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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#807: AI21 Labs CMO Sharon Argov on navigating promise vs. reality with AI adoption
00:23:47
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The agile brand.
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Welcome to Season eight of the Agile Brand Podcast. This season we're going all in on Expert Mode, MarTech, AI and Customer Experience, talking with the people and platforms behind.
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The brands you know and love.
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I'm Greg Kilstrom, your host and I help Fortune 1000 companies make sense of martech, AI and marketing ops. Hit subscribe or Follow to make sure you always get the latest episodes and leave us a rating so others can.
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Find us as well.
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And make sure you check out our sponsor, Tech Systems, an industry leader in full stack technology services, talent services and real world applications. For more information, go to teksystems.com now let's dive in.
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With every board member and CEO demanding a generative AI strategy yesterday, how much of the conversation is about creating real business value versus simply not being left behind? Agility requires more than just speed. It demands a fundamental shift in how we approach problem solving and storytelling, especially when a technology like AI rewrites the rulebook. Today we're going to talk about the real tension that exists between the incredible promise of generative AI and the practical, often messy reality of enterprise adoption. We're going to explore how to bridge the gap between deeply technical products and the clear, compelling narratives that actually convince customers and boards to invest. To help me discuss this topic, I'd like to welcome Sharon argov, CMO at AI21 Labs. Sharon, welcome to the show.
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Thank you Greg. Happy to be here.
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Yeah, looking forward to talking about this. Definitely a timely topic. Top of just about everybody's minds. Before we dive in though, why don't you give a little background on yourself and your role at AI21 Labs?
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Sure.
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So I've been in marketing for 20 years. In the last decade, leading marketing team in growth, deep tech companies, global companies. I've running basically every B2B aspect of those fast growing companies in Cyber Security, in HR Tech and now in AI. At AI21 Labs, I build the B2C, B2B and B2D business to developers motion. We're one of the very few labs companies that are developing LLM on scale and I'm responsible for brand product marketing and the go to market aspect. I think Greg, that you just said it very well. There is a gap between the perception of AI and and actually the adoption especially in the enterprise. And there's also a huge barrier between the technical aspect and the business aspect. And we have this unique mission of translating this super technical product into business discussions.
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Yeah, yeah. So let's, let's dive in and I want to start with that point of that gap that really needs to be not only understood, but ideally crossed as soon as possible. So starting from the strategic standpoint, AI21 Labs focuses on those barriers, things that anyone working in an enterprise understands, like AI adoption, things like reliability hallucinations. So from a strategic marketing perspective, how do you turn a conversation about a product's limitations into a compelling story about its strength and trustworthiness for that C suite audience?
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It's very interesting because I think B2B tech companies, there's always a deep technological aspect of the product that we are selling. I think with AI it's a bit different because one, the promise is so huge. You know, everybody talks about it as one of the huge revolution and you know, even compared to the industrial revolution. And we do see it impact every aspect, aspect of our life. And there is this huge FOMO in organizations in management, but nobody really understand the bits and bytes and the impact and also the risk of this ginormous technology. I think that we're doing two things that really helping us to get to the discussion at the right level. First of all, we have this superiority technology capabilities that we are bringing to the table. And second, we talk about not necessarily about the outcome of this technology, but more about the capabilities of the organization to gain what they put as the main targets. And I think that having the moving the discussions from what this technology cannot do and where is the risk and what are the limitation to what you actually can do and acknowledge the fact that AI makes mistakes, it will continue to make mistake. We talk about it very openly and we're trying to estimate the cost of the mistake and what is the cost of error that the organization could carry and where exactly they can implement it and what they need to do in order to acknowledge the fact that there are limitations. It's a new technology, but there's also capabilities. There's also a whole world of what they could do with this new technology. And it's two different discussion. It's a discussion with the technical people that really need to understand that they're seeing something different and a discussion with the business people, with the leadership of the organization about the options and the growth and risk elements of the technology.
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Yeah, yeah, there's also. You touched on this a little bit. But there's also just a fundamental mindset shift because you know, traditional SaaS, the features are fairly even. Even if the features of a product are broad in nature, they're still fairly deterministic. And what we're talking about here and what you touched on a little bit is, you know, AI is pretty probabilistic and you know, and again, prone, prone to things like hallucinations or, or potentially errors even. But you know, what, what's the, what's the mindset shift that's required to really make that, make that leap?
