Navigating AI: Unlocking Growth with Intelligent Agents

Brandi Starr [00:00:35]:
Welcome to another episode of Revenue Rehab. I am your host, Brandy Star and we have another amazing episode for you today. I am joined by Lillian Pierson. Lillian is a global authority on AI driven growth and fractional CMO for high growth tech brands. She is the author of the Data & AI Imperative: The Blueprint for Scaling Success. And her strategic data driven approach has empowered 10% of the Fortune 100 companies and countless startups to deliver predictable, measurable revenue results. Welcome to Revenue Rehab. Your session begins now.

Lillian Pierson [00:01:18]:
Thank you so much for having me on Brandy. I'm excited for our conversation and all of the nooks and crannies we get to explore with your audience.

Brandi Starr [00:01:28]:
Yes, I'm excited to have you and I, I know you are over in Thailand, so you are probably the guest that is coming to us from the furthest, which is really, really exciting. So before we dive into our topic, I like to break the ice with a little WOOSA moment that I call buzzword banishment. So tell me, what industry buzzword would you like to get rid of forever?

Lillian Pierson [00:01:59]:
I don't know if it's a buzzword, but it's definitely a prevailing trend in, across everywhere. A lot of people are branding themselves as AI experts and I have been working in the data analytics, data science, AI space for over a decade. So I started my business in 2012 exclusively like serving this sector and trained 2 million, 2 million workers on how to actually like do the programming that's required to build AI and stuff like that. So, and 10 books later and I still, at this point when people start saying like, I have a new book on data and AI strategy, right? And people come locally and like, oh, she's an AI expert. And I'm like, actually, please don't, like, I don't feel that's a, like, first of all, I'm not building these solutions. So I don't think like, as a person who's not technically in this, you know, I don't, that doesn't, that title doesn't resonate with me internally per se because I know I'm not building those solutions. So I do have expertise. But like, also there was a whole proliferation after ChatGPT.

Lillian Pierson [00:03:20]:
After version 4 came out, everyone and their mother comes and like the whole Industry just changed to overnight and there's like a million AI experts and a lot of them have like one year using ChatGPT and like, and so it's like, it's kind of. Honestly, it's also like a matter to me of speciation. So maybe they have expertise in applying AI in a certain domain and so there would be an expert of that domain and expect like an AI AI led strategy around that domain. Okay, that's, that's legitimate but like just the industry at largest, like, wow, like a lot everyone wants to be called, calling themselves an AI expert but like a lot of them do not have. Did not. I don't know, they don't, they had, they just jumped in, in the last minute and so maybe I can't say if they're experts or not, but it just is a little.

Brandi Starr [00:04:29]:
Yeah, I definitely would agree that there are a lot of people that are claiming more expertise than they really have. Like, you know, I, I do know some people who are extremely savvy, who live and breathe it all day and have for a number of years and they feel the same way that you do. Like they, they hesitate to use the term expert outside of a very small, you know, domain. Like I'm an expert in this use case or this scenario. But I'd say now that we've gotten that off our chest, tell me what brings you to revenue re.

Lillian Pierson [00:05:08]:
Well, I wanted to come on your show and just speak with you because I know that we've got a bunch of revenue leaders who are listening and fundamentally I think almost all, every, all and each and every revenue leader is looking for more efficient ways to grow their business. And for me, I can speak on how to use data and analytics and AI to. For marketing, marketing led growth in a more efficient way. So less basically less man hours, less faster, higher quality outputs sort of thing. So I thought that would be a really great topic we could explore together. One of the things that I wanted to make sure that I left your audience with was some insights into AI agents and marketing because especially for revenue leaders, it's really important what's happening for there to be awareness and adoption for revenue leaders especially.

Brandi Starr [00:06:15]:
Okay.

Lillian Pierson [00:06:16]:
Yeah.

Brandi Starr [00:06:16]:
And I, I think that's a great place for us to start because you know, the term AI agent is something that has, you know, come up. I mean it's, it's been really prevalent. But I'd say in the past few months I have heard it more and there's a lot of, you know, heads of marketing, heads of revenue that they're still just figuring out some of the basics. Like you know, you talked about chat GPT. They're just getting their feet wet in using chat GPT for some small basic use case. And then people start talking about AI agents and it's like, what is that? What do we do? So I'm going to ask a very basic question for the noobs. So what is an AI agent?

