With 40% of marketers experiencing more responsibility in the workplace according to a recent MarketingWeek survey, it is clear that marketing teams are being asked to do more with the same or less time and resources.
Today we’re going to talk about the role of AI in transforming marketing in regulated sectors with Nauman Hafiz, Chief Technology Officer at Constellation. We’ll explore how AI is reshaping the marketing landscape, particularly in industries like Automotive, Pharma, Insurance, and Banking, and discuss the implications for data privacy.
Resources
Constellation website: https://www.helloconstellation.com/
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Transcript
Note: This was AI-generated and only lightly edited
Greg Kihlstrom:
With 40% of marketers experiencing more responsibility in the workplace, according to a recent Marketing Week survey, it’s clear that marketing teams are being asked to do more with the same or less time and resources. Today, we’re going to talk about the role of AI in transforming marketing in regulated sectors with Nauman Hafiz, Chief Technology Officer at Constellation. We’re going to explore how AI is reshaping the marketing landscape, particularly in industries like automotive, pharma, insurance, and banking, and discuss the implications for data privacy. Nauman, welcome to the show. Hey, Greg. Great to be on the show. Yeah, absolutely. Why don’t we get started with you giving a little background on yourself and your role at Constellation? Totally.
Nauman Hafiz: Yeah, so I’ve been in sort of the software development product industry probably for, you know, maybe like 20 years now. And I started Actually finally in like the games industry mostly as like just doing some QA testing and things like that because I was really interested in kind of the intersection between software and art and sort of games as a good medium. It pushes a lot of those different verticals in but I was a computer scientist by trade and after that dabbling in that industry I kind of moved into the advertising industry with a product agency called RGA. And we were working with a lot of big brands such as Nike, Samsung, Disney, etc. And that was sort of early days, but we were helping Nike rebuild a lot of their digital websites and infrastructure. So Nike.com, Nike Store, Nike Plus, which was a big digital initiative they had. And working with them for a decade. And then the last thing I did at RGA was rebuild marvel.com for Disney. So that was a big content delivery product user experience exercise. And so that was really interesting. But after that I got an opportunity to jump aboard with Constellation. What they’re doing, what we’re doing, is essentially redefining how marketing content is created and how you scale exactly what you were talking about, where you have tight budgets, you have a small team, you need to deliver something that’s highly personalized and highly customizable. for all these various large enterprise clients and you need to factor in all of the compliance that comes in with that. So you can’t just launch an ad in the auto sector or pharma or in insurance without going through a lot of brand and regulatory guidelines and sort of approvals. And that’s really where our software comes in and it makes that process really much faster and much more efficient. And also at the end of the day, you just get a much better product for both the customer and for the brand themselves.
Greg Kihlstrom: Yeah, yeah. So yeah, let’s let’s dive in here. And, you know, as I mentioned, you know, we certainly talk about AI a lot on the show. I think it’s hard to escape it in some ways. And, you know, it’s not just because there’s a lot of hype, it’s because there’s also a lot of real applications. But as you just mentioned, and I mentioned at the top of the show as well, using AI and making it compliant in highly regulated industries brings some challenges as well as some opportunities. So why don’t we start there? What do you see as some immediate things that some of these highly regulated industries you mentioned need to take into account when making investments in AI?
