Jake Sanders
Content Strategist
The Marketing Leader’s Guide to AI Agents
Published:
April 14, 2026
Updated:
April 14, 2026
Table of Contents
Sandra Oono-Thomas didn’t come up through marketing, which might explain why her perspective on marketing AI agents is so grounded in reality.
Sandra, currently the Vice President of Enterprise Growth & Digital Commerce at Daye North America, leads teams responsible for turning complex, fast-moving, massive troves of marketing data into decisions that actually drive revenue. Her path started in data—first at Samsung, then inside Amazon account management when e-commerce was still taking shape. From there, she moved across digital marketing, retail strategy, D2C, and B2B, building a career that sits at the intersection of analysis and execution.
Sandra's background in data analysis shows up clearly in how she thinks about marketing AI agents.
Our interview walks through how Sandra's team moved from manual, multi-tab spreadsheets to a connected system of marketing AI agents—and why that shift had less to do with adopting new technology and more to do with strengthening the foundation underneath it. The questions they asked, the way they structured their data, and the habits they built as a team all came first.
“AI amplifies what you already know,” she says.
When you hear that in the context of AI agents for marketing, it lands differently. The technology speeds things up, expands what your team can see, and helps you act earlier.
But the starting point still determines how far you can go.
Before AI Agents: The 20-Tab Problem
Before AI agents, Sandra's e-commerce team ran on Excel. Not simple Excel, but twenty-tab Excel. Each tab a different lens on the business: sales, inventory, paid media, seasonality. The team was small, with no dedicated analyst, and Sandra’s analyst skills and curiosity were collated and compacted into every new tab.
"I'm probably the one who was asking the most questions and then the team would have to create another tab so I could view things a different way."
The tabs multiplied. So did the time it took to drill in and say anything meaningful with them. By the time a picture came together, two weeks may have passed. In e-commerce, two weeks is a different season.
"By the time you put it all together, the information is old, and things have moved."
This is the problem AI agents were brought in to solve — not capability, not skill, but time. The team knew what questions to ask. They just couldn't get answers fast enough to act on them.
Which brings Sandra to the principle she returns to throughout the conversation: the AI is only as good as you are at the starting point. The twenty tabs weren't a liability. They were the foundation.
"The work AI outputs, you have to check anyway. But I’ve found, as you grow, your AI agent can grow too."
There is no shortcut concealed in this technology. What was never understood stays murky. What was already understood gets faster and wider, when the tech matches the tenacity of the team.
What "AI Agents" Actually Means
Sandra has been to the roundtables. The ones where senior marketing leaders from major brands sit together and talk about AI. And her observation is precise: everyone is using the same words to describe very different things.
"Everyone's experience right now of AI is different. A lot of people are calling AI agents different tools, different levels of agents — kind of all lumped together right now."
Some organizations have strict legal constraints that limit what AI tools their teams can touch at all. Others are running LLMs, or tools with AI features bolted on, and calling the whole stack "agents." The terminology is soft enough that no one is technically wrong, and that adds to the problem.
Sandra draws a hard line between those tools and what her team uses through NinjaCat. The distinction is architecture. NinjaCat's agents are connected to her organization's actual data, trained for her team's specific use cases, and built to work together — not as isolated features but as an orchestrated system.
"When we use our agents — the tool we're using, which is NinjaCat — the game changed for us. For us, AI agents are on a completely different level."
At those same roundtables, Sandra found herself likely the only person in the room operating with agents of that kind. Not because the others weren't curious or capable, but because their organizations hadn't yet given them access, cleared the legal path, or made the structural investment.
"I'm wondering when that's going to change really, for these larger companies. AI implementation really is very beneficial."
The gap between what is possible and what most marketing teams are actually doing is not small. And most people in those roundtable rooms don't know exactly how wide it is.
How AI Agents Enable Proactive Strategy
The clearest proof point of AI agents for marketing in Sandra's account, is also the most specific.
Daye North America's business moves with weather. Seasonal demand patterns are real, predictable in broad strokes, and nearly impossible to act on in advance when your data is assembled manually after the fact.
When Sandra's team began building out their NinjaCat agent environment, they started where most teams start: analyzing one slice of the business at a time. Sales. Then inventory. Then paid media. Each in its own agent, each illuminating a piece.
