The Value-Driven Path to AI Adoption with Aby Varma

Hi, I'm Michael Hartman. I'm Naomi Liu.

And I'm Mike Rizzo. And this is OpsCast, a podcast for marketing ops pros and rev ops pros created by the MoPros, the number one community for marketing operations professionals. Tune into each episode as we chat with real professionals to help elevate you and your marketing operations career.

Hello everyone, welcome to another episode of OpsCast brought to you by marketingops.com. I am your host, Michael Hartmann, flying solo. I am hopeful that Naomi and Mike will be joining again soon. In fact, I know we're planning on doing a recap soon of MOPS Blizzard 2025, so watch out for that coming out soon. But today I'm diving into one of the biggest challenges and I guess opportunities facing marketers and our marketing ops folks today is how to adopt AI strategically and responsibly. Joining me to have this conversation is my guest, Abhi Varma. He is a global business and marketing leader and the founder of SparkNovus, where he helps marketing leaders navigate their AI journey from adoption to self-reliance, driving innovation and marketing and business growth. He also hosts the Marketing AI SparkCast podcast and leads the Marketing AI Pulse community, both focused on AI's real-world impact on marketing and business. So, Abbie, welcome to the show. Thanks for joining.

Thanks, Michael. Thank you for having me. Such a pleasure.

Yeah, we may have to talk about, I know we chatted briefly before this, maybe we can talk a little bit about the community. I think it's a local one that you have there as a part of this, but let's dive into it. Let's talk about what you're doing with SparkNovus and how did you, like, what's the origin story of how you got started helping marketing leaders navigate the adoption of AI?

Yeah, great question. So, this was all sort of timed well in the sense, well in quotes, because I got into AI right before COVID you know, with Hired as part of our R&D department in the companies to work for. We hired a person who was very focused in machine learning and I'm kind of a nerd at heart and got into it and everything. And then it was timing-wise, it was just before COVID And so that gave me plenty of time to sort of dig into it. And I went from a predominantly travel sort of role to being completely kind of in lockdown mode like we all were. And like, it just sort of afforded me the opportunity to dig in and learn more about it. And I was like, wow, this is game-changing. This is really going to kind of change the way marketing is going to work. And, you know, for the— this was— I was in a marketing leadership role in an enterprise organization. So I was just sort of— it started off with just me thinking about You know, how this was going to solve my problems. And the more I got into it, I was just fascinated that this was just going to be game-changing. And that was sort of the quote unquote spark, the spark of this.

Ah, okay. Love it. Nice connection there. For those who are watching this, maybe like, I'm not— now I'm wondering that piece of art that's behind you, Abby, like, is that AI generated? I'm just like, It may be.

No, it is not. That's my sister is a commercial artist and whenever she visits, I sort of twist her arm to act. There you go.

There you go. It's great in a human way.

Yes.

I mean, I'm worried like at some point here in the near future, it's going to be hard to distinguish. It's like I see posts a lot of times now on like Facebook or Instagram. I haven't seen it as much on LinkedIn that I'm aware of, but like where it's like, this story seems too good to be true. And then you start reading like, oh yeah, yeah, it's definitely like bullshit.

AI slob. Yes. A lot of it is making its way.

Well, so let's dive in. So one of the things that you and I talked about was that a lot of companies, when they're starting to dip their toes or starting to try to like, oh, somebody says we need to go do AI, right? It's, they think about it probably like ABM, right? They really think about it as technology. first, but you said that you think about and advocate for companies to think about on a value-based approach. So, I mean, kind of walk us through, what do you mean by that? And what does that look like in a data, you know, kind of at the ground level, if you will?

Yep, absolutely. So, the value-based approach is really changing your perspective and saying, hey, you know, what can this tool do or what can AI do to really what can, what can I do with AI to achieve my business goals, right? Very subtle shift. And when I say I, you can replace the I with me or my team or my organization.

Sure.

To achieve business goals. So, it's really kind of changing the framing that you are putting AI to use in a way that is adding value to what is relevant to your yourself, your team, your department, your organization at that point in time. And as simple as that sounds, it is often lost in the noise of AI adoption. Like, it's not uncommon where when we were working with CMOs and, you know, they'll, they'll ask me that, hey, can you help me doing— can you help me with AI? Can you help me quote unquote do AI? And I'm like, yeah, what would you like? You know, what do you, what do you want to do? And there's an awkward silence and that sort of This symptomatic where people have, you know, they're obviously getting overwhelmed. There's an assault on all senses these days with AI and whatnot. But really pausing and thinking about what is the value. You can use it, but use it towards something that is moving the needle forward, you know, with your department, your team, your organization. And that is what we call a value-based approach.