Nauman Hafiz: Yeah, no, it’s a great point. I mean, AI is amazing and we’ve seen it evolve beyond, I think, what anyone imagined over the last couple of years. It’s kind of taken stage beyond. I feel like AI has been around, obviously, for like 30, 40 years in some sense of the word, but it’s never really found application that’s really valuable until now. And for, you know, kind of what we’re doing, there’s a few different layers, I would say. It’s kind of like a layer cake. As you split out the stack from a product perspective, everything that you need to do, whether it’s decision making on the brand or the imagery or the actual taglines, etc., disclaimers, you know, you can’t expect AI just yet to be able to go from A to Z and solve all of those situations. and all of the complexity of that, I think we’ll get there as we customize these AIs and we fine tune them and we train them in a very specialized way for some of these situations. But what you can do right now is solve you know, very specific points in that journey. And some of the areas that we’re using it, for example, is if you think of GenAI from an image content creation standpoint, and you think of mid-journey, stable diffusion, some of those models, what they do well is they can create landscapes, they can create content that’s relatively abstract, as in it doesn’t have any specifics to the brand or specifics to humans or showing something that sometimes is a little bit in the gray area. But it’s still incredibly valuable to get that kind of content creation. You can present your brand and your messaging with a little bit more context to the customer and a little bit more personalized. So that’s one area. Another area is we use AI systems a lot for another validation check. That’s where we’re testing a lot of the systems, where it’s not necessarily generating net new content for you, it’s just validating that the content that was created checks off a number of these boxes. I think that’s another area which is really a test and eval that AI can do a really good job. You still have humans in the loop, you still have a lot of the source work that’s happening, in a relatively scalable way using the systems that people are a little bit more comfortable with and a little bit more predictable. But then you have another layer of evaluation that’s happening on top. And again, I think there’s going to be hundreds of more applications, and we’ve been testing many of them. But that’s sort of the approach that we’re taking is, OK, look at the bigger picture, see which smaller pieces you can use AI for in a really predictable, compliant, and scalable way, and then continue to iterate on that.
Greg Kihlstrom: Yeah, and to build on that then, what does that mean from the strategic perspective? I mean, so, you know, you definitely, you mentioned a few different potential use cases, and certainly, you know, generative AI, to your other point, is getting, I think it’s getting a lot of the oxygen in the room, so to speak, but AI, you know, has been around for for a while, there’s predictive analytics, there’s, you know, there’s workflow, automation, RPA, like stuff like that as well. So lots of different ways of looking at AI and machine learning as a subset of that. What does that mean from a strategic perspective, knowing that you have the potential, maybe some of them are not ready for prime time, so to speak, but you have the potential to this, how does that, you know, transform a marketing strategy?
Nauman Hafiz: Yeah, no, it’s a great question. And it really comes down to also accessing information. I think a lot of these systems make data available to a marketer. And we’ve been testing out, I mean, you think of TatGPT and just the way you can sort of query it and you can ask it for a background on specific topics or specific ideas. And what I think those systems do really well right now is they just explain things in a really clear way. They can kind of you know, present somewhat complex ideas and topics and they can kind of tell you how it got to that conclusion in a relatively, you know, clean way. And so making sure that, you know, marketing team is aware of how to tap into these tools and how to get access to information. What we also do is, so in automotive, for example, there’s these massive data sets of inventory information of vehicles and movement of different vehicles across every dealership. And we have those massive types of data sets, but the challenge has always been how do you make that presentable or kind of actually get insights from that type of a huge data set without spending hours and hours and working with the data science team and modeling this data. in different ways. And what AI allows you to do is actually, whether it’s through a chat system or through some sort of natural language, kind of poke and prod at those datasets and get access to some really, really incredible insights and incredible type of inference just through, you know, a very fast turnaround and very fast iteration cycle. And so that’s one way where we equip marketers with all of this additional information for strategy and tactics and kind of thinking. And it’s not like they need to, you know, kind of write SQL and crawl through all these different data sets and kind of get really, really technical before they can start to see some actual kind of guidance and some actual information. And I think that bridging those gaps and AI for training, I think, and education is in general where I’m focused on significantly, because I think it’s a huge, huge boom there.
Greg Kihlstrom: Yeah, and then plenty of opportunities, as you’ve outlined. And then, you know, as far as the challenges go, I think you touched on some of them already. And some of them, some of the challenge, I think, in highly regulated industries are not specific to AI. They’re just, you know, there are some challenges with just, you know, maintaining compliance and things like that. But, you know, in the context of, you know, whether it’s data privacy or other things, you know, How do you look at making sure that you’re staying compliant and all that when AI is being used? And certainly AI, it requires a lot of data and almost the more data, the better in some cases. So how do you look at that in context of data privacy?
