MEDIASCAPE: Insights From Digital Changemakers
Join hosts Joseph Itaya and Anika Jackson as they dive into conversations with leaders and changemakers shaping the future of digital media. Each episode explores the frontier of multimedia, artificial intelligence, marketing, branding, and communication, spotlighting how emerging digital trends and technologies are transforming industries across the globe.
MEDIASCAPE is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. This online master’s program is designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
MEDIASCAPE: Insights From Digital Changemakers
AI-Driven Strategies in Modern Marketing: Insights from Guillaume Dumortier
Join us for an enlightening conversation with Guillaume Dumortier of Maestrix.AI as he unveils his journey from traditional marketing to the forefront of AI-driven strategies. Guillaume shares how he transformed his marketing approach from the early days of Google AdWords to today's sophisticated, data-driven methodologies. Discover the growth marketing fit framework, which emphasizes the seamless integration of marketing with product development, audience engagement, positioning, and conversion channels. Learn how creativity, data analytics, and behavioral psychology are essential in navigating the modern marketing landscape.
Listen to Guillaume's fascinating story behind the creation of Matrix, an AI tool inspired by the fear of obsolescence, designed to streamline marketing processes. He provides an insider's view on overcoming AI output challenges and the evolution of Maestro X, a push-button marketing solution that delivers professional-grade, structured outputs. This episode underscores the significance of comprehensive solutions, moving beyond simplistic prompt libraries to achieve impactful marketing results.
We also delve into the critical aspects of AI privacy and security, discussing how Maestro X ensures user input protection and intellectual property safeguarding. Guillaume offers insights into transitioning from a side project to a product with market fit, catering to diverse users, from agencies to solopreneurs. Explore the shift from Web 2.0 to Web 3.0, emphasizing the importance of continuous learning and adaptation in the rapidly evolving digital media landscape. Stay tuned for more insights on how AI is revolutionizing marketing and redefining the future of the digital world.
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
Welcome to Mediascape insights from digital changemakers, a speaker series and podcast brought to you by USC Annenberg's Digital Media Management Program. Join us as we unlock the secrets to success in an increasingly digital world.
Speaker 2:Welcome to 2025 Mediascape. I am thrilled to have somebody who I've known for almost a year, whose tool I use in my classes in the digital media management program to introduce some of our students who have less knowledge about branding and executing AdWords concepts for brand integration with your marketing and your advertising. So, guillaume Demortier of Maastricht AI, thank you so much for being here today.
Speaker 3:Thank you, Annika, for having me on. It's a great pleasure.
Speaker 2:Yeah. So your background. You have had over 20 years in marketing, working with big firms, big brands that we all know and love and probably use every day. Can you talk about your experience transitioning from working in agencies, working with these big brands, to then realizing that there is a hole in the market that you needed to fill, and how you've been able to do that with artificial intelligence?
Speaker 3:All right, that sounds about right. So I'll try to keep it short for that first question. But yeah, so marketing background from you know, academic and practice obviously, but it was back. I graduated back in 2005. So that's 20 years ago this year.
Speaker 3:So the marketing landscape was totally different. I mean, if we're talking digital marketing, it was just Google and AdWords and period right, so nothing too exciting. Still, the keyword foundation, we'll talk later, but this time the marketing experience was really about CPG, traditional product marketing for supermarkets, and was promotion and things like that. So not too much the scale of digitization at the time. But then the social networks appear, like the Facebook, the Twitters, then the technical marketing with the pixel tracking and all this data-driven approach to marketing that has kind of taken over the entire way. We're kind of deciding and making arbitrage on where to spend the marketing dollar that we thought so hard to get.