Yeah, it's interesting. This is a, like, not a great analogy, but in my head, what I went to is sort of the, The John Kennedy thing about like, ask not what your country can do for you, but what you can do for your country. In this case, it's like, ask not what AI can do for you, but what can you do with AI? Isn't that kind of the—

There you go.

All right.

There you go.

You can steal that if you want.

I will. I will. But, you know, at the end of the day, you said it to me. It's the, you know, the AI is not the protagonist of the storyline, right? It's human beings still very much so. So, it's really, you know, really I look at it as AI is an enabler, but what is it enabling? It needs to add value, it needs to move the needle, it needs to do something more than using AI for the sake of AI.

Yeah. I mean, it's like, the way I've been thinking about it is it's almost like a, I was gonna say a thought partner, but it's like a partner to kind of like, it's Yeah, it's not even an enabler, it's a multiplier maybe in some ways.

Yeah, absolutely.

And I think that's the way I've been starting to think about it both personally and professionally, but actually honestly, like mostly personally, I think in my world. But I think I happen to work at an organization now that is really truly trying to adopt AI. And I don't know that they've thought about it as value-based per se. I know I've seen senior leadership talk about like a time dividend, Which I, and I like that way of thinking about it, but I think it feels like there needs to be a sort of a thoughtful way of doing it and finding early adopters maybe. And so like maybe like something like I have that example a little bit in my head from personal experience. Like, could you talk through maybe some examples where you've seen this either not done well and you've come in and helped kind of correct, or where you've come, been brought in and you did help, Right? Where there wasn't that, like, there was that silence and you go like, okay, well, let's talk about what your goals are and then figure out how to bring AI as a part of it.

Absolutely. So, I'll give you maybe 3 examples. We work with all sorts of firms. So, I'll give you an example of an enterprise company, a mid-market company, and a small business, because that value-based approach is sort of agnostic at the size of the organization. Work within. So, um, for an enterprise company, they're a Fortune 500 firm, uh, we are, uh, working with one of their business units that launched a new product. And there was, um, a lot of urgency in terms of launching the product, trying it out, and proving the product market fit early on before they put in more investment dollars into that. And that entire cycle.

Good. Just to like, what kind of product was it? You don't have to be specific, like software product, a widget kind of thing, or?

Yeah, it was, it was a, it was a, a software and solution consulting sort of package offering. Okay. Uh, within the sustainability industry.

Got it.

And, uh, yeah. And so longer sales cycles, but the idea was, hey, how can we do it? And If you did it the traditional sort of way, that process could easily be a 1-year type, you know, cycle where you're coming up conceiving with the idea, coming with the idea, coming up with messaging, your go-to-market strategy, activate your channels and all that kind of stuff. And that was something. So we were brought in and saying that we were brought in just really almost for the content piece of it that help us with the content generation.

Yeah.

But when we started working with the executives, we sort of basically the idea, hey, this is, you could use AI to accelerate the entire cycle. And the outcome of that, like within a 4-month cycle, we were able to take the solution from concept and take it to market with a qualified pipeline of over 2,000 leads. So that was, we were able to compress that within 4 months, which would just not be possible with, without AI, just in terms of the mechanics that sort of went into it. So, a great way where we sort of took— there were a lot of ideas on how to leverage AI, but we really took it to where, hey, let's— we kept asking the question, you know, so what? Like, hey, let's do content. Or like, okay, so what? To achieve what? Okay, we wanted to make sure that lands with the right people. And you keep asking that question, so what, till the time you get to that nugget of value that you were trying to extract. So, that's the enterprise example. A mid-market example is where we are working with a Mid-market, they're probably a million professional services firm out of the Midwest. And in that example, you know, 90% of their marketing dollars are spent for paid media. And their entire use case is that, hey, anytime they want to grow, there's an expectation of, you know, more budget dollars are needed in order to grow. So how can they take, get more out of the same amount of spend? And that's where You know, the higher sort of value was leveraging AI to really improve their return on ad spend, ROAS, and really help them grow without additional investment in their paid media strategy. And we've searched AI—