Nauman Hafiz: Yeah, I know 100%. I mean, a lot of it is sort of a couple of different sides to that. One of them is you have to have a number of checks and balances for anything that AI is producing as well. The whole testing part of the equation, again, is something that needs to evolve just as quickly as these AI systems are evolving. And there are a number of platforms and kind of thinking and philosophy is kind of going behind that. So that’s one part of it. The other part is also getting the customers and the companies familiar and comfortable with what these AI systems can do. So like if you think of some of these industries like pharma and auto, there’s still manual approval processes. So you submit content to a team. It can be the FDA for pharma. It can be a specific kind of approval center in auto. And, you know, there’s a manual team of folks that are approving all of this content. And that’s OK for kind of how the process works. But when you scale up the amount of content that you’re submitting and with some of the tech that we have, we can scale that up 10x, 100x. And it really becomes a much more complex exercise for those teams. And so it’s getting them to become more comfortable with some of the decision making that not only AI, but even some of our software is presenting. And that’s a journey that you need to kind of go on with them. You can’t just be like, OK, this is kind of how you do things now. You’ve got to trust it. So I think those are definitely two big parts. But just from a data standpoint, I think what AI is also presenting is everyone needs to just be aware of what these systems can do. I mean, from a kind of bot standpoint, from a hacking standpoint, from a, you know, getting access to your data, like, I feel like you hear about this ad nauseum, but, you know, I think we need to just be twice or, you know, 10x more aware of how our data is actually being stored and how our data is being presented because these systems are really intelligent and people are using it for a number of different use cases and you’re just as susceptible. In fact, way more susceptible now because people can do those types of attacks at scale, at a much grander scale than I feel like we could before.
Greg Kihlstrom: Moving back to some of the benefits and opportunities of integrating AI in these industries as well as others, but in these highly regulated industries, I want to talk about enhancing both creativity as well as compliance. So can you talk a little bit about how your technology enables marketing teams in these industries to both automate and personalize content creation while, you know, one of the big concerns is, you know, personalizing stuff and what happens as far as regular regulatory stuff goes. So, you know, how do you how do you do that while adhering to strict regulations?
Nauman Hafiz: Yeah, it’s a great point. And really kind of the backbone of our platform is using data and templates and a number of kind of business logic systems that you can customize to the brand and the product. to generate all of your content. So you can think of it as a few different pieces. It’s maybe like a little bit of a database that you can customize and then a Canva type of templating engine with a lot more data-driven functionality. And then the output can be you know, whatever type of marketing materials, all the way from an in-design doc, if you’re doing print to HTML and images and video, et cetera, et cetera. So just having that type of, those sets of entities and that sort of set of connections has put us in a really good spot to take advantage of AI because we’re already taking sort of the manual decision making out of that process and we’re adding the scale. a database or a set of data records, and this can be many thousands of records depending on how complicated the marketing strategy is and how personalized it needs to be, and use that type of source to drive all your content creation. So you get a lot of the scale without the time that it takes for a creative team to actually do all that work. And there’s still creative teams in the loop that are setting things up. that go into that larger data set and that rules engine, but they’re not actually doing the work of creating each of those individual content pieces. And from there, you know, really we bake in a lot of the compliance. So in automotive, for example, disclaimer, if it’s, you know, if you’re talking about a specific type of incentive in a specific state with a specific brand and it’s a new vehicle, it needs to show all of these very specialized things. So we can use the data to drive the creation of those disclaimers as well as making sure that it has all of the subjectivity and all the specificity that are required for that very specific version of the content that’s being created. And that’s sort of the scale of the pipeline. But then if you look at that pipeline, it’s easier to see where AI can sort of come into the loop and bridge some of those gaps and really take you in smaller steps from A to D or from L to P or whatever. And so it’s closing some of those gaps where, yeah, before I needed to do translation of all these things into different languages, yeah, I can support that and validate that and test that out. You needed to do audio generation, yeah, I can do a relatively good job. It’s getting better at converting text to audio. So those are areas where you can start to now take what we already kind of scaled from a general process and make it even more efficient and make it a higher quality product at the end of the day. And that’s really where AI is coming into play. Let me know if that sort of made sense.