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Yeah, I think in SAS you often sell some level of certainty, right?
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Yeah.
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If you work with Salesforce, you will gain better outcome from your customers. If you work with Riverside, you will get a really good impact on your videos and webinars. I think with AI it's a little bit different. What we're selling is confidence and the ability to control under certain level of uncertainty. We talk to our customers, we are very aware of the limitation and the risk of this technology and we're building a whole infrastructure in order to tame that technology. So I think it is a change in the mindset. Right. I can only promise you what we will do together with this technology versus those very shiny taglines on a SaaS production.
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And so to get maybe a little more tactical here, talking about, you know, the balancing, balancing the hype as well as rethinking something like brand identity. So you know, you, you've mentioned the need to rethink branding and, and brand in the age of generative AI. So you know, you know, what does that look like in practice? Is it surface level? Is it a deeper shift to how a brand communicates? What does that look like.
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To be honest, I think that from branding perspective, it's pretty much similar to any kind of branding project. Branding is not about technology branding, it's about people. It's about emotion, it's about perception, about dreams, feelings. And in that aspect, we haven't changed a lot, you know, in the last probably hundreds of years. So I think. Well, you know, my answer on the promise, on the value was that AI is very different. I think that from branding perspective, we use the same methodologies. I can tell you that we just launched a week ago a campaign under the tagline Build boring Agents. And the premise behind this campaign, the pitch behind this campaign is that, you know, while everybody talks about AI as something that is very humoristic and unpredictable and enabling and, you know, take you to the edge, right? Actually, when you think about enterprise, they want to have something that is very solid, responsible, controllable, predictable, maybe even boring. So we came up with this very funny brand campaign that took the most boring tasks and turned them into the most boring agents. And the purpose was to create an awareness within our target audience. And we did and we gave them name and we kind of like design them as people from the 17, from the 70s, very conservative and traditional and you know, with those very boring clothes and outfits, et cetera. But you know, they, they are the people that want invent facts. We, you know, one of the tagline, for example, was dull in chat. Never invent facts. You know, those will be the type of technologies that you can really rely on. And I think having something that is different than our competitor was what we aiming for. So bottom line, I. Branding is something much more deeper than technology. And this is the area that I still, that we can still use the same tools that we've used before.
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Yeah, yeah, well, and to your point, agentic AI. AI agents. You know, certainly that, that term is. Feel like it's, it's the current buzzword. You know, everyone was talking gen AI. Now it's, it's kind of evolved to, to agentic. So, you know, but to your other point, what companies that adopt agentic quickly realize is that yes, you know, it's, it, it can be great, but there's also. So it needs guardrails and things. So, you know, I think, I think to your, to your campaign, I think that's, that, that's great because it, it does alleviate one of those. One of the biggest risks is, you know, the, the rogue agent, right?
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Yeah, yeah, yeah, exactly. And I think that, you know, there's always, there's already a Few use cases where agent made mistake. That might be funny with in a B2C or might be funny when, when you're an independent, but actually can be very risky if you're an enterprise. I, I've heard about a flight agency that one of the agents just decided to give a refund for a person called Alex, but then he gave a refund for every Alex in the database.
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Oh, wow.
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So, you know, those kind of things really need a different approach when it comes to enterprise. And part of what we see that is actually really working for us is not only create those differentiative approach, but also take the opportunity of educate the audience, educate our customers and our prospect. You've talked about agent and even the definition of what is an agent is something very confusing. And everybody talks about agent, but everyone referred to completely different things. And we feel that because we have this deep tech capabilities and very unique talent, the people that literally building the LLM, we could also take it in front of our customers and use it to think with them, to train them, to teach them, to give them the full understanding of this technology. And part of the marketing team ongoing thing is to release what we called labs in the front. We are writing very detailed technical articles from the lab, things that historically our people wouldn't be that happy to expose. But we understand that this is part of our unique value and we are sharing tricks and methods of model training problems that we saw and how do we got over that? The technical people writing that of course with the marketing distribution and that's created a lot of impact on the ecosystem, but also on our customers.
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Yeah, yeah. So from a measurement perspective, certainly, you know, again to go Back to the SaaS analogy, you know, with a, with a product that has, I would say, clear boundaries of what it does, what it doesn't do, it's a little bit easier to tell the narrative, okay, if we invest this much in this function, we're going to get this much out. Potentially with agentic it's a little different. And you know, I know we've talked about some of the reasons why. What are some of the KPIs that you look at to measure success or even to tell the story of how to get investment in something like an agentic approach.
