10. Priceless Branding: Neuroscience, Emotion & AI with Jim Cobb
Episode 10 of Kinwise Conversations · Hit play or read the transcript below
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Welcome to Kinwise Conversations, where we explore what it means to integrate AI into our work and lives with care, clarity, and creativity. Each episode we talk with everyday leaders navigating these powerful tools, balancing innovation with intention and technology with humanity. I'm your host, Lydia Kumar.
Today I'm honored to be joined by Jim Cobb, founder of the brand strategy firm, The Bloodhound Group, and a veteran of the advertising industry. With decades of experience, Jim has seen firsthand how technology has reshaped marketing from the .com era to the rise of AI. We'll explore his fascinating work in the science of emotion, including his role in the iconic MasterCard "Priceless" campaign, and dive into how he's now using AI-driven analytics to build stronger brands, enhance customer experiences, and scale his business thoughtfully. Let's dive into our conversation.
Lydia Kumar: I'm so excited to have you on the show today, Jim. I've known you for a long time, but we had such an interesting conversation a few weeks ago, and I thought I have to have him on the show and hear more about your insights. Jim has been working in marketing and owns his own business, and has been in this industry for a long time. So I'm really excited for you to introduce yourself, to tell your story, how you ended up in this line of work. I'm curious about what first led you to brand strategy and to the launch of The Bloodhound Group, and anything else that might be helpful for listeners to know about who you are and how you approach work.
Jim Cobb: Well, thanks for having me on the show, number one. I started in the advertising industry in the eighties and during that time, we did advertising, marketing, and communications for both small firms but also some large global organizations. And what we began to understand was, this was not about advertising and creativity, but it was really more about how do you build a brand? And brand being, what are the intangible values that a company can basically label into their identity. And so we began to understand the financial implications of that and conduct a lot of test markets, which was a little unusual for advertising agencies at the time, so that we could understand how to build brand awareness and value that allow our clients to actually create a premium position and support their premium prices through an enhanced image. So we did that for 35 years and in 2017, I decided to close the agency that I had grown up in and was really very attached to. But the economics of the agency business had gotten to a place where it was very difficult to offer a full slate of services and achieve profitability. So we felt the most unique part of the organization was the brand consulting work. And so we decided to create a new company called The Bloodhound Group that just focused on brand research and brand development work, which included new product ideation and helping clients improve their market offerings.
Lydia Kumar: When you think about brand, what drew you to marketing and into branding at the beginning? Like why that, out of all the things you could have done professionally?
Jim Cobb: Well, you know, when I was in graduate school, I guess it goes back maybe to my father, 'cause my father was a really good salesperson. And when I went to graduate school, I always felt like advertising represented the purest form of salesmanship. Because if you could, in 30 seconds, convey a compelling message in a creative way, then it would allow you basically to have the pinnacle position of salesmanship. And so I felt like that was kind of, maybe that was a little DNA coming in, where it seemed to be the right thing to do from a career standpoint, but it also allowed you to be creative and it was kind of glamorous. You could be related to some big idea that everybody knew about. And so it was not something that a lot of my graduate schoolmates were interested in. But I pursued it and I ended up landing in an advertising agency in Raleigh.
Lydia Kumar: I think it's really compelling, the way you said advertising is the pinnacle of salesmanship, being able to craft that within 30 seconds. You talk a little bit on your website about emotion, like great brands being able to trigger emotion that moves people. How does that connect with this idea of advertising being this pinnacle of salesmanship?
Jim Cobb: Well, you know, most commercials, when you see them, you engage with them, you like them. And even if you're not going to buy the product, a big part of the messaging has to do with likability and entertainment or relevance. For many, many years we had great creative guys that did work that was just phenomenal. And when we did research on it, in terms of ad testing or we did work that would put it into the marketplace and see how it moved product, some of the more creative work always delivered the best results. And around 1998, 1999, one of our researchers who was a PhD, was reading and communicating with the guys at Harvard who began to bring forth the idea that 90% of the decisions we make are driven by unconscious motives, where we basically make the purchase based on a feeling, and then we rationalize why we made the purchase. This was very different from the model of most advertising agencies, but what we learned was you make the people buy, and then they do the logical part of the equation. And so then it began to make sense why some guys who are very creative, they understood that intuitively. It wasn't like they were trying to match it from a science standpoint, and now we began to understand how that actually worked.
