Are you throwing money at AI without seeing real returns? You're not alone. Businesses everywhere are struggling to turn their AI investments into actual revenue. But what if I told you that AI agents could become your most profitable team members?
The challenge isn't just implementing AI - it's making it pay for itself. Many business leaders are investing in AI agents without a clear path to ROI, leading to frustrated teams and disappointed stakeholders. The reality is, most companies are leaving money on the table because they don't understand how to properly monetize their AI implementations.
That's why I'm thrilled to welcome Eva Dong, Lead of AI Monetization at Google Cloud, back to the show. Eva brings over a decade of transformative leadership experience from McKinsey & Co and Visa, where she's helped countless businesses turn AI investments into profit centers. As a former entrepreneur who built a successful direct-to-consumer marketing startup, Eva knows firsthand what it takes to make AI work for your bottom line. Today, she's at the forefront of Google Cloud's AI monetization strategy, helping businesses of all sizes transform their AI agents from cost centers into revenue generators.
The AI Hat Podcast host Mike Allton asked Eva Dong about:
✨ Start Small, Scale Smart - Begin with focused AI agent implementations that solve specific revenue-generating problems.
✨ Value-Based Pricing - Structure AI agent pricing around measurable business outcomes rather than usage metrics.
✨ Human-AI Harmony - Success comes from finding the right balance between AI automation and human oversight in your revenue model.
Learn more about Eva Dong
- Connect with Eva Dong on LinkedIn
- Connect with Eva Dong on X
Resources & Brands mentioned in this episode
- Gemini
- NotebookLM
- Vertex AI
- Learn more about Custom GPTs, Gems, and Custom Instructions
- Christopher Penn on The State of AI around the world
- World Economic Forum: AI in Action
- Eva’s Smart AI Marketing newsletter
- AI Primer: A Comprehensive Guide
- Explore past episodes of the The AI Hat Podcast podcast
Full Transcript
(lightly edited)
Google Cloud Expert Reveals: Making AI Agents Work for Your Bottom Line with Eva Dong
[00:00:00] Eva Dong: Just have one and only one high impact, clear use case. So think about what is your biggest roadblock? Think about what do you need the most and just have one use case. If you're a small business, just start with one. With your limited resource and with your limited energy. Have that plan. Make a estimate, build a future for yourself.
[00:00:20] Like what does success look like for me and how much does success will cost me today to get there? So answer these two questions and then build a roadmap for yourself and just work on this one use case. Things may or may not work like perfectly in the beginning. That's totally normal. Just keep improving on it.
[00:00:38] Keep working on it until you see, okay, this is bringing me more customers. Oh, this is bringing me more revenue. So always have that goal. Don't just say, oh, I wanna use AI because everybody else is using AI. You, you should always say, I wanna use AI because I wanna grow my customer base by 30%, and I wanna make $20,000 more tomorrow.
[00:00:58] Mike Allton: Mike's got The Hat, the AI kind. Unlocking power in business, you'll find. Cut through the hype, explore the tech. Every week, new ideas to check.
[00:01:23] Allton leads us on a digital quest. AI secrets, he'll always invest. Tune in tight, don't miss a beat. Knowledge, groove, keep you on your feet. Listen up! To the AI flow. Business growth, here we go. Cutting edge insights, that's the plan. With The AI Hat you'll understand.
[00:02:07] Greetings program. Welcome back to The AI Hat Podcast, where I selfishly use this time to pick the brains of experts at harnessing the power of AI across all areas of business, from streamlining operations and boosting productivity to gaining a competitive edge and driving explosive growth. Oh, and you get to learn, too.
[00:02:25] Subscribe to be shown how to prepare yourself and your brand for this AI Revolution and come out ahead.
[00:02:31] Now, do you feel like maybe you're throwing money at AI without seeing real returns? If so, you're not alone. Businesses everywhere, sometimes struggling to turn their AI investments into actual revenue.
[00:02:43] But what if I told you that AI agents could become your most profitable team members? The challenge isn't just implementing AI, it's making it pay for itself. Many business leaders are investing in AI agents without a clear path to ROI, leading to frustrated teams and disappointed stakeholders. The reality is most companies are leaving money on the table because they don't understand how to properly monetize their AI implementations, and that's why thrilled to welcome Eva Dong, Lead of AI Monetization at Google Cloud back to the show. Eva brings over a decade of transformative leadership experience from McKinsey and Co and Visa where she's helped countless businesses turn AI investments into profit centers. As a former entrepreneur who built a successful direct-to-consumer marketing startup?