bending the cost curve is what I would—

Absolutely. Absolutely. So from, you know, campaign ideas to content creation, to imagery, to analysis, analytics, the whole value chain for paid media. is under review where AI is already adding value. And then for a smaller firm, we work with a training and services firm, small company, 25, 30 employees. And in that example, we enabled the entire organization, rolled out business ChatGPT. They don't have a lot of investment dollars for different types of technologies. So we just rolled out the business version of ChatGPT, which is $25 per user per month. Very affordable. And then we came up with like this series of custom GPTs designed very specifically for these different use cases. And my favorite use case was making sure that they can really improve their sales effectiveness. So the very first, you know, prospect discovery calls, we came up with a rubric of how to evaluate these calls. And this was designed for the salespeople. So they sort of uploaded their transcripts. At the end of the call and then just give them guidance on what to do. And the value there was prior to that, the CEO himself would be guiding the salespeople and spending 30, 45 minutes, which is not physically, humanly possible for him to do that with all the salespeople and kind of provide constructive feedback. So we were able to develop this and then, you know, now we're seeing a better result where, you know, sales velocity is faster. The close rates have increased and those sort of things. So again, in the list of use cases, there were lots of use cases, but this is the perfect example where this was value-driven. We prioritized all the sales use cases, sales enablement use cases, some of the delivery use cases, but we picked the use cases that added the most value to the org. So that gives you sort of a sense of enterprise, mid-market, and small business, but in all of these cases, in this sort of swarm of AI ideas, we really honed in on the ones that is going to move the needle for the business in some way.

I mean, the last one, it's interesting to me. It sounds like you get sort of 2 pretty immediate benefits, right? One is sort of embedded coaching from the tool.

Absolutely.

As well as probably insights and guidance on like, what are the best next steps? And if you do that over time, right, you can continue to make that better. I'm curious across those and maybe others, right? Have you gone in and you had like, these are the use cases and you figured out the one that looks like it's going to be most value or multiple. And then have you been surprised either way, right? It either didn't achieve the value you hoped or it was like off the charts, like, or is there something else that you didn't expect that you, like, what were the surprises you ran into?

Yeah, I mean, great question. I think in the In some of the examples that we see, there is an assumption that the concept of AI, leveraging AI is going to automatically sort of make everybody's life easier, faster, better in some way. And the surprising part is that it is so intrinsically tied to culture. And the bigger the organization is, The more sort of egregious resistance there is. And some of that resistance is subtle, right? That people won't even acknowledge it. We do, and we typically come in, we will do some sort of a baseline understanding of culture and knowledge and that sort of thing. But that to me has been really surprising where a lot of people just have a philosophical stand on no AI or it's a habit thing, right? Like they're like, hey, you know, my My workflow is this. I've been with the company for 15 years and this is working. I don't know why I need to change my process. So a lot of those sort of things. So that culture-based resistance is very surprising, even though you can prove out value. So I think those are the ones where you gotta put in some extra TLC, focus on really working with the team, you know, being heard, understanding. And it's not a, rip the bandaid off, some sort of a cultural transition in helping those sort of people. And most adoption with AI follows a very traditional bell curve. You have the early adopters and the majority of the people will sort of join, and then you have the laggards. And so it's that, but I think the most surprising thing pretty much across the board has been that sort of cultural resistance. And then the second part is, it's almost the underinvestment in training, right? So, that's sort of surprising to me, right? Where you have leaders and, you know, they focus on— I'm gonna add training and governance, right? Where people are like, yep, I just want the value and I don't wanna spend too much effort in training the team and investing in training dollars and that sort of thing.

It's easy, right?

Yeah, just give me the easy button. So, those are things. And as part of our implementations, we definitely try to impress upon the leadership that are making some of these decisions that those elements are important. And yes, it may not be, it may not show its head right off the bat, but I can guarantee that these things will matter during a longer term.

Yeah. I mean, to me, what I'm hearing is like change management and tailoring the change management to, like, to match the cultural readiness is still something that needs to be done here. Like it is in a lot of, it's just about any significant change, right? And this certainly would be a significant change no matter what you're focused on.

100%. And I see that that change management element is so critical when it comes to knowledge, there's just general insecurity about, um, about AI based on the lack of, with the knowledge for AI or the lack of how the organization is intending to use it, right? So, Yeah. People are just insecure that, hey, is, if I'm gonna use it eventually, are they gonna replace me? Or, hey, I have, I'm giving you real-life examples of where people are like, hey, I'm, I don't wanna use AI 'cause I've put in a budget for 2 people to increase my team and now I'm not gonna get that budget. Because now people are going to be like, hey, I'm going to already have that. And that somehow detrimental to their personal growth or whatever. So these are hidden things that are there that are in people's minds. Some articulate it, some don't. But till the time there is a kind of an effort put towards sort of nurturing and getting an understanding of these thoughts genuinely, and then finding a strategy to address it, whether one-on-one or as an organization, these sort of things come up pretty routinely.