So my business partner researcher, whose name was Bruce Hall, developed a working model on how to measure emotions for our advertising. And so that work would basically be, I'll call it like a lie detector test. We would hook people up to electrodes and we would show them the commercials, and we could see exactly what they liked about the commercials based on how they responded emotionally. And that became kind of a litmus test for sorting good advertising from bad advertising.
Probably the most famous campaign that we worked on was the campaign for MasterCard, which was the "Priceless" campaign, which did not perform well in traditional ad testing. We were partnering with McCann Erickson out of New York who had created the campaign, and they asked us to conduct the emotional engagement research. And what we found was the story completion component of the "Priceless" campaign—of a dad taking his son to the baseball game for the first time, of how you interact with dogs—those television commercials scored especially high. And so, McCann took that to MasterCard and they said, "You know, we really ought to continue down this path," because MasterCard wanted to make sure that they were spending their money in a way that was going to be productive. And when the traditional ad test came back and didn't show very good results, we ended up becoming the tiebreaker with the research we conducted. And so they decided to go forward with the campaign, which I guess they've probably done several thousand commercials by now.
Lydia Kumar: I've seen those commercials. I watched one of those commercials in my MBA program right now, in my marketing class we watched one of the "Priceless" commercials as an example of really strong advertising. So that's so cool that you played a part in that.
Jim Cobb: Yeah. So the interesting part about advertising and brand development is that typically brands create some additional meaning to your life. There may be something in your life that's missing, and so who you are today, plus the brand, equals some ideal image or experience that you're constantly pursuing. A good advertisement gives you a little bit of that information. It allows you to complete the story. The reason the "Priceless" campaign is so powerful, I believe, is because you fill in the blanks in that campaign. It gives you cues of the storylines, it allows you to go back to positive memories, and it allows you to think about how the brand related to something in your life. So then you fill in the details of when you took your son or when you took your daughter to the ballgame. And that makes it much more compelling because it is highly relevant at that point. And relevance is a key driver of brand purchase. Our test basically allowed us to demonstrate that because it was an emotional engagement. It's nothing someone could control. The research methodology basically worked at an unconscious level, and it got past the traditional bias of someone answering a question based on what they think you would want to hear.
Lydia Kumar: I'm going to move us to AI, because you talked about emotion and creativity, which we kind of think about as more qualitative and difficult to quantify. And you were able to take that and really get into the science of it. Like, what is the science of creativity? What is the science of emotion? And how do you use data to help you create something that resonates with people more effectively? And that led to a lot of very successful work. Did I get that right?
Jim Cobb: Yeah, pretty much. I mean, ultimately, with good research and good science, you're able to have some sense of how to recreate it by understanding the things you need to achieve in the execution of the work that gives you the results you're looking for. And sometimes those are intuitive and you're lucky, and sometimes they're not, and you basically have to learn how to build those into your approach.
Lydia Kumar: So I guess my question to build on this is, machine learning has been around for a long time, but generative AI is something that is new and allows us to glean results from large amounts of data in different ways. I'm wondering about AI-driven analytics. What opportunities do you see there? When did that appear on your radar?
Jim Cobb: When AI became a thing almost two years ago, one of the things that I kind of felt like it was going to be like is a little bit like computers. When Macs came out, it basically empowered people to be more creative. IBM could never see the potential in it. Steve Jobs and his team could; they could create something that would allow anyone to expand their creativity. So when we start talking about AI, I kind of did a flashback and said, "You know, this is going to be kind of like when Apple came out with personal computers. It's going to empower people in new ways. Things that they haven't even thought about, they will be able to do with AI."
So we hired a consultant back in 2023 to give us a tutorial and to train our team on how to use AI. And my first thought was AI would be most useful as a way to automate routine tasks. Things that would take you time to do, you could train AI to do them for you in less time. Anything that was left-brained that would take a process, you could shorten that time very quickly with AI. We had been developing social media posts for some time where I would work with a writer, and that writer would work with me on the content. I had worked really hard to train her to have a really good understanding of the voice that I wanted to create in the marketplace. And so my challenge to her was, "Can you train an AI to basically have the same voice over time so that you would just plug in a topic and it would write the articles for us, and then we could edit them?" We would eliminate a lot of time that was being developed. So she did, and she got very, very efficient. My idea was that it would make life more efficient and productive because you would take things which were not necessarily adding value and reduce the amount of time it would take to do that work.
In essence, it can add value though, if you train it right. With AI, just like any kind of research, if you have a really good database—and there's massive data out there now, right? So if you organize the data that you feed into an AI machine, it can do things and analyze results that would take you days and days to do, and it may actually reveal knowledge that you would never have gotten because it looks at it from different perspectives. But you have to have a really good data set under any circumstances in order to get the right kind of AI results. It's got to have a basis of facts from which to generate that outcome. So that is very similar to research, so it made an easy leap from having a research background to going into an understanding of AI.
