GTM Innovators: The Future of Demand Gen – AI, Data, and Personalization with Prashant Kaw

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Demand generation is undergoing a major transformation, and AI is at the center of it all.

In this episode of GTM Innovators, host Kyle James sits down with Prashant Kaw to explore how AI is reshaping demand gen workflows, disrupting traditional attribution models, and redefining go-to-market strategies. From hyper-personalization to the rise of dark social, we dive deep into the biggest challenges and opportunities for marketers in an AI-driven world.

Tune in for insights that will help you stay ahead in the evolving landscape of B2B marketing.

Subscript to GMT Innovators Series on the following platforms:

Transcript:

Kyle James 00:00
Welcome to another episode of GTM Innovators by 3Sixty Insights. I’m your host. Kyle James today, we’re diving into demand gen especially how it relates to go to market with my guest here, Prashant, who Prashant Kaw is a long time friend. Gosh, we’ve known each other for 15 years, going way back to our days at HubSpot, where we were runner ups at a ping pong tournament. And we still go by the name Slammers today. So Prashant really great to kind of talk with you and connect again today, and you’ve got a fascinating background. You know, serial entrepreneur, and you’ve spent many time. You’ve been marketing executive at multiple companies, specifically VP of demand gen at camunda, VP of Marketing at Code ship, and VP of Marketing at continuum. And today, your founder at Helmer. And you also do some fractional cmo roles for a couple of companies. So, you know, that’s, that’s kind of my high level intro to you. What else do you miss? Or what else do you think the audience here needs to know about you and your background?

Prashant Kaw 01:11
No, that’s, that’s really good. I just, you know, I’m, I like to tell people that, you know, the area I play in is part art and part science. So it’s what I like to call architecture, right? So whether it’s architecting marketing or it’s the marketing ops and the technology stuff, right? It’s, it’s sort of putting those two things to blending those two sort of parts of the brain together to sort of help companies grow successfully. Love

Kyle James 01:47
it. Love it. So I know, and I always think of you and your superpower of demand gen, right, like that’s what you’re known over the years for. And really want to dive in today and talk about, like, how you see AI reshaping demand gen, and what are some of the things that you’re doing out there with companies, but you’re also seeing on the landscape, and I’m sure, like, we’ll be able to fill all of our time kind of digging in here. So yeah, what are you seeing out there? It seems like it’s fascinating, and stuff is changing so fast. Oh,

Prashant Kaw 02:18
man, we live in interesting times before we sort of jump into the the specific demand gen aspect. I want to share something I heard from someone who said something crystallized, something for me, really, really well, okay, so this is from David boscovic. He’s the CEO of flat file, and they have an agent tech software that helps companies with data transformation, helping companies with the data needs. And what he said is that in any innovation cycle, there’s typically four phases, right? So you’ve got this, you know, phase of discovery when you’re first figuring out what is possible with the specific, you know, let’s say AI in this case, right? Yeah. Then you’ve got sort of the material science phase, like, what are the practical applications and use cases of AI? And then you get this sort of commoditization phase, where there’s sort of now broad manufacturing of, you know the tools and how to deploy them. And then finally, there’s this sort of phase of commercialization when you’re trying to sort of really work out the economics of the technology and innovation. And for the first time in history, things are moving so rapidly with AI that you have all four of these phases happening at the same time. Yeah, right. You’re still figuring out what is possible with AI. You’re still trying to figure out what are the use cases and applications, right? You’re still trying to figure out how to apply it broadly across your organization, if possible. And then the question of, How much is this going to cost, especially with how you know, all the AI platforms and tools are sort of evolving so quickly.

Kyle James 04:38
So why is that? Let’s dive in. Is that just because the iterations are happening so fast, or just as we wrap our hands around one thing, there’s already two other new things that have come out, or it’s like, Why do you think that is?

