Speaker 0: Good afternoon. Good afternoon, everybody. Um, I’m super excited for this next session that we’ve got hot takes, marketing’s future, and AI. Over the past years, I’m sure we’ve all heard some pretty drastic hot takes about AI. I think the biggest one that we keep hearing over and over again is, will AI take my job? Um, so we’ve got this little session for you. Um, we’re gonna kinda cut through the noise. No nonsense. Talk to some individuals here today that are breathing and living AI today in their work, um, as marketers. So we’ve gathered a a great lineup of Salesforce marketing pros to show and and share what they’re doing today with AI, um, and give some of their, um, takes. So with that, um, I’d like to introduce you to all of our panelists. Um, Laura, let’s Lauren, let’s start with you.

Speaker 1: Hi, everybody. Um, Lauren Noonan here. So I lead

Speaker 0: the the

Speaker 1: growth and alliances team here at Circante. But I’ve been working with data, data science, AI for my whole career. Um, I came up through the data data science world, um, and have been very, very close to the Datacraft product and a lot of the emerging Einstein features over the years. Um, East Coast based, just north of Boston, Massachusetts.

Speaker 0: Awesome. Um, we’ve also got Chris Dutton here from six cents. If you wanna hop on Chris and introduce yourself.

Speaker 2: Yeah. Hey, everyone. Uh, really looking forward to today’s, uh, discussion. Um, so I’ve been at 6sense now for it was actually my four year anniversary this month. Um, so I have the, um, wonderful opportunity of running and leading our marketing operations, uh, function here. So we’re always thinking about, you know, different ways for for us to continue to leverage AI, um, and then how do we continue to be at the forefront of, you know, not only what are we doing from a product perspective, but just, you know, making sure that, you know, we’re supporting our teams, um, from an efficiency perspective as well. So how can we use AI to, uh, you know, save hours, so to speak? Um, so really something that I’m passionate about. Um, and like I said, I’m looking forward to, uh, discussion today.

Speaker 0: Awesome. We also have Steven Stover with us. Steven is a five year more dreaming veteran joining us from the very beginning. Um, Steven, you wanna introduce yourself?

Speaker 3: Yeah. Back then it was par dreaming. Yeah. Steven Stouffer. I’m the director of automation solutions at Tre AI, which is an integrations, uh, company. And I’ve been in the world of Pardot or marketing account engagement for those newer folks for about a decade. Um, and I’ve been using AI or at least, you know, ChatChpPT since it first came came out. I’ve also been using, like, Anthropic and Gemini and some of the other ones, uh, basically every day since since it came out. So we’ve made the the big transition, at least within my organization, to include, um, an AI palette in our integrations tools. So I’ve been building a whole bunch of solutions for businesses across customer success, marketing operations, sales operations, revenue ops. So I’m excited for this. This is gonna be this is gonna be a really good session, but happy to be here.

Speaker 0: And if you don’t follow Steven on LinkedIn, he is always talking about something AI related. So Yeah. Go give him a follow. Yeah. Um, and best for last, we’ve got Emily Lee joining us from the Just Global team.

Speaker 4: Hi, everyone. Um, I’m Emily. I lead the analytics and operations and AI initiatives at Just Global. Really excited to be here. Um, I’ve been at Just Global for about three and a half years. I’m based just South Of Chicago. And from an AI perspective, we’ve worked really closely with AI as well for years now. Um, we have what we call our AI trailblazers. Uh, I know we know are very familiar with the trailblazer term, but we have our AI trailblazers internally, um, and they represent basically all of the, uh, uh, departments at JustGlobal. We essentially shifted what was sort of a wild, wild west kind of approach with AI to a more strategic and cohesive approach. Um, and we’ve had a really nice adoption rate, really nice sort of early survey results showing a reduction in, you know, repetitive tasks, improvement in decision making, increase in job satisfaction. So very excited to be here and getting to chat with all of you about AI today.

Speaker 0: Awesome. Um, so with that, let’s jump right into our first hot take here. Um, our first hot take is AI will replace 80% of marketing tasks in the next five years. I’m sure we’ve all heard some version of this, um, over the years. I think we all know that AI is gonna be a great source of taking over a lot of the repetitive and data driven tasks for us as marketers use today. Um, but it’s gonna have a shift a little bit, I think, more to be able to open our our our bandwidth to think more strategically and take on some some cooler things in the new year. So I’d love to hear from all four of you on this one of, um, what’s your take on on this specific, um, item here? Let’s let’s start with you, Lauren.

Speaker 1: Yeah. I think it’s a really interesting question. Right? And I think for all of us as marketers, you have that, like, inherent human reaction of like, oh my gosh. Are they replacing me? But I think the reality is we all need to reframe because I think AI offers an incredible opportunity for us to automate a lot of the stuff that we do on a day to day basis so that we can focus on what we love doing, which is figuring out how do we continue to improve and and move the envelope, push or move the the needle on, um, our strategy. How do we improve personalization? So just imagine, like, being able to automate your reports and a lot of just the admin tasks around, like, campaign brief creation and segment creation, how much time you’d actually have to analyze your results and think about really thoughtful strategies for improving.

Speaker 4: Uh, Yeah. I I agree with a lot of what what Lauren said. I think AI will absolutely change how we approach a lot of our marketing tasks, but I’m not going to assume that AI is going to replace those marketing tasks entirely. I think AI will automate many of the repetitive tasks like I mentioned earlier. Um, it’s gonna make us more work faster and more efficiently, and it’s gonna free up that time that Lauren was mentioning for the critical thinking and doing what we really love, like strategy. But AI still needs that expert touch. Um, when without someone who really knows the landscape, we risk significant, uh, accuracy and relevance for speed. I’ve heard many folks refer to AI as, um, adding like a supercharger to the marketing engine, but we can’t swap out the driver, um, of that engine entirely for AI.