"When we put it together," Sandra explains, "that's when for the first time we actually could see the big picture. Then we added weather data. Historical and predictive. All of a sudden we were able to go into a proactive model where we could set our strategies based on the information we had — and then see how those strategies performed based on what we are predicting."
The shift is not incremental. Reactive marketing means you're always reading last week's story. Proactive marketing means you're writing next week's before it happens. The technology uncorked that ambition Sandra's team had in their twenty-tab spreadsheets. What NinjaCat's agents did was leverage marketing data models to make the picture complete enough, and fast enough, for the team to actually use.
"It really allowed us to feel like we're driving things more rather than reacting to everything."
AI Agents Don't Require a Data Analyst
One of the more useful moments in the conversation is when Sandra is asked directly whether data analysis is a technical skill or an inquisitive one. Her answer is worth sitting with.
"I actually don't love math, but I like that numbers tell a story. The more information you have, the more full of a story you're going to get."
She approaches a dashboard the same way: high level first, then drilling toward the root cause of whatever the top line is signaling. It is a habit of questioning, not a technical procedure.
And this is where AI agents close a gap that has historically kept a lot of marketers at arm's length from their own data: you don't need to know how to build the analysis. You need to know what you want to understand.
"Technically setting up your AI agent, NinjaCat makes that easy. You don't have to be a data analyst. Agents are like having a data analyst that can read the numbers and tell you what story they're seeing."
Sandra's team didn't build their agents from scratch. What they needed was the data-driven marketing curiosity to ask the right questions and the organizational groundwork to make the answers mean something.
For marketers who have held data literacy at arm's length because it felt like someone else's job, which reframes the picture: analysis has always been about inquiry. The math was never the barrier, but access to a tool that could handle the math while you asked the questions. And it's a smaller barrier to overcome than most people think.
Sandra's advice for marketers in organizations that haven't moved on AI agents yet is straightforward: stay educated, find the content that's actually useful, and keep showing leadership what is possible. Demonstrate value inside whatever sandbox they'll allow.
The gap between any technology's capability and most teams' access to it might be wide, but it closes faster when someone inside the organization already knows what's on the other side.
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Transcript
# Sandra INTERVIEW FULL - Apr 9, 2026
[00:00:00]
**Jake:** Um, Sandra, welcome to the show.
**Sandra Oono-Thomas:** Thank you. Thank you for having me.
**Jake:** It's been a long time coming. Get our schedules aligned. Here we are. Finally. Um, you're busy. I'm busy. The audience is busy. Let's just jump in. I wanted to give some context here 'cause I wanted this to be a guide. To AI agents for marketing leaders. And so I wanted, uh, the audience to get a little history and context on your end.
How'd you get into it? I mean, not the full story, uh, but you know, how'd you get into marketing and then into data, and then into ai? Just that sort of progression because some people are missing that step. You could've just done the bare minimum is what I'm trying to say.
**Sandra Oono-Thomas:** Well, actually it was in the reverse order of what you just said.
**Jake:** then we're all learning.
**Sandra Oono-Thomas:** Yes. I actually started as a data analyst, that's where the journey started for me. And then I moved into account management for [00:01:00] manufacturers, retail. but because I, uh, am from the Seattle area, it was very natural for me to then move into Amazon account managing.
**Jake:** Right.
**Sandra Oono-Thomas:** and then, uh, just added on to my, I guess knowledge bank, I did a pivot and did D two c B2B learned digital marketing_, _ so that I could, you know, I'm a curious person. I wanna learn more.
**Jake:** Sounds like it.
**Sandra Oono-Thomas:** and so that's kind of where I am. That's where I am today
**Jake:** So, uh, so give us.
**Sandra Oono-Thomas:** and marketing and digital all coming together.
**Jake:** So timeframe, like what, what is this? Where, where, where did we start in data? This is,
**Sandra Oono-Thomas:** Oh, way back.
**Jake:** don't date yourself, but No, I, I, I, I just, yeah. No, I.
**Sandra Oono-Thomas:** it, it was during like I said, I grew up in Seattle, you know, went to college there. I'm a Husky, um, uh. Anyway, it was about the time when, e-commerce [00:02:00] was coming on board, so Amazon was just coming on board that's when I, I got into data analytics so it was a natural, you know, Amazon was, growing real fast and all their. Marketing and everything was brand new, so I really was, I've, I've started there as far as digital marketing, I don't think I even knew it was called that at that time.