Prashant Kaw 04:53
Yeah, I don’t know, right? Because I think last year a. Lot of people were using, at least in the demand gen marketing, right? You saw a lot of applications that were tied to sort of content, and maybe to aisdrs, right? But, but now it seems, and then there was a I felt there was a little bit of a lull, and now suddenly it seems like it’s going gangbusters, and there’s all kinds of applications, right? People are building CRMs. They’re building agentic workflows, right? You’ve got the idea of building mass agents that will be sort of workers for you doing all kinds of different tasks. So I think that’s again, it’s going through this new cycle of what are practical applications. But I think a lot of the cost part is still not figured out just yet, right? How much this will cost you, right? So I think that’s one angle of it. Yeah, the other angle, which I wanted to get in a little bit later, but we can sort of touch on it briefly, is sort of the governance and compliance, which is, you know, large enterprises, I’m sure they want to start using AI right away. But if you imagine if as a demand gen person, you’ve got a database, and suddenly some agent changes all of your opt out people to opt in for some reason, and suddenly people who aren’t subscribers start getting emails. Right? There’s some serious liability there, right, right, right, so

Kyle James 06:48
right, as the person who put it in there gets fired, or is that, like, a oops, it was AI, or, yeah, like,

Prashant Kaw 06:54
exactly. So there’s definitely use cases where there’s less liability. Can you pull together and assimilate like you can transcribe our podcast really quickly and create outlines and blog articles? There’s probably no liability in that. Companies that are doing, you know, content marketing are probably worried about plagiarism and citing sources appropriately, but I think a lot of that can be validated very easily, yeah, but if you suddenly have a bunch of software agents going and changing things in your system, you probably want to have a better, better understanding of how that’s going to work, when it’s going to happen. So I think it’s lot of people are being cautious about that too,

Kyle James 07:42
right, right? I mean, it seems like the content stuff is obvious, right? If you could, if it could help you create more content easier, like, obviously, that’s always a good thing. But what do you what do you think is kind of the biggest change that’s going on right now, especially in demand gen marketers, it is, it the ability to just be able to produce content faster or or do you see a lot of people doing a lot more of that workflow, agentic workflow, to kind of customize and segment their audiences a lot deeper ways than they ever have before? Yeah, I

Prashant Kaw 08:13
think, I think it’s a, it’s a it’s a little bit of all. I think the the content is the easiest use case to understand, and it’s also the repurposing of the content into multiple formats, right? So I think that’s that’s huge. A company I’ve been talking to recently is actually doing something even more interesting. What they do is that they have some agentic software that will analyze the content viewed by all your contacts within your HubSpot account, for example. So it starts to make a content interest graph for each user in your database, and if you have social profiles for them, it can actually go out and research and augment that content graph with additional content interest based on stuff you’ve posted on LinkedIn, stuff you’ve liked, and they can also augment that based on some of the tags on your profiles and some of the skills that are associated with your profiles, so and then, based on that, they can create a customized, personalized journey for you, whether it’s On your website because of the integrated CMS or through curating personalized emails directly within a HubSpot for you, right? So if you, if you think of the level of efficiency for the amount of personalization and customization possible here. Yeah, it’s incredible.

Kyle James 10:01
So, so you’re already seeing companies do literally cohort size of one, right exactly, because you could specifically target and could create custom content that’s slightly different for every single user, because you know who they are. They know that that’s wild, exactly.

Prashant Kaw 10:17
And then on top of that, then they use the same knowledge and content graph about you to help you build audiences. And then what you do is you take these audiences and you import them into your paid marketing platforms and display platforms, and then you can again hyper target them for relevancy, because you know exactly what their interests are. So it’s super, super interesting to be able to get that level of detail, at least for B to B. Yeah, right. I think B to C companies and probably have that level of detail, segmentation and information about their consumers, and are able to do this routinely, but for for B to B companies to be able to do this very efficiently, I think, is fascinating. This is the exciting part about using technology that’s so efficient and you program, you just use a few prompt prompts to sort of program and refine how you think about your segmentation, and you’re off to the races. That’s

Kyle James 11:30
crazy. So, right? All you need is a little bit of prompt engineering. Is I just, I like to just think about prompt basically writing the scope of work, right? But, but, like, what else? What else are you seeing is, like the foundation or the framework that people need to think about putting in place that everybody needs to do, which is part of good, clean living, if you want to start being up take advantage these things. Is there kind of like a step by step guide or playbook that you kind of think go through?