Lydia Kumar: Do you have an example of working with clients where you have data that you've been able to draw these insights from, or you have data that you want to draw insights from, but it's not clean and it's not usable in the current state and how you've navigated that?
Jim Cobb: So we're working with two clients right now on AI-driven results. One of them is using neuroscience results of a battery of tests that people take that would allow them to profile their basic behaviors and match them to the behaviors required for a job. A lot of people are really smart at certain things, and they get promoted to a new job, and then they're not successful because that new job requires a different type of behavior. It may require more leadership, more oversight. But if you are able to gather data on how people behave and you match that to the behavior needed for the new job, then you're able to promote people into areas where you give them a better chance for success. And number two, it gives you an opportunity to train them in the areas where they need more support. So that is a way that we are looking at AI because it basically gives a recommendation to both the individual who is being profiled, but also to the HR person that's trying to match up the right people for the right function.
The other work that we are doing is analyzing, let's say for example, an athlete, and we analyze the athlete's performance on the field in real time. You can run that video stream through an AI bot and you can understand the areas where that individual needs to improve, and you can do it very quickly. If a professional builds the model, then you can run any video through it, and that video would automatically give, say, a baseball player, tips on how to improve his swing. And that would be fed into the system and that system would allow recommendations that come out on the back end based on a professional observation. So those are two examples where we're working with AI to basically create a branded product that adds value to a business or adds value to an individual who is trying to be a better athlete.
Lydia Kumar: Those are such cool examples. It's like you have to have the ideal of what you're matching the data against. You need the ideal baseball stance so that the AI can evaluate it, or you need the ideal profile of an employee and the characteristics that a business is looking for.
Jim Cobb: Correct. And so in both cases, it requires data. It requires a standard set of data that you're basically using. So if you have poor data, you'll get poor results. Now, the beautiful thing about AI is that it can translate images very quickly into something that gives you results or recommendations. One of our other clients does monitoring of brand standards within restaurant chains. One of those is, what are their times of operation? All of those are posted on a window. We have one client that used to manually record dates and times of operation for retail locations into a questionnaire format. But now, they can just take a photograph of it and it automatically populates the form. So it gives them a way to make that research work and that assessment work that they do for brands much more efficient.
Lydia Kumar: That's such a new way of being able to process data. AI can process visual data in a way that we didn't have in the past. How has your mindset about AI evolved from where it initially started as you've learned more about how the technology works?
Jim Cobb: Well, I would say that it is a tool, like a lot of other things. And the more you use the tool, the better you are with the tool. We've embraced it as a company in a way that we want to really become experts at it so that we can make recommendations to our clients and add value to our consultation work with them, try to see opportunities for that tool to be used to make brand experiences better. And to the degree that we are looking at how to use it more effectively just to create content for our own company that allows us to shorten the time it takes to develop white papers or points of view, we think it's the tip of the iceberg.
I don't think people really understood the power of personal computers until people began to really work on the software that would increase the power of them. So I think there are probably going to be people who are going to continue to improve the artificial intelligent bots or the models that allow people to do more things better and faster and more intuitively. So there probably is going to be a user-friendliness about it that will make people less afraid of it and more engaged with it. You already see it with Google. You go on and do a traditional online search and it automatically takes you to an AI-driven result, which provides some insight to the question that you've asked. So I think it'll become part of the mainstream of how people interact online and how they compose their thoughts.
I know that the universities are trying to figure out how do they integrate it into education and how do they identify where people have actually done their papers and homework based on an AI-generated result versus actually understanding the topic and writing it themselves. Those are all things which we'll kind of work through, but I do think that it's not going away. It is only going to change the way we do business and the way we interact with each other.
Lydia Kumar: When you think about that part about the way we interact with each other, branding is built on trust and it's built on emotion. How do you think AI is enriching or complicating those relationships?
