Imperfect Marketing
Imperfect Marketing
How Can AI Scale Innovation 10x Faster in Your Business?
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.
đ The New Face of Innovation
- AI as a Catalyst for Innovation â Why Amir believes AI doesnât replace creativityâit amplifies it.
- Systematic Innovation â How innovation is more science than magic, with best practices, methodologies, and even ISO standards.
- Scaling Creativity with AI â How AI tools like ChatGPT, Claude, and Gemini can help teams ideate, prototype, and role-play with personas 10x faster.
đĄ 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 â How a gaming company now processes and localizes thousands of new titles using AI-powered workflows.
- Smarter Marketing Research â Why tools like ChatGPT, Perplexity, and Claude have become Amirâs personal âresearch assistants.â
- Avoiding the âAI Slopâ Trap â The importance of human oversight, editing, and providing examples to teach AI your brandâs tone and standards.
â ď¸ The Risks and Responsibilities of AI
- âHuman in the Loopâ Principle â Why every AI workflow still needs human review, editing, and approval.
- Ethical and Compliance Challenges â How organizations can balance innovation with data privacy and risk management.
- Three-Tier Risk Model â Amirâs framework for identifying low-, medium-, and high-risk AI use cases to guide safe adoption.
𧰠Tools and Tips for Getting Started
- Donât try every AI toolâpick one or two and learn them deeply.
- Master your tools before scaling: tweak settings, explore model options, and learn how to âsqueeze the lemon.â
- Start small, focus on use cases that save time and amplify creativity, not replace human input.
đ§ Lessons from Amazon on Innovation
Drawing from his time at Amazon Web Services, Amir reveals how Amazonâs âWorking Backwardsâ approach fuels innovation:
- Start with the customer, not the product.
- Write an imaginary press release describing your future product before you build itâif itâs not clear and compelling, youâre not ready to launch.
- Always focus on delighting the customer, not just building something new.
đŻ Amirâs 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 pointsâa mistake that taught him the importance of clarity, audience insight, and consistency.
đ 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?" â I've got all your AI curiosities covered. It's the perfect after-podcast snack for your tech-hungry brain.
Watch here
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 is called Imperfect Marketing. Join me and my guests as we explore how to grow your business with marketing tips and, of course, lessons learned along the way. Hello and welcome back to another episode of Imperfect Marketing. I'm your host, Kendra Corman, and today I am joined by someone who has a passion for AI and innovation similar to me. He actually might be a lot stronger in that than I am. Welcome, Amir. Thank you so much for joining me today.
SPEAKER_00:Thank you, Kendra. And I'm very happy to be here and thanks for having me.
SPEAKER_01:So let's go ahead and talk about how did you get started with innovation and AI and all this fun stuff?
SPEAKER_00:Yeah, I'd say innovation has always been a silver line in my career. So uh I've been in innovation consulting. I love innovation, so I I kind of read books and learn about it. And even when I wasn't in innovation roles, I always kind of did innovation workshops internally or with customers if it even if I was giving other services, everybody asked me for innovation stuff. So that's kind of uh always been uh kind of a fire uh that I've uh kindled. AI, uh I would say uh a few years ago when I was in Amazon Web Services, I was very curious about how does Amazon innovate? Elements, of course, is technology, and I got to work with uh some great customers because my role I was helping other customers think about innovation with the cloud technology, and in some cases it was around AI. And then, of course, when in more recent years, generative AI came about, I was very fascinated about it and I wanted to connect these two things. And I started to play around with ChatGPT, with other technologies, and see how can AI support, uh, accelerate, scale the rate of innovation.
SPEAKER_01:Very interesting because I have a tendency to hear that innovation is the one thing that AI doesn't do, right? And that's a lot of what I share is, you know, hey, you know, AI is going to flip the work pyramid, right? Right now, 60% of our day is spent on work about work. And like less than 10% that top of that pyramid is spent on us thinking, innovating, coming up with new ideas. And when I talk to people, I say, you know, I've been to several presentations and they talk about flipping the work pyramid. And when they do, we're gonna be hopefully spending 60% of our day on innovation and coming up with new ideas and the things that AI can't do. But you're saying that AI is helping speed up innovation. How is that happening?
