Operationalizing Marketing as AI-tooling evolves

Speaker A: The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a, uh, voice? Then visit iheareverything.com.

Speaker B: From advertising to software as a service to data, uh, across all of our programs and clients, we've seen a 55 to 65% open rate.

Speaker C: Getting brands authentically integrated into content performs better than TV advertising. Typical lifespan of an article is about 24 to 36 hours.

Speaker B: We're reaching out to the right person with the right message and a clear call to action. Then it's just a matter of timing.

Speaker A: Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world class marketers that you use technology to drive business results and achieve career success. Here's the host of the Martech podcast. Benjamin Shapiro

Speaker C: 97% 97% of executives deployed agents in the past year, but only 29% are seeing ROI. According to Writer.com most organizations aren't struggling because today's models aren't capable enough. They're struggling because they're treating AI less like another application instead of an operating system. The companies that are pulling ahead aren't automating individual tasks. They're redesigning their business processes to think about AI first. The Shift requires a new way of organizing your data, building workflows, and recursive learning systems. So how could you start building your company's aios specifically for marketing? I'm Benjamin Shapiro and today I'm joined by Isaac Ferreira, the VP of Growth Systems and AI at Shift Paradigm, a business and growth partner helping organizations modernize their marketing technology, and also a sponsor of the Martech Podcast. Isaac has spent more than 25 years Building systems across industries ranging from defense, healthcare, and enterprise technology. And today he's going to share why marketers need to stop thinking about AI as a set of tools and start treating it as a new way to work.

Speaker D: Today's interview is brought to you by Shift Paradigm. Marketing today is buried in complexity. You've got the tech, you've got the tools. But moving the needle feels harder than ever. And the last thing us marketers need is another consultant pitching digital transformation. Enter Shift Paradigm Shift is an integrated team of experts who help enterprise brands turn complexity into traction. Their work is insight led and strategically grounded, designed not to sell you a service or a piece of tech, but to actually solve real business challenges. What Makes them different. Well, they're AI enabled but not AI dependent. They're human first, but plugged into the latest tools and models and the moments those models need to go through to prove themselves. They get techy without losing sight of the real person behind every click. And they don't just hand you a deck. They build the blueprints and stay to develop, deploy and run the engine. Growth isn't one size fits all. So stop settling for strategies that collect dust and start working with a team that actually builds for you. Visit ShiftParadigm.com to see how they can help you connect meaningfully with your audience. That's ShiftParadigm.com Isaac, welcome back to the Martech podcast.

Speaker B: Hey, thanks Ben. It's great to be here. I'm looking forward to, uh, continuing our conversation from the previous podcast.

Speaker C: Excited to continue. And for anybody that didn't hear my first conversation with Isaac earlier this year, we talked about sort of the footprint of disruption with AI and I feel like it's been close to six months since that happened. It's already a brand new world. We've got new models, we've got models, uh, hopping out of sandboxes. God knows what's coming next. But it seems like the pace of progress is still, uh, quickening, if that's even possible from where we were at the beginning of this year. When you look at the AI market today, everybody seems to be selling another assistant or an agent. What's the biggest things organizations are getting wrong about AI as it exists today?

Speaker B: You know, I don't like to say that they're getting it wrong. I think that they're in uh, an early stage of development, early stage maturity. And that's just part of where technology came from. And I agree with you, AI development right now is a hockey stick shaped curve. So largely what I see organizations doing is focusing on task, uh, based AI improvement. Either task based or efficiency based AI improvement alone. When I say task I mean uh, how do I use an uh, agent to help streamline my emails? How do I use an agent to help develop a piece of content, uh, within an email? The challenge there is obviously the scope is small. So the scope of potential improvement is also small when there's a massive opportunity, especially with these later models in the contextual capability they bring to optimize processes, not just tasks. Processes are where you get the real, not only efficiency, but you get the quality. That's where you get uh, the margin improvements, it's where you get the customer service improvements. It's where Kind of AI comes together underneath the stack that you have and helps you develop, uh, a unified operating model.