Speaker 3: Yeah. I, uh, I was kind of in the camp of, like, AI is not gonna replace my job. Like, you know, like, good luck. Uh, but I I live here in Dallas, Texas, and there’s a McDonald’s in Fort Worth that is completely autonomous. Like, you walk into it. There’s there’s no person there to greet you. There’s no cashiers. There’s it’s, like, completely driven by AI. That was the first time I was like, no. Wait a second. Maybe AI, uh, can actually replace, uh, people. But my my thought is it it’s already kinda baked in, like, 80%. Uh, maybe not 80%, but but I would say a lot of what we do as marketers, we’re already using AI. The tools that we’re leveraging already have AI baked in. How many of us are using ChatGVT to come up with subject lines and boil down text and change the tone of voice of of the emails that we’re sending out? Like, I I might just be alone here, but I use AI a ton in my day to day. So is it going to take over all of my tasks or 80% of my tasks in the next couple of years? Maybe. But I also don’t think that marketing is just task jockeys. Right? Like like, we we we’re not just fulfilling tickets. That that that’s not all we’re doing. We’re also, uh, strategic. Right? So, um, especially on the the operation side of marketing. So, uh, hopefully, what it does is it removes the boring work and allows us to focus on more strategic and business driving decisions where we’re not having to spend all of our time, you know, correcting state values from a CSV export from LinkedIn. So I I see it as, like, a helper, not not in, like, a good thing, not not a bad thing, but also we need to get out of this space of just, like, having the entire business think of marketing ops specifically as just, like, the the the the people who just do tickets and and just fix things. Right? So, um, maybe, but I also think that it’s already kinda baked in right now. I I think over the next year or two, we might see AI kinda take over more in different tasks, but largely, I mean, it’s it’s already ingrained in our day to day. And then how we leverage it, I think, is gonna matter.

Speaker 2: Yeah. I I certainly echo, um, what the rest of the the folks here are saying. Um, we’re actually seeing AI help up level our team. Um, so if you think of, like, a BDR function, um, specifically, we had 14 BDRs that got promoted in q two, um, as a result of an AI sort of benefactor. So taking some of those mundane tasks that they’re, you know, having to do every day, we were able to increase their efficiency, have them focus on high value touches, and ultimately, um, sort of, you know, expedite, um, those individuals in those roles. So they were seeing, you know, faster success, Um, and as a result of that, they’re on, you know, doing other things within the org. So it’s been a great catalyst, um, for us specifically to be able to lean into. Yes. We’re saving hours. Yes. We’re, uh, increase efficiencies. But it’s sort of like just pro human in the sense that it is there to benefit us as individuals and help us just be better at what we do because we’re not having to do maybe those those sort of mundane tasks and those things that we can very much automate through, you know, different variations of AI.

Speaker 3: I I’ll I’ll add to that. Humans are very good at snowing when something is is off. Like, how many of us have received an email or read a LinkedIn post and gone like, okay. Like, you didn’t write that. So, like, we can spot the difference really easily. And, um, I think that there’s a quote. I don’t know who said it, but we probably have all heard it that, like, AI won’t replace your job, but but someone who embraces it and and someone that leverages it will replace you. Um, so I think the more that we can, you know, lean on it to to to help with the stuff that we just don’t enjoy doing, the the better off we will. But, like, do I think for for you mentioned BDRs, we’re leveraging it. Do I think that there’s gonna be, like, an AI BDR or, like, an AI, um, HR person who interview views you? I think we might see that bubble up in, like, the next year or so, but it’s gonna be very quickly squashed. Like like, I think someone’s gonna try to do it to, like, save a buck and a nickel, but it’s gonna fail so miserably, or at least I hope I’m right that it’s gonna fail so miserably that we’re gonna go back to, like, the human factor. Um, but I’m very curious to see where it goes, um, in the next couple of years. It’ll be fun, hopefully, unless it takes over.

Speaker 0: So I’m hearing a lot of we’re good. Our roles will just shift. It is not the end is near. Yeah. I think, um, at Sarcante too, we’ve just kind of beta launched, um, some custom GPTs specifically for our BDRs. So I think that’s an easy kind of real world, um, use case that a lot of people are being able to see the advantage of. So I’m interested to see where that does go. And to your point, Steven, does does it continue to be helpful, or do we continue to just snuff it out like we have over the years of the kind of fake dynamic content even now, um, and how we can spot that super easy. Um, so rolling into the next one here, I think another big hot take that we’re hearing a lot with organizations is AI isn’t compliant. Um, privacy regulations are holding it back. I think a lot of large enterprise clients, specifically, when you look at things like GDPR, um, I think a lot of organizations still have some, like, strict no policies to utilizing any version of ATI or AI into their, um, organization. Um, so would be curious to see, you know, what your guys’ take is on this. Um, Steven, why don’t you kick us off on this?