Prashant Kaw 11:56
There isn’t. And I wanted the conversation to sort of end on sort of this note, but back if you were, if you were, yeah, let me talk about sort of one or two other use cases first, and then we’ll, we’ll come back to what I think is going to be the bigger challenge, after a lot of these applications and use cases have been figured out. Give me some more fascinating stories? Yeah, and the answer is actually very simple, but I think it was something that you brought up, right, which is one of my clients, what they do is they don’t use very standard verticals for segmenting their go to market, right? A lot of companies will say, Hey, I’m going after telco companies, healthcare companies, blah, blah, blah, right? And you probably have, you know, the classic si C codes, NAICS, you know, NAICS codes for industry classification. But this particular customer has got a very unique way of sort of segmenting their ICP, okay? And we were running into issues, you know, it through our enrichment processes, through, you know, the typical data providers like zoom and Apollo, etc, and we just weren’t getting the right fit of customer based on that enrichment. But what we found was a lot of the sales people could eyeball the company’s website or description, and very like literally, within a second, they would see a few things, and they could say, yep, this one’s a fit. That one’s not a fit. This one’s a maybe, right? And unfortunately, a lot of this data is not really available in structured databases, yeah. So what we ended up doing was we use some prompt engineering to replicate their eyeballing.

Kyle James 14:06
So you train the algorithm on like keywords and specific phrases and stuff that you look for Exactly, exactly.

Prashant Kaw 14:12
So we use this tool called RE spell, which is essentially an agent tech workflow tool, and we used it to essentially look up LinkedIn profile of the company as well as the website, right? And we gave it criteria right. Look for companies that provide XYZ service, right, but exclude companies that provide these kinds of services. We fed it key terms, we gave it example companies, and then we also gave it negative examples, right? And this, this was by a little bit of a trial and error, right? When we first built it, we first, we started getting sets of companies, and. The tagging that came in wasn’t, wasn’t that good. And so step by step, we had to learn, and it’s almost like what you would do in AdWords, right? You put in keywords, then you put negative keywords, right? So we did the same thing with sort of the prompt engineering. So we gave them examples, and then we gave them negative examples, and then there were use cases where there was an overlap, right? And so for each vertical essentially we we essentially did that and gave them guidance for what was the match, what was not a match, and then what was an overlap. And based on that, we were able to enrich our entire database based on our custom requirements. And that really helped sort of define the go to market by these sort of sub verticals, and it sort of fed the entire advanced strategy. Interesting.

Kyle James 15:57
So it’s interesting you talk about this, because I just had a conversation with John Marcus, who I know you also know really well, talking about what he calls AI qls, right? Okay, yep, so it sounds like you’re doing some of that stuff too, right, using what you can enrich the qualified the lead that you have to kind of say, is this a good fit or not? And kind of helping hand that off to sales totally in kind of a lot better ways. That’s exactly that y’all are independently, kind of using these tools for very similar, probably maybe somewhat the same, kind of aspect. But he didn’t talk anything about the negative aspect of it. So

Prashant Kaw 16:33
yeah, it’s a really useful way to think too. I’ll touch a little bit more on that, but I’ll tell you one issue that we’re sort of running into, and we still have to find a solution for it. So while we have the ability to enrich our first party data using this methodology, when we go to systems like LinkedIn or Facebook where or tools that integrate with like a primer, where you sort of build audiences. We aren’t able to use the same methodology based on our vertical definitions, because they use classical definitions, right? So while we’re able to do some of our internal, sort of first party demand gen capability, whether it’s outbounding or through our email marketing, et cetera, right? We’re able to leverage this. It runs into some scaling issues as you start doing second party or third party. So it ends up being, how do you consistently build your operations and data to be able to do this consistently everywhere, right? And so the way around that is you’re sort of exporting lists and importing them as sort of list audiences, as opposed to using the native segmentation. And the good thing is that also these tools are going to get smarter, right, with their sort of predictive audiences, and as they’re sort of building their AI capabilities, right, Facebook or meta has almost completely abandoned being able to build audiences, right? They just use let their algorithm do all the targeting now going forward, so it’s just so these are some of the things that people are going to have to sort of figure out, right, like, right, while you’re able to successfully use AI capability someplace, are you able to consistently do that across the board and don’t, don’t start running into data silos across all your operations? So that’s a that’s a interesting. Gotcha. The other thing that other use case that we sort of working on, we just started doing, is one of the CRMs, one of my clients use is called Adio. So surprisingly, not a HubSpot, not a sales force, not not a Zendesk, not one of your classic CRMs, but this is one of the newer sort of CRMs that’s a little bit more AI savvy,