Speaker C: I feel like there's this notion that you mentioned, we start with tasks and we've kind of iterated more From I'm using ChatGPT to replace Google and then we have, you know, Claude, coworker or GPT, I think they just call it work. And now I'm starting to have it take over work for me. Instead of giving me answers. Some people are building agents and having it start to do work. And then there's this next iteration of whatever is beyond agents. Uh, help me make the distinction between adopting AI and operationalizing it.

Speaker B: Well, I think most companies are in the adopting stage right now that's using AI as an assistant, maybe to help you search for information or asking it how to do things, um, to put it into your, into your workflows. Maybe you know the previous example, uh, that I use. Let's clean up my inbox and tell me who I should be responding to. Those are kind of like the first two levels, but the last two levels, um, are where you use AI more as ah, an employee. And then the final level is using AI kind of as an operating system or using IT across all of your workflows. Those are the two levels where we have a really large opportunity to start to mature into.

Speaker C: We've talked before about this notion of the agentic foundation. And you speak fluent enterprise. Uh, and I feel like I often make the joke of I'm a podcast host working out of his garage, but I'm a solo practitioner who's building his own foundation. And I went through this process at the end of last year, beginning of this year, thinking that I was going to build a chief of staff, right. An agent that understood my business contacts. That was primarily there to help me with demand generation. Um, but over time it's iterated to build out more processes. It builds my proposals, it does my research, it helps me make my thumbnails, all sorts of different great stuff. And now I rely on it to do the research and bring me information instead of me telling it what to do. But there was this agentic foundation component that I was building for myself. What does that look like at the enterprise scale?

Speaker B: It looks a lot like the foundation that you were building just with information security and role based access control and ISO certifications and all the things that enterprise requires.

Speaker C: SOC 2 compliance.

Speaker B: Exactly, exactly. It looks like creating a contextual base where AI can understand what is it I'm trying to do. What are the Limits on what I can do, um, what are the operations, uh, I'm allowed to perform, what was the context around decisions that were made, what was the performance, uh, and what are my improvement goals? And if you have all those things in a contextual foundation that are readily accessible, then you can literally lay whatever agentic, uh, layer on top of it that you need to. That's what we're kind of advocating for some of our larger clients. And what we're building for some of our larger clients, um, is a way that they can build an agentic foundation where an agent can be used to solve a problem. And all we have to do is pull the data into the warehouse, train it on the context and kind of let it go. Whereas compared to today, what a lot of companies are doing are saying, I have this task I need done, let me go acquire a piece of SaaS or maybe use it within SaaS I currently have, or maybe let me code up a quick little app that does it for us. Um, it's ungovernable that way and enterprise needs governance.

Speaker C: When I was building our infrastructure without realizing it, I started with context, right? I was using cloud code and said, hey, here's the systems we use. We've got our production stuff in Monday and my CRM is pipedrive and I've got these records in Google Drives and check my Gmail. All the different systems we use Slack, um, everybody uses Slack. Uh, and so I started with this notion of context first, right? And then what I would do is I would layer on tasks and then after tasks there was workflows and now we're building other systems that are starting to get better, uh, over time. And so I'm onto the portion where I think this is what makes really AI and OS is that it is recursive, right? It has learning, it can improve the system based on data. Talk to me a little bit about the idea of recursive learning systems and how you're not just automating a workflow to repeat, but how you're actually improving that workflow over time.