[00:03:30] Eva knows firsthand what it takes to make AI work for your bottom line. Today she's at the forefront of Google Cloud's AI monetization strategy, helping businesses of all sizes transform their AI agents from cost centers into revenue generators. Hey Eva, welcome back to the show.
[00:03:46] Eva Dong: Hi, Mike, really glad to be back.
[00:03:49] Mike Allton: My pleasure. I'm so excited to see you and chat with you today. So I'd love if you could just start by telling us about your new role. 'cause when we first chatted, you were with McKinsey and Co. Now you're with Google Cloud. That's so exciting. Talk to us about how Google Cloud is supporting and how this work that you're doing relates to AI for businesses.
[00:04:07] Eva Dong: Yeah, yeah, for sure. So I am part of Google Cloud and under Google Cloud there is Google Cloud Consulting. And within that there is a professional service organization called Delta. So that's where I belong. And then my role specifically is on AI monetization. So that's basically to help my Google Cloud clients will answer, what does success look like for you?
[00:04:29] How much revenue, how much profit? And in order to get to success, how much will it cost you? So this way it be with my client, a very clear picture, okay, this is how much it takes to get there, and this is how your future should look like when everything works together. So I think my new is new, like relatively new in the organization.
[00:04:49] I think there's several reasons behind it. If we look a little bit back on the last few years, you can see 2023 really bring Gen AI to the public eyes and, and then more, more broadly to the enterprise world. And then 2024, all the companies are doing proof of concept, doing scaling, doing testing, and then scale gen AI to their business, to their clients, to their customers.
[00:05:12] And then 2025 is really about, okay, I did all of that. I tested, I implemented. What does it mean for me? What does it mean for my revenue, my profit, my stakeholders? So I think that's top of mind for everybody. And that's also bring my role which I'm doing right now.
[00:05:29] Mike Allton: Fantastic. That is super exciting. I'm, I'm really thrilled for you can't wait to see what else you do in that role. Now, today. I, I want your help to let me mostly, and also those listening, understand better how AI agents can help us in our role. So, but before we dive into that, I wonder if you can break down exactly what an AI agent is and how it differs from some of the basic AI tools that many businesses are already using today.
[00:05:56] Eva Dong: Yeah. Yeah. If I have to summarize the answer in one word, I'll say Action. So. I think AI agents have lots of different definitions and specifically has a different definition in the industry versus academia, but I think the core of AI agent we can all align on is that it is machine that can reason and can take action autonomously.
[00:06:20] But thinking about all the previous version of AI just to AI in general we also have like. Like Open AI O1 model that can reason. We also have like Gemini, like 1.5 Pro that can reason, but they can't really take action. And so I think that the key difference between AI agent and just AI in general is AI agent can take action.
[00:06:40] And then maybe to break it down a little bit more and put it more into life, I think there's three categories of AI agent. There's personal agent, persona agent and organization agent. So I think personal agent is like the easiest to understand. So that is like the open AI operator, which just got released several weeks ago.
[00:07:00] So basically, let's say the AI can book a restaurant for you, buy a flower for you, book a vacation for you. Once you, you guys align on a, on a. Vacation plan. And the second one is persona based agent, which I think Google Cloud is very anchored on. So this is more in like a B2B context. So we have all kinds of Vertex AI agent builder and we categorize it in many subcategories.
[00:07:24] There's like. Customer Agent AI to talk to your customers. You can have Employee Agent AI to provide efficiency for employees to summarize email for you to summarize meetings for you, write emails for you, that kind of stuff. There's Creative AI Agent to do all kinds of messaging, imaging creative for you.
[00:07:44] There's Data AI there's Coding AI. To replace some of the junior software engineers and their Security AI to take care of the basic security needs that you need for your network. So that is what Google is focused on. And lastly, organization agent. I don't think we are fully there, but I think the vision everybody's talking about is that in the future, maybe there's a startup that is just a AI agent.
[00:08:09] There's no human, there's no human involved, and the startup doesn't work itself.