Speaker 3: Yeah. Sure. So I was in London four weeks ago, um, for AntiCon, and it was an I it was an AI event, marketing event. Um, And, like, I was chatting with people on AI, and they’re so like, it’s just starting to scratch the surface of AI, at least in The UK. So I don’t know if there’s any folks joining us from The UK here, but, like, they’re they’re very wary of it. They’re they’re, like, very curious, but also kinda like, I don’t know. Like, it’s just not worth the risk. Um, but my thought is there’s definitely ways to integrate AI into your your processes in an automated fashion where you can be GDPR compliant. So for first, you can feed it, uh, technology information, company information, demographic information without giving the PII data. Right? So, like, you can feed it information. You just have to make sure you feed it, uh, the correct information. And if you do wanna include something like your the name, the email address, or anything like that, you can tokenize the data, um, in a in a context that you can feed to something like OpenAI, ChatGPT, Gemini, or Anthropic, or any any of the AI platforms. So, like, you can definitely leverage it in a way that that it is compliant. Um, the bigger question that I have around just, like, regulations and privacy and all of that is, like, there isn’t a lot of direct AI regulations in The US or even actually in GDPR. Like, GDPR largely, it’s not focused specifically around AI. It’s just sharing data with third parties and and how it’s shared and, uh, the the, uh, controls that, you know, consumers actually have over their data with with businesses. So I’m curious to see kind of, like, CCPA can spam is basically basically ancient and useless from a privacy perspective. So I’m curious to see, like, from a US perspective, if we actually have AI regulations. And if so, like, um, like, what those are and and how what the practical application will be with implementing it within a business. Because we’re one one, like, um, union job being taken away from AI away from just, like, the government stepping in and just regulating the heck out of it. So, um, yeah, you can absolutely be compliant and use AI. I I I’ve been there, but I’m very curious to see kind of where things go.

Speaker 4: Yeah. Agreed. I think it’s a bit of an a balancing act with privacy and AI. I think, I mean, in general, AI, especially in marketing, it wasn’t designed with strict regulations like GDPR in mind. So we’re having to kind of go back and make sure that we are compliant and we are ensuring that our data is protected Rather than seeing, um, AI and the regulations with AI as barriers, really try to view it more so as guardrails just to push us to be safer internally. So, Steven, exactly like you said, said, making sure that we don’t include PII data or anything else that could be sensitive that could put us or our clients at risk is always top of mind. I think from a leadership perspective, if anyone is trying to roll out AI, um, in the company or perhaps trying to do that, like, wild wild rest, um, controlling the wild wild west of AI across the company and you don’t know who’s doing what, I highly recommend in ensure having in place a weekly meeting with your legal and IT and constantly talking about where you’re using AI throughout the company. You’d be surprised how many use cases come up where you think that you’re being safe or this person thinks they’re being safe or no matter how many times you’ve repeated to people, you cannot use the client’s name or you cannot use XYZ if you’re using a free version of any kind of AI solution, people still do it. So keeping legal and IT included in the, uh, uh, rollout of AI or or perhaps control you know, as you try to control AI throughout the company really helps make sure that you’re respecting your customer’s data, your internal data, you’re meeting your your regulatory standards, um, and and aligns with your company’s values. Right? You wanna make sure that you are building trust and transparency. Um, so please make sure that if you do not have a meeting internally at your company that includes legal and IT when it comes to AI, I highly recommend putting that into place immediately.

Speaker 0: Awesome. Super interesting, um, to kinda hear how everyone’s kind of approaching AI and compliance, um, to be able to use and and break some of these barriers. Our next take here, um, a little interested in in what the the vibes are gonna be on this one. Um, the next one here is AI will make real time marketing the new standard. Um, so for this one, um, Lauren, haven’t heard from you for a bit. We’d love to get your take on this.

Speaker 1: Yeah. I mean, I I had almost like a visceral reaction to this one when I first you know, when we were first discussing it because I think there’s a fine line between being a predictive marketer and really meeting the customer where they’re at and being a little too real time and almost creepy. So what I think the benefit is in the world that we’re in today is we as marketers have access to AI that will really help align your different segments to your product or service catalog. Now that doesn’t necessarily mean that you have to action on that information immediately. You can hold it. You can do so in a thoughtful manner, but we finally do have an ability as marketers to really understand, like, if I’m Sarah, what have I browsed? What has been my engagement history with a particular brand or product and be able to really accurately predict Sarah’s next best product conversation or service conversation if you’re in a vertical that is services based. Um, so I think it’s really exciting, but I also think that as marketers, we need to take a pause and really be mindful about, okay, how do I use this information that I now have access to in a way that feels, um, like a cohesive experience for our customers?

Speaker 4: I agree, Lauren. I think knowing, you know, when do you engage, how to engage, why we’re engaging, that human touch and, like, the nuance throughout the process is so key. I think real time marketing could definitely become more of a standard in certain areas, but an automation can help with that. But it’s likely not going to replace the, like, thoughtful strategic planning that you’re mentioning that I think really good marketing, um, demands.