**Jake:** We,
**Sandra Oono-Thomas:** called that.
**Jake:** we've come up with so many cool new, new words and acronyms and phrases, so, but this makes so much sense. Then you took data awareness and then account management, which is talking to clients and like getting a sense of how they are, and then you become a marketer. It's all it, it's like.
**Sandra Oono-Thomas:** Yes,
**Jake:** I, it's so amazing.
Then this makes sense of how you've sort of come into your own with AI agents, um, you know, in your current gig, um, you, you said, we've talked before, but AI amplifies what you already know. You, you were [00:03:00] keen to, to hammer that ho home. So how did you and your team get clear on what actually matters before introducing AI agents like updated?
We're here today. How did you get people squared before you brought on the tech?
**Sandra Oono-Thomas:** Well, um, if you work on my team, we do a lot of analysis. so I run the e-commerce team right
**Jake:** Mm-hmm.
**Sandra Oono-Thomas:** Um, and, uh, our spreadsheets, our Excel spreadsheets, I think were are manual. Were manual. They
**Jake:** Hmm.
**Sandra Oono-Thomas:** anymore. And they're probably 20 tabs long. We like to analyze our business in multiple
**Jake:** Right.
**Sandra Oono-Thomas:** But you can only do so much as a human. And, we don't have dedicated, like we don't have a dedicated analyst on our team, so it was a heavy load for our e-commerce managers. I'm probably the one who was asking the most questions and then they would have to. Create another tab so I could view [00:04:00] things a different way so we could get a better view of what was going on in the business.
**Jake:** So it's interesting to say part of the way that you had Strati strategically used Excel in those tabs was because of your awareness of visibility on data layers that you were, so, did you help? Um, when you were asking those questions like, Hey, I wanna see this view, I want to, I want to analyze it this way.
Uh, what was the reaction from your team who didn't have data analysis experience as deep as you, or did they equally, or just what you're saying, like there's no analyst on hand. We all have to be a bit of an analyst. Did you help them sort of sharpen their skills there in a way?
**Sandra Oono-Thomas:** Yeah, I think it's more of the asking the questions
**Jake:** Hmm.
**Sandra Oono-Thomas:** and continuously wanting to like, find out more, right. The, the real answer of like what's happening. And, I would say that, uh, the e-commerce [00:05:00] managers I have, They, they are also analysts. They like data they like data and strategy. I think those two go hand in hand. So, it's a skillset I think that's natural and they're cur, uh, they're as curious and as excited as I am.
**Jake:** So strategy, we're saying data and strategy go hand in hand. I think everybody would say, oh yeah. But a lot of people don't see that I stop at the data. You know, a lot of people stop thinking at the data. Um, is, is, was strategy the thing that got everybody on board before you brought in AI agents?
**Sandra Oono-Thomas:** I think that, well, if I'm being honest, it was the ability to not see a whole picture,
**Jake:** Hmm.
**Sandra Oono-Thomas:** because we were doing everything manually and we didn't have enough time. That was our first intention with, AI agents, right? To be able to put a bunch of, um, connect the agents with a bunch of different data sets and see a bigger picture. [00:06:00] but what happened after that is we realized we could, see so much better, but also our AI agents would, be strategists with us.
**Jake:** And so, and so these, these are AI agents on Ninja Cat that you're using. Okay. Yeah. And so just to get everybody square, because there's a lot of. Agentic stuff. Um, you've, you've talked to a lot of people in the industry. You've been at these round table events that we discussed in one of our last, um, conversations.
Is there do, when people are saying ai, do you think they know what they're talking about? If people say agents, do you think, you know, they know what they're talking about? Or is this like just taxonomy, just using words? Do you see people misusing, uh. Phrases like that, or is, is that a part of confusion in, in the place where you're speaking to marketing leaders?
**Sandra Oono-Thomas:** Yeah, I think that unfortunately there's a lot of organizations that have really strict rules and
**Jake:** Hmm.
**Sandra Oono-Thomas:** and, you know legal [00:07:00] parameters around AI tools. So
**Jake:** Sure.
**Sandra Oono-Thomas:** I think that everyone's experience right now of AI is different.
**Jake:** Hmm.
**Sandra Oono-Thomas:** I think we use the word AI and AI agents to mean different things, but I also think that. A lot of people are calling AI agents different tools, different levels of agents, like, you know what I mean?
