Ben Ard00:00 — Welcome back to another episode of Content Amplified. Today I'm joined by Nav. Nav, welcome to the show.
Navneet Singh00:05 — It's great to be here. Thanks, Ben.
Ben Ard00:06 — Yeah. Nav, I'm excited. This is going to be a really fun conversation, super relevant to today's market. You have such a cool, unique perspective on everything. I'm excited to dive in. But before we do that, let's get to know you. If you don't mind sharing with the audience who you are, what you do, all that kind of fun stuff. So it sets the stage for the conversation. That'd be great.
Navneet Singh00:23 — Awesome. Thanks for inviting me, Ben. I, as you said, do have a unique background. The first 15, 16 years of my career were actually in product, building products. And I got into that accidentally as well, because I was with Deloitte, and things didn't work out because the dot-com bubble burst. And then I was with a startup. I realized that the work that I was doing was actually product management, even though that's not what it was called. From there, somebody at Oracle hired me into a product role because I was basically doing the same without the title. And then I moved to Cisco, some of the other larger companies, and then found myself in a startup, 16 years into product management, having built lots of products, up to a billion dollars of product lines. I found myself in a startup wearing multiple hats. One of them was running the website and getting leads and qualifying those leads, which was very interesting but very different for me. I thought, this is interesting. After doing it for two, two and a half years, I was looking for another job. I got an opportunity at a very interesting company called Palo Alto Networks. It was a marketing opportunity. I'd never done marketing officially, but I said, I've done product, I've seen what there is to see. Let me just try it.
And it turns out that the next 10 years were spent just building the marketing function at Palo Alto Networks. Amazing, amazing run, as we grew from 2,000 people to almost 20,000 people in 10 years. The stock price also was 10x. So it was an amazing journey for me. And that's how I got into marketing. And now I am at a company called Eightfold AI. For the last six months, I have been the chief marketing officer here.
Ben Ard01:55 — I love it. Very cool. And quickly, what does Eightfold do real quick for the audience?
Navneet Singh01:59 — Our mission is the right career for everyone in the world. So, in a nutshell, we match the right people with the right career opportunities. A little bit about our history very briefly. Our CEO has been doing machine learning for many decades. He was at Google, one of the early pioneers of the search algorithm, and then was at IBM. And then he started a company for e-commerce, predicting what people will search for next, predicting what people will buy next. And then after doing that for a while, he said, one of the biggest decisions people make is their career. So why can't we use the prediction algorithm to match people with skills? Why can't we predict what skills they are going to acquire next? What career are they going to have next? What job change are they going to have next? And based on that, we can match people and their potential with the right job opportunities, rather than only matching based on what you have done in the past, matching based on who you can become. That was the genesis of Eightfold AI.
Ben Ard02:58 — Love it.
Navneet Singh03:00 — And we continue to do that, have done that over the last 10 years, have customers in more than 100 countries worldwide, and some of the largest names in the industry are our customers.
Ben Ard03:11 — I love it. That's amazing. Again, I'm excited to have you on the show. I think this is going to be a fun conversation. What we're going to focus on today is a subject that a lot of people really care about. It's AI and marketing and go to market. And what I love is though is you have this cool little spin on it to create outcomes that were not possible before. So a lot of people talk about the efficiency of AI. It allows you to be able to do your same work but faster. But you're talking about outcomes that weren't even possible before. What does that look like? Like what's the difference? What's the distinction? What are you seeing?
Navneet Singh03:45 — Yeah, when people talk about using AI to do the existing job, what comes to mind is that they're trying to build a faster photocopier. The function remains the same. You can photocopy faster, but you're basically doing the same thing again and again, right? And that's okay when new technology comes onto the scene. That's what people try to figure out, how to best use it. I think because we are an AI company, because of our DNA, been around for 10 years, people are very open to asking what can the technology do for us that was not possible before.