[00:08:15] Mike Allton: Got it. Got it. Now, like I said at the outset, this is not something we've talked about much on the show. I have heard of Vortex. I have not used it, and I know many of the folks listening have probably never heard of it before.
[00:08:25] So if you could talk to me a little bit more about how that suite of products relates to everything else. 'cause I'm using Gemini every day. I use Deep Research, I use Notebook, lm, for instance. And I don't see those tools in relationship to Vortex because that's a different place different URL different it lives someplace else within the Google ecosystem.
[00:08:46] And so if, let, let me, if you could explain that relationship, if there is one, and perhaps pick one of those agents and, and share a little bit more about exactly how they work.
[00:08:56] Eva Dong: Yeah, yeah, for sure. So I think for a, just like a general public perspective, so you have ChatGPT, you have Gemini, that is more like a B2C app.
[00:09:06] So you, that is for you to use, for your personal use for your personal and professional use is a chat base. You ask a question, it can answer for you. So that is one type of application. And then you have more like the business type or enterprise app applications. So what Google does is like, we wanna play like enabler role in the market for AI.
[00:09:30] So Vertex AI is. You can see is a suite of enablers to help you build your own AI agent. So for example, maybe you have e-commerce website and then you want a shopper agent on your website. So maybe the customer is like, oh my God, what's the difference between this pans and this pans, and then, and maybe one is waterproof, one is water repellent.
[00:09:53] And then your AI agent can help explain that. Technical details for your customers. So for example, that is a AI agent, and usually that kind of enterprise grid level of AI agent is built by Vertex AI and the equivalent of others on the market. But for day-to-day consumer use, so we use chat, we use Gemini that's a, that's a user like a B2C interface application.
[00:10:18] Mike Allton: Got it. Got it. And I love Gemini. I'm using it every single day. Is it, is it Vertex or Vortex? Vertex. Ver
[00:10:24] Eva Dong: Vertex. Like VER vx. Yeah.
[00:10:27] Mike Allton: Got it. Okay. And we'll put a link to that in, in the show notes is how does the the access and the pricing work, you need to go into super details, but it, is it, is there like small business, medium business level pricing or is this pretty much an enterprise grade product across the board?
[00:10:43] Eva Dong: Yeah, I'm sure there's like pricing for all different kinds and there's, I think there's a small free version too. I, I'll also give you the link there. I think there's something called AI Studio that we just provide to developers. It does involve certain kind of coding skills. There, I think there's no code version, but of course the better, coding, the better AI agent you will build.
[00:11:04] I, I'll send you a link on that. I'm not super sure about the pricing, but but, but I'm pretty sure there's a free version for developers.
[00:11:11] Mike Allton: Awesome, awesome. And I know we just started at, at this role, so I don't expect you to know every, all the ins and outs of the organization, but why do you feel like AI monetization is so important right now?
[00:11:23] And, and how does it work within Google Cloud? Are these all Google Cloud services? How does this all work? I.
[00:11:29] Eva Dong: Yeah. Yeah. So just as I explained in the very beginning, I think 2025 is the year after all the, the excitement, the hype activity bring to the world. After all the, like, the proof of concept and the testing the enterprise has been doing last year, this year, like, everybody wanna see an impact on the bottom line because people and companies probably have invested money in bringing the proof of concept, buying all kinds of like, license of.
[00:11:57] Using all kinds of models and people are really wondering like, okay, I invested x million dollar of this. Where do I get, what do I get? So I think this is the year and this is why like AI monetization is on top of mind and how does work in my day-to-day or at Google Cloud is like, we really try to make it like a win-win situation.
[00:12:16] We wanna make sure like our Google Cloud customers can see impact. Before they do anything in order for them to make strategic decisions. So let's say you are already Google Cloud customer. You host your data the data is stored on Google Cloud. You maybe use like some, Looker dashboard to see, okay.
[00:12:35] Like, how, how much is my sales? How much is, is my orders? And then you maybe wanna, okay, maybe I do, do I wanna have a AI agent on my website? If so, how much impact will it give me? How much money will cost me? So that's more like my job to provide that kind of information. So we really want customers to have that information before they make decisions so they can really make like strategic and end-to-end decisions.
[00:13:01] And also. Hopefully the goal is that during the implementation or after the implementation, the customer will be able to track that impact to say, oh, I expect this model or this AI agent to bring me $10 million in the next five years. Is it actually doing so? Or if it, is it actually going beyond my expectation?