Speaker 3:And so first, and being based in Silicon Valley for the past 17 years, it was very important to formalize the approach to marketing as much as equal as the product. So the product is the star, right, it's mainly product-driven and the notion of product-market fit kind of epitomizes this view that everything revolves around the product right. But it would be a big mistake to forget marketing as kind of the vehicle, or at least part of the tooling of the vehicle, that brings growth right, whether it's into the way you present the solution or the tool that you're building, or it's really how you want to experiment against the market and see how the market resonates. And so there was a need to feel, which is putting marketing back in the equation of, you know, building a product, building a brand and whatnot. And, on that note, all the years starting 2012 where growth hacking was a very kind of very much buzzwords. My explanation and personal view on that is that those are all the product people and engineers that were actually doing marketing, building it in the product, without wanting to say that they were doing marketing right, so kind of a product-nested marketing that we cannot call marketing. And so that's kind of building the growth marketing fit framework. So that's the marketing framework I've designed, revolving around audiences, around positioning, around the content, the user, the buyer's journey, the conversion channel. So all these ingredients and building blocks, if you wish, for the growth marketing fit approach. So that's great.
Speaker 3:I've run that for 10 years as an agency with big groups, et cetera. So, to start to answer your question, working with big branches, everything they don't know, whether they have a kind of a scouting department or something that is hooked to the pulse of what's happening, which is almost the case via agencies. Well, they don't know what. They don't know right, and the way I've been trained academically as a marketer has never reflected in a bigger group or bigger company. As kind of a practitioner, I was more of a brief drafter for agencies to execute the job right.
Speaker 3:Today, the marketers in big companies they're expanding their resources but they're doing briefs all the time and chances are that a brief from one vendor to another or from one project to another slightly changes along the way, so it's never ending unsatisfactory kind of ecosystem, or I would say dynamic, if you will.
Speaker 3:So that's one remark. But I think framing marketing as a discipline that has kind of a predictable workflow and where you accept that it's less about being creative, as people think it is, but having the notion of psychology and behavioral psychology, having the notion of data analytics, everything revolving around numbers and also gut feeling, being creative yourself, meaning having taste, I think that's a good transition in the world of AI, because the next chapter of the story is taking that framework in the area of AI, and that is giving you maestrics, which is having all those tasks and building blocks and ingredients that help you do marketing on a day-to-day basis, whether it's from strategic thinking to the tactical execution of things. Well, it's about translating every of those elements into AI-driven or AI-enabled prompts that put you in the position of having taste as to what resonates the most, as to what the AI has helped you formulate, or at least accelerate, some very structured way of looking at marketing.
Speaker 2:A couple of things that you've mentioned. I also used to live in the Bay Area doing launch marketing for publications and coming up with interesting concepts that would attract both consumers and advertisers to the publications. It was more on the creative side. I didn't work with a lot of data and have that construct. Of course, unless you think about in terms of how much ad revenue were we bringing in right, those were the kinds of things that were our markers. More than are we reaching the right audience? You know, this is what we say is our demographic and psychographic, but is this, you know, really matching? Is our audience really matching with these advertisers? We didn't really get into that as much. It was really more about showing the differences between products, for instance, if it was Xbox versus PlayStation, because I worked on a lot of video game publications and at those times, to your point, we used to think of data in one bucket right data analytics and then you think of the storytelling and creativity in the other bucket. But the truth is they really have to be blended because otherwise you don't get the good results, and that's what I really love about Maestrics.
Speaker 2:I tried a lot of AI tools. I tested a lot of tools and when we first met and I tested out Maestrix and I know it's you continue to iterate and ideate and add to it I had not seen a product where I could literally put in a website or put in a descriptor for a brand and then have various personas based on that category, based on competitive analysis. See the competitors and competitors that you wouldn't even think about necessarily Right, get the psychographics, think about getting the key messaging down to even receiving. Here are some campaigns you could run for your advertising and here are the keywords and themes and here are some things you could do on your social media, your blog posts, your website, along with KPIs. So you really created this very robust ecosystem for any practitioner, whether they have no experience or lots of experience to test out.