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Yeah. So like every emerging technology, I think it's still very early, although we do see and we hear from customers that they want to start looking into ROI on the AI investments, especially when it moves from experiment to production and the costs are raising. I think it have two elements. One is kind of like what you've just said at the beginning, this fomo, every CEO and every board are saying, get me this AI. I want to have a budget of AI, I want to have an eye in my core systems and I want to see people using AI tools. So not having that in your plans is problematic. So this is number one. The second thing, I think it comes out to maybe three elements that we identify as the element that each organization should look into. One is scale and growth. What are the things that you could do with AI that will help you grow, that will improve your growth planning and will help you scale in different area of the organization, do more with less, do it faster, get to bigger reach, et cetera, et cetera. The second thing is around decisions. What are the AI capabilities and what are the AI insights that you will get and help you get to better decisions based on faster analysis of your data, larger testing areas, different views that AI could supply and will help organization get to better and more accurate and relevant decisions. And the third one is risk avoidance. What are the risk areas that you could look into very carefully and, and help you as an organization plan better, act better and reduce risk in the different area? Those. It's a very general perception that obviously each organization will need to customize, but those will be the three areas that we believe that AI will create. The main ROI and the main impact that will be measurable in the next few years.
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Yeah. So then looking ahead a bit as well, drawing on your experience as a CMO as well as the work that AI21 Labs is doing, looking ahead a year or two, what's one capability or skill that marketing leaders need to develop within their teams to successfully navigate all this change and the change to come?
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I love this question. So thanks for asking. I think, and you know, referring to your, to your program, I think it's, it's agility, it's being flexible, agile. It had to do with being curious about the future and about the, about yourself and about the environment. But I think it's being agile. I think, you know, we are in a situation where it's, it's very hard to predict what would organization would look like. Not only marketing departments and not only high tech companies, but you know, what will work look like, what will a role look like, what would a manager look like? And in order to get into this phase with confidence and curiosity, you need to be, you need to have an agile attitude. And I think it's, you know, it's probably, it's really the right answer for every department and for every role.
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Yeah. Yeah. What do you see coming? You know, certainly we, certainly we touched on the issues of things like hallucination and reliability. Do you see another friction point coming as more and more organizations are adopting, in this case agentic and integrating this, what do you see as a potential point of friction that companies are going to start running into?
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Yeah, I think there's, there's a, there's a lot of questions that are related to trust.
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Yeah.
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For example, who is responsible for a mistake that the AI is doing? How do you mark your policy on how do you enforce policy of organization on AI? How do you tain and train your tool to be exactly what you need them to be and how do you trust them? At the end of the day, you know, even with the current technology development, you will trust your AI to do, to answer questions, maybe to write, you know, your homework. But would you trust them to read a contract? Will you trust them to invest for you in the stock exchange? So I think the level of trust and the ability to decide on the responsibility of the AI, those will be the things that we will need to understand in the next future.
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Yeah. Well, Sharon, thanks so much for joining today. Got two questions for you as we wrap up here. The first one, if we were having this interview one year from today, what is something that we would definitely be talking about?
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I think we're still going to speak about the low adoption at the enterprise. It's going to take time and to be honest, I think we will still be surprised that it does take time.
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Yeah. Yeah.
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Last question for you. What do you do to stay agile in your role and how do you find a way to do it consistently?
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So to stay agile? I constantly question my assumption. I try to remind myself that I don't know everything, that there's still a lot for me to learn. I try to keep an open and a growth mindset and keep myself busy and open to new things.
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Yeah, I love that. Well, again I'd like to thank Sharon Argov, CMO at AI21 Labs for joining the show. You can learn more about Sharon and AI21 Labs by following the links in the show notes.
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This episode is brought to you by Tech Systems. They're leaders in full stack tech services, talent solutions and helping companies it all in action. You can learn more@teksystems.com and thanks again for listening to the Agile Brand podcast. If you like the episode, hit subscribe and drop a rating so others can find the show too. And if you're interested in consulting, advisory work, or if you need a speaker for your next event, feel free to reach out. Just visit GregKilstrom.com that's G R E G K I A H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
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The Agile Brand.