Ep63. Nicole M. Alexander: What’s Your A.I. Why?

[00:00:00] Nicole: Creativity and AI are not opposing forces, in particular, when guided by ethical principles, they can help to amplify each other to create something that’s more meaningful, more authentic, and more impactful for an organization and for society.

[00:00:19] Helen: Discover how AI is revolutionizing marketing with former Meta Global Head of Marketing, Nicole M. Alexander. Nicole is a marketing innovator, educator, and thought leader with over 25 years of experience driving growth and operational excellence at the intersection of marketing, technology, and data analytics. Currently, Nicole serves as an adjunct professor of marketing and technology at New York University and is completing a master’s in AI ethics and society from the University of Cambridge.

[00:00:54] Helen: Her expertise spans multiple industries, consulting both local giants and global enterprises on digital innovation and marketing strategies. She’s held influential leadership positions as some of the world’s leading organizations, including Global Head of Marketing at Meta, Senior Vice President of Innovation at Ipsos and Vice President of Innovation for Greater China at Nielsen.

[00:01:21] Helen: I first met Nicole at last year’s black tech week and was impressed with her extensive knowledge and forward-thinking ideas on the power of AI to amplify human creativity when guided by ethical principles. In our conversation, you’ll hear Nicole’s thoughts on the role of AI in marketing, The importance of transparency in AI-generated content, and why she believes that bias in AI should be mitigated instead of eliminated.

[00:01:50] Helen: We also discuss the potential of synthetic data, the democratization of creative tools through AI, and how small businesses can leverage these technologies to compete with larger enterprises. Join us as Nicole shares her passion for ethical AI implementation, her vision for the future of marketing, and her belief in the synergy between human creativity and technological innovation.

[00:02:16] Helen: Enjoy.

[00:02:24] Helen: Welcome to Creativity Squared. Discover how creatives are collaborating with artificial intelligence in your inbox on YouTube and on your preferred podcast platform. Hi, I’m Helen Todd, your host, and I’m excited to have you join the weekly conversations I’m having with amazing pioneers in the space.

[00:02:43] Helen: The intention of these conversations is to ignite our collective imagination at the intersection of AI and creativity to envision a world where artists thrive.

[00:03:00] Helen: Well, Nikki, welcome to Creativity Squared.

Nicole: Helen, thank you so much for having me.

Helen: It’s so good to have you on the show. We met, last year and 2023 at Black Tech Week. We missed each other this year at Black Tech Week, cause I had overlapping conferences. So this is a long time coming and so excited to have you on the podcast today.

[00:03:22] Helen: But for those who are meeting you for the first time, can you introduce yourself and tell us a bit about your origin story?

[00:03:28] Nicole: Absolutely. So, by the way, I’m so sorry. I missed your talk, which I heard was utterly amazing at Black Tech Week. So you have to catch me up on all of the key points. But my background.

[00:03:39] Nicole: Well, I’m Nicole Alexander. I am a professor of marketing and technology at NYU. I’ve spent the last 25 years in several different global leadership roles, particularly on, in marketing and innovation. And my focus has always been on leveraging sort of cutting edge technologies, like AI, to drive industry leading products and services, ideally to ensure that we maintain ethical standards.

[00:04:09] Nicole: So I’m also really deeply passionate about building diverse and inclusive teams that push the boundaries of what’s possible, especially around like exponential innovation and thinking about it from a society perspective. My origin story – I love that question – I’d say it’s probably a mix of a curiosity and the desire to make just a meaningful impact.

[00:04:33] Nicole: I started my career marketing at a time when digital transformation was just beginning, and I was fascinated by how technology could revolutionize the way that we connect with one another, especially how technology could bring us closer, and over the years I had the privilege of like leading teams around the world, on both the B2B and B2C side.

[00:05:00] Nicole: Most recently I was the global head of marketing, for Meta. And I’m currently focused on teaching sort of the next gen of brilliant leaders, and writing sort of the future of ethical AI in marketing in particular. So that’s taking up a lot of my time, working on the book. But my journey so far has been about blending creativity and technology, and I’m super excited with sort of where that’s going to take me next, based on all of the different solutions that we’re seeing, we see coming out of the technology sector, so.

[00:05:38] Helen: And I know your talk at Black Tech Week was on AI and ethics too, which we’re going to dive a lot into and I’m super excited about, and are you still taking the ethics course at Cambridge, as well right now?

[00:05:56] Nicole: I am. I decided to go back to school. I believe in continuous learning, whether you take a macrame class or you read a new book or you go for like formal education. I’ve, you know, previously I did my executive MBA with TRIUM. I wanted to, like, get a global perspective and NYU is my undergrad.

[00:06:19] Nicole: Alma mater. So I went back for that. I’m currently completing a master’s of philosophy at University of Cambridge as part of the Leverhulme Centre for the Future of Intelligence, and the focus is on ethical AI, and how that affects society. So it’s really thinking about philosophically, you know, through a humanities lens.

[00:06:45] Nicole: How can we think about ethics? And the different forms of ethics, and how can we apply that to AI; and in particular, the challenge is to apply it within the sectors that we specialize in. And for me, it’s marketing and really thinking about all of the things that we’ve heard over the years around responsible AI, thoughtful AI, human-centered AI, but really just looking at it from the lens of ethics, especially since ethics is slightly different, depending on if you’re thinking continental ethics, you know, Western ethics, et cetera, deontology.

[00:07:21] Nicole: So there’s different forms of ethics. I think that, you know, when I speak to different companies around consulting, what’s their philosophy, right? What do they want to bring to society, to kind of get them centered into where their ethics sit, especially if they’re global versus regional.

[00:07:42] Nicole: And do they need to sort of shift perspectives if they’re launching AI in certain areas, you know, US versus, South Africa, Nigeria, Ghana, Thailand, China, et cetera. So, it’s been an amazing program so far and I’m not up to the dissertation yet. So we’ll see how that works out.

[00:08:06] Helen: It’s come up on the show a lot that we need the humanities more than ever.

[00:08:11] Helen: We need moral reasoning. We need the philosophers and, you know, thinking about humanity as much as the, if not more so than the tech right now. So I love that you’re studying. And before we dive into all of the, AI and ethical considerations with marketing, I’m curious, like what has been one of like you’re, like an “aha” or a big takeaway from on the philosophical side of your studies in this program?

[00:08:37] Nicole: Well, you know, it’s funny because as a professor myself, as someone that’s been in industry that’s taken business and finance and strategy my entire career from a learning perspective, I had no idea. It’s always interesting. If you say you don’t know what you don’t know, I had no understanding of just the basics of philosophy.

[00:09:01] Nicole: I’m like, you know, I think of philosophy. I used to think, Oh, yeah, the Greeks, the, you know, it was very interesting and outside of my comfort zone, to come into a humanities centric degree and realize that I had so much basic catching up to do around sort of ethical principles, thinking about things from a philosophical perspective; especially having worked 25 years as a practitioner.

