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 re-writes 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’ll 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.
About Sharon Argov
Sharon Argov is Chief Marketing Officer at AI21 Labs. Argov has vast experience in building B2B marketing teams. Before joining AI21, she served as CMO of cybersecurity unicorn CYE, as VP of Growth at Hibob, Director of Marketing at 888 Holdings, and founded her own marketing boutique agency and is an expert in hyper growth VC backed companies. She will direct AI21’s brand and marketing functions, focusing on data-driven decision-making to grow the company’s presence among the dev community and the enterprise market.
Sharon Argov on LinkedIn: https://www.linkedin.com/in/sharonargov/
Resources
AI21 Labs : https://www.ai21.com/
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Transcript
Greg Kihlstrom: 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. Tell me to discuss this topic, I’d like to welcome Sharon Argov, CMO at AI 21 Labs. Sharon, welcome to the show.
Sharon Argov: Thank you, Greg. Happy to be here.
Greg Kihlstrom: Yeah, looking forward to talking about this definitely a timely topic. Top of 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 AI 21 Labs?
Sharon Argov: Sure. 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 cybersecurity, in HR tech, and now in AI. At AI 21 Labs, I build the B2C, B2B, and B2D business to developers motion. We 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 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 a business discussions.
Greg Kihlstrom: Yeah, yeah. So, let’s let’s dive in and I want to start with that point of that that gap that that really needs to be not only understood, but ideally crossed as as soon as as soon as possible. So, start starting from the the strategic standpoint, AI 21 Labs focuses on the those barriers, uh, things that anyone working in enterprise understands like, you know, 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?
Sharon Argov: Mhm. It’s very interesting because uh, I think B2B tech companies, there’s always a deep technological aspects 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, um, and, you know, even compared to the industrial revolution. And we do see it impact every aspect of our life and there is this huge FOMO uh, in organizations, in management, but nobody really understand the bits and bites and the impact and also the risk of this enormous technology.
[04:24] I think that um, we’re doing two things that um, um, really helping us to get to the discussion at the right level. First of all, we are we have this superiority technology capabilities that we are bringing to the table. And second, um, 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 target. And I think that having the moving the discussions from what this technology cannot do and where is the risk and what are the limitations to what you actually can do and acknowledge the fact that AI makes mistakes, it will continue to make mistakes. We talk about it very openly and we’re trying to estimate the cost of the the mistake and what is the cost what is the the cost of error that the organization could carry, um, and where exactly they can implement it and and what, you know, what they need to do in order to acknowledge the fact that there are limitation, 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 discussions, you know, 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 or with the leadership of our organization about the the options and the growth and the risk elements of the technology.
Greg Kihlstrom: Yeah, yeah. There’s also you you touched on this a little bit, but there there’s also just a a fundamental mindset shift because, you know, a traditional SaaS the features are fairly even even if the features of a product are are broad in in nature, they’re still fairly deterministic. And and what we’re talking about here and you touched on a little bit is, you know, AI is 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?
Sharon Argov: Mhm. Yeah, I think in SaaS you often sell some level of certainty, right? Uh, if you work with Salesforce, you will gain, uh, better, uh, outcome from your customers. If you work with, uh, you know, Riverside, you will get a really good impact on your videos, uh, and and and webinars. I think with with AI, it’s a little bit different. So what we’re selling is confidence and the ability to control under certain level of uncertainty. We 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 to tame that technology. So I think it’s a it’s it is a change in the mindset, right? I can only promise you what what we will do together with this technology, versus, uh, there’s very shiny taglines on the on a SaaS product.
Greg Kihlstrom: And so, to get maybe a little more tactical here, talking about, uh, 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, what does that look like in in practice? Is it surface level? Is it a deeper shift to how a brand communicates? What what does that look like?
Sharon Argov: To be honest, I think that from branding perspective, it’s um, it’s pretty much similar to any kind of branding project, you know, branding is not about, uh, technology. Branding it’s about people. It’s about emotion, it’s about perception, about dreams, feelings. And in that aspect, uh, we haven’t changed a lot, you know, in the last probably hundreds of years. So, I think, um, while, you know, my my, uh, my answer on on the on the promise on the value was that AI is very different. I think that from from branding perspective, we use the same methodologies. I can tell you that we just launched a week ago, a campaign under the the tagline, Build Boring Agents. And the promise behind this campaign, the pitch behind this campaign is that, you know, while everybody talks about AI as something that is very um, humoristic and uh, unpredictable and uh, enabling and, you know, take you to the edge,
Greg Kihlstrom: Right.
Sharon Argov: Actually when 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, uh, within our target audience. And we did and we, you know, we gave them name and we kind of like designed them as as people from the 17, from the 70s, very um, conservative and traditional and uh, you know, with those very boring clothes and outfit, et cetera. But, you know, they they are the people that want invent facts. We, you know, one of the tagline for example was, uh, dull in chat, never invent facts.
[10:11] You know, those will be the type of technologies that you can really rely on. And I think having something, uh, that is different than than the um, than our competitor was what we’re aiming for. So, bottom line, I think that branding is is something much more deeper than, uh, technology and, uh, this is the area that I still did we can still use the same tools that we’ve used before.