Speaker 2:So I think that's just a really interesting construct, because not a lot of people have put something together as robust as you have. So I'd love to hear what did it take to get from the very beginning? How long were you working on the product before you took it to market? And then how did you come up with each iteration? Was it customer feedback? Was it that you saw some changing trends in the market? Because we all know AI absolutely something as marketers we have to know. We have to know how to use it. We have to know how to use it effectively, and there's so many things coming out, so many different tools you can use, as well as all of the trends that we see with AI personalization, how it's helping with ad cycles.
Speaker 3:Well, first, thank you so much for your phrases. That's what fuels me to keep on building and iterating. So a lot to unpack here. But basically the first thing that drove me to build Matrix is Fubo, I think. As marketers, you all know Fubo fear of missing out. That was the Web 2.0, social web era and the era of AI. It's FOBO Fear of Becoming Obsolete.
Speaker 3:And so if you take the two-year timeline that coincides with ChatGPT 3.5 being released two years ago. That is the timeline. Then what is my prism within this? So that's the following. So first it's realizing that it was a fascinating tool, just in a couple of instructions to get a ready, fantastic results, but unstructured, generic, but still just the speed. And the magic wand effect was just kind of when we say, okay, there is something here that forward to the artist and there are going to be people on the right end of history and people who are going to be laggard, and I by nature I'm very curious and builder and I had a couple of these in the past. But here that was kind of a defining moment and say things are going to be big. And so that's when I started to take very specific tasks that I was doing with my agency, so taking landing pages. And then I started to have some layouts that say, okay, this is a pattern model that I can provide as a type of output. And these are the first couple of prompt engineering sessions. Basically that's what it was prompt engineering for marketing. But I was still frustrated with this back and forth because I realized that you could derail the AI very quickly. So it got better as we go. You know time is extending. Ai got better so it was more structured. There was GPT-4 in the spring. So you know there's going to be three years of GPT-4, which is kind of really when the model started to be super stable and incredibly useful.
Speaker 3:And so I ended up transforming the growth marketing fit framework into a collection of 30 prompts roughly, but that were a couple of paragraphs, so just one or two paragraphs, but starting to be detailed. But I still had this copy and paste. So I need to go on my Notion database paste there, go on the product description that is nested somewhere else. That needs to be unvariable, otherwise I know that I'm going to derail the output by a couple of degrees, but still that matters. And I said there has to be a better way of this copy paste etc. And then they came up with the custom GPT. They say, oh great, now I can package my instructions into this custom GPT, which I did. I did.
Speaker 3:If you look up for my street GPT, it's an all-in-one marketing person agent. Sorry, that is doing a workflow. I don't remember exactly what the workflow is, but I was super hyped by that and in the meantime I took a zero-prompt approach, meaning that instead of having conversations, I wanted to have massive output delivery that was kind of structured in a way. That is what expected from a marketing professional perspective what you would pay an agency for, basically, or a very good consultant or a subject matter expert, right, and having this level of thinking of the delivery output and thinking, okay, delivery grade in terms of structure, what's in it has to be refined right, but providing 80% of the starting material was my goal. So I start to merge those prompts in bigger zero prompt shots so there would be no more conversations, but still having the custom GPTs as kind of the vehicle of distribution.
Speaker 3:And last year so a year ago, it is the same time I started to build Maestrix because the GPT store was such a disappointment that I couldn't find a viable way of distributing all I've worked on in terms of structuring prompts. And in the meantime you had infopreneurs selling the 10,000 prompt library PDF for 100 bucks, which again was ruining and adding to the noise. That was unnecessary at the time. Still, you know, polluting the sheer native and nascent category of prompt engineering products, related products or AI products as a whole. But last year.
Speaker 3:So I decided I would build on a no-code platform, so I would own the entire stack, so I would skip the step between product requirement documents and the developer.