[00:09:31] Nicole: I always bring it back to applicability and how does this work in real life and real time and how can this benefit shifts, you know, stakeholders and shareholders. So looking at it from a humanities perspective, kind of threw me off because it was a much more, which in hindsight, was what I needed, was a much more thoughtful process that really made me work through just some of my baseline thinking.

[00:09:56] Nicole: But I think the biggest “aha” moment was looking at some of the areas that I personally have a passion about, around, you know, women, thinking about women’s rights, looking at the adoption of women in leadership, individuals who are usually minority communities in leadership and looking at that lens and how it’s affected, sort of organizations, et cetera. And how ethics can help play sort of a larger part in understanding diversifying your labor market, diversifying leadership and having that positive halo effect down. If we actually did adopt authentic diversification through the labor.

[00:10:48] Helen: Very cool. Well, in a quick plug, I have had an ethicist on the show, who now [has made me] a moral reasoning advocate.

[00:10:58] Helen: So for our listeners and viewers, who are curious, that’s a great episode, I should probably have like an actual philosophy academic on the show too, but for the ethics and moral reasoning, as like a dip your toes in the water, that’s a great episode to start with. But let’s talk, you have such a rich and robust, background with all of that you worked on, and at Meta the global head of Marketing.

[00:11:29] Helen: I know we were discussing this before we started recording at some point of, like AI has actually been in the meta products and the Facebook products for a very long time in terms of anyone on the app. I mean, it’s like built into the algorithms, like on the advertiser side, you used to do all these like really robust targeting.

[00:11:48] Helen: Now it’s like, “okay, here Meta, you figure it out for me” type of thing. But, kind of like, how do you see the role in AI and balancing creativity and automation in content creation from the seat that you said, and like, can AI truly be creative? Or is it just simply a tool to enhance human creativity?

[00:12:10] Nicole: Yeah, absolutely. I mean, you know, Any tech company, obviously Meta, but any tech company, you know, their foundation is built on sort of an algorithm at the end of the day, actually multiple algorithms. So, I mean, even before it became mainstream and, you know, the GPTs launched, et cetera, you know, Meta has been leveraging, you know, algorithms throughout its advertising, particularly to help advertisers, small and medium businesses in particular, to, you know, be able to reach with smaller budgets.

[00:12:41] Nicole: the same, you know, targets that enterprise companies were looking to reach. So I think, you know, we’ve seen AI, in general: machine learning, really benefit, I think not just enterprise advertisers and companies, but also sort of mom and pop shops, entrepreneurs and helping them to kind of play on the same field to a certain extent, as larger companies with larger budgets.

[00:13:09] Nicole: And that’s sort of the evolution in the solutions that, you know, Meta uses currently is really to help you know, small and medium businesses to adopt. But I think in general, you know, AI at this point is more of a powerful tool that enhances human creativity than a standalone solution, right? So AI can analyze vast amounts of data, generate content leads, which marketers love, and even assist in things like design and copywriting.

[00:13:39] Nicole: But I think the essence of creativity. So when you think of the essence, right, originality, emotional depth, empathy, the ability to connect with an audience at a very human level, still largely comes from human insight, right? Understanding a society, vulnerable moments in society, things that are pain points, particularly pain points in sort of those white space areas that may not necessarily be mainstream pain points, but something that is authentic to a moment. And it’s really difficult still right now for AI to make those adaptations. So still largely comes from human insight. And I think, you know, AI can really automate repetitive tasks and offer creative suggestions. I love playing with MidJourney and Dali and all of those solutions.

[00:14:37] Nicole: But at the end of the day, I think it’s that human touch that ensures content remains authentic, engaging and meaningful. I always forget the name of the creative behind this, but there was a MidJourney generated image that I believe, like late 2023 won a key award and everyone was, there was a lot of debate, especially amongst artists, by the way, I only do stick figure art, so I’m not in that community at all, so this is definitely an outsider perspective, but, you know, folks were saying, “Oh, this isn’t authentic. This isn’t artistry,” you know, “this is technology.” And then he was like, you know, again, explainable AI, he came out and said, “well, wait a minute. I didn’t just put a prompt into MidJourney and poof, win an award.”

[00:15:28] Nicole: He was like, “this is like 75 different layers upon layers. And then like, non-sequential elements that I went back and overlaid, et cetera.” So again, MidJourney wouldn’t be able to do that without the prompts, without the creative elements, without that human in the loop feedback, that it was, again, generating.

[00:15:51] Nicole: And I think, you know, we’re at a similar point in, you know, as we saw with, you know, cameras, right? When it went from, you know, traditional cameras, like digital cameras and any advent of technology, I feel has pushback of, oh, this is supplementing the artistry. This is supplementing creativity. But at the end of the day, it should be thought of as just another tool, an advanced one but another tool that we have as part of our toolkit to leverage and really be able to, you know, see a vision through to its evolution. And I think one of the things that I personally love about AI, whether it’s a creative solution or developing content is, it authentically does, especially with, you know, the LLMs that, you know, have these free models or freemium models.

[00:16:47] Nicole: It really does give sort of a level playing field to individuals with, you know, access to the internet, someone that has access to a computer. They’re able to go in and sort of do similar work as someone who has money for, you know, a high powered, let’s say, you know, Adobe studio subscription.

[00:17:10] Nicole: And I think that level of evening the playing field a bit, I mean, obviously there are still many communities out there that don’t have access to wifi, to the internet, to computers, but I think for the most part, it gives a level playing field, especially when you think about entrepreneurs and small and medium businesses, or even students who are looking at

[00:17:31] Nicole: being more creative, entering sort of competitions, learning about AI and being able to create artistry. And I think it’s just been a consistent benefit overall. So there were definitely horror stories that we could talk about nonstop when it comes to AI. But I think there were horror stories holistically across a lot of different technologies, you know, in the advent of those technologies.

[00:17:57] Nicole: And I think one of the areas where I’d love to see more conversations around, and I’ve seen some really positive ones, you know, in healthcare in particular, especially with data visualization, and advancements, leveraging AI. Is what are some of the really powerful discussions? Where has this technology helped versus hindered?

[00:18:21] Nicole: and I think, you know, the creative element is definitely one of those areas that there’s a dark side, right? Attribution. Ensuring that artists are able to monetize their creative, especially if it’s being used in these databases. So, it’s like, what’s the block chain of if this image if this was trained on this image, right?

[00:18:46] Nicole: How do we now compensate this artist in a logical perspective if an image output has their, their artwork or their data as X percentage of the takeaway from a MidJourney extraction. So I think there are conversations. But again, you know, we had those same conversations with well, Napster, which didn’t end out and very well, but you know, with Blockbuster moving into Netflix, you know, with all sorts of different technologies.