Greg Kihlstrom: Yeah, yeah. Well, and and to your point, agentic AI, AI agents, you know, certainly that that term is I feel like it’s it’s the current buzzword. Um, 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’s it it can be great, but there’s also so it needs guardrails and and things. So, you know, I think I think to your to your campaign, I I think that’s uh, 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?
Sharon Argov: Yeah. Yeah, exactly. And I think that, you know, there’s always there’s already, um, a few use cases where, um, agent made mistake that, uh, might be funny with, uh, in a B2C or it might be funny when, uh, when you’re an an independent, but actually it can be very risky, uh, 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.
Greg Kihlstrom: Oh, wow.
Sharon Argov: So, you know, those kind of things really need different approach when it comes to to enterprise. And part of what we see that is, um, actually really working for us is not only create those, um, you know, differentiative approach, but also take the opportunity of educate the audience, educate our customers and our prospects. You’ve talked about agent and, you know, even the definition of what is an agent is, uh, something very confusing and, you know, everybody talks about agent, but everyone refer to completely different things. And
Greg Kihlstrom: Right.
Sharon Argov: And we feel that because we have this, uh, you know, deep tech capabilities and, uh, very unique talent, you know, the people that literally building the the LLM, we could also take it in front of our customers and, uh, use it to think with them, to to train them, to teach them, to give them the full understanding of this technology and, uh, and part of, uh, part of the marketing team, uh, 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 but we understand that this is part of our unique value. And we are sharing, you know, tricks and and methods of, uh, model training, problems that we saw and how do we got over that. The technical people writing that with, of course, with the marketing distribution and that’s create a lot of impact on the ecosystem, but also on our customers.
Greg Kihlstrom: Yeah, yeah. So, from a measurement perspective, certainly, you know, again, to go back to the 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 or even to tell the story of of how to get investment in in something like like an agentic approach?
Sharon Argov: Yeah, so, uh, like every emergent, uh, technology, I think it’s still, it’s still very early, although we do see, uh, and we hear from customers that they want to start looking into ROI, uh, on the AI investments, especially when it moves from experiment to production and the costs are raising. I think it have two, uh, two elements. One is kind of like what you’ve just said at the beginning, you know, 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 AI in my, uh, core systems and I want to I want to see people using AI tools. So, not having that in your, uh, plans is, uh, is is problematic. So, this is number one. The second thing, I think it, uh, it comes out to maybe three elements that we identify as the 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 of the organization, do more with less, do it faster, uh, get to, uh, bigger, uh, reach, et cetera, et cetera.
[15:18] 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, uh, based on faster analysis of your data, larger testing areas, uh, different views that AI could supply and will help organizations get to a better and more accurate and relevant decisions. And the third one is risk avoidness. What are the risk area that you could look into very carefully 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.
Greg Kihlstrom: Yeah, yeah. So, then looking ahead a bit as well, you know, drawing on your experience as a CMO as well as the the the work that AI 21 Labs is doing, you know, look looking ahead a year or two, what’s what’s one capability or skill that marketing leaders need to develop, uh, within their teams to successfully navigate all all this change and and the the change to come?
Sharon Argov: I love this question. Uh, so thanks for for asking. I think and, you know, referring to your, uh, to your program. I think it’s it’s agility. It’s being flexible, agile, it’s had to do with being curious about the the the future and about the about yourself and about the environment, but I think it’s being agile. I think it, you know, we are in a in a situation where it’s it’s very hard to predict what would organization would look like. Not only marketing departments and, uh, not only high-tech companies, but, uh, you know, what will be what will work look like? Uh, what will a role look like? What what will a manager look like? And in order to get into this phase with confident 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 probably the right answer for every department and for every role.
Greg Kihlstrom: Yeah, yeah. What what do you see coming, you know, certainly we certainly we touched on the issues of things like hallucination and and reliability. Do you see another friction point coming as more and more organizations are adopting in in this case agentic and and integrating this, you know, what what do you see as as a potential point of friction that that companies are going to start running into?
Sharon Argov: Yeah, I think there’s there’s there’s a lot of questions that are related to trust. For example, who’s responsible for a mistake that the AI is doing? How do you mark your policy on how do you enforce policy of our organization on AI? How do you train 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 um, to answer questions, maybe to write, uh, you know, your homework. Yeah. But would you trust them to, uh, read a contract? Will you trust them to invest for you in the stock exchange? So, I think the the level of trust and the ability to to decide on the responsibility of the AI, those will be the things that we will need to to understand in the next in the next future.
Greg Kihlstrom: Yeah, yeah. Well, Sharon, thanks so much for joining today. Got a two 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?
Sharon Argov: I think we’re still going to speak about the low adoption at the enterprise. It’s going to take time. Um, and to be honest, I think we will still be surprised that it does take time.
Greg Kihlstrom: Yeah, yeah. 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?
Sharon Argov: So, to stay agile, I constantly question my assumption. I try to remind myself that I don’t know everything, that there’s still, uh, a lot for me to learn. I try to to keep an open and a growth mindset and keep myself, uh, busy and open to new to new things.