Speaker 3: Yeah. I’ve I’ve, like, heard this before too. Right? Like, personalization, I think back, like, Clearbit days, like like, lead enrichment days, like, it was like, oh, hyper personalization. And and it kind of flopped in a lot of industries. Like, the network security industry, uh, it it pretty much flopped. Federal governments, you know, anything there, you you you know, you can’t really do that. It comes off as creepy. And then on the consumer side, unless you can do it kind of low key, um, it doesn’t really come off very, very authentic because, like I mentioned before, we’re humans and we can we we we know when there’s something fishy is happening, and it and it can be kinda fishy. And then if you’re gonna do this, going back to our compliance and regulations conversation, you have to get them to opt in to to all of this. Right? If you’re gonna be compliant with GDPR. So there so what are you gonna do? Put a wall of, like, um, opt in check boxes in front of someone to basically be able to do this kind of real time marketing, and then you’re gonna use AI and and and whatnot. So it it almost becomes unreasonable with the amount of teams and and departments and people you have to work with to actually get this up and off the ground. I would love like, I like the idea of it. Like, I like the idea of going to a website and having the entire home page, the banner, the navigation, all personalized to, you know, Steven Stouffer, director, marketing operations, you know, company size 300 to to 600 employees. Like like, you know, look being able to look up what opportunities are from a customer, what kind of product I have, what would the upsell look like, and be able to put that up on the page and, like, guide me through it. That sounds amazing. But, like, the realisticness of it is I just don’t think it’s gonna happen. I I think we’re it it it’s out of reach for probably most marketers, um, when it comes to coordination amongst the product team, IT, legal, front end development, marketing, sales. You know, it’s it’s it it it’s a bit of a reach, but I think where it can make a difference is maybe through, uh, like chat. Like, how many of us have gone to, like, Verizon, T Mobile, AT and T, and had just a horrible customer experience from, like, a chat perspective where you’re just like, I just wanna pay my bill. And it’s and it and it’s like, sorry. You can’t do that. You have to reach out to so and so, and it’s just like this cookie cutter model that just doesn’t work. Like, maybe AI can help with that first before we try to, like, personalize an entire, like, homepage of a website.

Speaker 0: Yes. I think I heard a lot there of, um, a few mentions of personalization, intent, and just some of the things that we’re, I think, looking forward to, um, with AI, but also I think still a little bit of PTSD. I think this takes us to the a great segue into the next question, which is all about AI will predict customer intent better than humans ever could. When I mentioned that PTSD, I think we could all think of one time we made a report, an automation rule, a segmentation list that we created, and we all had that one, like, ugh of, like, uh, that’s that’s not accurate. Like, I just said, like, a really dumb message to someone that, you know, they’re a customer of ours and how did that possibly slip through the cracks. Right? There’s always one data point that just isn’t getting it right for us. Um, as far as intent data goes, Chris, I would love for you to start with this one to really talk about too, like, I know 6sense is doing a lot with AI intent signals today. So just love to hear your side of the story here.

Speaker 2: Yeah. Uh, love the question. Um, it’s always very top of mind for us, um, here at SixCent. And, you know, we’ve been at this now for, you know, ten plus years using intent to understand where our customers and buyers our customers and prospects are within the buying journey. So, uh, the the short answer is, uh, yes. I think AI will continue to predict intent better than humans, um, with the caveat of being, uh, the reasons for that is it’s it’s about scalability. Um, as humans, like, we can only focus on so much. Um, once we start to try to do too much or focus on too much, it just becomes, you know, inefficient and we become slightly watered down. AI really does not have a capacity. Right? And that is one of the beautiful things about it. It’s I like to think of it as, like, the eighty twenty rule. AI is gonna get me 80% of the way there. And then, yes, we are going to have that human oversight component, which is that 20%. So I would, you know, absolutely lean in on, you know, AI per predicting intent, but then still having that oversight of humans to make sure, you know, we’re not having those missteps that you talked about, Sarah, because those are absolutely like cringeworthy and we hate for that to happen. But the reality, uh, of this as well as as marketers, uh, right, like we’re being asked now more than ever to do more with the same or less. So we do have to find ways where we can do, you know, these types of, uh, these types of things at scale. Um, and it’s just not, you know, plausible with, um, you know, just focusing on, you know, just having humans, for example, do that.

Speaker 1: Yeah. Chris, I agree with you completely. Scale is, like, the key. Right? Because as humans, we all know that we leave so many bread crumbs as we interact with the brand, um, and all of the different digital channels that we have available to us. So, yes, you could take a marketer, and your marketer could go through every line item in your database and look at how they’re engaging and think and be thoughtful about, okay, what’s the next product that I could position? But we don’t have that time, and we don’t have that luxury. Right? And like Chris had said, AI doesn’t have that bandwidth constraint. So we’re able to use the technology to really troll through all of the data breadcrumbs that we all leave behind on a day to day basis. We’re sending so many signals to brands about what we’re looking for and what that next best conversation could be. So why not use the technology to help predict what that next conversation should be? Um, and I think we have to let go a little bit to the fear of the UGG moment because the reality is is where we are have come from or where we are still today as marketers, there are so many UGG moments that even just moving into the next phase, there’s going to be a reduction in those. So embrace it and, um, you know, just find ways to test how the AI capabilities can predict that next intention.

Speaker 3: Yeah. I would be I don’t know. I’m pretty I’m I’m a skeptic when it comes to this this part of the conversation. Like, I don’t think AI is gonna be I think it’d be too expensive. First of all, you have to have all your systems talking to each other, and the intent that it has to be in one place, which is gonna almost be impossible in order to have AI plugged into it. And then I’m reading the comments here. Donna just said, you know, I I have a hard time believing it. Essentially, even with AB tests, it can’t be really be trusted. And that’s a good point because if I am in, let’s say, the network security space or the cybersecurity space and I send an email and do an EV test, I might have a whole bunch of emails that that are opened and even clicked, uh, but that’s because it’s being clicked by the, uh, the the email service provider and the company to check to see if it’s spam. It’s not a human actually opening that email and clicking and clicking the link, so it could be a false positive. On a consumer side, if I were to run that same AB test to Gmails and stuff, it could actually be, you know, the data from from real people. So, like, I wouldn’t I I would treat one organization completely different than another. And and I don’t know if we’re gonna get to a place where AI can actually make that distinction and and be that intelligent where it’ll be able to plug into all of the different tools, all of the different data, um, and then also make those kind of business to business decisions and have it be cheap too. Like like, that just seems like we’re far aways out of there where, like, the the the amount of tokens being used, um, or or even just the server space and stuff for that to be, like, leveraged, like, would even be possible, but maybe. Um, I just I’m not sure I see it.