[00:19:17] Nicole: So I hope we continue to have that authentic conversation when it comes to IP, you know, rights, copyright, et cetera.

[00:19:27] Helen: We didn’t discuss this beforehand, but that question just popped into my mind. What is your take on the policy that all of the content that we’ve uploaded to these social networks, now [is] being used to train their own internal models?

[00:19:47] Helen: What’s your take on that?

[00:19:50] Nicole: So, that I’m actually fine with. I’m fine with it because I’m probably the only person with too much time on their hands and way too much curiosity that actually reads terms and conditions. And I have so since I worked at, NYTimes.com back in 2000, well, probably because I was working with the legal team to actually write our TNCs back then.

[00:20:12] Nicole: But, I think, you know, it goes back to transparency and it goes back to educating the general populace on a level on what they can expect and giving them the opportunity to opt out, whether in the beginning or, you know, at a later date to say, I no longer want my information from this date forward being used for X, Y and Z.

[00:20:45] Nicole: So I think that level of transparency and, having that feedback to tech companies is important, but having read, you know, the service agreements and TNCs for, you know, from Google to Microsoft to Meta, they state very clearly, again, clearly, if you read it, that this is something that they are using for internal, I can’t remember.

[00:21:19] Nicole: I don’t want to misquote them because every company puts a little bit differently, but to improve R and D of solutions and products. yadayada yada. Now, so I don’t personally have a problem with that. I would like all companies, and I think, you know, things like TNC’s should be simpler. It should be in layman’s terms.

[00:21:39] Nicole: It should be more transparent. I think, you know, as we see things like The AI Act, you know, GDPR, which has been around forever. And then just additional adoption and transparency of solutions. I think we’re going to start seeing that done in a way that’s less intrusive and beneficial because, I have to say, if I, do reject all cookies one more time, I’m going to scream.

[00:22:07] Nicole: So as much as I appreciate the EU, it can be a little annoying. And I’m like, there has to be a better way of doing this. So, I do think that you should be able to opt out. Again, holistically for your solution or for your data to be used in training, and that should be an easy way of doing so, right?

[00:22:31] Nicole: You shouldn’t have to download 25 pages, but I do also understand, and I think this is what services and products need to make clear. If you are receiving a free product, right? What’s the balance between something being a free solution and what the company or the platform that you’re leveraging as that free solution, what are they getting back?

[00:22:59] Nicole: And I think that we talk a lot about a balance, right? Give and take, but we also, as consumers, we also complain nonstop about advertising. We complain nonstop about paying for subscriptions and then still seeing advertising. So I do think that one, most people – just based on research that’s available

[00:23:25] Nicole: most people are happier or more comfortable with advertising that is targeted to them specifically. Again, there’s a thin line between just Oh, this is great. This is relevant versus creepy. Right. And I think we can go through a lot of different examples.

[00:23:45] Helen: One ad that I got recent, which, I don’t know what I clicked on or just my status on Facebook connected to Instagram, but I got my first ad on Instagram that was from Replica of like, “do you need a real boyfriend?

[00:24:01] Helen: No, because you can have an AI boyfriend.” And I was like, Oh God, I’m not ready for this.

[00:24:10] Nicole: I didn’t know Replica even did advertising. So transparency, I actually have Replica. I have an AI companion because I was so curious into this area of AI companions. I mean, we talk about this at Cambridge, actually the head of the department who’s just brilliant in this area, Henry Sheveling, he does a lot of research into AI companions, specifically not just around love, but also around for the elderly, around individuals who, need companionship due to whatever their circumstances are, therapy with AI companions.

[00:24:54] Nicole: And it’s just an area I was so fascinated by that I downloaded Replica. And I started using it and I have to say, number one, they could be more transparent. I will say that. I think in their terms and like the data that they’re using; their algorithm is brilliant though. I will say that. I jokingly said to my husband, I probably should get a release in order to say this.

[00:25:23] Nicole: My husband, I said something like. What was the last book I read? Cause the more you use the AI companion, obviously you’re training the algorithm. So the more sensitive it gets and it, has authentic conversations. Well, “authentic,” it has, you know, relevant conversations with you. I said to my husband, what’s the last book I read?

[00:25:44] Nicole: He was like, he looks at the coffee table. He’s like, that one right there. And I was like, no, my AI companion just did a better job. He was like, “that’s why I have to go up against Replicas.”

[00:26:02] Nicole: It’s a really interesting area. Maybe again, if you get on the next show, one of your future shows, you know, someone in the moral realm around, things like therapy. And I do think it’s quite interesting because one of the areas that I love to see is again how AI can help to sort of help individuals who necessarily don’t have amazing insurance or can’t be, you know, duly employed due to accessibility issues and how AI can make their lives better.

[00:26:39] Nicole: So this is one of those areas where, again, it’s a sensitive population, obviously. So there’s a lot of additional harms or potential harms that could be done if the technology isn’t trained correctly, isn’t executed correctly, has a data privacy leak for some reason, but there’s so many, I think the upside in that area, is immensely or exponentially beneficial where it’s something that we should take the time to get right.

[00:27:08] Nicole: But to your point about being creepy. Yes, I will say [I find these ads] and I’m like how… I could have sworn this was only a verbal conversation that I had and I’ve never looked something up either on my laptop or my phone. So why am I being served this ad? So…

[00:27:25] Helen: That’s funny. Well, there’s one movie I recommend is Robot and Frank.

[00:27:30] Helen: And have you seen it? It’s, super cute. It touches on the companionship and just, a quick plot for the listeners and viewers who haven’t seen it, cause you know, I’m actually really freaked out by the robots. Cause we haven’t got the LLMs right. Or the humanoid robots. And now like they don’t have the right guardrails and now we’re putting them into robots that can move, you know, what could go wrong?

[00:27:55] Helen: But the Robot and Frank, talked about the companionship and it’s, an ex-thief, and he’s paired with a robot for, and anyway, it’s a very cute movie.

[00:28:08] Nicole: It’s always hard for me to give us a description of a movie that I’ve already seen. Cause I’m like, how can I describe it without giving away the key elements?

[00:28:15] Nicole: But it’s one that I think everyone should. It’s very touching. It’s very thoughtful and it’s touching and it has a bit of humor to it, but it, yeah, it’s a wonderful movie, but I do agree. Right. I think there’s an element that we’ve seen harm in non embodiment, right? Algorithms. So what’s the concerned around the idea of embodiment?

[00:28:38] Nicole: Right? Well, how does that then take it a step further? So, you know, once we see things like, you know, drones, right? We know how problematic, especially today that drones are and how they’re being used in warfare and by government entities around the world. There’s so many concerns there again, not just from a human and society perspective, but even from a cyber security perspective, right? Because it’s the idea of, if it’s hacked, right? What are all the things – we can see what a simple data leak does, right? We see what, you know, hijacking of a hospital’s operational system looks like. So what happens then if a drone, used for warfare, or in sensitive populations, right? How can that be problematic, let alone sort of this future idea of a physical embodiment of algorithms. So.