[00:13:20] Why is it, if it's under my expectation, then why is it and what, what else can I do?
[00:13:26] Mike Allton: That makes a lot of sense. I was just reading an article that talked about Google Cloud's, CEO, Thomas Curian last year who said that the company's increasingly monetizing AI based on consumption models, volumes of data, and selling agents to line of business executives over it, which is a shift, I think, from how people have been looking at AI over the past year or two.
[00:13:44] 'cause we've been thinking about it from a subscription basis, like the chat GPT and the, and the Gemini. And so where we pay 20 bucks a month, maybe 200 bucks a month, depending on, on the model. And, and that's it. Whereas now we're moving towards, we have to move towards, I think, a different model because it's, it's based now on these agents that are doing things autonomously, as you said earlier, on behalf of the organization.
[00:14:08] And, paying 20 bucks a month or even a couple hundred bucks a month just doesn't seem to make sense when they could be going out and doing all kinds of research, interacting 24 7 with customers doing all kinds of things on our behalf. Am I, am I restating that correctly?
[00:14:23] Eva Dong: Yeah. Yeah, and I'm happy to give some examples if like bringing like more to life.
[00:14:29] Mike Allton: Yeah, please.
[00:14:29] Eva Dong: Yeah. So, there's several clients. Even though I'm new to Google Cloud, there's several clients like we always talk about, or like, like stellar clients. So one is Mark and Spencer, which is a grocery super, like a grocery chain in the uk. Very famous, like there's Whole Foods. And then we did something with their Contact Center AI.
[00:14:47] So we installed this Contact Center AI Agent in the, in the, in the call and the messaging system. So that reduced your, like, call volume by 50%, which means 7 million calls through agent. So that immediately means so much cost saving for your, hmm. Human agent and so much better quality and so much consistency.
[00:15:10] And then because of that, it also brings you a lot of like opportunities to bring in new customers to retain your existing customers. And then, which bring a lot of impact. And another example I can give is from the US, Wendy's. So you, I would have probably all go through this, like, you drive through a fast service restaurant and you, you peek out of the window really, really hard with your body and you try to order this hamburger and then you try to order this cheeseburger and then keep saying back hamburger.
[00:15:41] It's like, no, I want cheeseburger, I want more like french fries with like, three catsup please and then keep giving you five catsup. So like the whole season will probably go through that. So we collaborate with Wendy's and install this thing called Fresh AI, which is their the Wendy's version of Food AI.
[00:15:57] To have agent do the order taker order taking, added drive through. Mm-hmm. This. Obviously save a lot of trouble from the human staff. At the same time, improved accuracy just so much because drive through the, the biggest thing is like, just the mistakes. I said something, they heard something that the, the, the kitchen did something, blah, blah, blah.
[00:16:17] Everything went wrong. So everything is super like accurate and very smooth. And that was like a really good experience. But I think there's like one more client I think that really make Google Shine because, in this AI world, there's so much competition like besides Google, all the other tech companies and like companies like OpenAI, Anthropic, trying to take a pie, but there's like one client, I think Google really, really shine by bringing our whole ecosystem to it.
[00:16:44] So that one is with Alaska Airline. So. So this one, I, I think it's really relatable to myself. A lot of times when you think about vacation planning, the biggest or the most time consuming part is like, think about where to go. We we're probably all, like my, my have you have this kind of experience just like for a, what is it?
[00:17:05] Is it Cancun again or is.
[00:17:12] So a lot of time we're just thinking like where to go. So we together with Alaska Airline, built something called Trip Aspiration. So this one you can go to Alaska uh.com and today just try it out. Is trip aspiration and then you just type in, let's say, oh, I'm a couple of two. We're newly we, and we wanna do something really active in July, but I don't want it to be super hot.
[00:17:37] And then can like, give you recommendations on like the locations. And then, so this one is combined our Google BigQuery, which is our data processing and data storage service. Vertex AI is our ai. Service and it give, you also have Google Flight which are our flight and dynamic pricing service.
[00:17:56] So it shows you, oh, these are the possible locations and this is how much it costs. Have a round trip from your Destin from your city to this destination back, and you can click condom maps to see like, how does this city look like? Okay, maybe a, a recommend you, you can go to Mexico City. I click on Mexico City and gimme like a drone picture of Google Map to see, oh, this is.