Kyle James 19:12
okay, and more AI native or industry, yes, yeah, okay,

Prashant Kaw 19:16
exactly, exactly, I wouldn’t say that It’s as AI native as sort of the the day.ai the Christopher Donald sort of startup from Sequoia. Yeah, they’re, they’re completely native from the ground up, AI CRM, but these guys are new enough that they’re building a lot of AI capabilities into them. But essentially any lead that comes in, we are sort of grading the persona, and we are grading the company fit. So what this company does is they have a use case that’s relevant, but now. Along with the use case fit, we’re able to do the persona fit and the company fit. So in terms of how we can make sure that we’re hitting our ICP and that the need is the highest, we’re completely doing this using prompt engineering instead of hard coding, you know, formulas or automations that say, Oh, if the persona is, if the title is this, then, you know, the persona is this, and the grade is that, right. We, we used to have to write all these kinds of automations and workflows to do all of this, which was time consuming, it would break. It was just never perfect. And while our sort of prompt engineering isn’t 100% it’s still flexible enough and easy enough that we can do it really quickly and iterate on it really quickly and refine it to the point where it’s good enough.

Kyle James 20:59
So I want to dig into that. Let’s double click there, right? Like, would you say that it’s better? Like, because it has the ability to be a little bit more flexible and into it a little bit better. Is it in general? Is it always better than the workflows that you’ve built in

Prashant Kaw 21:14
the past? It’s, it’s better in the sense that I feel that it’s, it’s more scalable, because, sure, anytime there was any big data augmentation or any pivot, we would have to go, and yeah, we would go, have to go and re engineer all the automations to support that or accommodate that, right? So, hard coding a lot of this stuff the old way. And this is the classic area where AI fits in, right? It’s never going to be perfect, but it’s always going to contextually get better much faster.

Kyle James 21:50
Are you seeing that too? The more, the more you’ve kind of done that

Prashant Kaw 21:56
absolutely right? Because the ability to just change a few prompts and sort of give it a little bit of guidance, is so much easier than having to go and either try and hack the code yourself or then work with a data engineer and have them build something very specific for you, right? So, yeah, I think the ability for the lay business person to drive change much quicker. It’s sometimes it’s just about the time to market, right? How quickly are you able to do this stuff and sort of drive, drive change, right? So you don’t let good enough get in the way of you know, what’s, what’s the saying, Don’t let good get in the way of being perfect.

Kyle James 22:41
Yeah, yeah. Gotta keep moving

Prashant Kaw 22:45
perfect. Get in the way of being good enough. Yeah. So that’s

Kyle James 22:48
fascinating, that it’s, it’s really that quickly overtaking all the complicated workflows, and just now it’s, it just kind of handles that, and you can kind of just tell it how you want it to tweak, yeah. How are you because I know, a few years ago we saw a lot of the demand gen channels like get changed up. A lot of this kind of COVID work from home. How do you feel that like, as Have you seen the way that you’re generating leads, and different channels are using to generate leads change a lot through demand gen recently? And how is AI impacting that? Like your the way you’re spending budgets and

