, uh, a
lot.
I use them all for differentthings and in different ways.
What the question I get often iswhich tool do you recommend for
someone starting out?

SPEAKER_00 (12:02):
So, first of all, if I were to list all the tools
that I'm using, we'll probablyuse up all the time that we have
because I'm experimenting withmany.
Um, and uh, but the thing isalso if I were to recommend this
now, and this is going to belive in a few weeks, there's
gonna be so many other tools outthere and and new versions of
them.
So it's a bit risky to say whichtool to use.

(12:23):
Uh what I usually say is pickone or two that is most helpful
in your use case.
Get to know it well, know thetweaks.
Like even with if even if wewere to choose just ChatGPT.
I know so many people who useChatGPT, they don't use deep
research, they don't useprojects, they don't use um, you

(12:43):
know, all the tools that arewithin it, you know, choosing
which model do you want tochoose?
There's so like uh the uh do youdo you want it to think harder?
There's all kinds of tweaks thatyou can do with just with what
the major tools.
So choose two tools that are youknow, and if you do image
generations, don't go for all50, right?
There's MeJ Journey, there's uhyou know the Google models now,

(13:04):
the nano banana and the others.
Uh just choose one, know whatit's good at, learn how to
prompt it, learn how to tweakthe tool for the specific needs
that you have.
Think about costs.
There's all especially if you'rescaling, right?
So learn the tool deeply, youknow, squeeze, squeeze the lemon
all the way, right?
And make the best lemonade thatyou can with those tools.

SPEAKER_01 (13:25):
I love that because there's all of the tools do
different things, right?
And they do different thingswell, but you have to start.
You have to start, you have tolearn, and yeah, people aren't
even scratching the surface withwhat some of these tools can do,
you know, and they're notprompting to get the full
benefit of what it can do andall the other things.

(13:48):
So I think that's fantastic.
So talking about moving on intobusiness and businesses working
with AI, this will be airingafter I do a panel, but I'm on a
panel coming up on um complianceand ethics and issues around AI
for um the accounting professionor finance and accounting

(14:09):
profession.
They're really hesitant to startusing AI because of all the
risks.
But on the other side, I'veheard people say the risk of
open AI or Google or Microsoftusing your information
incorrectly is sort of on thesmall side because it'll

(14:30):
negatively affect theirreputation too.
It's more important that you notworry about those risks and get
using AI because yourcompetitors are.
And if you fall behind, you'renever going to be able to catch
up.
What are your thoughts about therisks?
What are your thoughts aboutusing things?

SPEAKER_00 (14:47):
Yeah.
So, first of all, there arerisks, of course, and there is
still it's also lots of movingparts.
There's regulation that is uhkeeps moving.
I say actually the biggest riskis not understanding what the
risks are.
Like, okay, there arehallucinations, there are biases
in those tools.
If you don't try them out,you're not aware of them, and
you're just, you know, somebodyon your team or or um a supplier

(15:08):
that you're working with isgonna use them and you're not
gonna be able even to spot thembecause you've never tried it
out.
So uh there are risks they arebeing mitigated by the tool
providers uh in in many cases,but there are also things that
you can do to mitigate thoserisks.
And by the way, there's thereare just to mention another
customer that I'm working withto help people use things uh in

(15:30):
a smart way.
We've uh initiated like athree-tier level.
So there's low risk.
So if you're not using anypersonal information, if you're
not directly interacting withcustomers, etc., that's low
risk.
You know, people just can tryout things, of course, still
being human in the loop.
And then there's uh the the morerisk you introduce, the more
steps you need to take care ofand do some more testing and and

(15:52):
do some more things.
So by all means, don't let therisks uh uh stop you from
getting to know these tools, uhfrom uh being aware of what it
what they're capable of.
And again, there are risks inhiring new people, right?
Are you not gonna hire newpeople because they they they
make some mistakes, right?
Uh so uh just do what you needto do.

SPEAKER_01 (16:16):
Yeah, I think that that's really important because
you know when we started out, wewere being a little hesitant and
slowly adopting AI.
Yes, I'm not saying to uploadyour bank statements or anything
crazy to it, right?
But you definitely need to startusing it because you don't want
to be left behind.
And the people that aren't usingit are getting left behind right

(16:36):
now, which is not good, right?
And you're talking about scalinginnovation, right?
And you don't want to be leftbehind.
If other organizations areleveraging AI to scale
innovation, they're gonna beleaps and bounds ahead of you.
One of the things that youtalked about earlier was Amazon
and that you were at Amazon WebServices.

(16:57):
So Amazon is definitely what Iwould consider an innovative
company and how they moveforward and what they roll out
and all of the different thingsthat they have.
Do they approach innovation orimplement it differently than
the rest of us?

SPEAKER_00 (17:12):
So I was fortunate actually to lead the digital
innovation program at AWS and uhat Amazon Web Services.
And in that role, I got to learna lot about how does Amazon
think about innovation, and thenI got to share it with others,
other mostly enterprises, butalso small and medium
businesses, and help them thinkabout innovation the Amazon way.
So um I can definitely speak tothat.