Speaker 0: I’m hearing a lot of data. Data. Data. Data. Do we trust the data? Do we have all the data talking to each other? Still unknown of of how we can make this, um, like you had mentioned, Lauren. Like, how do we scale this? Um, and that goes good into another one of the the next questions here, which is marketers will need to become data scientists or hire them. Who wants to kick us off on this one?

Speaker 1: I can take it if I could get myself off mute. Yeah. And I don’t know if I necessarily think that we need to, like, become data scientists or if we as marketers need to have, like, a data science hat in our repertoire of potential, like, roles that we can step in and out of. Um, because I do think having access to a lot of the data has opened a lot of doors for marketers to better leverage that data for things like propensity modeling and engagement scoring and really being able to predict that next best cross sell or upsell or even listening for customers or accounts that could be at risk for churn. So I think it does. Some of the newer technology capabilities have even brought an ability to build a propensity model into tools that never before had that capability, and it’s kind of getting it out of the scary data science propensity model tools like SPSS and r, um, you know, back in the early two thousands. So I think it’s making it a lot more readily available, but I don’t necessarily think we have to become, like, full time data scientists on a day to day basis in order to be successful at harnessing the power of data.

Speaker 4: I’m with you, Lauren. I I see it as a partnership as well. I think data scientists definitely bring a level of rigor to analytics that’s really invaluable, um, with complex datasets. But I don’t think that every marketer needs to dive into data science, uh, necessarily. I see it as sort of more of a hybrid role. Um, and hybrid sort of roles will emerge from from this long term where marketers definitely need to have a strong grasp of data analytics basics, and then data scientists need to be the ones that really focus on those deeper insights. So, um, I think marketers will need to understand enough to ask the right questions and make sense of the data, but they won’t have to do it all themselves. I think data scientists are you know, I don’t think they’re going away. I still think that there’s, um, certainly a need for them. I don’t think that the roles need to completely merge together. I think of it as more of like a skill upgrade for marketers rather than a career overhaul. Um, like I mentioned previously, we cannot remove the driver from the car. The expert still needs to be present.

Speaker 3: Yeah. I I see there being kind of the the analytical people who are kind of developers, coders, you know, data driven, um, and then there’s the creative. Right? Marketing tends to be lumped more in the creative side. And then you maybe have, like, the hybrid who sits in between the two. They become ops people, um, marketing ops, sales ops, uh, which is great. Those are my my people. But I don’t I don’t think that they have to become data scientists. I think you have to care about the data deeply, and I think you have to be very curious about what’s working and what’s not working. And you have to ask those, like, important questions and dig into them and and also hold people accountable when things don’t feel right, um, and work with the appropriate teams to to to get that. But, um, I think that as marketers, we try to do too much and we spread ourselves too thin. And it’s okay sometimes to be like, you know what? I’m not a data person. I I I don’t know how to make heads I don’t know how to make know how to make a pivot table in in in Google Sheets. This is this isn’t for me. But as marketers, we have to ask questions, and we have to get to the bottom of things, and we have to know what’s working and not working, and oftentimes that comes from testing and and and getting into the data. So, um, for sure, tap into it if if it interests you. Um, do lots of tests, but I don’t think we have to be data scientists.

Speaker 2: Yeah. I I I would agree. Um, I think one of the things that we did a couple years back was we, um, built out a revenue analytics team. Um, and that was that was, I think, a bit of a a missing, um, sort of plug for us. Uh, we were we were quantifying marketing impact the best that we could, but just bringing in this sort of agnostic team that is looking at the data more holistically, less about where it’s necessarily coming from, um, um, just brought another perspective, you know, to the conversation. I think as marketers, though, we certainly need to be, um, very aware around the different, um, variations of data that we can see, and we do need to be data driven, Data science, I don’t think to that level, but, um, you know, focused on the right KPIs. What are your lead leading and lagging indicators? Right? What are those traditional kind of boom stats that we tend to report out on or vanity metrics that, you know, may or may not be impacting the business and, you know, more focused on those those those business impact type of of metrics that are gonna essentially move the needle. But, you know, making sure you do have someone of some various skills that is that has the capability to look at the data, but not only look at it, but then be able to interpret it and then be able to get recommendations based off of that. Because I often think that’s the point, that’s the part that’s missing the most. It’s not just reading the data, but it’s what do you do with that data once that once that you have it.

Speaker 0: Awesome. Um, moving on to our next question here. Um, we have, AI will make customer data the most valuable asset a company has. So more chats about data. I think we’ve all said it almost on every single topic here, um, that we’ve had so far. We know that data is huge. It’s been a big priority for marketers for years now. Um, would love to kinda dive in a little bit deeper to what we see with with data and and how we’re gonna be handling that.

Speaker 3: Wait. I I may I may have missed a memo. Was there was data not always the most valuable asset a company has? I mean, other than the, like, the customers. Sorry to interrupt. Go ahead.