Lillian Pierson [00:07:06]:
Okay. An AI agent is an autonomous, it's an autonomous piece of software. So you could think of like a robot. If you had a robot in real life and it was cooking your breakfast, it has agency because it can act autonomously and make its own decisions in a series of events, can guide itself through a series of transactions. So we have also AI agents in digital work. So it's basically a self governing system where it's got intelligence that's guiding, it's got its own intelligence that's guiding each of its operations. And that's powerful. Yes, and I'd love to explore that deeper with you because there's different types of architectures we can use with AI agents.

Lillian Pierson [00:07:52]:
And something that's really remarkable is what I've seen happening with multi agent systems. Multi AI agent systems. And, and I think that the multi AI agent systems is where the rubber hits the road. I mean, I'm running them in my business. I just, I can tell you the story behind that and I'm blown away.

Brandi Starr [00:08:19]:
Okay. I was gonna say, well, now I gotta hear the story so. Because I do think that understanding like what this could look like helps all of us who are less familiar with these types of technologies to start to see what not only the future could look like, but the now. So talk a bit about how you are leveraging the multi agent systems, especially around increasing efficiency, optimizing what you're doing. I'd love to hear your story.

Lillian Pierson [00:08:54]:
Sure, yeah, of course. The backstory on this is I was doing an interview from my own YouTube channel of Madhukar Kumar. He is the CMO of Single Store, which is a series D vector database vendor essentially. So he's in Silicon Valley and he knows everything. Cutting edge, right? Because he's out there leading these things and he. The interview was on multi agents systems, multi agent AI and marketing. Okay, great topic. I was working with my team to put this thing together and I'm like, it's just taking like, because we don't have our process, normally I get handed off, but right now our process, Kristen, is phase where we need to recreate process.

Lillian Pierson [00:09:45]:
I'm like, this is just like so much. I'm publishing a YouTube blog series on AI agents and marketing, how it creates efficiency. And I'm like, this is like overwhelming amount of work. This is so much work to get this out there. Why don't I start looking for multi agent AI systems to help me do this? Because that only makes sense, right? So born out of the distress of just like as much the amount of man hours and just like tedious details of goes into producing an interview like the one we're having, actually I started searching and within, actually I found this solution within 10 minutes I would say. And it's not for YouTubes and podcasts, although they're out there. But I found a system that. Okay, the use case sounds lame because every.

Lillian Pierson [00:10:42]:
Because this is not new. So it does, it does SEO blocks. It does SEO blocks. Okay, so I say that sounds lame because this isn't new. There's been SEO blogs, AI generated SEO blogs for a long time. Applications that generate, you know, content like that's everyone and their mother has been doing that, right? So I actually have not been doing that too much because I worried about like the pin penalization like by Google and didn't capture brand voice and just the quality was low like overall compared to what you would add if you were giving your own expert opinion in something. It was low. And I just didn't want to go down that pathway.

Lillian Pierson [00:11:32]:
Even though I saw a lot of people using AI to blow up their search, their SEO organic search return. But this is not that. This is not one of these like SEO, like content writers. Yes, it has that, but I feel like I'm just talking a lot and like, do you have any questions? Is this like, is you want me to continue with the story?

Brandi Starr [00:12:01]:
No, this is helpful. So just recapping. Your objective was to figure out how to reduce like the human effort in being able to create these blogs. But you want it to do so in a way that is going to have, you know, quality content. So not just stuff trying to keyword stuff that's going to have your brand voice. And so you. So is this a system that you acquired so something you purchased or something that you built?

Lillian Pierson [00:12:34]:
No, I didn't build it, I purchased it. I was just like the fundamental. I was just looking to adopt AI agents and marketing. Based on the interview I just published. This just makes sense. Why don't I look and see what's there? Searching around 10 minutes, I find this tool. What? This tool. I think that there's probably 20 agents running inside of this tool.