So on the change management piece, is there— it sounds like there's maybe layers to it too. Like, there's like, you talked about senior leaders maybe don't fully understand it, but also people kind of the middle levels and then kind of individual contributor levels within organizations. Yeah, there's resistance or Maybe beliefs about what's possible that are a little bit not aligned with what's real today and that kind of stuff. Are you doing sort of, are you adjusting that change management at all those different levels? How do you approach that? Yeah.

So, as part of our process and methodology, definitely. We have sort of a 3-pronged approach where we talk about change management and it's sort of a tight feedback loop. So the first is really discovery. You don't know, if you don't ask, you'll never know. So you've gotta have a formal mechanism of asking. And we recommend anonymous input, which is much more richer, right? Because people are not afraid to sort of answer or, you know, communicate what they're thinking. And some of those feedback elements are, Could be reflective of the leadership or the organizational culture itself. So that is really good to know. So for example, it could be like, hey, we are a very conservative organization. We don't typically lean in on technology. So I am not optimistic about how, what AI can do for us because I haven't seen that based on historical technology. And that has nothing to do with AI. That's really everything to do with the culture of the organization. So some of these sort of anonymous sessions will reveal that. And then we have pointed sessions which are tied to the nature of the, or the type of org, you know, is it a B2B, B2C, services, product, global, local, size of team, workflows and all that kind of stuff. Like this, the mid-market firm I was telling you, they're 90% of their budgets are going in paid media, right? All the other channels, Not a big thing for them. So that has an impact on how AI would be leveraged and the emphasis on the various AI-focused programs within the organization. So all of those things sort of are a key part of it. So one is listen and learn. The second is, I already said it, enable and train. So making sure that there's that enablement and training happening on an ongoing basis. Third is culture of experimentation. So allow people to fail and fail fast and recover. So that's another thing. There's an assumption of perfection, right? And to me, it's like, there's AI and people are going to try it and voilà, that's the silver bullet and it's going to, all my problems are going to go away. Definitely not happening. So to me, just be realistic about that. You know, there's all the technology providers, their sort of perspectives are definitely rosier than what real-world teams experience. And so, just acknowledgement of that is going to go a long way. And that, those 3 things just need to be repeated. It's not a one and done, especially when it comes to training. People are like, hey, if I train people and they're going to leave my team and I've just funded their education and Now they're gonna leave the org. And my thing is like, is that worse than if you don't train them and they stay? Like, to me, what is, what is, what's worse? So my take is that invest in your team, empower them, let them play with it, get hands dirty. And that could be something as simple as even making time. Because, you know, I don't know of any marketing team that works 40 hours, right? So to me, you know, that's the thing of the past. But if you are carving out and articulating that, hey, Take 2 hours on a Friday or whatever, you know, whatever is in alignment with your organization. Let people play with it. And, you know, hackathons is not limited to technical teams. You could have marketing and sales organizations do that and encourage that culture of experimentation. Very essential.

Yeah, I love that. Yeah, yeah, certainly there's no teams up. I actually just talked to a coaching client recently and I said, nobody in the executive team cares that you're busy. There, I was busy, right? Like, so get over it. So all this implies that there's, and I think you called it like there's a North Star for AI, right? And so what do you mean by that? And then if you were like, when you go in and talk to these new clients or others, like how do you help them think about what that should be and identify it and then articulate it well?

Yeah. So Think of it like a pyramid almost. Like, so the value-based approach that we spoke about earlier is right at the very tippy top. And that to me is your strategic north star of what that value is. So for example, your value in your organization could be all about business growth. Your value could be all about client acquisition or client retention. Or the value could be, and this is a real-life example, working with an agency where their entire sort of north star for their AI strategy was not to get rid of people within their existing teams, but to reduce the headcount per new client that they got. So, they wanted to make sure they're pivoting the entire organization. They get a new client, which is worth, it's a $2 million account. What they don't want to do is go hire 4 people now to service the account, which is what they traditionally did. They're like, hey, we want to make sure We can add one more person and then some AI or 2 people and AI, and that 2 people plus AI is more cost effective than 4 people and faster and maybe improved quality and insights in some instances. So, making sure that that underlying sort of your north star becomes that underlying decision point. And I, we physically do this exercise in workshops, but just for your listeners, imagine if every use case was a Post-it note.