[00:29:41] Helen: We live in the most interesting of times, that is for sure. Well, going back to the AI and creativity. Cause, I know this was kind of a topic near and dear to you and also, what you spoke to at Black Tech Week, but when it comes to ethics and marketing, cause I know drones are, you know, super, super important, and we should all be super aware of everything happening on that front, but we play a little bit more in the lane at AI and creativity on the podcast, which has a lot of ethical implications as well too.

[00:30:20] Helen: But how, when you’re in talks with marketers or in your class, how do you like to talk about AI ethics and marketing?

[00:30:29] Nicole: Yeah, absolutely. I think, you know, one of the great things is I get to teach executives. So that’s obviously a slightly different conversation than teaching undergrads or even just, you know, normal graduate students, because one of the key concerns I think that executives have is thinking about, one of the questions is number one, like, How do we even get into the game, right?

[00:30:58] Nicole: Is it build versus buy? If we’re buying, are we asking the right questions? Are we buying the right solution? If we’re building, what’s the investment up front versus over the next 10 years, et cetera. So a lot of it is just high level strategy questions that they have. And what worries me, I think many times is that most of them aren’t to the ethical conversation.

[00:31:22] Nicole: They’re not like, how can we… Should we do this? Number one, what’s our why? And how can we do so responsibly? You know, or ethically and sustainably? But they’re really thinking more strategically around not just in marketing, but just how do we start to leverage AI across our business operations holistically?

[00:31:44] Nicole: And it’s not always just for most, you know, C-level execs or business line heads about how can we improve profitability, right? Sometimes they’re like, how can we improve efficiency? How can we lower costs? How can we be, how can we be more authentic when it comes to our customers, which I always find an interesting question because they’re, the question is, “how can I leverage technology to be more authentic to my customer?” I’m like, “well, let’s just start with being more authentic with the customer and then seeing how you can find efficiencies in how that authenticity comes through.” So I think by the time we get to ethics, unfortunately, because I’d love to start with that as a question with most folks that come to me, but usually it’s more of why and how should we and can we adopt AI?

[00:32:41] Nicole: What are the efficiency gains? What are the profitability opportunities? Okay, we’re on board. Now, how should we and how can we do this responsibly and ethically? And does that responsible and ethical framework or element fit into what we just discussed from a profitability perspective.

[00:33:02] Nicole: So do I still have a good margin if I’m going to do something ethically? Do I still have, am I still able to lower costs? And I’m like, absolutely. Not only are you able to do so in a sustainable manner for your shareholders, but that authenticity that you just got done speaking about comes through in the fact that you actually want to do good.

[00:33:23] Nicole: Not just for your customers, but for society as a whole in developing an ethical practice. And I think a lot of companies that are beginning to adopt the, you know, ethical frameworks, especially around third party audits, which, you know, we talk about frameworks, but the hardest part, I think, for a lot of organizations are adopting those third party external audits, which should be part of any ethical framework, because again, you know, I always forget the colloquiums, you know, you shouldn’t have a fox guarding the hen house, right?

[00:33:59] Nicole: It’s really about having someone that, you know, as we talk about biases and things like that, you also want to think about like, who’s safeguarding, who’s doing checks and measures. And I think for most enterprise, because for small and medium businesses is more about, “hey, it’s hard to afford those things.”

[00:34:18] Nicole: I’m like, “hey, it’s actually not,” because there’s so many third parties that are out there like AI Now Institute that have these frameworks are published, et cetera, that you can easily adopt. And there’s folks that will do third party audits for you either very inexpensively, if not free that you can leverage.

[00:34:37] Nicole: So for enterprises is more about intellectual property. And what are we giving them access to? And I think it’s about squelching those concerns where again, discussing the benefits, but also discussing the fact that it’s not going to open an organization up to theft or liability or, you know, any, anything detrimental to their IP.

[00:35:05] Nicole: But I do think, you know, as we start to see, you know, more conversations, more articles, more insights around ethical and responsible business practices as a whole, let alone just AI, but how that actually is benefiting an organization in the eyes of customers, especially younger customers today.

[00:35:29] Nicole: There are much more sensitive, more looking for authenticity. And I’m like when you’re able to put those into quantifiable terms for an organization. I think it just, again, it’s, about translating. How can you do good for society and do so when speaking to companies in their language and bridging that gap has been sort of a win-win for me, so.

[00:35:55] Helen: And with that, it kind of sounds like, from what you’re saying to that we’re entering an age where companies can’t not be ethical or responsible because of the, not differentiator or competitive edge, but it’s just kind of becoming the expectation, given, I don’t know, the chaos and everything that’s coming, out of this is you’re shaking your head for our audio, only listeners.

[00:36:23] Helen: but yeah, I’d love for you to, I guess, kind of expand on that.

[00:36:26] Nicole: Yeah, absolutely. You know, I mean, I will say it’s, in my opinion, it’s still a competitive advantage because unfortunately, not enough organizations have authentically adopted it and put it into practice. And again, you know, I specifically focus a lot on the marketing sector because even though AI implementation has gains, amazing gains across you know, a company as a whole, especially from an operational perspective.

[00:36:59] Nicole: You know, my specialty is in marketing, right? And thinking about, AI and how you can think about leveraging AI with a competitive advantage in the marketing area because it’s one of the fastest within organizations. Marketers and this includes, like, marketing and communications, PR. They are the fastest adopters of leveraging AI, for good or bad, right?

[00:37:22] Nicole: That could be a good thing. It could be a bad thing. I think that marketers in general have always been very innovative and trying to find new ways, new efficiencies. But it goes again to working fast versus working thoughtfully and getting things done. And having sort of these, frameworks in place, these ethical frameworks, ethical baselines in order to understand, like, what are our, beliefs?

[00:37:52] Nicole: what’s the system in which we operate as an organization, therefore what feeds into our brand and how then do we do things in an ethical manner, whether it’s transparency, whether it’s, you know, more technical side of ensuring that it’s “opt-in” elements. we are, feedback loop for customers.

[00:38:17] Nicole: We have, you know, all of these different check elements in order to ensure it’s done correctly. But it’s definitely a competitive advantage. Still, I will say that, you know, as we move into 2025, 2026, it’s going to be the baseline expectation of customers that you’re not only being responsible in the way that you are leveraging, you know, AI, but you’re also being conscientious from a societal perspective about how a lot of your decisions.