**Jake:** Yeah. Yeah.
**Sandra Oono-Thomas:** kind of all lumped together right now. And when, when we use our agents, you know, I'll say, yeah, I use, we use ai, but we have AI agents for us, the tool we're using, which is Ninja Cat, the game changer for us, different than, we also do use, you know LLMs and you know, other tools, uh, as well that have AI functionality.
But the, to me, the agents, are completely different.
**Jake:** Yes. Completely different. Yeah. And so when we're talking about Sandra's experience here, it is a Ninja Cat specific one, but it's the same technology that's a bit lumped together, maybe [00:08:00] transferable, but it's a specific use case because the data is tied in, there's a central place where they can pull layers from, and that's kind of what I wanted to get into.
Like, there's a lot of people playing with ai, like we said, and there's a lot of lump together terms, so people might not even know if they're playing. Um, what changed when your team moved from experimenting to actually operationalizing ai Th.
**Sandra Oono-Thomas:** well, uh, it was like, uh, every, the door was opened. yeah, they, I think we're all really excited to see, the capabilities of the agents and how much more work we could do, like in depth work. even connecting our own data to the agents made a difference. and. Like I said, the strategy, um, input from the agents, of course, we always have to check it, right.
but, it, it's [00:09:00] incredible and the speed, it, it really, uh, has affected, how much time we're working on putting data together to get a story right.
**Jake:** And so, and what it, was it, the technology that helped, um, change it? I mean, of course it, it was here, but just like what you said, you, you could. Do it in Excel. You could craft these things on your own. You could thread it all together on your own. Why didn't you do that?
**Sandra Oono-Thomas:** Oh, we just didn't have the time.
**Jake:** Doesn't it come down to that time is a non-renewable resource. Friends
**Sandra Oono-Thomas:** Yeah, we just didn't, and by the time you put it all together, it's like insignificant. The time has passed. 'cause we're in digital, right? So it
**Jake:** good.
**Sandra Oono-Thomas:** Yeah. I mean it, by the time you put it together, it's two weeks later and, and
**Jake:** I,
**Sandra Oono-Thomas:** moved
**Jake:** I know, I, I hate, I hate that. I was still seeing, it takes three to 10 days on average to produce, [00:10:00] like, to produce a report. And I'm like, by that time things have changed. It isn't that. Everything happens in real time, but if you're trying to get a glimpse, you want to get it as close as you can. And I love that analyst awareness that you bring.
Do, let's take it aside here on a, on analyst awareness, because I, I, I came into it as a marketer, which I was like, I'm just the marketing guy. And then you're like, guess what? You need to know statistics. And you're like, well, I'm not big on math. And they're like, that's a problem. And you're like. You have to get a sense of what math, what math is important.
Like a lot of people are like, Hey, this number's down, and you're like, that number doesn't matter. I think it's really a challenging reality for marketers to have limited data analyst skills like you have legit, I'm in the field, I'm in the trenches, and I'm [00:11:00] asked to bring that same level of awareness to a certain data set.
I, I might not be able to, I mean, I, I'm kind of how, like, what's your thought about that? That for marketing leaders do. Uh, is it assumed that we have this analyst capability? And how do you sharpen yours and what's your advice for people like me that are just like, you know, I know over time I figured out what matters, but it's, I had to hit my head on a lot of hard surfaces, you know?
So, just kind of, uh, the marketer's guide here. How do you get people to kind of get their analysts skills tighter?
**Sandra Oono-Thomas:** Well I probably have a different path 'cause I started with data analysis, right?
**Jake:** 'cause you're legit. 'cause you're legit, Sandra.
**Sandra Oono-Thomas:** Well, well, I love numbers and what I love about numbers is as long as the data's correct,
**Jake:** Hmm.
**Sandra Oono-Thomas:** it always tells a story like, you know what I mean? And the more [00:12:00] information you have, um, the, the more full of a story you're gonna get. So for me I think as a marketer, um, the benefit from making sure you ask questions outside of just like what you're working on, right? And get, get more information outside of that so you can get a, a, a bigger picture story. I think also my account management and retail background helps a lot because I understand what it's like working with buyers.