Lillian Pierson [00:13:08]:
And so it Takes you from cradle to grave. It takes you through the entire life cycle of like. It does the SEO keyword research, it identifies it. It also scans your entire website and it collects from you all of the like, the brand voice things and then also the caveats of like, who you're serv, your icp, all of that it collects from you. It gets its stuff off of your website as well. And it used that to basically guide its decision making for content ideas. It does the SEO keyword research and then it proposes headlines that are already SEO optimized for you to say yes or no. And I'll share this with you after.

Lillian Pierson [00:13:58]:
But I'm also keeping this secret. Like, ah, this is like one of these things. Because I'm good at finding things that don't catch on until like five years later and get really big and riding them out and getting a lot of growth off of them. But there, this is something that's out there. It's easy for. It took me 10 minutes to find it. Okay. And I'd be willing to share with your audience if they reach out to me directly or something.

Lillian Pierson [00:14:25]:
But it does the keyword research. It proposes headlines and then you say yes on the headline once you say yes. Or you can edit the headline, you can check that the keyword is the right search volume and all of that if you want to, but it does, it's not needed. And then you just hit run and it goes. And it scrapes 24 different websites, it scrapes the data itself and it does a full analysis of like the entire topic area. And it basically introduces like one main main way of doing things and another main way. There's different types of like, it's got different formats of articles, right? So maybe it's a listicle article, maybe it's a like a thought leadership post. Maybe it's like a, it's got different blog formats, right? So it will take the topic and suggest.

Lillian Pierson [00:15:23]:
Okay, I think that the best format for this blog is this piece. It will scrape all of the sources on the intern that it can find the top sources and then it will like evaluate one against the other and come up with not just like a, like a blog post about this topic, like that's like just another thing anyone else has. It produces a full 4,000 word analysis with like key takeaways. Not just key takeaways like the summary, but like getting. Asking the audience to think, you know, I'm like bringing up examples of each of the different instances and saying like, how would this work in your business? Sort of thing. So it does a whole analysis of the topic and pits like, takes all of the stuff that I've seen out there and like takes it one step further in terms of like adding value. In terms of like considerations. Like, we have multiple considerations here.

Lillian Pierson [00:16:20]:
We need, we can't just go down this path or this path. You need to know all the nuances here. And so if you've considered this, this and this, you might be wanting to go down this path. So it does all of this in 4,000 words. None of it is like, you cannot see like chat GPT, you know, those buzzwords. And like the, you know, something is crucial. It doesn't like the word crucial. Can, can that be one of those annoying.

Lillian Pierson [00:16:46]:
No, but like doesn't read as robotic or ChatGPT generated. It goes and it pulls a YouTube video that is on the topic and inserts it into the. So a person can watch a video for more information. It goes and it's got like 30 outbound links to the highest, most credible sources on the topic. So that it's not pulling from, you know, so it's showing here is the proof and support of this reasoning and here's the data and also getting the outbound, high quality outbound links. It does all the internal links on your website and then this thing it publishes for you. So you can review it if you want to, or you can change it if you want to, but you can just run it. I, after the first one I'm like, I'm just running this and then you can like edit it though.

Lillian Pierson [00:17:41]:
I put my own YouTube videos in there, you know, so you can make it your own in whatever way, shape or form you want. And yeah, just. Oh, and it updates.

Brandi Starr [00:17:55]:
Yeah. So I want to take this example and just dig deeper into how. Because I think this is a great picture that you've painted of what these agents can do and having. So each agent is kind of doing its part and all together they are able to run and do all of these things.

Lillian Pierson [00:18:16]:
And so, and it generates the image. It generates the image, it SEO optimizes the image, it publishes everything. And when you look at the SEO optimization tool, what's the score of this? It comes out at like 79 or 80% without touching.

Brandi Starr [00:18:32]:
Okay. And so thinking about. So I think this is a good use case to get those listening to really understand what agents can do for you. And so let's step back from this specific use case and think about like when it comes to data and AI and understanding or figuring out where organizations may want to try to deploy AI agents or any other form of AI. What do you think they should be thinking about in being able to identify use cases for themselves and really embrace this as a technology?