SPEAKER_00:Speed up and scale. I mean, it comes back to the what I believe about innovation. And again, uh, and here it may be also a surprise to some people. I think that innovation is actually a systematic endeavor, right? And and there are there's a way to do innovation in a right way. Of course, there is a creative spark there. There are things that are uh special, but if you really want to do innovation for the long term and at large scale, it takes a systematic approach and it takes methodologies. Basically, it's a profession. I'm uh here in, I mean, I'm based in Stockholm in Sweden, and I'm a member of the Innovation Leaders Association. And you know, last year we even were supporting a new ISO standard for innovation. And you think, how can that be like ISO standard, right? And you know, and innovation, how does that work? Yes, because innovation is a system, it's a methodology, there's best practices, and connecting it back to AI. If you teach AI to be that super innovator accord, and you give it the context, uh, you make it part of the team. Then it can help you scale and innovate and play roles. There's so many things you can do with AI. Again, you have to understand what the capabilities are, you have to understand what it's good at and what it might not be good at. But I find it that I can innovate now 10 times as much, not 10% more, like 10 times much faster and bigger with uh innovation. I can prototype, I can ideate, I can roleplay with imaginary customers and personas. So, so many things you can do with AI if you know how to guide it.
SPEAKER_01:Yeah, I mean, it's amazing how quickly you can test and try and do things with AI, right? I mean, things that might have been hard for somebody to do and they would have had to put in a project to IT to get things coded. You don't need to do that anymore. It's just amazing. So you covered a couple of things that AI is doing, but how do you see AI being used in marketing or in general in business? Is it beyond innovation?
SPEAKER_00:Yeah, I'll focus on marketing because this is this is what the podcast is about. And I have I have quite a number of use cases that I've done with customers. Uh, just to give you a few examples. So I was working with uh a gaming company, and uh they have hundreds of games, if not thousands, every year coming in from studios, and they need to you know put those out on their uh portfolio that the gamers can play. And they come from different sources, very unstructured data from PDFs from different studios, and just processing that data and making you know, creating a web page for that game with all the tagging and all the categorization used to take a lot of time from the humans, and now you know we've we've built some uh AI-powered workflow for them. There is a human in the loop just checking things, but now on not only can they do that like uh a hundredfold uh uh faster, but they can also almost automatically translate to more markets and more languages, so that's like SEO-focused uh AI workflow. Another example is is marketing research, especially in recent months where we had like more powerful uh large language models that are capable of doing long-term research. So even I, when I want to look at my competitors at uh uh at some of the ideas that I have for products, right? Because I want to build them, uh I start by doing some some marketing research, and I don't have a marketing research team or analysts uh by my side, I just have my friends, my uh perplexity, and I have Claude and I have uh Gemini and and Chat GPT, and I can you know send them off to do good research on my behalf. So that's like marketing research example, and then there's you know content generation. That's uh uh so much to be done there again. If you but we don't also have the the AI slop, right? So we have to be careful uh when we do content generation, um, to do it in a smart way. Uh so yeah, that these are just kind of uh selected use cases.
SPEAKER_01:So, in talking about work slop, because I've been hearing a lot about it, I'm gonna have a friend of mine on on the show in a couple of weeks, hopefully, um, where she's gonna be talking about work slop. But some companies are actually investing a ton of money in AI and they're finding that it's actually not saving them money because too many people are phoning things in using AI, not correctly, right? Because I think that's the big thing, right? They're not using AI the right way. They're not reviewing and editing the content they're getting. They're phoning stuff in and they're just sending it out. And when it gets down to the person or up to the person that needs to put it all together, they're having to start from scratch because they don't have any good content to work with. How do we fix that? Any ideas?
SPEAKER_00:Yeah, so a couple of thoughts there or suggestions. Uh, first, uh, for the people who are creating the content, producing the content, there are ways that you can guide the AI to come up with something which is human-level or you know, even high quality, right? You need to give it examples of previous or you know, high-performing content, right? So it learns the style and it learns what is relevant to this audience. So think of it as you know, this is like a really talented um uh marketing professional that you just hire to your team, but they don't know anything about your product, about your brand, about your tone, about your audience. 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 train those those uh models and how to work with those tools. That's the the first thing I would do. And second, you do need to think where does the human need to be in the loop? Okay, when do we need reviews? So maybe we'll start with just three concepts and then the human will review and give some feedback, and then we'll the second step will be from these three concepts, generate ideas for copy, and then okay, now with this copy, a human is going to check this. So don't just you know give it one prompt, create a full campaign for me, and just wait to get the best on the other side. You need to see where do the humans come in the loop in order to work together with AI as a team member.
SPEAKER_01:Well, and AI can save so much time, but humans do need to be in the loop. And I think people, I don't know if it's like, oh, this is so cool and so great. I don't need to do anything anymore, or if they don't care, or what that attitude is. But it's like, yes, it's saving you time, it's allowing you to do other things. That doesn't mean that you like just wash your hands of everything that it creates, right? You need to really create things that are going to be of use to other people. And I love what you said was give it examples, right? That is so important. You always AI thrives on examples, so give it examples, and it still doesn't always follow those examples 100%, right? But you can edit it to get there, it'll get you like 90% there, which is just huge.