Prashant Kaw 23:23
such, yes and yes and no. I don’t think I’ve necessarily seen the impact of AI on channels, or at least some of them. And I’ll talk about which one is sort of the the odd one out there. But in general, I think the go to market function and industry is going through an upheaval. Right? I think the whole notion of attribution has been upended recently. Do tell it’s just the whole idea behind dark social, and I don’t think any company has still solved dark social, but I think there’s probably explain dark social, just in case. Yeah, and I’ll explain that. But there’s almost ubiquitous recognition that dark social plays a huge role in attribution. And dark social is the following, right, you look in your attribution results at the end of any cycle, and you say that I got X number of, you know, deals or opportunities from AdWords, right? Guess what? It technically wasn’t AdWords that prompted that user to come to your site. It was a conversation with someone on LinkedIn. It was probably in some thread where there were some comments and maybe. They did a Google search, and maybe they clicked on a Google AdWords, right? So you think that as a company deciding where to place their bets with their budget, you’re seeing AdWords in your attribution data, and you said, Hey, we should spend more on AdWords. The reality is, you probably need to be spending more time engaging with conversations on organic, yeah, social, so that. So that’s kind of the notion behind dark social and attribution is just so hard to do, right? What? What do you use? I, nobody can agree. At least in B to B, nobody can agree. Should you use first touch attribution all the time? Do you use last touch? Do you use a classic W model or a U model, right? So, what

Kyle James 25:59
do you have a strong opinion. One, where is it really? It really depends on the company.

Prashant Kaw 26:04
I think it depends on the question you are trying to answer, right? So I look at multiple attribution models to sort of answer different things. But still, at the end of the day, it is never perfect, and even if it does get really good, it only ends up being good for 60% or maybe 50% of your database, you’re still 50% completely blind. Yeah. So it’s no matter what you do, it’s it’s never good enough to really say that, Oh, my God, this is the holy grail of how we should be allocating our energies and resources, right? So I think most of most of the brave minds have now sort of come out and said that you just have to do good community engagement and customer experience to do healthy marketing, right, and to be successful with your funnels. And guess what? There’s, there’s no quick There’s no quick answers to this, right? You have to build up a brand. Building up a brand takes time. It takes dollars to right? It takes also people to sort of engage. You can’t use AI generated content. That’s crap. It doesn’t work, right? We recently learned that HubSpot has lost 80% 80% of its I’m not saying that their content was aI generated, but it necessarily wasn’t the content that sort of is not ranking anymore, was not tied to their core business, right? It was, it was,

Kyle James 27:51
Do you also think was just like, people don’t search for things, they just go ask chat, GPT or something now and and where some of those long tail search results just aren’t happening anymore, because we’re not assuming the content the same way.

Prashant Kaw 28:05
Absolutely we we’re just having a discussion with a customer. They they wanted to start using their g2 crowd badges on some promotional materials, and our basic sort of feedback to them was that g2 crowd is reviews are kind of dead right now, right? They’ve lost 80% of their organic traffic. Everybody knows that g2 crowd is pay to play, right? And there’s a little bit of a grain of salt, because, you know, you offered everyone who did a review, half of them got a Starbucks card or something to say something nice about you. So the real hard way, and the good way to do it, is work with, you know, Gartner pay reviews, or make sure that industry experts understand what your technology does and how it’s better than the competitors, and have them write reviews about you, yeah, right. So or nurture influencers, right? So it’s, I think people see plays and they want to rep, they want they want these formulas to replicate, yeah. And I could probably go into a little bit of a tangent and say how the movie industry, you know, tries to follow formulas, and those formulas don’t work anymore. It’s the same thing over here, right? The old plays aren’t working anymore. So you just have to go back to the basics and just do good do good marketing. So you’re

Kyle James 29:37
saying creativity and originality are still really important. Super bingo. So I do want to, I do want to go back to kind of the whole HubSpot like thing, because we it was all over the but I don’t remember that was a week or two ago now, but yeah, basically, a lot of the charts shown was it sometime in the last 18 months, their organic traffic just cratered and has gone down. Down to something like 30% of the volume that it was. I’m curious. One, like, have you seen similar trends like that on any of the customers you work with? And two, how are you working with your teams thinking about SEO, and are they now becoming like, how do you even call it to like, educate the AI is on your company. Like, are you doing some of that so that you know when someone goes and asks for, hey, is this a what do I use for this that your company shows up in? You know, the AI approved recommendations?