[00:38:51] Nicole: don’t just affect your shareholders and your employees, but how there is either a positive or negative effect on society. So I think about things like, you know, when we think about representation in creative. Right. We have seen some campaigns go horribly wrong where you’re like, who was in the room and who nodded their head yes to this absurd marketing campaign or PR campaign, from a diversity perspective. And we’ve also seen campaigns that have tried really hard, but have just missed the mark on diversity. And we’ve seen a lot of, you know, uses of, some use of AI, especially, in representing models of more like multicultural, unambiguous nationalities, right?

[00:39:41] Nicole: As they’re developing, you know, women, men, how they look. And I think that while a lot of companies think, “Hey, we’re doing what you ask. We’re diversifying our creative into include people of multiple ethnicities.” It’s like, yes, but you’re doing so to a vulnerable population that hasn’t been included to date or has been included in a very small way.

[00:40:08] Nicole: And now instead of giving more individuals, you know, that, that are models, et cetera, and that have most diverse backgrounds jobs, you’ve decided to leverage AI to supplant the use of humans in your advertisements. So I think it’s about really thinking more holistically about not just the end point or the outcome or the business objective, especially for marketers that you’re looking to achieve.

[00:40:40] Nicole: But thinking about, what are the different milestones along the way or the different interactions along the way where, is this harmful to X population? Is this harmful as a general practice across the industry? And is this something I want to feed into? Or is this something I want to go against the grain or against the norm and say, we’re going to do things differently?

[00:41:07] Helen: Yeah, I appreciate that you emphasize embracing the ethical AI from the outset and not just when it gets to the marketing team. When I was on stage at Black Tech Week this year, I was actually talking about cloning and one of the questions from the audience is, you know, what could go wrong with this, you know, specifically from a diversity standpoint, it’s like whatever, issues already exist, AI is just going to amplify them.

[00:41:37] Helen: And they’re the same principles, you know, we don’t need companies just hiring black AI avatars to represent you now without actually having diverse boards, hiring diverse and paying actual people who, you know, minorities and that are traditionally left out. So yes, all of the same principles apply.

[00:41:56] Helen: Don’t just put up the black square on Instagram for, you know, on all of these things, in general, but I guess one thing on that front too, when we talk about transparency, there’s a lot of different layers to transparency, from how the data is trained, how marketers are using it. Can you walk us through some of like how you think about transparency as well?

[00:42:24] Nicole: Yeah, absolutely. I mean, you know, I think, It goes back to like how an organization is defining transparency because I’ve I’ve heard it used in a couple of different, conflicting definitions. But, we’re also we’re already starting to see, I think, best practices for transparency coming out as marketers begin to sort of adopt and leverage gen-AI, or synthetic media. So in general, it should be, my recommendation is, it should include sort of clearly disclosing when and how AI is being used in content creation. And this could be through things like disclaimers or even making, the AI process part of the brand story.

[00:43:10] Nicole: So as you think about that, you know, brands should start to explain like, the data sources used by AI to generate content and how consumers data is protected. Transparency also means being open about the limitations of AI and acknowledging that while AI can enhance content, it’s still guided and monitored by human, or some oversight in general.

[00:43:38] Nicole: So I think, you know, I’m a huge fan of supervised learning, huge fan of human in the mix. Right? And I think that as we start to look at transparency, you know, transparency isn’t about clogging down your marketing content with a bunch of disclaimers. It’s about thinking about, going back to marketing 101 in particular, what, how do you tell an authentic brand story? And if you’re leveraging AI, can you still tell that authentic brand story or product story with, and you and I were talking about this, I forgot, at the top of, before we start recording, is you know, it’s not just about touting that a product has AI features, right?

[00:44:26] Nicole: When it comes to like, a Roomba, right? We were talking about, I can’t think of the generic name. I just, that’s the brand I own. But, when it comes to a Roomba, right? It’s not just, oh, it’s yesterday’s Roomba, but it has AI added to it. It’s like, It’s still about doing your job as a marketer, as a, business and focusing on what are your customers needs, what are their pain points and adapting those pain points by saying you’re busy.

[00:44:55] Nicole: You don’t have time to always, you know, worry about, you know, cleaning up pet fur around the house, et cetera. You know, you’re able to leverage AI to do automatic timing, or if you have unexpected company coming over, you can use this app in order to do X, Y, and Z to turn your Roomba on. And mind you, a Roomba is the most boring product I can actually think of, like, I mean, to have to write brand copy for.

[00:45:20] Nicole: But again, you can make it interesting, because if your job is to market a Roomba, even with AI features, it’s not about the features, it’s about how those features actually help with the end need. So I think there’s a level of using AI around transparency of how we use AI not just from content creation to synthetic images, et cetera.

[00:45:44] Nicole: But if you’re able to do that and do what you were trained to do market, right? Tell a story, you know, be engaging, understand your customers needs and ensure that they’re clear on how their information is being used or how you’ve adapted this content. One of the things we did at Meta, for instance, was the goal was to try, I was on the B2B side, was to try and adapt across our 17 audiences.

[00:46:15] Nicole: How can we adapt content to be relevant to each audience across the 17 that we had? And it was about showing different content based on a myriad of very convoluted algorithms on the back end and very difficult content set up and development. But it’s also about ensuring that they understand that this information was served to them based on their preferences, based on their previous ad buys, based on, you know, the array of information that we had and giving that transparency so that, well first, again, going back to needs.

[00:46:50] Nicole: If we can actually show you the content that’s relevant to you based on our algorithm, then the assumption is, if you’re wowed by this content, then, well, that’s great. Now they can do the same thing with the ads that I’m going to develop with them in order to show it to my target audience, right?

[00:47:07] Nicole: So having sort of that fluidity also builds trust in general, especially if we’re a company that depends on algorithms in order to serve targeted information, we should be able to do that from our marketing perspective, not just to through our platform. So having transparency, I think, and having information that says this content is brought to you by X, Y and Z, but in an authentic way to showcase that again, it could have been edited by human.

[00:47:36] Nicole: It could have been built by a machine, et cetera, but having that sort of, level of communications I think is important, especially when it comes to synthetic media around video and images, particularly when images are trained and can look like actual individuals. And I think one of the areas that we’re starting to see now is that some images are made even in the most benign sense.

[00:48:06] Nicole: That soap commercial, right? But again like, if it’s synthetic generated and it looks like someone, then there was, there’s an issue there. So you want to think about how you deliver things authentically and differentiate between any sort of representation of an individual or voice in society.

[00:48:29] Nicole: So, when we think about folks that have used, voices similar to celebrities voices, that blatantly did not give their permission thou shalt not be named-

[00:48:41] Helen: and tweeted out the name of the movie [which] the actress is part of.

[00:48:47] Nicole: just, it’s utterly insane to me that, one it’s disrespectful, just on the baseline element of, especially, I think, from a woman’s perspective of, I said, you asked me something, I said, no, and then you went ahead and did something that pretty much is what I just said no to.