Mm-hmm.

And in a half-day workshop, it is not uncommon for the wall to have like 50 pull-off Post-it notes.

Right.

And it's funny because the moment you add, you work with the leadership and you add a strategic north star and saying, hey, our focus is going to be on retention because our churn is really high, you'll see magically all the non-retention Post-it notes sort of wither away. I'm not saying that they're not important, but the relative importance importance, and those become sort of secondary. So to me, when we say strategic north star for AI, it is really making sure that you are answering the question of why and why now and what are you focusing on? And a great example was we were working with a company where their CMO and marketing leadership wanted to redo their digital asset management system. It was there for A decade. And, you know, if there's a dam for a decade, it needs some cleaning, right?

Right.

They had like millions of assets and they wanted AI. They were like, oh, perfect use case. Can AI just scan all these things and recategorize and blah, blah, blah, based on our new go-to-market? Great, great use case. Definitely AI can do that. You know, that's one. The second thing is they're entering a brand new market. They're entering new markets in Europe. And they want marketing to support them in, in, you know, that go-to-market, uh, play. What do you think got the most value? Again, the, the digital asset management was not a bad use case, but in the relative importance of that north-starving business growth, you know, all the effort, uh, where AI could play a part in was for the new market play, the new market go-to-market play. So that's an example of where your North Star can guide everything. It prevents random tool adoption, disconnected pilots, you know, kind of displaced investments. Everything that you do is aligned to something bigger.

Yeah, that makes sense. So another thing that you talked about, and maybe this feeds into the pri— like, it feels like what you're describing there helps with prioritization, which totally makes sense. One of the things you said, I think when we last talked, is that, you know, getting like productivity improvements are sort of table stakes in adoption of AI, but there should be like, there's like, but that's like the table stakes. So what should be people thinking about beyond that? I mean, you've kind of touched on it, I think, but, and maybe that, like, it feels like that idea you just talked about with the dam. Is because it falls in that category, like that would be a productivity benefit, at least short-term, right? Maybe there's more too, but the other one felt like it was more of a high value aligned with business goals. Like, is that what you were talking about there?

Like— Yeah, I think that when it comes to AI use case, productivity is like, when I say table stakes, it's pretty much kind of embedded. That's why AI is so popular, you know, already. So you, you'll see people, regardless whether it's generative AI or analytics or creative, whatever use case, it is essentially helping you do the same thing less time or helping you do the same thing in more and better quality or whatever in less time than what you would, you know, traditionally do.

Mm-hmm.

So when I say, when I said, you know, productivity is sort of table stakes, that's what I meant. But I think teams should really think beyond that, right? Like the value addition discussion that we had. And to me, there's sort of 3 pillars. Productivity sort of is the underlying pillar or sort of a horizontal layer. And then on top of that, think of it like as 3 pillars. And to me, that is speed, precision, and data-driven insights. And those 3 things, again, sound simple, but those are things that we have chased in the past and with moderate success at best, right? Like, and I think the best example that exemplifies like all of these 3 pillars would be personalization, right? Like we've all, it's been the holy grail for marketers.

Sure.

You know, we wanna personalize things and, you know, pre-AI has not been easy. Like it's time consuming and, you know, we—

It's not truly personalized. It's like you're in, Maybe it's a, you know, part of a group that's bigger or smaller maybe, but it's not truly personalized. Correct.

So it's been like we come up with segments and then we personalize messaging to the segments and then we do the outreach and hopefully those segments line up with the messaging and the message resonates. That's been sort of the traditional way of doing things. But now with AI, you can have a segment of one. You can really do things and have a segment of one and really go personalize things and do it fast. So to me, if, if your AI use case is able to, beyond productivity, is able to achieve all 3 elements, all 3 pillars, speed, precision, and data-driven insights, amazing. But even if it is able to achieve 1 or 2, to me, that's a win.

Got it. So, so one of the things that you, I don't know, at least in my own life, and I think maybe, I think you've echoed this as well, is that like, for me, it took me a little while, but like on a personal level, like I, like I, unless I know that something's going to be a very binary, like this is the answer, like I almost never use a search engine anymore. Like I, if it's anything that even has the hint of being complicated or is going to require, I'll call it in quotes, research, right?