(17:32):
And Amazon starts to think aboutinnovation by starting with the
customer.
And you can say, you know, manycompanies try to do you know
customer-centric thinking andinnovation, but it it Amazon,
it's Amazon, it's like at thecore of things.
It even the methodology thatAmazon uses internally, it's
called working backwards.
So it works backwards fromcustomers and what customers

(17:52):
need, and then it basically asksyou ask yourself, okay, if this
is what customers need and want,and you need to bring some
evidence on or some proof thatthat is the case, how do we work
backwards from that and make aservice, a product, uh a program
that delights them?
You don't start with, okay,these are our products, how do
we sell them in the best way?

(18:12):
How do we find the best productfit?
Maybe we don't even have thecapabilities today to deliver
these services, and we're gonnabuild a complete new offering to
delight those types of customersif we decided that this is in
interesting and importantenough.
So it starts by choosing a veryspecific customer and like a
specific problem that you wantto uh work towards, working

(18:35):
backwards from that, and onlythen thinking about what is the
solution.
And then the interesting thingthat uh that happens is before
you actually invest in buildinganything, you spend some time uh
thinking about that future, andthen you write an imaginary
press release, not your regularmarketing press release, the one
that kind of sells the productand markets it, you jump to the

(18:57):
future six months, eight monthsfrom now, and you basically
describe what you just releasedand how it delights customers.
It's one page and it has to bevery crisp and it can't have any
marketing fluff or technicaljargon.
So if I gave it to anybody onthe street that doesn't know
this market, doesn't know thisproduct, it will be very clear

(19:18):
to them who is this for, howdoes this delight them, how does
this work, and what's the valuein this?
If you can't articulate veryclearly that one page, you
shouldn't start building it.
And that is basically amechanism for making sure that
you are buildingcustomer-centric,
future-thinking, delightfulcustomers.

(19:38):
And this is just a taste of howAmazon thinks about things.
And then the other pieces, okay,once you do that, then you
iterate quickly, you experiment,you get feedback.
Uh, there's a lot of this isrooted in Amazon's culture.
So uh, and if you haven't lookedat like Amazon's leadership
principles yet, I encourage youto go with them.
You're also welcome to join to Ihave a newsletter where I share

(20:00):
this thinking around uhinnovating and innovating in the
age of AI and Amazon leadershipprinciples.
So people are welcome to also uhcheck that out.

SPEAKER_01 (20:09):
Awesome.
Yes.
If you get me a link for howpeople can sign up, let me know
and I will uh include that inthe the show description below.
So I love, love, love the factthat they start with the
customer and work backwards.
Coming up with ideas is nothing,right?
If the customer doesn't have aneed or have a pain or what

(20:33):
you're gonna deliver isn't gonnamake them happy, right?
You really need to think aboutwhat you're creating, whether
it's a product or a service,whether you're a solopreneur or,
you know, a Fortune 50 company,it doesn't matter, right?
You really need to look andstart with the customer.
And I love the fact that youstarted with that first because

(20:56):
knowing and understanding thecustomer is so underrated in I
think all of business, right?
People think they know betterthan their customers, and we
just don't.
And so that is just huge.
I love that.
Love that.
Well, thank you so much forsharing so much.
I love talking about AI and Ilove this conversation about
innovation because I had neverthought of innovation as

(21:19):
scalable before.
So thank you for that.
And I really appreciate yourthought process on getting
started and trying tools andgetting people going with
everything related to AI.
Before I let you go though, I dohave to ask you the last
question that I ask all of myguests.
And that is this show is calledImperfect Marketing because
marketing is anything but aperfect science.

(21:42):
What has been your biggestmarketing lesson learned?

SPEAKER_00 (21:45):
Yeah, and actually that that will tie us back to
working backwards.
And so I used to be a directorof products in um virtual and
augmented reality company thatand I was brought in to to take
that company to selling uh thesekind of uh services to
enterprises to use uh VR and ARfor training.
Very interesting uh um uhstartup, and and we had some

(22:07):
very cool stuff, but then ofcourse I was in love with the
product and with the the idea,and I I just started pushing it
the wrong ways, right?
And I I went to all kinds ofconferences, but I wasn't I
didn't start with working withthe customers, right?
I wasn't I and then I you knowmy messaging went was all over
the place because you know I uhonce I heard one one thing in my

(22:28):
conversation one conversation, Iwould change my messaging.
I should have started the otherway around, right?
Uh I should have understood,yeah, what who is my target
audience, who is the persona,what is their pain, not start
with my product.
You know, uh that's uh as goodas it was, as great that as as a
development team that we had ina great studio, that's not where

(22:49):
I should have started.
Um, and yeah, and we in the endwe pivoted, we did something
else which uh did uh um happento work out, and we worked with
as actually OEM providers toSiemens and other companies.
So it was even B2B, not evenlike very different approach to
the product that started byactually starting to listen to
the customers and what theyneed.

SPEAKER_01 (23:09):
I love that.
Thank you so much for sharingthat because I think we all make
that mistake at some point,right?
And a lot of us make itrepeatedly.
Like I want to raise my handhere because I definitely have
made that mistake a few times,um, a few times too many at
times, it feels like.
But it's yeah, it's hard to keepthe customer front and center

(23:31):
all the time.
And it's so important because itmakes such an improvement in the
consistency and the quality ofour messages.
Thank you again so much, Amir,for joining me today.
I really appreciate it.
For those of you listening orwatching, wherever you're
listening or watching, it wouldreally help me out if you would
rate and subscribe whereveryou're at.
Thank you guys all and have agreat rest of your day.