[00:18:17] How Mexico City look like. So really bring like the whole suite of Google together. I think this is where our value proposition compared to other our competitors.
[00:18:28] Mike Allton: I couldn't agree more. This is in fact something I talk about a lot when I'm helping my clients and my audience understand the difference between different large language models.
[00:18:36] Just pure Chat GPT versus Gemini. It's that access and embeddedness within the entire Google ecosystem that I can call up Maps. It can, use YouTube. I asked it to create a playlist for me. Once I was just expecting a list of songs, it generated an entire YouTube playlist for me, automatically, and I hadn't asked it to that. And, and I love this example from Alaska Airlines because I did something similar with my kids over the holidays. We drove from St. Louis, Missouri to Southern California. It's like a 25 ish hour drive depending on what route you take. And when you do that from here, you can drive through Colorado and Utah, or you can drive through New Mexico and Arizona.
[00:19:15] It's about the same amount of time either way. And I told my kids, use AI. Just have a simple conversation. Say, Hey, I'm going on a road trip from here to here. What are some great places that I could stop along the way that allowed them to give me as the driver suggestions and everybody got to vote on, on what they wanted to see.
[00:19:32] So like we stopped at Zion, we stopped at Vegas, we stopped at the Grand Canyon in the Petrified Forest, which is not a stop I would've made because I'd never heard of it before. It's just down the road from the Grand Canyon. So those are the kinds of things that I love. That AI is just so good at it, knows so much more about all these different things, it can really shine.
[00:19:53] And I love those early examples where you're obviously saving employees time saving frustration for customers. What about revenue? Have you seen some really interesting use cases of AI agents where revenue is what's being driven and impacted so positively.
[00:20:09] Eva Dong: Yeah, yeah, for sure. So this one is not really a public case case, but we, Google Cloud is very focused on one of the industries, which is healthcare.
[00:20:19] So we have many healthcare clients and healthcare as an industry. Especially for those leaving the US and listening from US. We all know the healthcare system is not great here. We have many basic, basic problems. Like for example, there was one day I took some ultrasound and I wanna send it from one hospital to the other hospital, and they keep asking me to like ship it in the cd.
[00:20:41] I'm just, oh my God. Like, don't you have anything?
[00:20:48] I know it's like when I work in AI and working hospital AI, I was just like, okay, don't even mention AI. Can you just have like Google Drive?
[00:20:56] Mike Allton: Right?
[00:20:58] Eva Dong: Yeah, yeah. Okay. But jokes aside we have many healthcare clients and then a lot of things we trying to make the healthcare system a little bit more efficient.
[00:21:06] So for example, let's say the doctors take a lot of notes during the visits and have to time to put it into a, a visit summary or a health summary after the surgery or after the visit for the patient. That wastes a lot of time on the doctor's side. So we try to use AI to sim to streamline that.
[00:21:23] And then from the patient perspective, I think there's more like revenue generating. So when patient is navigating through the healthcare system. Them. So in the US we always have to start with primary care. Then you get a doctor referral and you refer to a specific specialization department and you navigate there to find a doctor, and then you get a lab, and then the lab have to circle back to your doctor.
[00:21:45] And maybe in between you, you feel a new symptom and you really need to communicate with the doctor. So all this process. It's very time consuming and sometimes not efficient and bring a lot of frustration to the patient. So we try to do a lot of work in this like whole cycle. Like how can we make the patient experience better?
[00:22:03] How can we have AI answer some of the basic questions with caution? Because it's healthcare, everything is has with caution. And then really put the staff and the doctor's time into the really things that matters to create a better experience for the patient. Therefore. They will be more comfortable.
[00:22:21] Therefore, it could be long term they will coming back to our hospital for further other things.
[00:22:28] Mike Allton: Got it. Got it. And as someone with aging parents who are trying to wrestle with the US healthcare system, I can completely relate. Folks, we're talking with Eva Dong about how Google Cloud's platform is supporting businesses and AI agents, and I've got so many more questions for her that you're not gonna miss.
[00:22:45] But first, a quick word from our sponsor.
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[00:23:46] So we were joking before we started recording that businesses who haven't yet started using AI today, those are, those are probably few and far between, but for businesses maybe haven't started using AI agents.