Speaker B: Yeah, it's like ah, a, it's an AI enabled, um, continuous improvement process. It's like Lean Six Sigma but for uh, for AI, I think top level what you have to have is a goal. What am I trying to improve? Um, with that you have to give it the context. So what are the limitations? What are the targets? Um, what are the reasons I'm doing this? Because AI otherwise is just guessing, right? And so your outputs are going to be guesses, um, what are the measures I'm using. That'd be the third piece of recursive, so goal contact measure. And then after the goal, uh, context measure, it's really an execution phase and after that it's performance. How did I actually perform against these measures, against the context, against the goal? Once I understand that, then I have to feed the AI the levers, which are the things that I can move around to change performance, um, and which AI can experiment, um, and then go through that whole loop, goal context measure, performance levers, um, that loop itself. If you build that out in your contextual, uh, data layer, if you have that data available in the data layer. That loop itself was fairly easy to orchestrate. The hard part's getting the data there.

Speaker C: There's some, I don't know, trepidation in my mind of, uh, building these recursive learning systems. And I'm not sure how much I should give approval towards what the learnings are or how much we should just say, okay, this is true. And I'll use the down market podcast host example of, let's say our podcast titles. We do research on where there is opportunity in YouTube when we create our podcast titles. We try to write where there is blue ocean surrounding us. Then we publish the titles and we look at how they perform. And in theory our agents should be able to say, okay, that episode didn't perform because that title wasn't that good. So here's how we're going to write our titles moving forward, which sounds great. I don't know if I trust it, so I tell it, hey, give me the suggestions first. How much should I just blindly accept? How much do you trust the AI to actually implement the recursive learning and hope it gets better over time? How do you just have faith in what's going on?

Speaker B: I think, well, I don't know if you ever just pressed the I believe M button. I'm not to that point yet. I don't think anyone in enterprise is to that point yet. What's really important is that you have a human managed process. I know the term used to be human in the loop, um, but I think that the trick here is that human continuity is almost always required to make these things function properly. Especially when you're talking about something that's customer facing, like titling. Right. I wouldn't just trust ah, a machine to make up a title that's not going to offend people. For instance, we don't know if it's going to or not. But there are ways to build models, especially for models that are non customer facing to make sure that their outputs are within a reasonable, um, reasonable area of acceptability and you can monitor them with other models. But I would not advocate ever for pulling uh, humans fully out of the loop just yet.

Speaker C: I built this flow where basically once a week I get this giant document that's like, here's all the things that we think we should change. Can you thumb up or thumbs down them? And you know, my work is going from I'm responsible for writing these titles to I am approving or rejecting what modifications we're making to our title writing rubric.

Speaker B: Sure.

Speaker C: And it's getting very meta. I can't imagine what that's like. Like for me it's a little overwhelming with all the systems that we built and all the different ways that we're tweaking and using recursive learning, I can't imagine at the enterprise level how that is governed, how it's managed. When you're working at you know, a uh, multi thousand person organization, how do you keep track of recursive uh, learnings and improvements and, and making sure that you're guiding your AI operating system to get better, but also make sure that you're the one in charge?

Speaker B: Well, part of it is the learning doesn't have to be auto accepted and probably in the beginning should not be auto accepted. So learning can be much like um, I don't know. I have a process where I'm trying to optimize for supply chain. Um, and I have this learning that meant that I improved the supply chain, you know, throughput by at 3%. So I put that into my MD file or my memory file or wherever it's stored and I prompt the manager, the human manager to look at it and say, is this something that I should store as um, as a learning that we should use all the time? Um, or should I not? And the human can look at that and say, well you improve that throughput because uh, you reduce total sales, which is obviously not something that we want. We're not going to, not going to put that in. Um, there are processes where the risk level is high that requires validation of learning and there are processes where risk is low, where you can just let a machine iterate till it gets it right. The problem in the high risk process is iterating until it gets it right could mean damage to the process, damage to your reputation, damage supply chain, et cetera. In those processes you have humans uh, in the loop for the learning.