I understand. they're looking for. I understand what we're looking for as a company, right? And so when I'm working with my marketing agencies, I know what to share with them to help them do their job better. But if you're on the agency side, you do need to ask the questions to help fill in the gaps, because I think a lot of times agencies just don't have all the information.
They're only to do what they can with the information they have,
**Jake:** I, I love [00:13:00] account management. Seems like a real bottleneck there because it's like, if that information isn't flowing, um, but what, what about questions? Um, some people feel dumb when they ask them. Um, what's your advice on that? Like, I mean, I, I, if somebody says, this me's important, I'm not gonna question that.
You know, like. Especially if that person's getting paid more than me. Um, well, how do you, how do you bring up questions without feeling dumb? How do you get to that? Is there a safe, you know, place or, you know what I mean?
**Sandra Oono-Thomas:** Yeah, totally. Um, I think that comes from wanting to learn, right? And not being afraid to ask questions, even if they're dumb. However we are able to do our own homework before we ask those questions. Right. Especially with, uh, chat GPT or clot
**Jake:** Or, or, or, or, or, or, you know, a, a [00:14:00] coworker or someone who might know. That's a great point. Before you ask what you think might be a stupid question, maybe do some research. Maybe do some, yeah, that's a great, 'cause that's exactly what I've found. Whenever I have a problem with. A, a, you know, a stat or some data point, and I'm trying to find that story.
You gotta go ask and say, what are you seeing? What do you think? Here's what I'm thinking. So collaboration is a great place to bring analyst skills to, to bear, to ask those questions. And is data analysis, is it a technical thing or is it inquisitive? Of course you need to know what you're. Technically inquiring against, but what?
What do you think? Like if some math you love math, do, should I love math? How do I get into math?
**Sandra Oono-Thomas:** I actually don't love math, but I
**Jake:** You said you like numbers. Okay.
**Sandra Oono-Thomas:** I, I like, I like that numbers [00:15:00] tell a story. That's what I
**Jake:** Hmm.
**Sandra Oono-Thomas:** Um, but what I'll say is this, is that I actually think that okay, technically setting up your AI agent and all of that, like Ninja Cat makes that easy. Like that was one of the, that's one of the things that, if we were just to make an agent on ourselves by ourselves, I, we wouldn't even know how to do it. But because we have the tool through Ninja Cat. But I think what happens is people that are curious or you know, they have a task and if it's to grow your business or whatever, it takes the numbers part out of it.
'cause you can just talk to an agent like a human right, and just be like, I'm curious about this. What is this showing me? Um, it can. You don't have to be a data analyst. It's like having a data analyst that can, can read the numbers, tell you or to you and tell you what they're seeing.
**Jake:** You, you just unlocked it. Uh, those stupid questions you should be asking inside a dedicated place. Like an [00:16:00] AI agent that's tied to your organization's. Uh, there wouldn't be a stupid question there, and there would only be 'cause it's just you. One, two, the stuff you could get the inquiry material. I love this.
This is also my experience with ai when, when I'm trying to gain sense of something, you have to inquire and then you bring the artifact out of it. Have a conversation and then that sets the strategic guide. Maybe that gives you a piece of a tactic, a thought that you might need, but I'm using it as much of a inquiry kind of engine, and then it sets us on the path.
But it only works when I know it's tied to the PDF that I'm trying to like, don't go ask Google. You know, or search your old training data, you know what I mean? So it's like those limitations, you do need to have technical awareness, but I love that [00:17:00] analysis is tied to inquiry and it isn't about math, but it's about numbers and stories.
I love that. Um, so is there, do you have maybe like a step, like some kind of quick drama gras thing when you go into, uh. Say you got a line chart, is there something that you're looking at? I'm looking at the highest point, I'm looking at the lowest point. I'm looking at the disparity in between. Is there sort of a guiding sense when you look sort of the dashboard?
Do you have.
**Sandra Oono-Thomas:** Um, so are you asking, uh, like how I would work with an agent with numbers? Is
**Jake:** Well, no, I'm just kind of wondering when you are looking at some dashboard, it's got line charts, it's got all this stuff, do you have a way that you approach that sort of like a key.
**Sandra Oono-Thomas:** Oh, I always go high level first. With numbers, and usually what I can do is at the highest level, you can kind of see areas that you need to [00:18:00] dive deeper into, that's, that's usually the path that I go to. And then continuously asking questions until I can find the root cause of things that make sense with the top line.