Speaker 3:So, and you know, having the knowledge of what has to be in the code as a subject matter aspect, subject matter expert. And so the secret sauce of that is I have structure based on 20 years of marketing and this tool has been built for me first with my knowledge, in the way I structure my outputs. So it's professional grade from the get-go and it's structured in a way that it hits all the concepts that revolve around the marketing foundation, whether it's positioning, when you're talking strategy, or by your journey, when it's about audience research. You have also specific elements and components that pertain to it. So really, the idea is to be able to prompt engineers, so really code the instructions so you don't have to. And today, how it translates in Maestro X is it's a push-button marketing. Basically, all you have to do is create your product seed, which is you describing it? Or just a URL, as you said? But then, after all, the tasks are seed-centric, meaning it's just push button, you don't have anything else. So that's for the long answer.
Speaker 2:It's a great explanation and I think that's what a lot of people miss. People think it's as easy as, like you said, downloading a list of prompts. It's as easy as, like you said, downloading a list of prompts. I know that there are things that you can do with fine-tuning prompts, with uploading documents that speak in your own voice or your brand's voice, but that's still not going to give as much robust information as a tool like Maastricht will.
Speaker 3:It combines the structure, it combines the templates that you're talking about, but then there is a variable component, which is more how do you keep up with AI, which are the models? And so, again, I think people don't know the difference or can't take the time to evaluate those prompts against different models, because, first, why would they do that? I can't do it for them. So that's why, in Maestrics, I don't ask you which model you want to use. You're using the most advanced model, regardless, via the APIs that I've built behind the scenes. So the story I can tell is it's a blend of GPT-4.0, so OpenAI, cloud, anthropic perplexity. But more recently, and I think maybe, if you don't go back to Friday, then you should, because I've replaced 80% of the models with Gemini Flash 2 that I have access to as an experimental, and it's fascinating In my evaluations against the prompts that are geared toward marketing, etc. Today, gemini 2 is the best model that you can get.
Speaker 3:Really, because it grounds well, gpt-4 grounds also with real-time research. But it works well when you use their tool, right. But when you work with the APIs so really the kind of the behind-the-door connections with data they make it harder. But with Gemini 2, they make it easier. So finger-cross isn't going to cost too much in the end, but have it for free, experimental right now.
Speaker 2:Amazing and I think that, like you said, it doesn't matter what you're doing, for instance, in podcasting, there are always new AI tools popping up. Oh, this one will help turn all of your content into blog posts, captions, show notes, transcript, short form content, videos, images, all of these different things, right, but then I've had to test out so many different ones to really see which one is going to be most valuable and which one sounds more like me and my guests. And it's the same thing with any AI tool and especially in the world of marketing, you want to make sure that everything sounds authentic when it comes out of the tools that you're getting.
Speaker 2:I wanted to ask at this point about privacy, because data privacy, of course, is something that we're all talking about. Most of us know that it's a free tool, we are the product, but I will caveat we know that with OpenAI, we know, with Meta, now they train on the data that we're inputting, and unless you have a closed model, I use Claude more, because I know that I have to approve my data to be used to. You know fine tune the models, and so it feels a little more private to me. So it feels a little more private to me. So I would like to hear more about that aspect of Maastricht. And what is the level of privacy? Or how do you make sure if somebody is creating a new product and they don't really want other people to know about it, can they use Maastricht, input the information and then know that this is only going to be stuff that they can see, that they have access to?