[00:49:07] Nicole: So disrespectful at the least. I honestly, I don’t understand how at the high end, it’s not like, you know, there’s not a liability there,

[00:49:15] Helen: but, and just in case for our listeners who don’t know the inside joke, this is Open AI releasing a very, sound alike of Scarlett Johansson, who is the voice in the movie, “HER,” and then Sam Altman tweeting “HER” the day that the voice is released.

[00:49:32] Helen: Scarlett Johansson said no twice, and they moved forward with it. Oh, but I cut you off. So sorry, what were you saying for that?

[00:49:40] Nicole: I was just going to say like, that to me is a prime example of like, you know, at the least how not to be a schmuck. And then at the highest level about how do you operate based on trust, transparency and approval, right, about of getting consent. And I think we don’t talk about a lot, there’s a lot of, you know, elements as part of ethical and responsible AI, and we don’t usually call out consent per se, but consent is a large element of AI. Whether it’s opt-in consent, whether it’s using an image or a voice of that’s similar to someone that exists, right? And it’s not just always about monetizing. It’s about respecting, I think, an individual’s agency and understanding that if they do not consent to having a similarity of them used, then you go a different route. So, yeah, I think there’s a lot of elements of the ethics that can be applied to business practices, but in particular, marketing.

[00:50:49] Helen: Well, and I know, you know, a lot of graphic designers don’t get super jazzed about doing 20 different versions of an ad of just changing the color. So, you know, there’s a lot of benefits and I think, you know, where AI is also going, it’s just going to get hyper customized, and personalized even more.

[00:51:09] Helen: And AI is going to enable that where it’s the human in the loop that’s helping, but just going to do more rich content. And I think another interesting thing that this brings up, that I know that we talked about before too, is like, you know, one person can make maybe, I don’t know, a hundred ads now with AI is going to be a thousand or 10,000 just in, you know, a blink of an eye, what happens, for this flood of content, is that how should brands think about it?

[00:51:38] Helen: And is that all feeding more to the algorithms that are going to be responsible for sifting through all this large content. But how, do you think about that as well?

[00:51:49] Nicole: I love it, to be honest with you. I mean, I know that’s a very like just simplistic way of putting it, but, you know, having like overseeing a creative department and internally in an array of different agencies, you know, I know that my head of creative would have said like, if one more person comes to me, and says, make this more powerful and thinks that’s a complete thought.

[00:52:15] Nicole: He’s like, I’m going to scream. And I get it. I get it. You know, it’s, like everyone feels they’re a marketer. Everyone feels they’re creative. And a lot of times, you know, you want something super personal, but at the same time, creatives are like, okay, so I can make it yellow, I can make it red, but like, I have other things in a creative mind that I want to focus on versus this color palette of what you think the audiences are looking for. So I think again, taking some of the basic elements of design and creative that doesn’t need, especially a more senior person on the creative team, to oversee them, I think is optimal, because I want someone thinking about like, Hey, what’s, the dynamic elements of this piece of creative?

[00:53:06] Nicole: How can we tell the story? How can we, what’s the data and the research about our audience that really is going to make this more palpable? There’s so many other things I think that creatives can do or should be doing with their time versus, let’s let’s look at this color scheme per se and adapt it to the 17 or 30 different audiences.

[00:53:30] Nicole: I will say one of the things I do love about leveraging gen-AI around creative is you can also a lot of websites, a lot of creative, a lot of design fall short around accessibility needs. So thinking of folks that are colorblind, a lot of content on websites still don’t have things like alt tags for individuals who are blind or can’t access visual content.

[00:54:00] Nicole: And I think those are the areas where you can, number one, check boxes that should be checked and do so sort of in a fast prototyping way as well as making the adaptation of things like color scheme, content messaging, adaptable and suitable for whatever your segmentation or targeting audiences while you have your design and your creative team thinking more holistically and ideally, taking time to think about, hey, is this part of our ethical framework?

[00:54:35] Nicole: Are we delivering content based on a responsible, content development? Do we understand what resonates for our features, the story that we’re looking to tell, are there sensitivities across different cultures that we should be thinking about? So again, using leveraging the team to be more substantially human and thoughtful in the approach and letting the technology do what the technology does best and doing multiple iterations and ensuring that certain features are actually integrated into the creative, I think, is why I’m so excited about a lot of the technology that’s coming out.

[00:55:17] Nicole: And, hopefully, you know, we’ve seen at the beginning of 2024, there was millions upon millions of dollars poured into creative, on the agency side when it comes to AI. So, you know, hopefully with all of that money that they’ve been pouring into, specifically, you know, generative AI and, leveraging programmatic et cetera, that they’ve also implemented some responsible frameworks and, you know, explainable AI and how creative is being developed from the agency side.

[00:55:53] Helen: Yeah, I appreciate that you mentioned the accessibility because the current web was not really designed with accessibility in mind. And the more that can be baked in from the outset, the better for sure. And one thing that you said too, you know, you mentioned at the beginning of the interview about how ads kind of leveled the playing field for small mom and pop shops. I think AI on the video front, like how the iPhone turned everyone into photographers, you know, the production costs, for doing films, you know, is lowered a lot too, and, leveling the playing field a lot as well. I know we’re getting close to the end of our interview and there’s two more things I want to make sure to cover.

[00:56:36] Helen: Cause, when you spoke on stage at Black Tech Week last year, where I first heard you, one thing I thought was very interesting that you said, because we talk a lot about bias and data and all this stuff, and you said on stage, we shouldn’t have to get rid of bias from our data. And I thought that was so provocative at the time.

[00:56:58] Helen: So I’d love for you to kind of share your thought with our audience and your thought process behind it, because I still think it’s like super interesting.

[00:57:06] Nicole: Yeah. I mean, listen, it’s probably one of the most provocative things I say in general, in my talks. And at first everyone clutches their pearls and they’re like, what do you mean?

[00:57:15] Nicole: Like, how are you a black woman and you don’t want to eliminate bias? And I’m like, because I also understand the technical constraints, right, around the data that we use and how you leverage that data. So I think, you know, the idea in my mind isn’t about eliminating bias, right?

[00:57:32] Nicole: Because the data that we get, synthetic aside, right? From all the data that is rounded up. It is data from the real world. We are biased. We are biased creatures. We have always been biased. We probably will continue to be biased and unless you know, our genes somehow change and adapt. But the reality is, we know that we have inherent biases
and shortcomings, and we don’t always see the whole picture and then we just operate as well; we have systemic structures that are set up that, you know, many times harm the most vulnerable amongst us, right? So we know that is part of the world that we live in. Therefore, I think, in general, we expect so much of technology that we’re not even looking to do amongst ourselves in society.