[00:23:58] That's probably more common, I think. And we're talking about, major corporations who probably invested a lot of money and time and resources into developing systems like Wendy's and so on for some of the smaller businesses who are probably just getting into AI agents, maybe they're not familiar with Vertex and they wanna get started.
[00:24:16] What do you think their, their fastest path forward should be? Whether it's for generating ROI or at least building in more efficiencies in the organization?
[00:24:25] Eva Dong: Yeah. Yeah. I think three suggestions from me. I think the first one is always start with a very clear and impact driven use case. So for small businesses, you probably look at like, the, the enterprise is like, oh my God, they have this.
[00:24:41] Oh my god, they have that. That's cool. But for small business, when you have like limited. Budget, you really have to put things that, things like most needed. So maybe like you are like a small business and you wanna put all your AI power on marketing because you really need to expand your customer base and do more customer acquisition.
[00:25:00] Or maybe let's say you are like a. Tax, like, just help business and, and human beings like do tax is almost tax season. So maybe you wanna focus on, okay, how can I make my job easier when I'm doing tax for my customers? So like, just pick one use case to start and start with a clear agenda. And clear.
[00:25:20] Start with the impact in your mind. Like think about it for yourself. Okay, if I'm gonna spend this money and time and energy to get this AI implemented in my business, what am I expecting? Am I expecting 30% more customers? Am I expecting 30% time me saving to do the work? So have very clear expectation and then translate that into a dollar sign.
[00:25:42] Let's say, oh, you can, if I can have 30% more customers in one year, that will mean like, I don't know. Like $20,000 more income for me. So have that target in your mind and build that estimation, build that roadmap for yourself. The second layer, I think is very easy to leverage existing tools and platforms nowadays.
[00:26:03] There's a lot of like companies provide that kind of platform for you, so you don't, you never have to start from scratch. So Google, we talked about, we have all kinds of like, Vertex AI suite so go into AI studio and take a look for yourself, whether, see whether it's good for you. And then even easier ones.
[00:26:19] I've seen business build like Custom GPTs, which are even like easier to build for small businesses and can just use your Custom GPT to do things for you. So never start from scratch. Always leverage what other people have built for you. And the lastly will be focus on data, 'cause every business a little bit different and you definitely wanna show your personality to your own business regardless whether it is you are doing marketing to attract more customers, or maybe it is your trying to like deal with your tax specific questions and workload.
[00:26:53] So pay attention to. Which tool you are using, what data is trained on, and if you are gonna provide your own data, train a little bit further. So we're talking about like Mike. Mike actually has like AI agent for himself, like his assistant. So you have to train your assistant, provide your data. So make sure that data is like very quality data and you give enough data to train that.
[00:27:16] Because you don't wanna just provide generic output in any use because just. Because gen AI is so cheap nowadays, you just only need to pay $20. And, and also you don't need to pay, like, like Gemini has a free version. It's, it's even free just to generate tons of tons of content because of that, having good quality content, getting more and more important.
[00:27:40] So always focus on the data, focus on the input, focus on the training to get your quality output.
[00:27:48] Mike Allton: I could not agree more. You're right. I've got my AI Chief of Staff that's my AI assistant that I've trained on my voice, my target audience, but also my core business. Information, who I am, what I do, the channels that I have, my goals for the coming year, so it knows everything about me.
[00:28:03] So I can turn to that anytime of day and I have a very strategic conversation about whatever question I might have, whether it's a marketing campaign or a business decision that's set up in Gemini as a Gem, which is for folks who are listening and maybe not as familiar, that's Gemini's version of a Custom GPT.
[00:28:22] It's a set of custom instructions. It works and operates exactly like Chat gpt, so you're not limited necessarily to Chat GPT for using that kind of functionality. I also mentioned, Magai in, in the, at a moment ago, Magai has personas which operate exactly the same way which allow me to use whatever LLMI want.
[00:28:39] With whatever Persona I want, and I strongly recommend that you guys are listening. If you haven't played with Custom Instructions, do it. I've got a couple different resources I'll link to in the show notes that you can check out and, and get yourself up to speed on that because that really is taking your AI usage to the next level and it's gonna help you in your business immeasurably.