Speaker 3:Sure, that's a great question. I have many different answers at different levels of the product. So I think, yes, privacy is very much important to the extent of training models and making sure that what you have cannot be used somewhere else, and the idea of, I would say, spoiling or spilling the beans around something. That's a little bit far-fetched, to the extent that if you're using the product, that's a different story. But I'm talking really within my streaks. There are a couple of things. Let me go from the get-go. In every prompt it's hard-coded that whatever is being input by the user shall never be used as training data, so it's built in the prompt. The other thing that you would think of is if there were prompt injection, like people that want to have my exact instruction. It's also protected against that, which is another way of looking at privacy and security, which is there is also a reason why my prompts are not exposed is because I want them to. They're hyper-valuable, right? They're my IP. Yes, so they're backed at prompt level, every prompt. And you're right, cloud has also some built-in things where you can toggle on and off with the API to make it private. The secondary level that I have is based on my own OpenABI account and every developer account where in the settings, api plus browser enabled or iOS, I don't really know and I did not investigate that, so I'll confess that. But the two steps I've taken in terms of privacy and there's a third one I can talk is trunk level account settings level for the API key. So, alternatively, if I want to use a custom solution, it's a requirement, a SOC 2 requirement for an enterprise-grade account. So I have no other choice for that customer and that's good news for me to have a custom app for them that will leverage their own custom API keys. That will be ensured on their end. So that should be service level agreement between them and OpenAI or Azure or whatever. That is, security is insured at service level agreement level and API is included. Nice, right, I mean that's kind of the theoretical framework that I've been working on.
Speaker 3:But there is a third way and actually I've been working with such a company a year ago. So they have created private inferences so you could get the model in a private enclave and basically get the result encrypted from that private enclave. That would ensure end-to-end privacy. But the idea that you're spilling the beans just by entering text, no, because AI will look at it at text probability. The idea that you're spilling the beans just by entering text. No, because that text AI will look at it at text probability. It's just going to change the weight of each word so minimal that there is no way that you type in this in AI. It will keep the idea and say, oh, that's a good idea, we will do that.
Speaker 3:But that said, on Maestrics, you don't have document import and I think there is a reason to that. Maybe I'll have a third-party partner, maybe Carbonai or one of those providers, or vector database provider. Then the security aspect will rely on their terms of service. But again, you need to make trade-offs as to how do you distribute the value proposition of your own product and as to who you really rely on. And today I think the maturity of Maestrix is less about you know, kind of it's a Series, a level type of conversation, if you will.
Speaker 2:Yeah, yeah, no, that's very helpful, thank you. Speaking of Series A, did you fund this yourself? Are you going after funding?
Speaker 3:So it has started as a side project. As to Guillaume, you need to build your second brain with AI and you have the prompt, so now you need to do something with it Then to have, okay, is this a product that people would want? He's like I've been spending the past nine months handing it hand to hand and onboarding privately people, so it's not scalable at all, but it has proven there is market fit. So, in the persona of agencies, fractional CMOs, freelancers, technical founders, small teams, solopreneurs all those people marketing noobs you have the entire spectrum of anybody who needs to do marketing at some point, is eligible to use Maastricht, and it's built in such a way that you can get the template ready to use. So all you need to do is okay, what is the product I'm working on? So the business model also is copy-paste of a SaaS product playbook or an online product, if you will, because it's AI.
Speaker 3:People don't know how to buy AI. The only thing they know how to buy is a chat, gpt subscription or cloud subscription or subscription to one of the foundational models right, or cloud subscription or subscription to one of the foundational models right. Then you have a smaller fraction of people who are actively building stuff or looking across the spectrum for all the tools made available and it's overwhelming. It feels like this big MarTech map of 2000 plus, like this ever-expanding chaos of MarTech tools. That's the same, that's going to be the same for AI tools and even in a more expanded fashion, because it's going to disrupt so many different areas. But the reality is people shouldn't care about the model that they use. So basically, the conclusion of that is AI is not practical today because when you arrive on chat, gpt, it's open DAW, you can do everything, so you can really do. It takes work to get what you really want and then you compare that again your effort or your investment in time and you get the result and say, huh, it takes time to get convinced that AI can bring you way faster and a higher standards and higher delivery output level.