Yeah.

I tend to use an LLM for that now. But it's pretty much a normal thing for me on my personal level. But even at work, even at the place where they're really pushing for adoption, I still, like, I'm not personally doing it and I don't see a huge amount of adoption that's obvious other than sort of a small set of people. Like, what do you think is the challenge there?

You mean personal adoption versus organizational adoption?

Yeah, yeah, yeah, yeah.

I think the, to me, when you're in, when you're sort of playing with AI and experimenting with AI or using AI individually, it's just liberating. There's freedom. You can do whatever you want. You can play with it and sort of get whatever results you can. There's no barriers. There's no box you need to fit into. There's no governance. There's no compliance, none of that. But the moment you sort of extend that out, to a larger organization, there needs to be a level of orchestration because everybody's not doing things the exact same way. So you want to make sure there's 4 copywriters using AI to write content. Well, you can't have the content sound 4 different ways. You want to make sure that your brand voice and tone and messaging and everything is all in alignment. So now you want to put in a way where regardless of whatever the prompting Happens with those 4 copywriters, the output is sort of consistent in some degree. Okay, then you have to have your governance, your dos and don'ts, you know, make sure our tone of voice is not, you know, bombastic, it's humble and fact-based or whatever. I'm just making it up. But, you know, now you gotta kind of come up with those sort of rules and make sure you're doing it. And then, you know, you start talking to compliance teams and You know, all those sort of things where in IT organizations and legal organizations, there are things that they are very careful about what kind of input is going into your AI, right? So I think the challenges become very different. It is just basically more strings attached to the way you use it. And the success of that is predicated not on just individual performance. It's all sort of, you know, different performance or orchestrated performance, if you will, like team performance. And that is to me sort of, you know, critical.

Yeah, that makes sense. Yeah. Yeah. I mean, I think even I'm thinking about this, like even personally, like where I'm, I think I talked to you before we started recording, right? That I'm doing some stuff in a with my wife, right? And just even adding a second person into the mix makes it complicated, let alone broader organization. So that definitely makes sense. When we talked before, you mentioned another phrase that I want you— you said that there's a— you think about something called the human-AI sandwich.

So—

What is that? I mean, I see you smiling. So like, what is that? And How should we be thinking about that?

Yeah, absolutely. So shout out to one of the members of Marketing AI Pulse community who we use this term. So not my term, just acknowledging it, but I loved it because to me, I feel that AI for the foreseeable future is going to be the human-AI sandwich where really You have humans at the start and the end of the process, and you have the AI in the middle, right? Humans are triggering and defining the problem and sort of articulating what they want AI to do. AI does the heavy lifting, whether it's the research generation, the analysis, what have you. And then you have the human in the loop on the end of it, kind of looking at the output. And I feel that that Sort of keeps the— that's the best way to sort of leverage what AI can do for you today. It is keeping the output intelligent and emotionally resonant, if you will, based on the human values of the human that is driving and seeking the output. Very often I refer to the term as AI slop in the beginning when you and me were talking. And to me, I think when I see AI slop is where the quote unquote, the 2 slices of bread on either end fail, right? Where the input given is very generic and there's no human review at the end to guide it. So you pretty much taking AI output and leveraging it. And that to me doesn't give you sort of the best output. But I feel the human-AI sandwich is That premise is going to change in the agentic future that stares us all in the face, right? So with agents, agents are going to be designed with what today human beings are the trigger in terms of what we want AI to do. And that is changing fast. I think humans will still have a role to play in it, but that, you know, how that sort of translates into the human-AI sandwich remains to be seen. But I certainly feel that is critical recognition of what I said earlier, that humans are definitely the protagonist of the story.

Yeah, it's interesting because I remember talking to someone, it may have been on one of our episodes where someone, we kind of got into a discussion of if you were to onboard a virtual employee that's AI-based, right? Why would you, how would you handle that in terms of giving it more autonomy? And my take was, I was like, I don't think I would treat it much differently than, you know, a new employee, especially one who doesn't have a lot of experience because I would probably early on spend more time with that employee, give it guidance, support, handle specific situations, and slowly give it more and more autonomy to make decisions independently. And I think I would do the same thing with With an agent, agentic kind of employee as well. Because I think, you know, part of it is just like, oh, trust is not really the thing, but like, I need to trust it to make reasonable decisions. And I want to give it guidance on, you know, if you feel how confident, you know, have a good model for how confident it is about a decision or direction it's going to go. And if it doesn't meet a certain threshold, right? And over time that threshold might get, may get lower and lower and lower just because it becomes better at doing that. So I don't know, the nervous system. Yeah, I think that's where I'm kind of seeing it. It's interesting because like every time I say like, ah, for the foreseeable future, use that term, I'm like, I don't know how far the foreseeable future is anymore. Like it's just, it's just moving so fast.