One example is personalization at scale. So if you do a campaign, let's say campaign execution, or you create campaign blueprints, can you personalize them at scale for ABM? Some of our largest customers are actually the largest companies in the world, and we do ABM. So the question is, can we do personalization at scale, something that was not possible in the past? Can we do data analysis, for example, of all the sales calls of closed-won opportunities in the last three years, and see based on data what signals early in the pipeline can be predictors of success in the future? That was not really possible unless you hired a lot of data analysts.
And then cross-functional expertise, right? So instead of doing a kickoff call with all the functions in marketing and saying, this is the campaign we're going to launch, this is how we're going to do it, come back with a plan of how your function can contribute, we built an agent which we call Mira. And that agent takes in the ICP, the goal of the campaign, what you want to achieve, the outcomes you want to achieve, the time frame, and all of that. And it spits out a blueprint. And that blueprint has a social component, a PR component, an AR component, the digital paid media component, and all that. And the best part is not that the campaign execution goes from 10 weeks to three days, although fast is one of the areas we look at. Acceleration, right? But the biggest unlock in my view is that you're not dependent on humans and their interpretation of what we are trying to do in the campaign. Or somebody was not able to attend the call, listen to the recording, somebody didn't ask questions, they were not fully clear, or they had their own interpretation of what we are going to do. The blueprint takes care of all of that and gives everyone a really integrated marketing plan. That term has been around for a while, but I think now we can really make it a reality. And then what we have is skills, let's say a social media skill or a PR skill and so on, which are skills in Claude, which we can plug in to actually create the drafts of the social media posts, drafts of the PR articles, and so on. So that becomes a really integrated marketing plan.
Ben Ard06:32 — I love it. And I think that's really cool. I love how you're talking about this cool technology that is unlocking a lot of these different opportunities, like you said, that weren't possible before. And yeah, like obviously taking something down to three days is incredible, but the real magic is where you can do things that weren't possible before. So I want to double-click on this Mira concept. Like, what does a campaign agent actually do? What examples of outcomes can you share with it? What is it doing, really, that a traditional team really couldn't do in the past?
Navneet Singh07:04 — Yeah. Mira is short for our marketing intelligence and response architect. It takes a single campaign brief. So it takes in the product marketing messaging, what we are hearing from the market, for example, what the AR team has produced from industry analysts. It also can take in the analysis that we have done of, let's say, Clari Copilot sales calls over the last year or more. We are only limited by the amount of data that we have. If you've really done due diligence in terms of recording every call with Clari Copilot, you can use that data to find insights. So we did the analysis over the last one year. It takes in all that input, all that information, the ICP, the goals we want to hit, the outcomes we want to produce. And then it creates a campaign blueprint. That campaign blueprint is something that contains a high level plan of what each function should do.
And we've done it for one of our launches. For example, we just announced a partnership with Oracle, where customers can use our AI interviewer product on Oracle Cloud. And that partnership was announced within a couple of days. We had the campaign blueprint ready. And this was something that, I remember from the past days, two years, three years ago, we used to hire an agency, create messaging. Even though PMMs had the messaging, they said, no, we don't have the messaging in the campaign format that we want. So we're going to hire an agency that is familiar with how we do campaigns. And then they would produce the output. And then there would be some lost knowledge between all of the different people. So we were able to do that in just a couple days. We created the campaign blueprint, and then we asked each campaign member to say, you already have a Claude skill that you've created. Like, for example, the social media person knows what times of the day are best to post these articles or social posts. What is our tone? What is our brand guideline? What kinds of images work well or carousels work well? So the social media person uses their own skill, feeds it into Mira, and gets the entire draft of what social media posts should go at what time, in our company brand, image, and tone guidelines. So that's about execution.