[00:28:59] I'm wondering, Eva, how you see all this playing out over the next 12 to 24 months? It's, I always joke that Claude. Helps you write a lot of these, these questions for these interviews. And Claude is always wanting me to ask folks how they see AI in five years. I'm like, Claude, that is ridiculous. Nobody knows where this is going in five years.
[00:29:19] Even the experts like Suleman and Demis Ababa, they're always refining and revising their estimations to be. Faster and faster. They think they're gonna, we're gonna hit a GI or a SI within the next couple years. But when it comes to AI monetization, what are you seeing? Just within the next few months and years.
[00:29:36] Eva Dong: Yeah. Yeah. I see it becoming more and more important and more and more top of mind for everybody for sure. As, as you said, like AI development is like so huge. Not, not to say five years, we can't even think about six months, right? Yeah. Six months is even hard. So, but I think for sure monetization will become more and more important with this like fast based technology.
[00:29:58] People would just have higher expectation. Oh my god, the technology's great. What does it mean for me? What does it mean for my business? So I think everybody will be thinking about it, and maybe that will also bring more jobs to this type of positions, across all kinds of industries. And then secondly we always talk about, like, before we always talk about SaaS, right?
[00:30:18] Software as a service. So right now as have, have like a. New thing called AI is a service. So I think because AI is getting so cheap, so accessible and so common, there will be a lot of like platforms offered AI as a service to people just so like you can just have this tool and then you input this and then you can have your AI agent or you just have that and you put this two together and you just have another thing.
[00:30:43] So I think steam to will be like, so easy for us. It just like as easy as like you go to, android Play Store. You go to Apple Store and you download this app and you start using this app. For us, it's like that, just like super normal, super easy. Maybe in the future, just like building an AI agent, having your personal AI agent will be so easy for everybody regardless whether you have any technology knowledge or experience.
[00:31:08] So that could be another thing. And I also think just looking at things more internationally for a second. So. So I, I know this episode will be aired a little bit later, but as we can see, the international market is becoming more and more active. We heard about Deep Seek from China and there will only be more and more AI models from China from going on.
[00:31:29] And then b besides China, there's many developing countries with lots of talent like Brazil for example. South Africa for example. All these countries are trying to produce their version. And then gas ai get more and more democratized, the world will have more emerging markets with more innovations.
[00:31:48] So AI is no longer a thing like US, which is hold it to itself. So I think as the entire world is developing, ai, ai monetization will also become a global thing for everybody to think about. How do I bring value to myself, my enterprise, my country, and eventually, like my, my word, my earth.
[00:32:11] Mike Allton: That is such a great point.
[00:32:12] In fact, you're, you're right. We're, we're recording this the first week of February, and it was within the last week that not only did deep Seat come out but friend and former guest of the show, Chris Penn did an entire video about the current global state i of AI development to help illustrate what you're saying right now, which is that it's not just US, it's certainly not us versus China.
[00:32:31] There's, there's so many other labs around the world that are developing really, really interesting, either full-blown large language models or AI solutions. Mistral out of, out of Paris, France is, is another such example. So I'll link to that in the show notes below folks. You wanna look at that? I, I had to laugh.
[00:32:48] I had to keep myself from laughing out loud. 'cause you talked about SaaS versus AI as a service. I think we need a better name than AI as a service because you can't pronounce it the same way you do SaaS. That's not gonna sound right to our American friends. But I'm so glad you you brought up jobs because I've talked a little bit, not on the show per se, but in, in other episodes and, and locations the World Economic Forum released a report in January of this year, and they said that due to AI, 92 million jobs are gonna be lost or displaced. But then they went on to explain that by 2030 they estimate 170 million jobs will be needed, which means a net increase of 78 million jobs created or changed. And it's, and it's interesting because you are the example of that. This role that you're in today did not exist, would not have existed without AI. And I'm just wondering if you have any thoughts about the state of jobs and where people should be thinking about when it comes to employment and their jobs and their, their careers and their future.
[00:33:54] Eva Dong: Yeah, yeah, for sure. I think this is a very hot topic for sure, and probably top of mind for many people. So AI will replace some jobs for sure, but it will also bring new jobs like my job and like, and, and my sister team at Google and many other organizations. So I think as an individual, as a professional, like as a person who need to make money for their family, I think you need to think about how can you stay ahead of the curve.