Speaker 1: It’s funny you say that because it’s like I have said this, I think, throughout my entire marketing career that if you ask a marketer what data they need and you ask a toddler what they want for Christmas, you’re going to end up with the exact same result, which is a very, very long list of things, most of which they have absolutely no use for. And so I think it’s a cautionary tale. Like, yes, marketers, they think they need a lot of data, and they I think they’re excited by the opportunity that they now have a lot of access to data and data sources that they maybe didn’t historically through a lot of the improvements and innovation that have come in the the way of integrating data. I think it’s going to continue to be a challenge for marketers to really distill down and identify the why behind the data. So why do they need this data and what is it going to support from a campaign perspective, a reporting perspective, or predictability perspective. Um, and I think we’re going to continue to have a lot of the same conversations that we’ve had for the last few decades with marketers, which is just helping them kind of think through their data strategy. I think the positive is is we now have a much more efficient way to action on the data once we have determined the why and the what it will support. Whereas even a decade ago, you could create and identify the data that you needed, and then it would take the data science team a month to create the model. And then the model would be two weeks old by the time it hit, you know, the the end consumer market and it was already stale. So I think we’re really accelerating the time to market for actioning on the campaigns. But we still have to help marketers with the question of, like, you don’t need everything in the Christmas catalog.

Speaker 0: That hurts a little bit, Lauren. I

Speaker 1: wanna Sorry, Sarah.

Speaker 3: I I mean, if we’re gonna lean into the AI conversation so much, I’m gonna say that, like, I don’t think necessarily yes. The data is is a valuable asset. But I think that if we fast forward several years, like, today, we’re using the large language models from, like, anthropic and OpenAI. I think we’re gonna move into a world where the most valuable asset to an organization is actually their their AI data model that they’ve created in house, that they’re then leveraging in, you know, downstream systems from marketing automation to their CRM to customer success. So I’d be very curious to see how, like, the industry of building models changes because right now, we’re leveraging an OpenAI where we have to give it the context of, like, I want you to write an email in this tone of voice, you know, from this perspective for this person. But, like, we’re gonna probably move into a world where a business is gonna have their own large language model, if you will, in house where you don’t have to prompt engineer the context to get an output that makes sense for that business. It’s gonna know who the customer is. It’s gonna know what your tone of voice is. It’s gonna know based on past email performance what’s worked subject line wise and and content wise and call to action wise. You’re you’re not gonna have to go do that research. So it you know, as long as there’s not any major roadblocks put forth by governments, um, you know, the most most valuable asset is gonna be their the model that they’ve they’ve they’ve built out, that all their systems are gonna be plugged into.

Speaker 0: Awesome. We’ve got one more question here from our deck here, and then I do wanna open it up to the q and a, um, with the with the audience here. We’ve got a few q and a questions. If you got any burning questions for this group, throw them in q and a, and we will get to them in just a second. Um, our last one here is all organizations will have an AI budget for 2025. I think this is being consistently I mean, everybody’s earmarking significant portions of their budget for AI investment. Um, and I think it’s I think I’ve read a stat at some point that it’s, like, 68% of orgs increased an AI budget in 2024 alone. I think that’s before we even had all this, like, crazy, crazy hype around AI. Um, so I’m interested to see what your organizations might be doing for this or what you guys are predicting budgeting specifically for AI to look like.

Speaker 2: Yeah. I can jump in. Um, so what I have personally seen, um, just having the opportunity to speak with a lot of our customers is the sort of directive from the executive suite is the expectation that a certain percentage of marketing budget will be allocated towards AI. So, it’s not only is your budget needs to be sort of redefined, what proportion is going to be AI focused versus not, but then also coming up with, you know, what does that plan start to look like? Where where are you gonna double down on AI? What is what is your kind of strategy as an organization going to be with leveraging AI? And then, I guess, more importantly, how do you quantify that? Right? Because then that’s that’s gonna be the next big question as is, do we continue to double down on AI and invest? Um, how do we quantify the impact that it’s having? And I’m not entirely sure, um, there’s a really good way to do that. Um, when we see all the different sort of variations of how AI can be used, some are a little bit more, you know, I would say, more direct in the sense that you can connect the dots. Um, others may not be. So just kind of thinking about, like, any type of marketing investment, yes, that’s the directive and that’s sort of, uh, I think what what we’re seeing and what we’re hearing, but is that going to be long term? Is that sort of quantifiable ROI going to be able to be measured and then, you know, kind of reallocate that budget for for years to come? I think that is gonna be more interesting than our our organizations, you know, budgeting for ’25. It’s what happens in ’26 and ’27. Are they gonna be able to reinvest because they’ve been able to, you know, measure ROI from their investments?

Speaker 0: Any other thoughts from anyone else on this one?

Speaker 4: Uh, I I have a quick thought. So right now, AI seems to be falling kind of under the data and analytics sort of category within companies. And I think it’s assumed that it’s just a function in some businesses, not all businesses, but it’s a function that will sort of grow on its own. And the, like, simple fact is it’s not. It is something that needs to it requires a road map. It requires pulse reads to understand efficiencies gained. Um, I mentioned in my intro that we just did a pulse read, and we’re already seeing strong results, um, when it comes to AI. And, Chris, to your point, I think it’s going to be difficult to attribute ROI to, um, AI. Well, that’s a mouthful. Attribute ROI to AI. Um, but I think through, like, pulse rates like that, I think it can really help. We’re already starting to see those efficiencies gain, and I’m really glad that we established that baseline a couple months ago. Um, to the hot take, though, I totally agree that AI will need to have its own budget from a management and support perspective in the future. I think, um, as we’ve mentioned, AI is going to open up a world of automation, but it’s going to take time and support within each company to make sure that the integrate integrations are, uh, safe, that they’re compliant, that efficiencies truly are being gained, that it’s not the wild, wild west, and that’s really difficult to do within a company unless you do have budget set aside. So, um, I do think even with the budget set aside though that the efficiencies gained will essentially help, you know, pay for AI itself.