Greg Kihlstrom: Yeah, yeah, absolutely. And I think the, you know, so from the compliance as well as the technical perspective, I think that really, that really helps. And, you know, marketers that They need to reach people they know that personalization works. There’s plenty, there’s many, many statistics that support that and real life examples that support that as well. From that creative standpoint, I’ve run into some folks that are a little reluctant about using too much AI because they feel like it might be holding them back a little bit. But when you explain this, to me it’s almost liberating because you get to have some really good ideas and then they get to be, you know, there can be, you know, near infinite variations and personalization and everything like that. How do you recommend that both marketers and creative teams think about this idea of, you know, automating content generation and, you know, how it can be a liberating in addition to speed and efficiency. Certainly, I think those are anyone can understand those, but how can it liberate creativity as well?
Nauman Hafiz: Yeah, I know 100%. I totally, I mean as someone who’s familiar with the design and the artistic side of things, you know, totally get where, you know, like people would spend, I mean it can take days creating, you know, an image or content and especially in illustration and AI just kind of spits these things out in like nanoseconds and you’re basically kind of you have a completely different approach to how you’re thinking about things and I totally get where people are afraid or just like worried about how that’s going to change the perception of some of that work. But I completely agree. I mean, you said a great word, which is like this kind of liberation of like, you know, being able to create content at a different scale. So it’s really not about replacing that team or that role. It’s just sort of giving, taking them out of the what usually is a mundane. I mean, it’s a lot of like, OK, you come up with the strategy, you come up with the vision and you kind of you know, and how have to, the execution part is where it can become a grind a little bit and it can really become work that, you know, it’s just, you’re kind of doing somewhat similar tasks again and again. And that’s really where I feel like we’re focused on kind of making that part of the work much better and much more rewarding. because you see the rules and the systems and the guidance that you’re putting in place actually manifest in this really, really comprehensive way. And in some way, it’s a good… I draw a parallel to how software developers are using AI as well. I mean, When you write code, there’s a lot of creativity to that. There’s a lot of enjoyment that you get just making stuff happen logically. But then there’s a lot of the grind of, yeah, you’re fixing bugs. You’re coming up with a lot of the same stuff that you’ve done 100 times in the past. You’ve just got to do it again. And again, I think AI does a fantastic job of coming in there, doing a lot of that stuff for you. The way we focus AI is helping someone who’s a subject matter expert and who knows exactly what they’re doing, because that’s when you can, I think, more reliably apply AI as opposed to giving it to someone who doesn’t know what they’re doing. And then sure, you might get some decent results, but you can’t have the confidence in that result necessarily.
Greg Kihlstrom: Yeah. Yeah. Yeah. And I like the way you characterize the, you know, I’ve in a past life, I was a designer, I remember making, you know, 20 variations of a banner ad. And that is not what I would consider highly creative work as well. So you know, I think some of that, some of that comes down to, you know, just, Rethinking how we define creative work in some ways and stuff. Yeah, definitely. I think the potential is so great for those that really just kind of embrace that. Yeah. I want to talk a little bit now about this idea of, you know, anyone in marketing, certainly over the last few years, if not longer, is seeing a bit of, let’s call it expansion of roles, more work being done by in some cases, fewer, fewer people. So, you know, marketers facing increased responsibilities. And I mentioned a stat in the in the survey at the top of the show, you know, how do you look at how AI tools can help this as far as, you know, a growing workload, growing expectations?
Nauman Hafiz: Yeah, no, it’s a really, it’s a really interesting question. And, and it is, we’re sort of in that little bit of the gray area where, you know, I don’t think it’s had a huge effect on jobs and employment just yet, but, you know, it’s obviously like, that’s something that people are worried about, people are thinking about. And I think, It’s just going to change. I mean, the way I like to see it, and obviously we’re going to see what happens in the next five years or so, but I think it’s really just going to change how people work and the quality of the work. And I think at the end of the day, I like to take it back to the enjoyment that people get out of doing the work. it sort of allows you to have like this sort of assistant that’s actually somewhat intelligent and can give some really interesting insights and really interesting ideas and allow each member of the team and the team collectively to work faster. I mean, sure, that will, you know, can have impacts on kind of the size of the team and the scale of the team. But I really don’t necessarily see that having a huge impact just in the kind of short midterm, just because like there is so much that still needs to go into driving kind of marketing and understanding all the different channels and how you optimize towards them and how you think about, you know, kind of taking advantage of each of them. And, you know, I think, again, AI can help us do a better job in all of those different sectors and help us scale to get more personalized content. and to not have to go through approval many, many times because we’re getting ahead of some of the things. So that’s how we’re focused on it and how we’re trying to make sure that we equip our teams with a fundamental understanding of what AI is and what it does well and how they can tap into it to make their day-to-day work more fruitful. and more engaging as opposed to like, okay, we’re just going to use AI instead of this team of people now.