Prashant Kaw 30:33
Yes. So I’m not the most knowledgeable person about this, but I read and I attend webinars, so I’ll regurgitate some of what I have learned here, but I would just like the audience to sort of go and sort of dig in with their own experts too, right? So I definitely see that overall, Google based SEO is taking and that’s the primary classic SEO is has taken a big hit, and that’s probably across most industries, right? That that’s happening, but it also has to do with the type of content, right? So if we were talking about HubSpot, yes, they lost a huge bunch of traffic, but I would say that that was kind of the vanity traffic that really wasn’t doing much to them. It made them right it it made them tell everyone, hey, we’ve got billions of page views, but I don’t know if all those extra page views are really driving their business, right? It

Kyle James 31:37
was the long tail we ranked for some keyword that helps somebody solve a problem because we had a blog article

Prashant Kaw 31:43
on it. Yeah. So I, I think that a lot of the content was sort of for the attention economy, right? It’s like, okay, we have all these people here coming to our site. Now, let’s give them all kinds of content that will keep them here and keep them keep us top of

Kyle James 31:58
mind, right? So it’s very much top of the funnel, general awareness content Exactly, right? For example, in order to get general awareness

Prashant Kaw 32:04
Exactly. And I think the AI engines are also doing a much better job of sort of saying that, okay, this is what your offering is about, and this is the content that’s relevant to your offering, right? So anything that’s tangential, they’re not putting a lot of weight on. And so as people are searching, you know, through prompts, Hey, what is, you know, XYZ company do they they’re not going to pull in all the ancillary information about it, right? And it’s doing a much better job of being concise. There’s less just, there’s less junk being put in front of people using,

Kyle James 32:47
you know, that actually sounds like a good thing for us, all right? Like, like, if I’m understanding you correctly, like, you’re kind of saying, like, we’ve the the signal to noise ratio is starting to change. Where we’ve gotten to this point over the last 15 years where there’s so much content out there, and let’s be honest, most of it’s not great, exactly, but everybody was told, content, content, content. So everybody’s out there writing five blog articles a week for their company, and there’s not five. So as

Prashant Kaw 33:18
a user, I love it, okay? As like a head of experience, I love it, yeah, right? As a demand marketer, I’m scared, sure, right? I want to be able to track it. I want to understand the source. I want to I want to gamify it like I’ve gamified every other channel in the past.

Kyle James 33:43
Well, it’s a playbook that’s no longer working. It’s worked for over a decade now. That’s just

Prashant Kaw 33:48
exactly Well, I think burning I think the playbook is create really good experiences and content and go deeper. Don’t try to be all things to everybody, right? Yeah, I think you have to really pick your niche and just go really, really deep in that and do that really, really well. How do

Kyle James 34:11
you feel about storytelling content? Though, it seems like that’s as important as valuable as ever, though. Now, right? Like being able to tell that. Give

Prashant Kaw 34:20
me an example. Well,

Kyle James 34:22
I mean, you know, the stories of your customers that are having success solving X, Y, Z with your product, like being able to actually, really tell those stories instead of, I think B to B companies have gotten personally, like, I’m gonna throw this out there. I think they’ve gotten really bad in the last five years of just what I call logo soup, right? They just stack on their website all the logo the bit of the big companies we’ve heard of, but they don’t actually tell me why they chose you and what interesting and valuable things they’re getting out of it. They’re just slapping logos because it’s easy.

Prashant Kaw 34:53
Yeah, so I’ll just again, share something I read recently where we. The Age of writing a how to is almost dying, right? There’s just so many How to articles right, but if you have a very unique way of doing something right, so how does Kyle James do something right? And all your customers have bought into the Kyle James methodology of doing that specific thing. There is tremendous value and depth in that, and that is what companies should be trying to do, is, right, is. And you remember, because we were both at HubSpot, what was the HubSpot way? Right? We built a methodology around inbound, right? So I think that that is what a lot of AI engines are looking for, and how they’re ranking and sort of organizing things in their data sets right now, so that that that’s got me thinking, and I think it again comes down to how you brand, how you position yourself, how you differentiate yourself, right? So these are, again, some of the harder things, and usually not always, the top of mind things the average marketer or content person is doing while they’re creating content right? Because we lived in the age of mass content production, the more strategic content creators, the product marketers, right? The SMEs, they’re probably thinking of these sort of flavors of their content, right? And I think just, again, using chat, GPD and other tools to mass produce content is not going to be good enough, right? It has to be the right content with the right flavor. So it’s just, I think again, the tool is still going to be an assistant or a co pilot for you.