SPEAKER_00:In order to do this, you did you do need to play with the tools, you need to understand what are their limitations. Uh, by the way, there's also risks involved. If if you in your marketing campaign promise, give some sort of promise, and you can't, you know, you can't really deliver on that promise, you are liable to it. Nobody's gonna sue the AI, right? So that's another reason to have uh the human in the loop as the the responsible manager who approves the campaign, approves the content, approves the the language that we're sending out and the the marketing promise, right?
SPEAKER_01:AI adds things into what I ask it to do, even after I tell it not to add anything in all of the time, right? So you're I'm constantly going, no, that's not what I'm talking about in this presentation when it helped me create my my talk description or whatever it is. I'm like, no, that's not what we're doing. Um, and have to edit it. Again, the human in the loop is so important. And making sure you deliver on what you promise is also so, so important. That's just just crazy. So let's talk about these risks that you were mentioning, right? We talked about tools. Actually, let's start with the tools, right? So you're using Gemini Perplexity, Chat GPT, and Claude. I don't use Gemini. Um, I probably should a little bit, but I use Chat GPT Copilot, which is basically ChatGPT Perplexity and Claude, uh, a lot. I use them all for different things and in different ways. What the question I get often is which tool do you recommend for someone starting out?
SPEAKER_00:So, first of all, if I were to list all the tools that I'm using, we'll probably use up all the time that we have because I'm experimenting with many. Um, and uh, but the thing is also if I were to recommend this now, and this is going to be live in a few weeks, there's gonna be so many other tools out there and and new versions of them. So it's a bit risky to say which tool to use. Uh what I usually say is pick one or two that is most helpful in your use case. Get to know it well, know the tweaks. Like even with if even if we were to choose just ChatGPT. I know so many people who use ChatGPT, they don't use deep research, they don't use projects, they don't use um, you know, all the tools that are within it, you know, choosing which model do you want to choose? There's so like uh the uh do you do you want it to think harder? There's all kinds of tweaks that you can do with just with what the major tools. So choose two tools that are you know, and if you do image generations, don't go for all 50, right? There's MeJ Journey, there's uh you know the Google models now, the nano banana and the others. Uh just choose one, know what it's good at, learn how to prompt it, learn how to tweak the tool for the specific needs that you have. Think about costs. There's all especially if you're scaling, right? So learn the tool deeply, you know, squeeze, squeeze the lemon all the way, right? And make the best lemonade that you can with those tools.
SPEAKER_01:I love that because there's all of the tools do different things, right? And they do different things well, but you have to start. You have to start, you have to learn, and yeah, people aren't even scratching the surface with what some of these tools can do, you know, and they're not prompting to get the full benefit of what it can do and all the other things. So I think that's fantastic. So talking about moving on into business and businesses working with AI, this will be airing after I do a panel, but I'm on a panel coming up on um compliance and ethics and issues around AI for um the accounting profession or finance and accounting profession. They're really hesitant to start using AI because of all the risks. But on the other side, I've heard people say the risk of open AI or Google or Microsoft using your information incorrectly is sort of on the small side because it'll negatively affect their reputation too. It's more important that you not worry about those risks and get using AI because your competitors are. And if you fall behind, you're never going to be able to catch up. What are your thoughts about the risks? What are your thoughts about using things?
SPEAKER_00:Yeah. So, first of all, there are risks, of course, and there is still it's also lots of moving parts. There's regulation that is uh keeps moving. I say actually the biggest risk is not understanding what the risks are. Like, okay, there are hallucinations, 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, somebody on your team or or um a supplier that you're working with is gonna use them and you're not gonna be able even to spot them because you've never tried it out. So uh there are risks they are being mitigated by the tool providers uh in in many cases, but there are also things that you can do to mitigate those risks. And by the way, there's there are just to mention another customer that I'm working with to help people use things uh in a smart way. We've uh initiated like a three-tier level. So there's low risk. So if you're not using any personal information, if you're not directly interacting with customers, etc., that's low risk. You know, people just can try out things, of course, still being human in the loop. And then there's uh the the more risk you introduce, the more steps you need to take care of and do some more testing and and do some more things. So by all means, don't let the risks uh uh stop you from getting to know these tools, uh from uh being aware of what it what they're capable of. And again, there are risks in hiring new people, right? Are you not gonna hire new people because they they they make some mistakes, right? Uh so uh just do what you need to do.