Speaker 0: Awesome. We’ve got a few minutes of time for q and a from the audience here. Um, one of the questions that came through, um, is when Amazon first came out, everybody wanted to have an Amazon like online experience. Do you think more customers will expect a GPT like shopper or consumer experience?

Speaker 3: I guess it depends on what industry you’re in and whether or not that’s kind of expected. Um, I think in the ecommerce space and, um, you know, maybe subscription SaaS space, I I would think maybe yes. Um, but, like, enterprise SaaS and enterprise sales, uh, I don’t I’m not so sure. I’d probably not. Maybe maybe not, but I I do think we’re every year, we seem to be, you know, stepping closer and closer to an automated experience, removing as as many, like, human to human touch points as possible. People just wanna get the dem on demand demo. People just wanna, you know, do it without having to talk to a person. So, um, it seems to be trending in that direction, but I think depending on what space or industry you’re you’re in, the tolerance level for that cut type of experience is drastically different.

Speaker 1: I agree completely with Steven. I mean, I think I think, obviously, for verticals like retail, you are expected, um, to be that hyper personalized like Amazon. And I think as marketers, we’re probably a little less forgiving, you know, as consumers of retail brands when they don’t have experiences like Amazon. Like, I myself have I’ve always joked I have, like, a personal role at the decks of brands, like, I would love to get my hands on because their experiences are so bad or, like, I’ll have a visceral reaction to an email that I that I receive. But, you know, in some other verticals, it’s just it’s just not there yet. But I do think we’re seeing a lot more shift in the buyer life cycle for verticals like high-tech or even comms, media, and entertainment where you are expecting, um, that more real time turnaround that you have as a consumer. So I agree.

Speaker 3: I guess the bigger question would be, like, would you buy a $60.70, $100,000 SaaS tool without speaking to a person? Like, I don’t know if I would. Like like, honestly, like, the I think we all wanna be like, oh, yeah. You know, do it yourself. Like, remove all the friction and roadblocks. But at the same time, like, I’m wondering, like, the recipe for disaster and what that churn would look like, uh, if you just allowed someone to buy, like, an 80,000, you know, tool. Like, would you would you sell anything? Like, how how do you communicate the value? Yeah. That’d be really, really interesting. I’m not sure if I know of anyone who did who who does that. Um, but

Speaker 1: Yeah. And I don’t necessarily think it’s they go all the way through the buyer funnel, but I think they can definitely get further in the buyer funnel, right, to, like, weed out competitors, um, before having to get to a point where they do speak with a person. I think for those bigger purchases, it doesn’t necessarily matter what vertical you’re in, even in the retail space. There’s large appliances that people do typically narrow down, and then they they do choose to go in person and have a conversation and touch it and feel it and interact with it. But I think it really helps kind of narrow down the choices.

Speaker 3: Are we gonna have, like, AI buyer agents and seller agents talking to each other, AI bots duking it out before they do the interest to people? What world are we in?

Speaker 2: I I I think one of the things that I’ve noticed is that sort of b to c experience is is bleeding over into the b to b experience. So, like, even as a consumer of retail, there are certain things that we’ve just become accustomed to as what what we would consider, like, a good overall experience. And if for those of us in the b to b space, we wanna take that experience with us and we’re, you know, ultimately wanting to provide that same level of experience at the b to c level at the b to b level. So, you know, Steven, to your point of will somebody buy an $80,000 purchase without talking to anyone? No. I don’t think so. But I think there is a way to, you know, use AI to streamline the process where you don’t have to do anything essentially but say, hey, I wanna talk to someone and the organization knows exactly who they need to connect you with because, you know, we know the account you’re with, we know the account executive that owns your account. Um, we’ve got all of the information and then essentially just making that connection happen without having to go through a form fill or any kind of, uh, you know, other sort of hoops. So I think there are ways and and we’re we’re seeing it today already where we can expedite expedite that process and you know, to that point, like, we we see people, um, you know, we see people, like, doing that over the weekend. Right? Because that’s the other like, people, you know, wake up, maybe they got some downtime, they’re doing some research, they wanna talk to someone. Oh, but historically, they’ve gotta wait for someone to call them back using AI to kinda automate that end to end process, get them connected. So now you’re scheduling meeting with your prospects over the weekend. So that to me is a really good example of maybe that b to c bleeding over into that b to b and kind of making those connections happen, making that experience be a little bit more, you know, um, more and more of what we’ve come, uh, become accustomed to.

Speaker 0: Yeah. I’m seeing that right now.

Speaker 3: Oh, go ahead.

Speaker 0: Just gonna say that reminds me a lot when we first had, you know, the the Google Homes and the Alexa’s come out and everyone talking about how instead of just asking questions, it’s like, hey. Like, find me a a local plumber, but not just that now. It’s like, figure out my schedule, figure out their appointments, automatically book that appointment, like, do it all for me. So I think there’s some of that that’s coming into the B 2 B space with some of this this new tech, um, that I’m gonna be excited for. Because I I think we’re all, in some ways I don’t wanna call us all lazy, but, like, we just need things to be easy. We expect them to be easy. So the more things can be easier and we can kinda take a lot of the the time suck out of our day, um, I think we’ll all be accepted to it.