Jim Cobb: Well, emotion is something that is really hard to replicate with a machine. I'm not saying it would not be able to show empathy, because there are some models out there now that you can plug in, like for coaching, certain personality traits when providing feedback, for example, to an athlete. Some athletes need certain types of experiences and others respond better to other types of experiences in coaching. I think it probably will evolve, but I don't think the emotional part of it is something that's going to happen immediately. I do think that when we think about brands and empathy and responsiveness and general experience, I think it can enhance the experience that people might have by being more responsive and reactive to their needs. And how that gets translated as emotion, sometimes that might translate positively into a positive emotion that, you know, "The brand actually cares for me," because it's responsive to my needs. And that has to do more with the speed in which the individual is receiving feedback. And in some cases, it's really hard to tell the difference between a human interaction and one that's driven by a machine today. So I think that will evolve. To answer your question more specifically, I think it will evolve over time. I don't think it will replace the emotional direction that is needed in order to create the content.
Lydia Kumar: Do you have a sense of what content you think humans need to be solely responsible for? Where do humans need to be just humans and where does AI come in? Do you see any things that are sacred for the human behind the work?
Jim Cobb: Well, there are certain interactions between individuals, the relationships that are spontaneous and those are intuitive. So when you're providing counseling, for example, you're looking not for verbal cues, you're looking for facial cues. I think those things can be taught as well with AI, but I do think that on certain, in certain cases, that might push the limit of responsibility that you might want to put on a machine in terms of how it affects the individual. So anytime there's a high risk of someone being harmed by virtue of an emotional connection that comes out of the machine, I think that gets a little bit into the gray area that we have to really be careful to manage properly. I mean, I think you go to a therapist to listen to you and to respond to you based on the responses that you provide. And I think that's a high standard that that person has been trained to understand and to respond to the cues, not just verbal, but other cues that the individual gives you. I'm not sure that I would put that into the area of artificial intelligence until a heck of a lot more work has been done on it.
Lydia Kumar: In your work, have there been ethical or confidentiality challenges that you have had to navigate as you integrate AI?
Jim Cobb: Not at this point, we haven't really dealt with that. But what we are trying to do is understand, if someone puts personal information into a system, you want to make sure that that personal information is protected. I mean, that's just basic data security, but the way in which the machine sometimes takes data from different people that it learns from, you just have to be really careful about how you co-mingle information that's being collected in the data sets. I think 'cause some of that should not be public.
Lydia Kumar: Absolutely. And knowing even internally, how do you separate data? How do you make sure your data is not, as you said, intermingled? That's important for clarity of insights as well.
Jim Cobb: Yeah, because there are certain experiences that we have across different people, you know, our different experiences with different brands or different individuals that provides a collective understanding and knowledge that we use to make future judgments, right? That's called experience. And that experience ends up making your life easier or harder based on how you envision the task at hand. And that has to do with a lot of different data sets from a lot of different individuals, some of which is confidential, some of it's not. In our brains, we end up knowing, okay, this part of my experience I can't divulge. I can draw on it, but I can't divulge the source of it. And I think those are kind of the areas where a breach in data could create exposure just like any other breach of data that's not even AI-related, right? I mean, we've got hackers out there to get into your social security number and everything else. So I'm sure those will become key guidelines that probably will end up being legislated, is my guess, in terms of the rules of how that type of data is being used.
Lydia Kumar: I'm going to ask you to kind of look into the future. It's cool that you were able to see how personal computers impacted industry, you saw the dot-com era, now you have AI. So you've navigated these huge technological events while you've been in the branding industry. I'm curious about how you see behavioral science and AI reshaping brand strategy in the next few years, drawing from your experience of seeing huge technological advances reshaping the industry over the past four decades.
Jim Cobb: Well, from a behavioral science standpoint, number one, I think a lot of people have been a little bit fearful of how that information is used, but I also think it's been very expensive to collect it. I think AI will allow us to collect data around emotions a lot more efficiently, which will make it a lot easier for us to use it in a broader sense for creating experiences that are more enjoyable, more efficient. A lot of the work that we are looking at today, like in supermarkets and museums where we are actually tracking how people move through a museum or through a grocery store, we know that at certain points, if they dwell in front of a certain area for long enough, that experience makes it better. If it's highly crowded, then they may not enjoy the experience that you're looking for them to enjoy.
So I think in the future, what we'll end up doing is using a lot of this data analysis. We'll basically plug in the actual results that we are seeing, but it will allow us to design better experiences. So if I'm designing a museum, I might learn from a database of how people interact with exhibits in another museum to better design the flow of traffic for the one that I'm getting ready to build. I think it will improve the customer experience in that way because it'll allow you to use that kind of intuitive data that you never really understand until people interact with it to make for a better starting point, not perfect the experience, but make it a better starting point. I think it probably would help ultimately in scheduling algorithms that are very complicated that would allow us to move from one place to the next more efficiently. All of those things could significantly improve future infrastructure and retail and how we behave.