[00:34:22] I. So AI will only replace like the basic, right? So, so one angle is like, only AI will only re replace the basic. So AI, a Coding AI Agent might replace a junior software engineer, but the best software engineer will never be replaced by anybody because this person has domain knowledge. He can communicate with executives, he know end to end how this infrastructure should work, how this code should flow, how this.
[00:34:48] Pieces interacted with like other different pieces in the organization. So that person's job will never be replaced by AI. So if you are top of your field, regardless what it is, it's coding, it's fashion, it's marketing is is anything like you won't be replaced. So. One ways to think about. It's like, if I'm the best of my industry, I won't be replaced by AI.
[00:35:10] And secondly, most people are not best of their field. We're just ordinary people doing ordinary things. So maybe you can think about how can I reposition it or pivot it to an angle? That so that I'll be growing with AI instead of, instead of like replaced by AI. So for example, Mike is doing AI podcast.
[00:35:31] I think that's a great way because you're doing AI literacy, educating more people. That's like growing with AI. I'm doing AI monetization, which is like, trying to like help my customers, we, there's ai and with AI impact, that's another job. That's another work. So, so think about in this lens, like, within my current capabilities and within my current experience, how can I grow with AI together?
[00:35:54] Is it to help other people to learn about AI? Is it to help with AI? Is it like to help AI to impact my customers? So from this angle I think the majority of the new jobs will be created in the second category.
[00:36:09] Mike Allton: Love it. I appreciate that insight. I've got just one more question for you, Eva. I'm wondering if you could share in the moments we have left, just what's one piece of advice that you would give the listeners today who are wrapping their brains around, not only how they can leverage AI, but how they can actually monetize it and impact their bottom line.
[00:36:27] What would be your advice to them?
[00:36:29] Eva Dong: Yeah, yeah, I think it's circle back to my previous answer. Just have one and only one high impact, clear use case. So think about what is your biggest roadblock? Think about what do you need the most and just have one use case. If you're a small business, just start with one.
[00:36:46] With your limited resource and with your limited energy. Have that plan, make a estimate. Build a future for yourself. Like what does success look like for me, I. And how much the success will cost me today to get there. So answer these two questions and then build a roadmap for yourself and just work on this one use case.
[00:37:06] Things may or may not work like perfectly in the beginning. That's totally normal. Just keep improving on it. Keep working on it until you see, okay, this is bringing me more customers. Oh, this is bringing me more revenue. So always have that goal. Don't just say, oh, I wanna use AI because everybody else is using AI.
[00:37:24] You should always say, I wanna use AI because I wanna grow my customer base by 30%, and I wanna make $20,000 more tomorrow.
[00:37:33] Mike Allton: Fantastic advice. Thank you so much Eva. You are always amazing and an absolute delight to catch up with. I appreciate that. For folks though, who want to learn more, if they've got more questions for you, where should they go to reach out?
[00:37:46] Eva Dong: Yeah so I'm pretty active on LinkedIn. You can search me. I. Eva Dong I'm pretty active on LinkedIn. Feel free to like, connect with me, message with me. I have a newsletter which I need to do a better job. I'm trying to keep up with it because Smart AI Marketing. So I produce some of the content to help like, not like super advanced AI people, but more like, day-to-day people.
[00:38:06] How do you use AI to benefit you in marketing in business? In, in your life. And then I'm trying to be more active on x Eva, Don on X. Trying to build a little bit more connections there. So feel free to follow me and interact with me too.
[00:38:21] Mike Allton: Brilliant. Thank you so much, Eva. We'll have all her links in the show notes below.
[00:38:26] Thanks to all of you for listening. That's all the time we've got for today, friends. But remember, if this is your first foray into the world of AI and business, I've got that perfect resource for you in the show notes below the AI Primer designed to quickly get you out to speed on the terms, the tactics, and the underlying technology that's powering this AI revolution.
[00:38:43] Check it out and I'll see you. In our next episode. Welcome to the Grid.
[00:38:47] That's a wrap on another episode of The AI Hat Podcast. I hope you've gained some valuable insights and actionable strategies to boost your business with AI. If you enjoyed this show, please subscribe, leave a review, and share it with your network. And don't forget to visit the ai hat.com for more resources and to connect with me.
[00:39:06] Until next time, keep innovating and remember, the future of business is human and it's powered by AI.