Speaker 3:If somebody is showing you how to and I think Maestro X makes it practical because it's prepackaged and the observation across those past six months where it's been active into having prospects and customers. So there are 15 customers right now who are loving it and all the users are really, I think, trying to wrap their head around. How can I extract maximum value of this because there's so much to do or, on the other hand, because they've been stuck onto one thing, they haven't even seen the rest of what's available. So the SaaS playbook with a trial period and a monthly license, that's fair for starting very well-identified tasks on my street. What I found is at least proving to work in a repeatable way right now is to do paid pilots of three months where you customize the approach and the plan, the game plan, with what do you need to accomplish? Let's see how the tool adapts and how you play around with that in a very much white glove and hand-holding fashion, more than self-service, because all products will go to the most laziest workflow Input output.
Speaker 3:But what you give is what you get and if there is the depth and structure of what's running the prompt behind, chances are you'll be disappointed every single time and I think I'll wrap this everything. I think I'll wrap this everything. It's expertise packaged in easy-to-use structured outputs that you can use to finalize the 20% of your marketing work. The promise is not it's going to do 80% of the job. It does 80% of the job for you, but you still have 20% to make it stellar and that's kind of the promise. It's not a 100% promise. It's a really 80% acceleration and many more data points that I could share, but it's less important than that.
Speaker 2:Yeah, nice, you mentioned before we jumped on that you also now have expanded into personal branding. So we all know at least hopefully everybody who's listening knows that your personal brand, that's your calling card. We all must have one, you know, because that shows who we are, what we're an expert in and helps not only our own perception of ourselves but other people's perceptions of us. So what was the impetus behind adding the personal brand into the Maastricht's mix, and is it very different from you know, from what we see on the business?
Speaker 3:side, so that one is directly customer feedback. So if you take a step back, you can start with just some text description that you come up with with a URL. But a subset of URLs can be LinkedIn profiles and I've been challenged with the question what can you do with LinkedIn profile? Because from a technical standpoint it's tricky to manipulate or to scrape data from LinkedIn profile or at least to get accurate, consistent, safe, without challenging the terms of use et cetera. So it's more challenging to set up from a I would say plumbing perspective. But it's more challenging to set up from a I would say plumbing perspective.
Speaker 3:But it's an interesting twist to maestrics, because maestrics would be very much product marketing. So you get a bit of product. It does the entire flywheel of product marketing. But person is okay. It starts with you and you are maybe the face of a product, but you are more than that and I think that kind of first, when I got access to being able to scrape consistently the profile data and the post and content posting history, I was able to draw a better picture and kind of apply prompts to it.
Speaker 3:So then I kind of reverse engineered and say, okay, people want to write LinkedIn posts, so they want to have the templates that all the guys are kind of showing et cetera, because everybody's doing the same thing and people want to do what is seen on LinkedIn. They want to crack that impression. They want to have that viral post that will hit 10K impression, 100k impression, et cetera. But I think it's more like I wanted to build a content engine. It's less about that viral post that you need to create, but more the consistency of being able to have your main themes, like owning your main themes, knowing your style, who you are, what you represent, in some cases showing the gap between who you are and what your profile reflects. And I think that's kind of always a good surprise, especially for high-ranking execs or who don't invest, or business owners who haven't been digital native from the get-go, where you can see there are discrepancies in the profile. Well, that tells everything.
Speaker 2:Yeah.
Speaker 3:So taking that data and kind of reverse engineering it into prompts that I already had for some, but applying like a disk approach for finding like hey, from this, what can you tell me?
Speaker 3:Or from the content posting history hey, what's the frequency? What are the most recurring themes? Where did you get the most engagement? What was the rationale between all those engagement looks in relation with your profile, right? So then, after the next step is okay, what are your main themes and how can you leverage that against templates that people are known to wait or consume, against linkedin, and obviously, obviously, linkedin is the main driver of that. But, yeah, no, that is the same ingredients that needs to be repackaged from a prompt perspective to apply to a different set of data, which here are LinkedIn profile data, as opposed to perplexity API results fetched from the previous prompt of you know you inputting a URL, right.