And then I think the very interesting part is about what we call recursive learning. When you put something out in market fast, I tell people velocity is more than execution, meaning focus on velocity more than perfection. So when we put something out in market, there is going to be some feedback. And we can have the agent do some recursive learning. For example, what kinds of social media posts had higher engagement, what kind of digital channels worked best, what kind of content resonated in the content syndication that we put out. And based on that, make some changes. I think this is the first time that I've seen, honestly, and I've been in marketing for 10 plus years now. This is the first time where I've seen truly sales call insights feeding into a campaign and then doing recursive learning. So that is really creating great benefits for us. We are still in the early stages, so I don't have specific data. Maybe if we get together three to six months from now, I can share more information in terms of the win rates and the difference that we saw in our pipeline and ACV.
Ben Ard10:29 — I love that. And I think that that's really cool. And I love the honesty there saying, hey, we're kind of right at the forefront of this technology. We're right at the forefront of everything. There's a lot of details to come, which will be cool. One follow up question. We're starting to run out of time, but I'm really interested. When a marketer is now not executing on campaigns and handing that off to a creation agent, what does the marketer become? Like where do humans in the loop play a role? What does the marketer play a role? Like what does the job change into, in your opinion?
Navneet Singh11:00 — Yeah, so I was thinking about our team, right? Our team was like cooks who spent most of their time washing dishes. And now the dishwasher handles that and the cooks can actually cook. So that's where I think the campaign managers or marketers can talk to the RBTs, align on the goal, brainstorm with sales, marketing, operations. They review the output of the agent. They use human judgment. For example, the social media person would know what other campaigns are going on or what else is resonating right now in the world. It was Juneteenth, right? So we said, how can this campaign align with that and what can we do around it? So I think humans are still important. They have the judgment, they do the orchestration, they review the output, they set the goals, and agents can do the execution.
Ben Ard11:52 — Okay, that makes perfect sense. Well, now we're almost out of time. So I'm going to kind of double up a couple questions, get your honest feedback and let people get back to their daily grind of getting things done. First and foremost, what has been like the hardest part of building Mira and getting this going, getting adoption? And then with that, where can marketers start today to maybe embrace more of this kind of technology and move forward on this pathway moving forward?
Navneet Singh12:18 — Yeah, let me share briefly. The hardest part has actually not been the AI, the tools, even the adoption. The hardest part has been the knowledge architecture that feeds it. For example, if we don't have the Clari Copilot calls, the sales transcripts for the last year, which is based on the sales discipline, we won't have the insights. Or if we do the call recordings in different tools, we won't have all of that data consolidated. So I think having access to the data and creating the right architecture for us to execute, I think that has been the hardest challenge.
And as far as the second question on where do people start, that's a very valid question. I would say what not to do is to start with the flashiest use case. Start with finding out what outcomes you want to achieve. And based on that, see what tasks will lead to those outcomes. Which tasks out of those is AI most suitable for? And then apply AI to it. And the best part is you can do this thought exercise with AI. You can tell it, these are the outcomes we want to produce. It can tell you the tasks and which tasks AI would be most suitable for. So the biggest mistake I see people making is asking, what can I do with AI? Instead it should be the other way around. What are the outcomes I want to produce? Where can AI help me? And that's where my team spends most of the energy, which is what tasks that lead to my outcome can be performed by AI.
Ben Ard13:44 — And I think that's so cool, figuring out the end in mind and then working backwards into it to say, how can AI help me accomplish my goals? Rather than just saying, okay, well, I'm going to change everything just because AI can do it, even if it doesn't really match my outcome and what I'm looking for. I think those are all really good insights. Again, Nav, this has been so cool. Thank you for the insights. For anyone wanting to reach out and connect with you online, how and where can they find you?
Navneet Singh14:08 — I'm on LinkedIn, so just my full name, Navneet Singh. Just find me on LinkedIn with Eightfold AI and that's where we can connect.
Ben Ard14:16 — I love it. And for everyone listening, scroll down to the show notes, regardless of what platform you're on. Nav's information will be right there. You can just click on a link and say hello and say you came from the podcast and all sorts of fun stuff. Nav, again, thank you, thank you, thank you for the time and insights. This has been really, really amazing. Appreciate it.
Navneet Singh14:34 — Thank you, Ben.