Lydia Kumar: When you were talking about using data to design a museum, you can use that data to design the type of museum that you want to design. I think there are things that you communicate through a design about your brand identity or your values. So you can improve the consumer experience, but also you communicate something through the experience you create. It's exciting to imagine the possibility of alignment between what a company wants to communicate about who they are and the experience that the person engaging with that company or organization gets to have.
Jim Cobb: Yeah, I think that ultimately a lot of the work that we currently do, I'll call it left-brained, through observation—a lot of times you will observe something, then you record the observation or you do time-motion studies—all of those kinds of things will become supercharged with AI that allow you to get to the more ideal points faster, right? We get there today, but it just takes us more trial and error to get there. I think we'll get there faster through AI-enabled learning.
Lydia Kumar: That's really cool. And I think there's a lot of potential for the future that is hopeful and really cool. My last question is, what idea or question about AI do you keep thinking about?
Jim Cobb: How do you actually make, you know, how do you scale your business better with AI? So, can I use AI in my business to handle more clients? And I think that becomes a big economic advantage. If you consider that someone could train artificial intelligence to basically replace you, then you're thinking the wrong way. The way we are trying to think about it is, how do we provide a more—how can we handle more clients with a given staff, scale our business up, and at the same time maintain the quality of our product? So for me, it's really looking toward how to apply it to scale the business that we have and the kinds of insights and services that we offer.
Lydia Kumar: That makes sense. And it's exciting because there are opportunities to use this data to create an enhanced experience, enhanced guidance. This kind of takes me full circle back to the beginning of our conversation with our "Priceless" commercial, where you were able to use data-driven insights to create something that was more resonant with people. And so you have a tool now that allows you to create a lot more data-driven insights that can allow you to connect with the brands, to connect with the people that they're trying to reach, while also making some of those challenging and dry or repetitive tasks easier and automated. So you have time to analyze what the data is telling you. So I think it's an exciting time. Well, thank you so much for sharing your insights and talking with me today on the show. Is there anything else that's on your mind or things that you want to share about AI or The Bloodhound Group?
Jim Cobb: No, I think the issue ultimately for me is I commend you for really studying this because I think the application of AI ultimately is going to expand, and it will expand at the pace in which people want to experiment with it. And so, any of the ideas that you find that might apply to our business, we'd entertain a conversation with you about it, because we basically understand it based on how we're using it today, what we do today. So if there are things that we can do... the learning that I mentioned with the training of athletes, I never would have thought about that until someone brought it to my attention. So we're very open to understanding anyone else who has AI, who's trying to commercialize it, to help them become better at maybe presenting it and packaging it so that it becomes more commercially viable.
Lydia Kumar: Awesome. Well, thank you so much, and thank you for sharing your insights. The combination of practical experiences of people like you and folks studying the new technology has been really fascinating to learn about.
Jim Cobb: Yeah, I think it's a pretty bright future with AI. I don't see it as being something that is bad. I think it's just going to open a lot of doors for us.
Lydia Kumar: Yeah, I think so. I think that there's a lot of possibility, and it's also important to be conscientious about how we use anything, because it's important to be intentional and conscientious. So it's good to have these conversations. Well, thank you so much.
What a cool conversation with Jim Cobb, thinking about the intersection of brand strategy, human emotion, and artificial intelligence. I want to thank him so much for sharing his journey and deep expertise with us. I was particularly struck by how he connects the dots between using neuroscience to understand consumer emotion in the nineties and using AI today to analyze and create better, more resonant brand experiences. From the evolution of branding to the future of the classroom, join me next time on Kinwise Conversations when I'll be speaking with Ben Gordon Sniffen, a guide at the innovative Alpha School. We'll explore their unique two-hour learning model, which uses AI-powered academics in the morning to create more time for human-led, passion-driven projects in the afternoon. It's a fascinating look at how more AI might actually lead to more humanity in our schools.
To dive deeper into today's topics with Jim, I've put everything for you in one place. Just head over to the resource page for this episode at kinwise.org/podcast. There you'll find the full transcript, links to Jim's work with Bloodhound Branding, and a list of resources inspired by our conversation. For the leaders and teams listening, if Jim's insights have you thinking about how to build a real AI strategy for your own work, I invite you to learn more about the Kinwise Pilot program. We partner with organizations to create practical, human-centered professional development and policies that empower your team to use these tools with confidence and care. You can learn more at kinwise.org/pilot.