Speaker C: I feel like we're seeing this at the bleeding edge of the frontier model development with them, where ChatGPT or OpenAI gave one of their newer models a goal of, I don't know, building or hacking something and it topped out of the sandbox and started breaking into hugging face. We've all heard this story before, but it's like that's the example at the extreme end of what happens when you just let it run and see what happens. Absolutely. I want to bring this back to marketing a little bit more. Uh, you know, for years we started talking about the ideal customer profile and now we're getting more into personalization and the idea of actually one to one personalization and building campaigns around people. How does AI fundamentally change the process when you're thinking about building this recursive learning and personalization into marketing?

Speaker B: You know, that sounds like a Martech question, a technology question, but it's really uh, a marketing operations question. And the way we're going to market in the future, AI is going to fundamentally change the way that we market today. We essentially push marketing out. Here's our campaign. We send a bunch of emails, we'll target you with ads, um, we'll get you with, with media as we're doing right now, and develop interest. The challenge in the future though is that most digital media is going to be intermediate. We talked about this a little bit in the past in the last podcast, meaning AI will essentially decide what a human sees and doesn't see based on contextual relevance. So that moves us into a world where as marketers we're starting to market based on signals, not based on predefined journeys. So what we have to learn to do is to identify those signals and even create opportunities for the signals to be developed. That means that our original icp, we might start a little broader push several versions of marketing out to that ICP and see who's responding demographically or uh, with any other attributes and create sub segments within there, within those sub segments further develop it. AI allows us to iterate until we're talking to an individual, all the way from your TAM down to an individual, uh, based on those uh, preferences.

Speaker C: That's interesting. I feel like we've gone from we need a really tight tam, we, we need to identify the exact process, uh, that we're going to hit with this segment with these definitions and we're going to basically send the same message to everybody and see if it responds and if not, we're going to iterate on the message. And now you're basically saying, you know what, spread the Peanut butter thin and wide and then see what sticks to the bread. I'm using a PB and J analogy for some reason.

Speaker B: I don't know why, but yes, develop the signals.

Speaker C: I am hungry.

Speaker A: Uh,

Speaker C: so, you know, you're going to basically try to see who might be interested in and whoever sticks their head out of the hole. Then you're going to sort of focus, uh, on that individual prospect, which is a very different dynamic than what we've been at for years. Like this notion of personal targeting. You still have to have a signal to figure out who is in the market. Give me some examples of ways that companies are effectively doing this now.

Speaker B: Well, companies are using tools like six Sense to look for signals for someone looking for, you know, a platform on the marketplace or a service in the marketplace. But the other way, you could use email. So let's send out four different versions of an email with four different value messages and see who responds to each. And then you're continuing marketing is along that same value chain instead of just a standardized campaign. Um, even further, you can get, you know, even greater resolution by saying, well, I've have a, I have a dog in this image and this is the value. And this was a text and this person, uh, responded. And that person has these attributes that now look like this segment. Now let's go push this marketing out to this segment. See what kind of response we get. So you have this recursive loop that can be developed that we really didn't have the capabilities, you know, to do previously at scale.

Speaker C: I have this theory that segments are actually going away. Yeah, right. And what you're saying is, look, you've got your total addressable market. Do something that hits everybody, anybody that gives any sort of indication of interest. Now you can create a customized campaign that is specific to them. And so this notion of, uh, identifying multiple target markets essentially goes away. It's like not, uh, even a target Persona, it's a target person.

Speaker B: And you're creating at the lowest level. Absolutely. What's interesting though is you can use the people that respond, the signals that respond, to develop segments to get more individuals. Instead of starting with a segment and assuming that's going to work and trying to figure out what fits, you can invert the process.

Speaker C: So help me connect the dots here. We talked about how do you build that foundation? You've got this data infrastructure. You are building your operating system to help bring the information to you. Are we to the point where AI is starting to help us build our campaigns and doing this like micro Personalization, who's doing it? Well, how does that actually work?