Greg Kihlstrom: Right, right. Yeah, it’s augmentation, not replacement of people. And that’s not to say to your earlier point that, you know, there’s going to be, whenever there’s new technologies used at scale, there’s going to be some displacement. And, you know, we can look through centuries of history and there’s always that, but there’s usually more jobs created in the long run. So, you know, I think even if there’s some temporary shifts, I just, I think the more realistic and better approaches to think of it as augmentation anyway. Totally. One last topic I wanted to just get your thoughts on. Certainly, you know, you’re seeing a lot of things and thinking through a lot of things. And as far as future facing developments go, where do you see, you know, in the, let’s say, you know, short term, near term, what do you see on the horizon as far as AI and marketing? And, you know, particularly as it applies to some of these highly regulated sectors?
Nauman Hafiz: Yeah, I mean an area that we’re focused on pretty extensively is sort of making these agents available to all of our various customers and clients that are incredibly aware of the subject matter and the data set that’s specific to that business. So, you know, you can think of ChatGPT, but ChatGPT that understands automotive and understands inventory data and how a dealer needs to sort of make decisions about their inventory or about, you know, how they kind of make sales. So that’s an area where, you know, there’s obviously a lot of technologies that, you know, are attempting to do that in different ways. And I don’t think we’ve really kind of found like the perfect solution just yet. I think it’s going to be very iterative. But that’s an area which I really think is going to be incredibly valuable. I mean, a lot of bigger companies are kind of rolling out a lot of different types of LLMs and agents like within their business. And I know Salesforce is always making some kind of big push or the other. But I think it really does take someone who understands those businesses. And that’s why I mean, we very specifically have focused on a few industries. And we started within the automotive space. And that’s sort of where like probably 70% of our client base is still. And it’s because you need to have decades of experience within that industry to really understand the complexity and the nuance of that industry. And then as we’ve moved into pharma and insurance, we’re starting to gain more traction there. I think that’s one area that I’m really excited about is some of that type of a subject matter expert and a data expert within your very, very specialized field is one thing. And then I’m also just personally really interested in how a lot of the generative AI image content and visual and design content is scaling. I mean, NVIDIA is obviously a big player in this space. the way things are moving, and this obviously has many different ramifications, but the way upscaling technology works and they can run this stuff in real time, and they can essentially generate a photorealistic image from just some very simple and rudimentary inputs, and I think it’s gonna be interesting how it changes just how even going back to games and video and other things are created. Yeah, I’m just personally interested in how that it’s going to take shape and how that’s going to evolve over the next couple of years. But yeah.
Greg Kihlstrom: Yeah, absolutely. Well, Nauman, thanks for all your great ideas and insights. One last question for you. I like to ask everybody, you know, what do you do to stay agile in your role and how do you find a way to do it consistently?
Nauman Hafiz: Yeah, it’s a good question. You know, I really feel like, well, A, I kind of got into running like many years ago now and that’s always a great way to, I kind of use it more than anything just to sort of like collect your thoughts, kind of go for a run, collect your thoughts. You get this kind of moment of like, you know, just somewhat silence or in your head and or listen to a podcast or listen to an audio book or something like that. But that’s a way for me to sort of reset and kind of rebalance. I also have, daughter Who’s four and you know, I love context switching between like, okay, you’re doing some some complicated thing whatever it is, but then also like going back to being a kid and understanding how Everything is just so interesting like any different thing like, you know Yeah leaf to like whatever like is there’s so much complexity there. It’s so much interesting knowledge there like, you know, and it’s important to kind of continue to kind of go back to that sort of, you know, the sort of enlightenment and the sort of imagination that it kind of is about being a kid. And I think we are in that, like the technology allows us as adults even to kind of be in that cycle continuously because things are changing and things are evolving, but it’s also important to just kind of stay grounded. So anyway, kind of a lot there, but