I hope this discussion has encouraged you wherever you are on your own AI path. If you enjoyed this conversation, please subscribe and consider leaving a review. It truly helps other thoughtful listeners find us. You can learn more about how to approach AI with intention, explore resources, and join the Kinwise collective by visiting kinwise.org. And if you or someone you know is doing interesting work at the intersection of AI and humanity and has a story to share, we'd love to hear from you. Until next time, stay curious, stay grounded, and stay Kinwise.
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I hope Jim Cobb’s deep-dive on blending neuroscience, emotion, and AI into brand strategy sparked new ideas for you. If his perspective resonated and you’d like to keep learning from his work, here are a few easy ways to connect:
The Bloodhound Group – Official Site: Explore how Jim and his team use emotion-science research and AI-driven analytics to build brands that command a premium and inspire loyalty. https://www.bloodhoundbranding.com/ Bloodhound Branding
CEO Weekly Feature: Get the full story on how The Bloodhound Group helps companies “future-proof” their brands by aligning neuroscience insights with business strategy. CEO Weekly
Connect on LinkedIn: Follow Jim’s thought leadership, recent posts range from neuromarketing tips to practical AI use cases in branding, and send him a note if you’d like to collaborate. LinkedIn
Follow on Instagram: See behind-the-scenes snapshots of client work, team culture, and branding inspiration at @bloodhoundbranding.
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1. The "Automate My Routine" Prompt
(Inspired by Jim's first use case of training AI to handle repetitive, left-brained tasks like drafting social media posts.)
"Act as a business efficiency consultant. I am a [Your Role, e.g., Marketing Manager at a software company]. I want to use AI to automate my routine tasks so I can focus on higher-value work. Here are three repetitive tasks I perform weekly:
[Task 1, e.g., Summarizing our team's meeting notes into a list of action items.]
[Task 2, e.g., Drafting initial social media posts based on our latest blog article.]
[Task 3, e.g., Creating a first draft of the weekly internal progress report.]
For each task, create a simple, step-by-step plan for how I could train a generative AI on my specific context, voice, and requirements to produce a reliable 'first draft' for me to review."
2. The "Ideal Standard" Prompt
(Inspired by Jim's examples of using AI to compare employee behaviors or athlete performance against an ideal model.)
"Act as a process improvement specialist. I want to define an 'ideal standard' for a key process in my business, similar to defining a 'perfect baseball swing' for an athlete to be measured against.
The process is: [Describe a key process, e.g., 'Onboarding a new client,' or 'Conducting a first sales call'].
Based on the goal of creating a world-class experience, generate a checklist of 5-7 key behaviors and outcomes that would define the 'ideal' execution of this process. For each item on the checklist, suggest one way an AI tool could help a team member prepare for or review their performance."
3. The "Unstructured Data" Prompt
(Inspired by his story of using AI to analyze a photo of a restaurant's hours and populate a form.)
"Act as a data analyst. I have a piece of unstructured qualitative data below. Your task is to analyze it, structure it, and pull out key insights.
The data is a collection of customer feedback comments from a recent survey: '[Paste 5-10 anonymous customer comments here, e.g., "The user interface is a bit confusing," "I love the new feature, it saves me so much time," "The customer support was very responsive."]'
Please do the following:
Identify 3-5 recurring themes (both positive and negative).
Categorize each piece of feedback under one of those themes.
Suggest one actionable business improvement based on your analysis."
4. The "Thoughtful Scaling" Prompt
(Inspired by his final question about how to use AI to scale a business while maintaining quality.)
"Act as a strategic business consultant. My business, a [Your Business Type, e.g., brand strategy consultancy], is looking to scale and handle more clients using AI, without sacrificing quality.
Our brand is built on [e.g., high-touch client relationships and deep emotional insight].
Your task is to:
Suggest 3 ways AI could help us scale our operations (e.g., client intake, initial market research, project management).
For each suggestion, identify the critical 'human-in-the-loop' step that is essential for maintaining our quality and brand promise."
5. The "Brand Emotion" Prompt
(Inspired by his work on the "Priceless" campaign and connecting emotion to brand strategy.)
"Act as a brand strategist in the style of Jim Cobb, blending emotional insight with data-driven strategy.
The core emotion I want my brand, [Your Brand Name], to evoke is [Choose an emotion, e.g., 'quiet confidence,' 'joyful discovery,' 'deep security'].
My target audience is [Describe your audience].
Based on this, generate three distinct concepts for a marketing campaign. For each concept, explain how it uses storytelling to trigger the desired emotion and how AI could be used to research relevant themes or draft initial content for the campaign."