It is true. And I mean, an interesting part is I've said, I'm seeing this already. And it's sort of a double-edged sword because the more, you know, LLMs themselves or AI itself is moving at a galloping pace, it's getting better, more parameters and that sort of thing. But in addition to that, the more information and context that it has, the output keeps improving, right?

Yeah.

And to a point where now people do have a little bit of trust in it, especially if you're using an app or tool or whatever, where you've trained it on your brand voice and your style and All of those things and facts and competitors and keywords and whatnot. And you suddenly start getting content, which is more in alignment with what your brand would have. But despite that, I feel that there is, there's more than anything else, sort of a word of caution that we get very complacent. Humans get very complacent where you trust it 3 times and you're like, great, the 4th time.

Yeah.

The, the human in the loop portion can sort of, the risk is it withers away. And I would caution people against that, where I would want that, you know, despite, great, you may have gotten amazing outputs the first 10 times, that the 11th time still needs a human in the loop. So every single time, human in the loop. But, you know, I'm sure in the future that may change, but that level of trust, don't let your guard down after just a few times. You know, still remain in the loop. It's, you know, especially if you have, if you're leveraging AI to represent, you know, your word and your ideas.

Yeah. It's funny, the analogy I just, just popped in my head, but you said is like, I'm in the middle of teaching one of my children to drive and, you know, the reminder, you're going every once in a while when you forget to look over your shoulder before you change lanes, right? You find out that somebody's there and It's that reminder that, oh yeah, I need to continue to do that, right?

100%. Love that. Love that.

Yeah. So that's interesting. So, okay, so let's maybe, it feels like you've been a little bit of a downer here, but I still think there's positive stuff here. But like, okay, so these were all the challenges and we've talked about some ideas on how to overcome that. Like, how do you think about Or how do you guide organizations to build guardrails, you know, that are responsible, but still enable teams to move quickly and adopt over time? Like, what do you, what's your best recommendation on how to approach that?

Yeah, I think we have a very simple framework, and it is input, output, transparency, right? So, people get lost in the Acronyms and the laws and the compliance. And anybody who's been through GDPR knows all about this, right? It's a slippery slope. But if you were to really abstract it and you think about it in terms of input, output, and transparency, I think that would serve as sort of a framework for how to approach responsible use. So when we say input, establish rules for what kind of data goes Into AI. Some of that would come, if you're a marketing organization, some of that should come from your overarching company-level IT folks, compliance people on what you are doing. So PII data, don't download a CSV of your Salesforce and upload it into ChatGPT and ask it to do analysis. Don't do that, right? That sort of stuff. So establish rules for input. Establish obligations for output. So whatever comes out of chat or AI, not ChatGPT alone, but whatever, any kind of AI, make sure that there is, you look for compliance, look for brand voice, look for factual proficiency or factual data, factual efficacy and those sort of things. So establish rules for what is the output. And from a transparency standpoint, This is part governance, but part culture, organizational culture where you establish, work as a team. If you have an AI council or AI working team, I've seen different names within organizations like a working committee or whatever, but whatever that is, establish rules for transparency of what point and how are we going to inform people that AI has been used to leverage this? So for example, I see that, you know, I'm going to give you a very simple example of video, right? Like where in the case of video, one of our clients, their CEO is completely okay with an AI avatar and like, great, the avatar comes in, he's okay with it as long as he's approved the content, it's his voice, he doesn't want to go through the logistics of recording and all that kind of stuff. So great. So he comes on on the video. But at the bottom of the video, it says, hey, this is an AI-generated avatar of the CEO, but approved in alignment with approved messaging and da da da da, whatever, you know, some sort of term like that. So that's one way of your disclosure. The other way of disclosure, same video, you watch the video, at the end of the video, it says video was made in alignment with our AI policy, go to website.com/policy. Same way, but the way you— the same video, but the way you've disclosed it is 2 separate ways. And that is a business decision that teams have to make. So, so the input-output transparency, obviously I'm super simplifying it for the sake of this podcast, but that serves as a very broad framework for every organization to think about it and then make sure that you are formalizing it and implementing it. But especially within marketing teams and marketing ops folks are, have a great role to play in this. They should really make sure that there is a body within the marketing organization and then some marketing—

Body meaning like a small team or body meaning a person? Okay.