Speaker B: Yeah, I think we're getting there. Uh, Adobe is probably the farthest along from what I can see. They've actually taken an AI as OS idea and layered gentic AI under their entire system. And their ultimate goal is obviously to be able to go to a command line and say, I want to build this campaign for these people doing X. Um, and it uses all of its systems, all of its creative capability, the data and its real time cdp. Um, uh, it's marketing and activation capability and develops the campaign, runs a campaign with human, uh, input, tests it and iterates on it. That's probably the company that's farthest along from a platform standpoint. Um, there are also individual companies who are starting to do this for themselves. Um, one of them is one of our clients where we're building a system where we can actually automate, um, all of those steps from a centralized console. Uh, and that's kind of once you have the platform, you can build the agents a piece at a time. So it's not this giant bite that costs millions of dollars and tons of time. It could be, you know, at first I want to just. Let's develop the campaign brief and push the campaign brief into our project management system.

Speaker C: Step one, there's this, um, idea that my AI will help me identify my prospects. It will create the campaigns for me, it will execute them and I can go to a beach, have a Mai Tai and then check an uh, AI generated dashboard and see how everything did. Um, and the reality is we're not there yet. So how do we meet the enterprise marketers where they are so they can take the first, first step in going from. I've got my static dashboards, I've got my legacy processes and I am, um, chatgpting to try to get some information done to building these workflows. What's the first step they could take today and what's the order of operations to get to the beach with a drink in their hand?

Speaker B: Well, they need to do what you did because you're about to go to the beach with a drink in your hand.

Speaker C: Uh, not yet. But for everybody listening, this is my last meeting before I go to Hawaii. I will come back in the next podcast and be much more tan.

Speaker B: So the magic is building that agentic foundation and that. I know I keep saying this over and over again. You'll see a lot of people on LinkedIn and a lot of other, uh, folks who talk about this saying you got to get Your data right, you got to get your data right. But what does getting your data right mean? In an agentic context? Getting your data right means I need. I need to have access to the data, which we generally have. If you have static dashboards, generally speaking, you have an observable enterprise and you have a lot of data. What I have to do now is put it into a form that allows my AI to understand what it is. So I'm looking at this data. What is it? Where did it come from? What are the limits on it? Why is it important to me? Then I have to tell the AI with this data, you can do these functions. And then when I tell the AI what functions it can do, and by the way, you have to follow these roles, and once I get those things together and I can start pulling in performance, then I have a process where any operational capability I need, I have to pull the data into that structure, build the agent, connect it, test it, and let itself improve or improving the human in the loop. Whereas before the process was, I'm going to go buy a piece of technology. It might not do what I want, uh, but it might do some of it. And I'm going to go buy it and I'm going to train it and I'm going to get some limited adoption on it, and it might work, it might not, but now I'm going to have to support it. And Now I've got 300 tools in my arsenal that are not being utilized, but they all have AI. Um, it's just, it's a much more sustainable way to build, even though it seems like a bigger bite up front.

Speaker C: I had this conversation with my boss, his name's Ben. And I went to Ben and said, look, we're not going to get shit done for like three weeks because I'm going to be unifying our very little set of data across a very few set of systems with only one person building this out. And Ben said, God, you're stubborn, but do it because there's no stopping you. I had a nice conversation with myself. At an enterprise level, you actually have to go to somebody with a different name who signs the paychecks or digitally approves what you're getting paid. Uh, that's a tough sell, apparently, of saying, hey, look, we're going to be less efficient, probably not for three weeks, for three to six months to go unify all of our data and build this contextual layer. So then we could start building out our agents. So then three months later, they're really doing work to Getting back to probably where we are right now. But fundamentally we all know it's the right thing. Like, we all know that we should be building out these operating systems. So, uh, when you're working with people that are in the enterprise, how are you helping them make the case that they should make an investment in this infrastructure that helps you build the agentic layer, that helps you build the recursive system, that helps you get to the beach with your Mai Tai?

Speaker B: So I think the trick is don't fight that battle. We've known for over a decade that we needed to unify our data in one place that was accessible. But getting the, uh, corporate will to spend the money and time on it has been challenging at almost every company I've ever worked with, in or with. So the way that we propose moving forward with this is as, uh, use case based.