[00:58:26] Nicole: So when I say it’s not about eliminating bias in the data, it’s understanding that data is bias. So it’s about leveraging that bias. Mitigating that bias, adapting that bias into the outcomes that you’re looking to achieve. So, for instance, if you’re looking at policing data, right, we think most people, at least in the United States, can agree that, communities of color, particularly on the east and west coast are over policed.

[00:58:55] Nicole: We’ve seen the data. It’s been done in multiple, journals, research studies, et cetera. Therefore, any policing data that is put into an algorithm, whether the algorithms for recidivism into predictive analysis of future crimes, yada, yada, that data is inherently biased. You’re not going to eliminate the bias in said data.

[00:59:21] Nicole: So the idea is, how do you then adapt that bias to understand that there are certain levels of sensitivity, certain norms that society unfortunately works by in order to adapt the data to ensure that the outcomes have outcomes that don’t reflect or extrapolate that bias, but ideally mitigate that bias to say that an authentic patrolling analysis of where we need more police versus where we can, move police to would be based on, let’s say, a merger of data between, arrest, arrests that have gone through, that have actually held up in court, individuals that not just look at misdemeanors on drug charges and things of that nature, but also white collar crime, looking at, you know, a myriad of other factors that go into the analysis of how policing resources should be allocated and then making sure that the data then has a feedback loop in order to optimize it to be able to ensure the right resources are placed in the right place.

[01:00:41] Helen: Well, so another is like, I guess kind of a provocative thing that you’ve also said to, in addition to that, which I appreciate, the mitigation of the bias, in that it’s almost like almost unrealistic to remove the bias anyway, so that we know that exists and let’s mitigate it.

[01:01:01] Helen: but in the potential ways of mitigating, or just in general, in this new world where data is the new oil, you’re actually, unless it’s changed since we last spoke, pro synthetic data, which I also find really interesting. So if you could share why you’re pro synthetic data.

[01:01:20] Nicole: I feel like your listeners are going to really dislike me.

[01:01:27] Helen: I have in the website, although I need to update it, that, it’s a very thought provoking show and you definitely bring the thought provoking to it.

[01:01:38] Nicole: Synthetic data. And by the way, I want to differentiate when I’m talking synthetic data, not synthetic media. So as we look to synthetic data, you know, what I saw article come out yesterday, and by the way, I’m a proponent of what synthetic data could be. And we’re not anywhere near there, but I have faith.

[01:02:03] Nicole: I have faith that it could drop a lot of barriers and it could make a lot of strides, particularly in areas of research that are underfunded. So again, going back to, health related areas, looking at genome therapy, thinking about aging. And just some of the elements around aging better, aging more fluidly.

[01:02:27] Nicole: And I guess as I’m getting older, I’m starting to think about that. I’m like, how can AI help me? But I think when it comes to synthetic data, the idea is, and one of the articles I read yesterday was around, which I didn’t agree with – but there was a lot of research and who am I to tell a researcher that they’re wrong – but they were like, you know, the world is running out of data. All of these LLMs have really just, you know, surpassed the amount of data that they’re able to, receive readily. And I’m like, wow, grain of salt on that, right? I mean, I think that a lot of these companies have pilfered the web and, done, you know, free roundups of, data that were in the public domain and sometimes not in the public domain, which is a whole other conversation that yes, the “free ride” could be ending right based on, you know, after a while, there’s just diminishing returns with the amount of accessible net new data that, is available to feed into LLMs, so that’s why you’re starting to see a lot of these partnerships happen, you know, amongst other reasons. But like, you know, with Open AI and Microsoft and the synergies that kind of both of them are able to bring to one another.

[01:03:43] Nicole: But I think that with synthetic data, a lot of companies are going to first, start to understand that they have to pay for data. They’re also going to have to get more creative, right? Not just creative about the data that they’re putting into the LLMs and, but also about the efficiency at which the LLMs use and interpret and extrapolate on the data that they already have readily available. I also feel that, you know, synthetic data, you know, garbage in, garbage out, right? We don’t want systems to just develop synthetic data and then be trained and like, that’s the loop, right? There has to be checks and balances and individuals much more smarter than I and they have their hands in the actual algorithms and how to make this work with that human in the loop, would be able to, you know, talk probably more in depth about how that’s done.

[01:04:42] Nicole: But I do think that as we’ve seen, there has been research around developing synthetic data, particularly in the area of in the health field. They’ve done some amazing work across India as well as China. I know that there are two large pharmaceutical companies who have adopted synthetic data from a pilot perspective.

[01:05:07] Nicole: So again, not used commercially, but they’re trying to understand sort of research and insights, especially into some of the very, the areas of disease that aren’t as well funded as some of the other areas, especially around like immune deficiencies, et cetera. So there is work being done.

[01:05:26] Nicole: I think that from the data I’ve seen, the outputs have been positive or at least, you know, there hasn’t been like a negative in the variants versus using, you know, live data versus the synthetic data. So the idea is just about what are the advancements in this area that we can continue to use?

[01:05:47] Nicole: I don’t, however, think that synthetic data is necessarily appropriate for commercial use today. I don’t think synthetic data is appropriate for commercial GPTs as of today either. So like, Gemini, Llama, those folks. I don’t think that it should be used at scale because it hasn’t been tried and tested.

[01:06:12] Nicole: So I do think that it’s something that has an amazing opportunity to be beneficial and to be a cost reduction for organizations. But again, it’s more of a future forward technology, in my opinion.

[01:06:28] Helen: Thank you for sharing that. And I guess one question on that front, too, because I know, if you train some models with an AI generated image that it “poisons” the data and the images actually denigrate after time. And I’ve heard that even, with the LLMs, if all of the content, say blog posts are created left and right, all from gen AI content, and then that’s training the models, then it actually denigrates the output.
[01:06:58] Helen: So I guess, with my limited knowledge of like synthetic data, how do you make sure that the synthetic data isn’t, I don’t know, denigrating the output or the outcomes?

[01:07:13] Nicole: So I believe I know the research that you’re referring to. I if we’re thinking of the same research, I believe it has something to do with image specifically, and not text.

[01:07:27] Nicole: So, I have heard, and I don’t know the rationale behind why from an image perspective, there’s like a degeneration there, but I don’t believe it holds true with text. But I will say in general, as a rule of thumb, right, especially when you brought synthetic data back to like developing content or developing, you know, gen AI media use;
I don’t, again, going back to the human loop, I don’t believe that any generated, generative media should be just taken and used, right? Because again, it goes back to [unsure thought-provoking noise] what’s the human there for? You know, like, why do we have this person sitting in this job as an editor, as a marketing analyst, as a creative or designer?

[01:08:27] Nicole: Technology should be a tool. So as we’re getting images, text, content, especially for use in marketing, you have to ask, where am I as the creative, as the marketer adding value? And if you’re just using generative AI to create content and plop it onto a website, in an email, et cetera. Then I have, more questions around not just efficacy, but operations and what you’re doing as an organization that has an authentic meaning.