It depends on the size of the team. So, you know, we have a marketing council where it's the CEO and us. So that's the marketing, that's the AI council in that case, that's the full organization. But in some of the bigger organizations, there is a marketing council where there is key members of the functional and geographical, like regional leadership form that council. Any decision-making that needs to be made goes through that organization. So make sure that there is some structure in that as you are deciding rules for input, output, and transparency. That you can go to that body and sort of have those open-ended discussions and debate and come out with something and formally publish something and spend time, again, educating your organization as to what the, you know, what that outcome is, what the policy is. And another thing I'm seeing is that those lines are moving fast, right? So, it's not, again, one and done. I would encourage teams to look at their governance every 6 months. So, I mean, when they, when all of these AI note-takers started, you know, everybody was like, whoa, apprehensive about allowing a note-taker in a meeting or whatever, or announcing that, hey, I have a note-taker. Is it okay for me to record it? Well, you know, water under the bridge. A year later, there's like, you know, 4 people and 8 note-takers and nobody bats an eyelid, right?

Right.

So, so to me, that line's moving fast. So from a governance standpoint, make sure that Whatever, you know, input-output transparency guidelines you're putting into place is reviewed on some sort of a regular cadence, and I wouldn't go beyond 6 months at this point.

Yeah. I'm wondering, you touched on marketing ops' role in this. Is there— so let me finish up here. Is there anything else, like, given our core audience of marketing ops folks, right, that you like, how, what role should they be playing in this? Should they be proactively pushing it? Should they be following along? What's your suggestion on what they should do at this point?

I think, to me, marketing ops, the role for marketing ops professionals is critical. Like, to me, I really see that they are the connective tissue between the strategy and the execution. They understand processes and data and governance and all these elements which are critical for AI to scale. And so, and at the end of the day, you need marketing leaders, CMOs or CEOs need ops people, need, they need a Sherpa to sort of guide them along, right? In this journey. And to me, I think ops people, just because of the nature of what they do, they understand the processes and roles and responsibilities, org and technologies and workflows and all those sort of things, are really well positioned to play that role of being that kind of quote unquote AI sherpa for an organization or a team. So to me, I think it's sort of critical in a lot of organizations where, and we work with organizations where there are AIOps leaders and we sort of help them enable them to do that for their organization, then that works really well. And in organizations where AIOps or ops doesn't exist as a function, marketing ops doesn't exist as a function, there is a, there's a little bit of a struggle as to who should own this.

Yeah.

And while there is a perspective that really everybody should be doing AI, which I completely agree with, because in 5 years it's gonna be like the internet where no one's gonna be talking about AI, because if you don't do AI, that is newsworthy. But at this point, there is orgs without marketing ops people suffer from that ownership thing and You know, that's where we work with a lot of CMOs and help them and play that role, if you will. Obviously in the context of AI where there is no ops people, but I think it's the perfect kind of role in that connective tissue for strategy and execution.

Awesome. That makes sense. Well, Abhi, I'm quite certain we could have gone on for another, whatever it has been, 40 minutes, 50 minutes. But I appreciate it. Thank you so much for joining. If folks want to continue the conversation with you or learn more about what you're doing, what's the best way for them to do that?

Yeah, I think obviously would love for people to hit me up on LinkedIn, but also you can go to sparknovus.com. That's S-P-A-R-K-N-O-V-U-S.com, sparknovus.com. And on the top navigation, you should see community where you can learn more about The Marketing Apples community that we host. It is, we definitely have events here locally in Atlanta, but it's not only a local community. We do a lot of online events and we're doing different events. We did one in Charleston. We have one planned in Miami and Chicago. So, there's more coming next year. And then, yeah, would love to have folks tune into my podcast.

Awesome.

Marketing as Sparkcast. Again, if you go to sparknovus.com, you should see podcast in the top navigation.

Perfect. Well, again, thank you, Abby. It's been a fun conversation. I've got some good ideas myself, so I'm sure it's going to help our audience. As always, thank you to our audience for continuing to support us and listen and now view. If you have ideas for guests or topics or want to be a guest, you can always reach out to Naomi, Mike, or me. We'd be happy to get that started. Till next time, bye everybody.