Speaker 3: All I want is an AI tool to be able to look at the calendars of everyone who who needs to be on a meeting and find a freaking meeting slot that that is available. Like, we can’t even do that, uh, let alone sell sell products and tools. Um, so, um, yeah, I I I think that, uh, to Chris’s point, I I think that there is some weird friction in in in, like, enterprise sales. Like, even if you’re a customer, um, let’s we’ll just use Salesforce as an example, Pardot specifically. Like, let’s say you wanna buy another, like, ten ten thousand other or 10,000 prospect block to add to your Pardot instance. It’s like you have to go through an account executive and you have to have a meeting and then they have to review it. They have to do contract and amend them, then they have to send it and they have to like, it seems like there there is a little bit too much friction in that, but that’s not really an AI problem to solve. It it’s just a business problem that that needs to be solved. The way that sales is compensated, I I I think, like, incentivizes some of these, like, human touch points that probably don’t need to to have happen. But in order for that to change, you have to completely change the way that, like, sales is compensated, which I think is just difficult. Um, so I I don’t know if that’s an AI issue, but it’s definitely something that I think we can do better on from an enterprise add ons and upsell, um, perspective.

Speaker 0: We got quite a few other questions here, um, around privacy, um, specifically looking at, like, your takes on how we can make AI more sustainable and respectful of privacy. Um, Also, similarly, when it comes to, like, compliance and and privacy there, um, is it more targeted as, like, generative AI or predictive that where you guys are all seeing?

Speaker 3: How can we you how can we make AI more respectful of privacy? I mean, from an from a GDPR and compliance standpoint, you don’t give it any PII data to be compliant. Um, as far as having it be more sustainable, I I think that it it’s only gonna be sustainable. Like, AI is only gonna be sustainable from a compliance perspective if if people are actually being compliant. Because the minute that that too many businesses start abusing it and using and abusing it is when they’re gonna ruin it for everybody, and then the government’s gonna step in and just create crazy, you know, legislations. Google’s gonna get sued. Amazon’s gonna get sued. And and, you know, it’s it’s gonna it’s gonna blow up real quick. But I I would say that tokenization is a big first step, um, in making sure that you’re using AI in a, like, sustainable and and, you know, being respectful of privacy. Because it but, basically, tokenization, for those who may not know, is taking PII data, like first name, last name, email, address information, converting it to a token, d like, anonymizing it in the prompt that you sent to something like OpenAI, ChatGPT, Anthropic, uh, where the AI model has context as to what you’re giving it, but it doesn’t have the specific pieces of information. So tokenization is is at least that’s how I how I solve for that.

Speaker 4: Yeah. Just to I guess my simple and, like, straightforward answer is it is all based on how you treat the AI solution. So it’s what you feed the solution that will determine the kind of impact that it has. Right? So to Steven’s point, you can you can tokenize it or just not put that data in there at all. Um, and that will ensure that, you know, you you aren’t being or that you are being compliant. Um, when it comes to just overall AI usage across a company, again, make sure that legal and IT are included and that you have, uh, compliance documentation. We even had people sign paperwork so that they, uh, confirmed that they knew what to do and what not to do. Because again, it’s really what you feed, uh, the AI solution and the integrations that you set up. It is in your hands. It’s it’s then the model that does it’s what the model then does with that data, and that’s what you have to be careful about. So, uh, keep in mind that what you are putting into it is is the key point here.

Speaker 0: Alright. We’ve got just a few minutes left. Any further parting words as the audience departs on AI and takes away any any tips and pointers here?

Speaker 3: I would say don’t let AI scare you from getting started. It’s very easy to start using something like a chat g p t. For those who aren’t experienced in it, play around with it. And then, um, I I read a a a question from Christina, which I’m not sure if we covered, but it was, like, what what do we like, advice, uh, for teams to get started with it. And, essentially, I would say is audit your day to day, figure out what those repetitive tasks, things that you don’t wanna do, or things that you’re doing repeatedly that you feel like could be automated and and start there. So, um, I’d say, um, you know, AI is not gonna take over your job. But the the quicker that I think that you can find and kind of weave it into your day to day, the more invaluable that you’re gonna be to the business over, um, AI if you can leverage it.

Speaker 2: Yeah. And I would say just lean in to AI. I’m I’m sure many of you already already are, but do your research. Um, you know, I think, you know, lean on your on your community. Find out how others are using AI to, you know, kind of help with efficiencies, you know, in their org or up level, you know, their team players. And at the end of the day, you’ve gotta do what’s right for the business and the decisions that you make. So just, you know, lean in, but also just be smart on your decisions and what’s gonna have the most impact for what it is that you’re looking to do and achieve.

Speaker 4: Agreed. Lean in, start somewhere, share positive use cases, especially if adoption is is low. Hopefully, adoption is increasing over time, but share the wins, uh, uh, host workshops. Uh, I would try to get a pulse on where things are at from an efficiency perspective now versus later so that you can showcase the ROI later, um, and and make sure that those guardrails are in place.

Speaker 0: Awesome. I also heard one person say once to me, if you don’t know where to start with AI, take, uh, an hour or so of your day and just start talking to yourself about what you’re doing and upload that to AI and ask it, hey. How can I be more efficient? And I haven’t done that yet, but I am just imagining all the things that it could tell me that it could save me time with.

Speaker 3: That’s the first thing I did. I’m like, hey. This is my role. This is the company I work for. What are the what are 10 ideas that I can start using AI with? And ChatGPT was like, boom, boom, boom, boom, boom. And then I just kinda dove into one of those, and then from there, it was a rabbit trail.

Speaker 0: Yeah. Awesome. Alright. Well, thank you all for joining us. Um, we will be back tomorrow. Um, so make sure to join us where we, um, we’ll be kicking off with our awesome demo jam.

Speaker 3: Alright. Cool.

Speaker 0: Thank you, guys.

Speaker 3: See you.