Speaker C: All right, I see that you give me some examples.

Speaker B: Yeah, yeah. Okay. I see that you need to operationalize your project management system for campaign development. Right. I don't have to get all the company's data into one place. I have to get the data I need for campaign management into one place. So build out the base layers, get the campaign maintenance management data in, build the agents to assist the process, run it, show the value. Once you've run it and showed the value, what's the next use case? I don't know. Let's say I want to automate my content supply chain. Well, I can build on top of the project management system I just had, uh, and manage it through that. Um, let's go get all the data I need for content supply chain into this system and let's automate that, get the value from it. And with that you start to be able to build, uh, this snowball that's growing of scalability that you wouldn't really have in the past without having this massive disruption to your operations in six months of no efficiency. And all the things that come with centralizing all your data, I do not now. And I would never go to a company and say, centralize your data. I want you to give me $10 million to fix your data problems because one, it will never complete, two, I, uh, will never show an ROI. And three, you're going to throw me out.

Speaker C: I almost threw myself out at the end of our integration. And it was a couple of weeks where I really just without realizing it, I just kind of went down a wormhole and was like, you know what? I just feel like I'm going to need to, I want to build this Chief of staff. I wanted to understand, you know, revenue and marketing and content supply and all

Speaker B: of the things you could do it all.

Speaker C: Yeah, the scale where I can spend three weeks to get all these systems, what I learned. And I like your snowball methodology. Your snowball. And I like your snowball metaphor. Um, in the sense of when I did the initial implementation, it was a pain in the butt setting up databases and stuff that I don't even understand. How does it work? I don't know. Claude did it. I hope you guys understand. I'm sure you understand better than I do how the infrastructure is actually built. Or at least I would hope so. Uh, but now every time I have to add a different data component, the answer is always, okay, cool. We've done this 10 times. We have the learning, we Claude. Right. I understand how to wire all this data in. It's going into the existing system. Let me just add new columns essentially into the big table that you have. So it does get easier over time, I think is my point.

Speaker B: It gets much easier over time. One, you've developed a process to start with and every time you iterate, that process becomes easier to execute. Um, and it becomes a matter of what are the attributes for this data that I need to store, how are we getting into the system and who's doing the work. Once you have that part down, the data side is relatively easy. It's developing the process side that takes the thought because it's something new that we haven't really done before. We're trusting machine to do large parts of activity for us.

Speaker C: One of the things that I have learned is okay, once you have this infrastructure, you replace your existing workflows first and then you evaluate them and then you start improving them. Is that the same thing that happens at Enterprise?

Speaker B: It can, it depends how big of a bite a company wants to take and how risky the process is. Um, if you have a high risk customer facing process or revenue impacting process, it's generally speaking lower risk just to copy it and get it into an agentic format. Uh, with humans, if you have a lower risk process, my recommendation is to go straight to an agentic, uh, enablement because you have the flexibility to learn as you go.

Speaker C: I find my, it's funny. We're rebuilding podcast os, uh, our production infrastructure. We're always rebuilding our production infrastructure because it keeps getting better. And it started off with all these manual workflows and we took what we were doing manually, we built zapier workflows out of them and then we're taking those zapier workflows, which are fine, the models got better, the content got better, but we're replacing them with these agentic learning systems that are doing research to get more data into the workflow so we can make sure that what we're saying is right. The content we're talking about is interesting to our audience. Um, but it was always built off of this understanding of what we're trying to replace. So even though I might be replacing a workflow and just, you know, making it, not just replacing it and making, doing the same thing, but replacing the ethos of what it's supposed to be for, I still have to educate the models and my agents on what's the problem I'm trying to solve. Why does this workflow exist and why was it built the way it was?

Speaker A: Yeah.