Kyle James 37:03
Interesting, interesting. It’s fascinating. I think, I think you’re right. It makes sense when you really start thinking about it that way. So this is, this has been fascinating person. I know that you had something where you kind of wanted to end it before we kind of wrap things up. So, you know, yeah, you want to bring back up that kind of line of thought again, I

Prashant Kaw 37:22
do, right? So I, and this is again, more from sort of running a software company that helps demand gen people, and sort of having been a career demand gen person, right, that at the end of the day, it’s going to be garbage in, garbage out. Yeah, right. So you can have the most brilliant prompt engineering. You can have the best agentic workflows. But if you don’t have good data, then whatever your workflows and your engine prompt engineering is doing is not going to be good enough. So I think marketers are going to have to continue to think critically about how they keep their data sets really, really clean and healthy so that they’re always feeding these AI engines the best context about their business. Otherwise, as I said, garbage in, garbage out, right? You won’t be, you won’t be getting the best outputs, right? So that that’s any tool will none of these tools will take the responsibility of having bad data fed to them that’s on the teams that are responsible for building the data sets and making sure that they’re good for them. So I think there is still time, over the next year or two, as you know, we move into that commoditization and commercialization phase of AI, but I do think that teams are going to have to think critically about how they sort of make sure that all their segmentation data and all the Information is good and where they get it from.

Kyle James 39:21
Well, yeah, yeah, that’s a good reminder. I think for everybody

Prashant Kaw 39:26
I know, simple, simple, not fancy. Well, it’s

Kyle James 39:30
getting back to basics. And to me, kind of what I’m also hearing you say is, like, look, so much of this stuff that we’ve been doing for a while has gotten to the point where it’s really easy to do, and we could start getting agentic AI to do some of those things for so let’s get back to the basics and focus on the messaging, the branding, the value props and those things and marketers can really like own their foundation. The other stuff is easy now,

Prashant Kaw 39:58
and that, to me, is the beauty of it, right? Right? Is just that if AI makes the more monotonous, repetitive tasks go away, you and me, we can spend our times on more strategic, creative tasks of it’s not just about getting a campaign out the door, but getting the best campaign with the best message right to the right audience, right at the right time, right? So

Kyle James 40:25
awesome. Well, Prashad, thanks so much for joining me today. And hey, thanks for having me. It’s been incredible insightful. And I’m sure our audience will find lots of value, like thinking of Newtown ways to do marketing and branding and such. And you know, I want to ask you, like we kind of teased a little bit over Helmer, any What do you want to plug? What are your lats and longs? How could people you know contact you or follow you or ask follow up questions of you?

Prashant Kaw 40:52
Yeah, I’ve got an agent, tech, demand gen tool in the works. Sign up on our website, we got some great news coming your way. So go to helmer.com and sign up on our guest list. When we have news, we have some beta customers that are trying our product and really excited to get other people in it soon.

Kyle James 41:21
Where else are you on Twitter, as much anymore, you mainly a LinkedIn person, mostly

Prashant Kaw 41:25
on LinkedIn. I’m, I’m a lurker on Twitter, an occasional Lurker on Twitter. I’ve probably pretty much abandoned, I

Kyle James 41:33
guess it’s anyway shows now, like, yeah, kind of retired from most social media. I’ve abandoned

Prashant Kaw 41:38
Facebook, and I never really adopted Insta, like the cool kids have, yeah, that’s what our kids are for now. So, so, So LinkedIn is where I’m at, awesome,

Kyle James 41:49
awesome, or all you demand, demand, Gen marketers out there, check out Helmer what per shot stood over there. It is super cool. The way that you could start breaking out funnels and and measuring stuff. So you know, if you enjoyed this episode, please subscribe, share, leave a review. If you have a question or a recommendation for a future subject or guest, please feel free to reach out, email me at research, at 3SixtyInsights.com, and once again, Prashad. Thank you so much for coming on this episode, and thanks everybody for tuning in. We’ll be next week. We’ll be back next week with another go to marketers, innovators and guests, and until then, everybody, keep growing.

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