Speaker 2:Yeah, fantastic. What is one thing every marketer needs to think about? What's one question they need to ask or one starting point? Whether they are a digital native, they're using AI for the first time, they're experienced with AI and marketing technologies. What would you say is that base?
Speaker 3:I think, well, the main takeaway for the professional practitioners in tomorrow's world, I think, how do you want to stand out in the pack? You need to have your own flavor. So my own flavor has been deciding I would put out a marketing framework that is the same ingredients that all the marketers know, but that I've packaged it as a plan. That's me thinking marketing as a whole. And I think it's think marketing as a whole because when you become very much practitioner into, let's say, google Ads and you make it your specialty, of course it's going to Google Ads and you make it your specialty, of course it's going to differentiate you from a skill set perspective that you still need to have to compete against other people who will maybe have the same foundation but maybe they'll have other tools. So I think it's think while and own all the marketing ingredients that are part of the marketing stack. I think it's the full stack approach. There is no other way today that to become a full stack marketer, at least from a knowledge perspective. That knowledge can be facilitated, slash, accelerated with tools like Maestrix, with prepackaged forms and, I think, the future of AI helping this, not only marketing, but it's practical AI for verticals. So here. It's practical AI for marketing.
Speaker 3:Prompt engineering is basically the second takeaway is know what you want and learn how to describe exactly in human language what you want, and not necessarily in a one-liner. And yeah, play with AI at least 30 minutes per day and challenge it. Challenge it and you'll never get the same result twice. So the way that, when you provide superstructure templates is you minimize that delta between two outputs, but, yeah, you'll get what you give AI. So the more you train yourself to ask AI more detailed things when you're not choosing maestrics, that does it for you. Well, that's how you develop this new skill of prompt engineering, which I think tops everything and will differentiate.
Speaker 3:I think prompt engineering has been discarded from the get-go. Everybody will be able to prompt or the machines will be able to replay the prompt engineering has been discarded from the get-go. Everybody will be able to prompt or the machines will be able to replay the prompt engineering. Truth is no, because we have taste. We have this intangible spark that makes us think or appreciate or disregard stuff based on our own human way, and that's that unique thing that people need to be able to translate into the prompt engineering and the capacity to create instructions that would help them with their own flavor. So, full circle, develop your own flavor, whether it's an approach, whether it's a unique workflow, whether it's a unique structure or a way of things. And yeah, turning to a prompt, and develop that skill again and again.
Speaker 2:Fantastic. We, of course, are going to have the website link for Maastrichtai, which is fairly easy to remember, in the show notes. Guillaume, is there anything that the audience needs to know? I know that if they sign up, they get five free tasks to try.
Speaker 3:Actually I changed it to 10.
Speaker 2:Okay, so anybody who's listening, if you go onto the website and sign up, you'll get to try 10 different tasks for free, to get your feet wet a little bit more with AI tools and particularly in the world of marketing, branding, and you know those things that you need to know for advertising, social media, all the other parts of your brand.
Speaker 3:Yeah, and follow me on LinkedIn or connect with me, because we're putting out some webinars to how to use it, kind of showing you know AI and marketing like practical AI, as I just mentioned, so I will show exactly some workflows on how to use it, so it's a good place also to follow me.
Speaker 2:Fantastic. Thank you for being here, for telling a little bit more about your journey, your story and how that has taken us to, from you know really Web 2.0, into this new, 3.0 and beyond world that we're living in right now. That's changing every day and I love the fact that you're able to use the most current data that you are always researching as well to figure out what's going to be most valuable for your customers, your audience, and for your own needs with your clients actually that's the best conclusion, like they need.
Speaker 3:You always need to stay a practitioner and get your hands dirty, because that's the only way to keep up with things.
Speaker 2:Yeah, fantastic. This is Annika Jackson here with Guillaume Demortier of Maestrixai, and we will be back with another episode of Mediascape next week. Until then, make it a great day.
Speaker 1:To learn more about the Master of Science in Digital Media Management program, visit us on the web at dmmuscedu.
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