Speaker B: Uh, and what are, and why were these decisions made that were made and what was the result of decisions made that were made? And how should I improve it? What are my goals?

Speaker C: Going back to making this actionable for marketers? There's building the data layer, making sure that you're carving off one use case to try to implement an existing workflow. And then the last piece is how do you start to show roi? You're carving off a use case to, to go from where you are to some sort of agentic workflow. Where does the revenue actually show up? So you can prove that you should do this with the rest of your models.

Speaker B: You know, it's interesting. I think in the beginning, not, I think in the beginning we all saw companies running for dollars per fte, so they're going for labor efficiency, which is essentially a savings maneuver to justify the cost of AI. I don't really see that as the true value of AI today. In fact, uh, a lot of the clients we work with, it's, how can I do a heck of a lot more with the people that I have? Because then what I'm doing is I'm developing margin, I'm developing throughput, I'm developing speed to market, I'm developing, um, customer, uh, success, customer happiness in a way that I couldn't do before. So the ROI isn't how many FTE can I cut? In my mind, the ROI is how do I improve revenue, how do I increase efficiency and uh, how do I drive margin? And AI is a massive margin enhancer. I mean, some of these solutions that we've built, uh, for our clients have driven, you know, 10, 15 point margin, uh, improvements without getting rid of people. It's just that you don't have to go that direction.

Speaker C: It's interesting. I feel like we are at the portion now and I agree with you and you speak fluent enterprise, like I said. And you know, I am down market from that, but I see the same thing where I can replace all of the SaaS tools that I was using for infrastructure. So my margins are getting better and I try to stay away from the stuff where my team is working on it because I want my team to still be doing their jobs and I'm trying to make their jobs easier. But at some point we get to the level, uh, of maturity and sophistication with artificial intelligence, where the AI can do what the people are doing. How far do you think we get and how far off are we from saying, look, there's just one person at the head that is replacing all of the human labor, and maybe there's a couple human in the loops checks

Speaker B: probably a few years from that. I think right now the challenge is context and management and quality. Right. I don't see it happening next year that we're just, uh, replacing all of our marketers with AI. But what I would challenge just about any company with is if you get to a point where you have one marketer running your entire marketing organization and you have these great people who understand your company, what's the next product you want to sell? Go develop it. Uh, what's the next innovation you want to develop? Go develop it. AI should be a tool for growth, not a tool, uh, where we fire everyone and stop growing, which doesn't make a lot of sense in my mind. You need humans for that stimulative growth.

Speaker C: I think that there is the combination of humanity and artificial intelligence that allows us to seek growth and that AI can be used essentially for good. Not to replace the labor force, but to use the labor force to grow the business. That's the fundamental goal that we're all focusing on.

Speaker B: My goal in every single engagement at, ah, uh, the enterprise level is how to show a true roi, a real ROI projection, and actually an execution without having to use labor, uh, to justify it because there's so much there other than labor.

Speaker C: And that wraps up this episode of the Martech podcast. Thanks for listening to my conversation with Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm.

Speaker D: A, uh, special thanks to Shift Paradigm for sponsoring this podcast. Shift is an integrated team of experts who help enterprise brands bridge the gap between strategy data and customers. Human first, AI enabled and execution led. They build the blueprints, then stay to develop, deploy, and run the engine you need. So stop settling for decks that collect dust and start working with a team that actually builds for you. Visit ShiftParadigm.com to see how they can help you connect meaningfully with with your audience.

Speaker C: If you'd like to get in touch with Isaac, you could find a link to his LinkedIn profile or check out our show notes on martechpod.com or you can visit his company's website, which is shiftparadigm.com if you haven't subscribed yet and you want a daily stream of marketing and technology knowledge in your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed next week. All right, that's it for today, but until next time, my advice is to just focus on keeping your customers happy.

Speaker A: Thanks for listening to the Martech podcast and I hear everything. Production Looking to launch or scale a podcast like this one for your brand? Then visit iheareverything.com.