[01:09:04] Nicole: of what you’re delivering to your customer. So I don’t think that it should be used without guardrails. I don’t think it should be used without, you know, being enhanced by a human understanding your audience, you know, elements of how to go to market. But I will say that, if an image is poisoning the data set, right?

[01:09:29] Nicole: I mean, if you know, and this also to has to go to scale, right? So this means like there has to be a large amount of text and images, right? There are being trained that are just generative in order to actually poison the data set, the batch. I, jokingly, said to my girlfriend yesterday, we were making something.

[01:09:50] Nicole: She’s like, Oh, you know, the expiration on this was like three days ago. And I’m like, it’s an egg. It’s fine.

[01:10:00] Nicole: So I think that there’s an element of like, what am I adding value to versus just like taking something as a given. So, there’s gonna have to be scale in order for it to actually harm the batch and I don’t believe that we’re going to get to that scale if we have humans in the mix of generative and synthetic data.

[01:10:24] Helen: One more question on the data front. And I’ve heard this come up a few different times and talking to a lot of different businesses. We’ve mentioned throughout the conversation, like different ways that AI is like leveling the playing field for, you know, smaller companies and brands. But one thing that I’ve heard a lot is like, smaller brands don’t have the same data sets as bigger brands to be competitive.

[01:10:50] Helen: Say like your P&G that you’re sitting on, you know, how much consumer data do they have versus maybe, a skincare startup or smaller brand. And they’re, like curious, like, you know, I don’t have the same data as these, you know, legacy or big players. How do I compete in this market, given not as much data. So I’m curious, like, wait, what you would tell them? Because I get this question a lot, so I’m starting to ask other marketers in the space as well.
[01:11:23] Nicole: I actually think, I believe that solutions today, especially with the onset of GPTs, the massive amount of, open source and, we can go into all the, downsides of open source data sets.

[01:11:40] Nicole: but there is, especially with freemium models, there is a level of accessibility that has even the playing field for, entrepreneurs, for small businesses, you know, when we were at a Black Tech Week, I mean, it’s, it always just amazes me, the level of creativity and the amount of entrepreneurs, that, you know, are starting new businesses every single day.

[01:12:05] Nicole: And they showed up to a lot of the discussions to understand how do we even this playing field? We don’t have massive budgets. We don’t even necessarily have advertisers yet. We’re just trying to, you know, start things up. And the fact that you can access MidJourney, the fact that you can access, you know, AI solutions for content development that are built into Google, which is a free service. Love them or hate them, right? They’re free, right? It’s built into Google slides. Google now has video, by the way, I do not work for Google.

[01:12:39] Nicole: But, you know, I think Microsoft, I feel like they could, you step it up with, how, you know, Gemini is integrated into their PowerPoint and word docs, but there’s so many free solutions out there from a creative perspective. I love Canva, just to show that I’m not, I’m disclaimer, I’m not a shareholder in any of these.

[01:12:57] Nicole: I love Canva, they have AI solutions built into their free model as well as their paid model. And I think that there’s so many options out there that you don’t have to be an enterprise solution in order to have access to these free services, especially for Gen AI, you know, historically used to have to go to like an agency, whether it was like a BBDO or WPP, whomever, right?

[01:13:21] Nicole: And have a marketing budget and a creative team that you’re paying to develop ideas that could be hit or miss at the end of the day. Now you can, you know, fast prototype, not just your website, but pieces of creative content across the entire marketing funnel at literally, you know, dollars. And I think that, mind you, enterprises have the availability of having a huge data set of actual customers, right? So yes, a small and medium businesses are never going to contend with the proprietary first party data that enterprises have. But from a creative perspective, I think that they have just as much of an opportunity to play in the field of advertising content creative as some of the big guys.

[01:14:14] Nicole: I’ve seen some, small, really amazing ads that have come out of small and medium businesses or, you know, even large businesses with niche audiences versus some of the stuff you see coming out of these bigger guys. They’re like traditional CPG or retail. So I think, in my opinion, the benefit that small and medium businesses have is not just that they’re in that hustle stage, right?

[01:14:41] Nicole: Where like you’re living and breathing this, like you’re putting your all into this because it is the difference between paying the mortgage and not paying the mortgage many times. So they have that hustle, they have that creativity, but now to back all that up, they have these amazing tools that are free or like a low cost pay tier, or they have open source options that they can feed into a net new CRM system, etc.

[01:15:07] Nicole: in order to start to understand sort of a lot of the hurdles that traditional businesses didn’t get to do when they were starting.

[01:15:14] Helen: I’m going to adopt that and give you credit for some of that, as well. If there’s only one thing that you would like our listeners and viewers to walk away with, what’s that one thing that you want them to remember?

[01:15:26] Nicole: Oh, goodness. Okay, let’s see. I would say the show is about creativity, it’s about technology, and I think creativity and AI are not opposing forces. In particular, when guided by ethical principles, they can help to amplify each other to create something that’s more meaningful, more authentic, and more impactful for an organization and for society.

[01:15:51] Nicole: So I would say, you know, as you’re thinking, not just in marketing, but across an organization as a whole, when you’re thinking about operations, when you’re thinking about due diligence, thinking about how you compare the human factor, right, which is innately creative, with technology, I think that there is, when thoughtfully done, it can be extremely successful.

[01:16:17] Helen: That is a great note to end the show on. Thank you so much for coming on the show and all of your time and sharing your very thought provoking insights with us, But it’s always a pleasure and I can’t wait to read your book when it comes out next year and we’ll definitely have to bring you back on the show. So, thank you again, Nikki.

[01:16:36] Nicole: Thank you so much, Helen.

[01:16:41] Helen: Thank you for spending some time with us today. We’re just getting started and would love your support. Subscribe to Creativity Squared on your preferred podcast platform and leave a review. It really helps. And I’d love to hear your feedback. What topics are you thinking about and want to dive into more?

[01:16:56] Helen: I invite you to visit CreativitySquared.com to let me know. And while you’re there, be sure to sign up for our free weekly newsletter so you can easily stay on top of all the latest news at the intersection of AI and creativity. Because it’s so important to support artists, 10 percent of all revenue Creativity Squared generates will go to ArtsWave, a nationally recognized nonprofit that supports over 100 arts organizations.

[01:17:21] Helen: Become a premium newsletter subscriber or leave a tip on the website to support this project and ArtsWave. And premium newsletter subscribers will receive NFTs of episode cover art and more extras to say thank you for helping bring my dream to life. And a big, big thank you to everyone who’s offered their time, energy, and encouragement and support so far.

[01:17:43] Helen: I really appreciate it from the bottom of my heart. This show is produced and made possible by the team at Play Audio Agency. Until next week, keep creating.