**Jake:** I think there, I, I just figured out there was probably a lot of people that do the reverse. They're trying to find the root before understanding the top
**Sandra Oono-Thomas:** Yeah. I mean, you have to look at it both ways, right? Like technicians are usually in the weeds and doing all the things they need to do for their specific role, um, right. But those roles have, or what they're doing has, has to roll up to a common goal.
**Jake:** Uh, so, and, and so is that when, when you were talking about the way you and your team are working with AI agents and you have sub-agents that are tied and tethered and stuff, can you explain like how, how, like what that unlock was in between the team and just realizing the [00:19:00] benefit of organizational orchestration, you know, capabilities.
**Sandra Oono-Thomas:** Yeah, I think for us for example, um, if we're ana we, when we first started with the agents, we realized, oh, look like we can, we can analyze this part of the business, like our, our sales, right? Our POS wait, we can also analyze inventory. We can, and then separately we can analyze, uh, what's going on with our paid media. Right. But when we put it together, that's when for the first time we actually could see the, the big picture. It took time to build
**Jake:** Oh, of course.
**Sandra Oono-Thomas:** and then the, what, what the switch was for us was this, very al re reaction. Right.
**Jake:** Driven, right, of course.
**Sandra Oono-Thomas:** because you, again, with attribution and all this stuff, you get your information afterwards.
**Jake:** Yeah.
**Sandra Oono-Thomas:** When we, when we were able to put all this [00:20:00] together and our business very seasonal based on weather, and so we added weather information past and predictive, of a sudden we were able to go into a proactive model where we could set our strategies based on the information we had. And then see how those strategies performed based on what we are predicting. So it it, it really allowed us to feel like we're driving things more rather than reacting to everything.
**Jake:** I love that responsive, reactive, but I just want the audience to know it wouldn't be anything without that 20 tab spreadsheet. Like I feel like, wasn't that the skeleton key? Sort of like, like because you had this data awareness that goes back years.
**Sandra Oono-Thomas:** Mm-hmm.
**Jake:** You were able to just put that thing in the ignition?
You know, I.
**Sandra Oono-Thomas:** the AI is only as good as you are. Right. [00:21:00] And the starting point. And then as you grow your agent can grow too, right? So, 'cause when it comes down to it, uh, you know, the work they're outputting, you have to check anyway.
**Jake:** Of course, like you would anyway, like you would your own work. If, if somebody's sending anything out, just hit send. It's perfect.
**Sandra Oono-Thomas:** Right, right.
**Jake:** You will find a typo.
**Sandra Oono-Thomas:** yes, you will.
**Jake:** No, I love that. Well, so, um, couple more questions. Um, so. You explained how it's changing the day-to-day workflows. I mean, it, it, what's your, what's your day-to-day with AI and your business?
You know, like you, you have meetings and you have all of this. How are you kind of engaging with technology? I think maybe people think I need to be in there daily. I have to be in Claude for hours. You know, I have to give up my family to learn ai, you know? How do you, how are you doing it day to [00:22:00] day?
**Sandra Oono-Thomas:** Um, I am really, uh, passionate about ai, but um, won't give up my family time
**Jake:** Okay. I'm not saying that that's an option, but I'm saying it does feel like you have to dedicate a ton of time.
**Sandra Oono-Thomas:** Yeah, well, I think it's, um, there's a small learning curve. Um, this is a new technology and things are changing fast, right? So there is an investment in it. However, whenever you learn a new way to use it, it does, um, save you time or do your work.
Um, a little could be the output a little bit better. So for example, like my weak areas might be designing slides.
**Jake:** Right.
**Sandra Oono-Thomas:** The design part. Uh, so instead of getting someone to help me design it, now I have a tool that will design it for me, but I'm working, you know, on the content in the slide. So, but I would say AI probably is in the majority of my work day
**Jake:** Yeah.
**Sandra Oono-Thomas:** even like. Changing meetings where you can take notes and [00:23:00] then they can summarize, right?