Lillian Pierson [00:19:18]:
Okay, so there's always the risk and reward. There's the risk and reward analysis for like adopting any new tool. So in general I've been slow to adopt any tool like this because I've seen it as risky because, because you know, you don't want to like not just reputation, because if you create low quality content, it's like spam reputation sort of thing, but also being penalized by people. So you have to look at like what, what's inherent in what am I up against is this, is this tool going to create a risk for my business in any way, shape and form? What is that risk? And then what is the, what is the payoff? So what am I saving in terms of time and money and what am I generating in terms of return, like increased return? And then I mean I would encourage, I would encourage business revenue leaders, business leaders in general to be exploring every use case. So this is what I could find in 10 minutes. But okay, so if someone goes and searches for this tool, they're not going to find it. It's buried under, it's buried under a whole industry of like well funded startups that are selling something similar but not as good. So I found one tool that actually does this well and it's only in this one use case and I haven't had time to go and search for more things.

Lillian Pierson [00:20:54]:
So what I would say is like one is like actually identifying multi agent tools that are effective and powerful and finding them instead of going for like, I don't want to say outdated, but these solutions were built on a different architecture. The things that came out last year are built on like software development, like a huge architecture that's rigid and these AI agent systems are nimble and can be like something breaks, it can be fixed very, very quickly. So it's like the ones that had a lot of funding like two years ago and stuff, they're like market dominance and they're the ones you're going to find. But the, the ones that are like coming out, the AI agent systems are hard to find because another consideration is like I can tell you another story about a different architecture for an. It's, I guess you could say it's multi AI, it's agentic marketing, it's AI agents. But startups have ideas, but the ideas and they maybe have a prototype of something that's working, but then they don't have demonstrated success. And so you don't really want to be a guinea pig. So finding a tool that's actually, that works.

Lillian Pierson [00:22:14]:
So I would say, like, test, like I did one test article on this thing to see what it was. And like, don't go full, like, don't get on a bunch of calls with these founders, like, do voice of customer and like, understand all their things and then find out like five months later, six months later that what they had in mind was never going to work. You know what I'm saying? So, like, just like a lean, lean test. Does it work? Does it work? Well, what's the payoff if I adopt this and then proceed from there? Yeah, so that would be another thing because. Because I haven't found any. I haven't found any. I wouldn't say there isn't other cases. When you're using CHAT GPT to answer questions for ChatGPT and you're running multiple instances, so you're basically.

Lillian Pierson [00:23:03]:
It's not agents because you are the agent. You're basically using one automation to feed the other automation. Do you know what I mean?

Brandi Starr [00:23:11]:
Yeah, yeah.

Lillian Pierson [00:23:12]:
But it's like, okay, we've been doing that. Like, if you're using ChatGPT all the time, you're already kind of doing this. But yeah, so those are the things I would.

Brandi Starr [00:23:21]:
And I think the hardest thing is I think that's a good way to help people who are not familiar with agents to really understand what they are. Because in. I mean, and that's a great example where using Chat GPT, we are the agent, essentially. And so thinking about what you were doing and what you're inputting and being able to automate that, to have it make those decisions and give that feedback and do all of those things, I do think is really valuable. I do want to shift gears a little bit. I know you wrote the book the Data and AI Imperative, and so tell me a bit about the premise of the book and what does that have for those that are leading revenue teams?

Lillian Pierson [00:24:18]:
Sure, yeah. So the premise of the book is that there is a clear and repeatable strategic framework you can adopt for building and designing strategies that drive business growth. So in that book we talk a lot about marketing. Marketing led, like product led growth and growth marketing, because I'm the cmo. But that's in the first half of the book and we talk about go to market. But the second half of the book is like, where I'm basically sharing my strategic. My strategic framework that I developed. So I've done technical strategy like I did, I've done strategy for 20 years.

Lillian Pierson [00:25:05]:
So I started out of college, I'm an engineer and I started doing technical strategy first even before I had graduated. So over those years I developed a framework and did action data and AI strategies for really like Saudi Aramco, I helped them develop their strategy there. And like many, many big companies and as I evolved I became much more into the marketing and way, way getting further, further away from the actual technical aspects. And I just felt like, okay, this is not appropriate. Like I can share my strategy, I can share the strategic approach I developed over these, these 20 years to help other people build these type of strategies for low risk, high reward executions, whether that's a project or a program or a product. How do we de risk and really pick that optimal use case for either achieving a quick win or if you're a more established business, for driving growth over the long term and integrating multiple strategies at the same time. Going into too many details here, essentially what I did was I gave away the keys to the kingdom because I'm not offering data and AI strategy per se. If it's related to marketing, then, if it's related to growth and marketing, then yes, of course I do data and AI strategy to support that.