SPEAKER_01:Yeah, I think that that's really important because you know when we started out, we were being a little hesitant and slowly adopting AI. Yes, I'm not saying to upload your bank statements or anything crazy to it, right? But you definitely need to start using it because you don't want to be left behind. And the people that aren't using it are getting left behind right now, which is not good, right? And you're talking about scaling innovation, right? And you don't want to be left behind. If other organizations are leveraging AI to scale innovation, they're gonna be leaps and bounds ahead of you. One of the things that you talked about earlier was Amazon and that you were at Amazon Web Services. So Amazon is definitely what I would consider an innovative company and how they move forward and what they roll out and all of the different things that they have. Do they approach innovation or implement it differently than the rest of us?
SPEAKER_00:So I was fortunate actually to lead the digital innovation program at AWS and uh at Amazon Web Services. And in that role, I got to learn a lot about how does Amazon think about innovation, and then I got to share it with others, other mostly enterprises, but also small and medium businesses, and help them think about innovation the Amazon way. So um I can definitely speak to that. And Amazon starts to think about innovation by starting with the customer. And you can say, you know, many companies try to do you know customer-centric thinking and innovation, but it it Amazon, it's Amazon, it's like at the core of things. It even the methodology that Amazon uses internally, it's called working backwards. So it works backwards from customers and what customers need, and then it basically asks you ask yourself, okay, if this is what customers need and want, and you need to bring some evidence on or some proof that that is the case, how do we work backwards from that and make a service, 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? How do we find the best product fit? Maybe we don't even have the capabilities today to deliver these services, and we're gonna build a complete new offering to delight those types of customers if we decided that this is in interesting and important enough. So it starts by choosing a very specific customer and like a specific problem that you want to uh work towards, working backwards from that, and only then thinking about what is the solution. And then the interesting thing that uh that happens is before you actually invest in building anything, you spend some time uh thinking about that future, and then you write an imaginary press release, not your regular marketing press release, the one that kind of sells the product and markets it, you jump to the future six months, eight months from now, and you basically describe what you just released and how it delights customers. It's one page and it has to be very crisp and it can't have any marketing fluff or technical jargon. So if I gave it to anybody on the street that doesn't know this market, doesn't know this product, it will be very clear to them who is this for, how does this delight them, how does this work, and what's the value in this? If you can't articulate very clearly that one page, you shouldn't start building it. And that is basically a mechanism for making sure that you are building customer-centric, future-thinking, delightful customers. And this is just a taste of how Amazon 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 is rooted in Amazon's culture. So uh, and if you haven't looked at like Amazon's leadership principles yet, I encourage you to go with them. You're also welcome to join to I have a newsletter where I share this thinking around uh innovating and innovating in the age of AI and Amazon leadership principles. So people are welcome to also uh check that out.
SPEAKER_01:Awesome. Yes. If you get me a link for how people can sign up, let me know and I will uh include that in the the show description below. So I love, love, love the fact that they start with the customer and work backwards. Coming up with ideas is nothing, right? If the customer doesn't have a need or have a pain or what you're gonna deliver isn't gonna make them happy, right? You really need to think about what 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 and start with the customer. And I love the fact that you started with that first because knowing and understanding the customer is so underrated in I think all of business, right? People think they know better than their customers, and we just don't. And so that is just huge. I love that. Love that. Well, thank you so much for sharing so much. I love talking about AI and I love this conversation about innovation because I had never thought of innovation as scalable before. So thank you for that. And I really appreciate your thought process on getting started and trying tools and getting people going with everything related to AI. Before I let you go though, I do have to ask you the last question that I ask all of my guests. And that is this show is called Imperfect Marketing because marketing is anything but a perfect science. What has been your biggest marketing lesson learned?
SPEAKER_00:Yeah, and actually that that will tie us back to working backwards. And so I used to be a director of products in um virtual and augmented reality company that and I was brought in to to take that company to selling uh these kind of uh services to enterprises to use uh VR and AR for training. Very interesting uh um uh startup, and and we had some very cool stuff, but then of course 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 of conferences, but I wasn't I didn't start with working with the customers, right? I wasn't I and then I you know my messaging went was all over the place because you know I uh once I heard one one thing in my conversation one conversation, I would change my messaging. I should have started the other way 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 good as it was, as great that as as a development team that we had in a great studio, that's not where I should have started. Um, and yeah, and we in the end we pivoted, we did something else which uh did uh um happen to work out, and we worked with as actually OEM providers to Siemens and other companies. So it was even B2B, not even like very different approach to the product that started by actually starting to listen to the customers and what they need.
SPEAKER_01:I love that. Thank you so much for sharing that because I think we all make that mistake at some point, right? And a lot of us make it repeatedly. Like I want to raise my hand here 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 keep the customer front and center all the time. And it's so important because it makes such an improvement in the consistency and the quality of our messages. Thank you again so much, Amir, for joining me today. I really appreciate it. For those of you listening or watching, wherever you're listening or watching, it would really help me out if you would rate and subscribe wherever you're at. Thank you guys all and have a great rest of your day.