**Jake:** yeah.
**Sandra Oono-Thomas:** um, put it into a task list or list, um, things like that. And it has a history of all the meetings you had. So, um, and then all of my analysis now, um, strategy, I'm usually using AI to help me in some sort of
**Jake:** Mm-hmm.
**Sandra Oono-Thomas:** uh, for our entire team,
**Jake:** Oh yeah.
**Sandra Oono-Thomas:** a, it's a tool that we're just working with all day long.
**Jake:** It just seemed like it's just sort of interwoven. It seems like people are thinking it needs to be this new battery or something, and you're like, ah. It's kind of, I love to hear it because it's a natural weaving in of processes that were already there, you know? Um, and just making it better. So I think I know your answer, but I, I'm interested to see, because a lot of people are thinking AI's gonna give me.
Superpowers. If you had to choose one enhancement that you think AI is good for, would it be [00:24:00] visibility or velocity?
**Sandra Oono-Thomas:** Um, I'm, I can't answer that question with just one choice 'cause I think it goes hand in hand.
**Jake:** You, you, analysts and a marketer? It depends. Ah, no, of course.
**Sandra Oono-Thomas:** Yeah, if, if I had to just choose one, it would be visibility.
**Jake:** I you, 'cause you mentioned it earlier and I was like, it really does help. 'cause uh, that's why I think a lot of people would be frustrated if you're getting AI to think that it's going faster. You might be sad because you're gonna have to set it up, you're gonna have to work hard, you're gonna have to tease things out.
But if you get in it that you are looking for visibility, higher level, just like what you were saying with analysts stuff. See if you can get as high as you can and see as far as you can. That just seems like the better [00:25:00] idea. Um, so, but that's just spoken from an analyst who knows exactly what she's talking about, who's using AI to make her team and workflows.
It's so exciting to talk to. A a a, an actual marketer who's doing things. 'cause everybody gets this unicorn idea, like, I'm gonna become a robot. Uh, you know, and, and Sandra, really quick, before, before, before I wrap it up, give that experience, you told me before you went to this round table, uh, event with big wig marketers from big wig brands, and you.
We're looking like a wizard. Can you just kind of explain how that goes? 'cause it explains the jagged frontier really well.
**Sandra Oono-Thomas:** Yeah, I've been in a couple situations like that and I think that I've been fortunate enough to have tools to explore, um, and have the support from, um, my management, um, to be able to test out new AI tools that have really, really made a difference [00:26:00] in what we do. think that a lot of companies, um, don't, um, provide that.
And so therefore there's a lot of marketers who have, um, very limited AR resources and um, can only do so much. So that, that's kind of what I heard in general. Um, I think two different roundtables I've been to. I'm probably the only one that have used AI agents that are the type that Ninja Cat offers, you know?
Um, I think.
**Jake:** Like working ones.
**Sandra Oono-Thomas:** well, like I said, everyone calls AI agents different
**Jake:** That's what I'm saying. Oh, nice. Demo. No, uh,
**Sandra Oono-Thomas:** we can, that we can make ourselves and train for what we want,
**Jake:** Customize. Yeah.
**Sandra Oono-Thomas:** yeah,
**Jake:** your instance,
**Sandra Oono-Thomas:** like limited to a platform or whatever. Um, um, I'm wondering when that's gonna change really, uh, for these larger companies,
**Jake:** Yeah. It.
**Sandra Oono-Thomas:** It really is [00:27:00] very beneficial.
**Jake:** the leadership angle is interesting though. I, I, I think that it, it's, it's heartening to hear that you have a team that wants you to have access to tools. Um, and it probably is that those organizations also have that desire, but there's things holding them back. What would be your advice for someone who might find themselves in that hampered organization?
Maybe the leadership isn't taking the initiative. What do you think they should do? Um, in, in a safe way, you know, that can maybe strengthen them and, you know, get them in line
**Sandra Oono-Thomas:** Yeah, I think, um, really staying educated on, on the trends with AI and marketing. I mean, there's so much content out there right now, podcasts, um, yeah, there, there is too much, but, um, find your favorite one,
**Jake:** Yeah.
**Sandra Oono-Thomas:** And. And see what's possible. Like you can learn a lot from, um, there's a lot of experimenting going on and honestly, I learn a lot from [00:28:00] that too.
There are things that I'm like, oh, I definitely wanna try that or no. Um, I'm not ready for that. Right. So, but, um. And I think sh uh, continuing to like, share with leadership like what is possible based on what you're learning. And maybe there's something within, you know, a box that they'll allow you to test it in. Um, really showing the value really, I think is, uh, key to getting leadership support.