Lillian Pierson [00:26:40]:
But every other type of use case operations, telecommunications, data and AI strategies to support factory operations, even decision support, like, like that's more data, that's like for data professionals. So I just said I used to make like a thousand dollars an hour developing these strategies for these big companies and I just am not doing that anymore. I don't want to do that anymore. It doesn't feel aligned with. I'm a marketing person really. So I'm just gonna share with everyone exactly how they could do that for themselves. And I take them step by step in emphatic detail exactly what needs to be done to, to, to build these strategies themselves.

Brandi Starr [00:27:32]:
Okay? And talking about our challenges is just the first step. And nothing changes if nothing changes. And so I, you know, I. In traditional therapy, the therapist gives the client some homework, but here at Revenue Rehab, we like to flip that on its head and ask you to give us some homework. And so for those listening, if what you have said has resonated with them, where should they start? What do you think should be the first action that they should take?

Lillian Pierson [00:28:05]:
Thank you for asking. And I wanted to. Okay, here it is, the homework. Because revenue leaders, you guys have got all different use cases you need to explore. My little thing was just like One little tidbit. Go to future tools, and it basically will have all the AI tools you can imagine for all the different use cases, and start exploring and looking what's in there, and if they've got tools in there that can support your use case, and then just do some very quick and lean iterative testing to see if this might be something you could start adopting now. Because this future tools is where you're going to find these. These kind of, like, early movers, AI agents that are actually working, that aren't, like, overhyped with a bunch of, like, funding and stuff like that.

Brandi Starr [00:29:00]:
I love it. I'm like, now you've given me something to research. I am not familiar with future tools, so I will.

Lillian Pierson [00:29:07]:
I didn't give you the exact tool I used, but I gave you the stepping stones where you could explore and discover.

Brandi Starr [00:29:14]:
Yeah. And I think, you know, honestly, I think that is great because every leader is going to have different pain points that they can leverage to, you know, leverage AI to fill in some of those gaps. And I know sometimes the hardest part is kind of figuring out what's out there because it does grow and evolve so quickly. And so this is a good opportunity to, you know, to. To see what's coming, what's out there, to play around, do some tests, see where it may work for you.

Lillian Pierson [00:29:47]:
Well, Lily, there's so much on there.

Brandi Starr [00:29:50]:
I have enjoyed our discussion, but that's our time for today. But before we go, tell our audience how they can connect with you and definitely do the Shameless plug for the book.

Lillian Pierson [00:30:04]:
I feel like we already did.

Intro/Outro Voiceover [00:30:06]:
Okay.

Lillian Pierson [00:30:07]:
Yeah, so I. The best way to get a hold of me would be just like through LinkedIn DMs. I'm Lillian Pierson on LinkedIn. And yeah, and I'm just available. I'm big on DMing, so just send me a DM and we can start a conversation. And then the Shameless plug for the book. The book is available on Amazon. It's actually available in Barnes and Noble, so it should be, like, on your corner, your local corner.

Lillian Pierson [00:30:36]:
It's all over the world, so. So it's called Data and AI Imperative Designing Strategies for Exponential Growth. And if you need to get a hold of me by email, it's lillianata-mania.com awesome.

Brandi Starr [00:30:50]:
Well, wherever you are listening or watching this podcast, check the show notes. We will make sure to link to Lillian's LinkedIn as well as to the book. Well, thanks again so much for joining me. I really appreciate you taking the time. It's been really interesting and I've got some things to research now.

Lillian Pierson [00:31:14]:
My pleasure. Thank you so much for having me on. I love speaking with you and getting the chance to go in front of your audience, so it's much appreciated.

Brandi Starr [00:31:22]:
Awesome. Well, thanks, everyone, for joining me. I hope you have enjoyed my conversation with Lillian. I can't believe we're at the end. Until next time.

Lillian Pierson [00:31:30]:
Bye.

Brandi Starr [00:31:31]:
Bye.