**Jake:** Yeah. And key and key to proving that value is knowing how you bring it, how you can analyze, how you can ask those stupid questions earlier, get to better insights, faster. Um, what a wonderful treat to hang out with you, Sandra. If, what if people wanna hang out, learn, connect, uh, chat about ai, how can they do that?
**Sandra Oono-Thomas:** Oh, uh, reach out to me on LinkedIn. Um, yep. I meet a lot of people on LinkedIn, so you can [00:29:00] search up my name, Sandra Ono Thomas, and, um, uh, I'd love to chat with you guys.
**Jake:** Oh, I'm okay. I'm not gonna let you go until we play a game called cheese or chocolate, where I ask you two questions and you have to choose one option. Are you ready?
**Sandra Oono-Thomas:** ready?
**Jake:** Cheese or chocolate?
**Sandra Oono-Thomas:** Oh, chocolate.
**Jake:** Oh, I don't know why I'm asking that anymore. It cheese is such an bygone era. Uh, you know, if I,
**Sandra Oono-Thomas:** cheese too, but I like chocolate more.
**Jake:** let's be honest. Um, okay.
Ooh, this is interesting. Ballet or break dancing.
**Sandra Oono-Thomas:** Oh, uh hmm.
**Jake:** I mean, I.
**Sandra Oono-Thomas:** Yeah, I'll say ballet, but I really love both.
**Jake:** But I think we all remember the Olympics, right?
**Sandra Oono-Thomas:** yeah.
**Jake:** Break dancing. Well, that,
**Sandra Oono-Thomas:** cool.
**Jake:** was great. But then also. It needed one more thing. You're [00:30:00] like, and then what? You know, it's like, I'm happy, but there's no ballet in the Olympics. Hmm. I bet I wonder who's working harder. That would be interesting. Okay. Unfair question.
Okay. Uh, next one. Zoo or Museum
**Sandra Oono-Thomas:** Oh, museum.
**Jake:** Zoo. What's I, I, I have kids. I have kids.
**Sandra Oono-Thomas:** Yeah. Yeah.
**Jake:** I.
**Sandra Oono-Thomas:** It depends what zoo, I guess.
**Jake:** Gotta be a nice zoo. Uh, an adult zoo. I don't know, you know.
**Sandra Oono-Thomas:** No. No.
**Jake:** Anyway, hike a mountain. Walk the beach.
**Sandra Oono-Thomas:** Oh, hmm. Hike a mountain.
**Jake:** Mm. You down for the strenuous activity, Sandra.
**Sandra Oono-Thomas:** Uh, well, it doesn't have to be straight up,
**Jake:** I, I like a flat hike.
**Sandra Oono-Thomas:** Yeah.
**Jake:** It's just outdoors. I get it. Um, yeah. 'cause the beach, I, I remember you get a little nervous 'cause here comes the ocean. Oh
**Sandra Oono-Thomas:** Oh, I love, I love the [00:31:00] ocean, but I, I, I don't know. I like both.
**Jake:** no. Sandra won.
**Sandra Oono-Thomas:** is, this is why, um, hike a mountain. You got trees around you. They're beautiful. Especially if you, you're in the redwood forest.
**Jake:** Oh, of course.
**Sandra Oono-Thomas:** quiet, you know? Um, you might run, run into some wilderness, but,
**Jake:** If you're concerned about that, we have a, well, it's a different podcast. I, I don't know, don't be concerned about wilderness. Um, thank you. Uh, two more and then I'll let you back into the real world. A really loud pet bird or Ed Sheeran.
Gotta choose one.
**Sandra Oono-Thomas:** Shean, I would pick Ed Shean. I don't wanna have a really loud pet.
**Jake:** Do you gotta meet this bird. It's probably a better singer. Oh, I'm just kidding. Any Ed Sheeran fans out there? I'm sorry, I just said that. Okay. Finally, uh, [00:32:00] paper or plastic?
**Sandra Oono-Thomas:** Oh, um, well, can give you a politically correct
**Jake:** I.
**Sandra Oono-Thomas:** which is paper, but when I go to the grocery store's, plastic,
**Jake:** Oh, you remember? They were like, no, no more bags. And you're like, oh, well I didn't like 'em anyway. I guess I a good plastic bag.
**Sandra Oono-Thomas:** yes.
**Jake:** I know. I'm sorry. I know that was, that wasn't the right answer, but I think it was the right one. We'll just let it go.
