Being Human In The Face of AI (Guest Su Jella)

Speaker A: Hello everybody. Recording live from somewhere. Uh, hello listeners. Uh, thank you for tuning in to the Created for Marketers by Marketers podcast. My name is Billy Louisu and we've been working hard to find marketing topics which will help you level up your skills and that will blow your mind. Today is episode nine of season three and the topic for today is being human in the face of AI. We'll be discussing, well, is AI a fad, Is it actually useful and how can businesses apply it? So my guest for today is Sue Jeller. Sue has been previously recognised as Australia's top 25 analytics leaders. She was recently awarded the prize of winner Women in AI Asia Pacific by the Women in AI committee for apac. Wow. Okay, so sue has worked across a diverse range of industries solving various data challenges and opportunities. She helps to drive innovation and opportunity using data and capabilities. A visionary in data analytics and all things AI. And she doesn't merely confront the question, what do we do with all this data? Instead, she transformed this challenge into an aligning journey of discovery, driving actionable insights that fuel smarter business decisions. I think we could all use a person like sue in our organization. Sue, how you doing?

Speaker B: Hey Billy, thanks so much for this opportunity to share some of my learnings and experiences with you and your audience. I'm really looking forward to the chat today.

Speaker A: Oh, it's great to have you. We've been on numerous, uh, you know, supper club dinners together chatting about this topic and I thought, you know, something we're to capture on, uh, on digital and be able to bring it to all my listeners. But before we dive into that topic, I have the hardest question of all, probably the one I judge you the hardest on. Marketers and music. What is your favorite album or music you listen to when you work?

Speaker B: Oh, uh, there's so many, um, I always like high energy music in general, um, things that just make me move, um, gets me tapping. Um, but the irony of the question or the answer that I'm going to provide to you is Adele's 21 album, which is not exactly high energy as such, um, but I do love her album. That's one of my favorite albums. But the song that I do love is not actually from that album. Um, it's by Kelly Clarkson, Stronger. And that's totally, um, everything that I like in music. The high energy, the momentum, the lyrics, everything that goes.

Speaker A: That's a catchy song. I can hear it playing in my head at the moment you mentioned it. Adele.

Speaker B: That's the background music for today's episode.

Speaker A: It Might not be full energy, but she reaches frequencies I've never heard any voices achieve before. So she's in a completely different stratosphere with that, with that lung capacity. I don't know, I kind of, uh, she just, yeah, she blows me away. I've never seen her live, but I know people who have and have said by far, uh, one of the top three or top, top two concerts they've ever seen.

Speaker B: Yeah, amazing. Yeah. Hopefully one day we can make it to that concert when she decides to visit us.

Speaker A: Yeah, it's probably one person I've never heard anyone say they dislike. So you're in good, good company there. So before, before we jump into the, um, the discussion, give me a brief introduction on yourself, um, why this topic in particular is relevant to you.

Speaker B: Um, I guess just in terms of my background. I'm actually from a former marketing background. So I worked in, um, marketing across brand advertising, um, sales research being my more important and more prominent, um, experience, um, over the last 15 or so years. Um, and of course, you know, research, market research has been one of the key areas that leverages and really drives capability through insights and data analysis. And so I'm very passionate about data from that perspective. It really delves into human psychologies, behaviors, why do we do what we do, what influences us, what are our interests and how do we sort of build on those interests. And that's all true data. Right. Um, but it's not just what you see, it's what you know, it's the information you collect and how you actually manipulate that information in a way that is meaningful. Um, and so that really is one of the things that, you know, goes back into my background, but then being able to bring that now into an area that is more relevant, um, resonates with audiences, resonates with organizations as well. But being able to make that into something that can be felt and seen in a way. Um, and of course now we know an era of AI and AI is pretty much nothing without the data. So you can go back, um, pretty much for centuries, millennia, um, to be able to use data to make meaningful conclusions. And so that felt like a really great spot for me to marry in my background in marketing, but now in a more analytically driven, um, high tech, um, enterprise level capability.

Speaker A: Oh yeah. It's such a great position and such a great spot to be in. I mean it's interesting you started in marketing. I think it teaches you the application of data and the outcome that data allows you to facilitate. And you know, it is the lifeblood of the organization. It is the fuel source to drive personalization. And the principles of marketing. Once started with, you know, proposition and understanding your territory, which all and market which is all data anyway and now has evolved into like this right time, right moment, right content. We've all heard the jargon a million and one times, but there's so much we can do if we get that right. And AI is just the next maturity section of the data journey. And AI is such an interesting topic. I know a lot of people always say like, what is your philosophy on it? What do you think about it? But I'd love to ask you that question. Can you give us a bit of an AI101 and conceptualize like what is AI and why is the data so important to be able to utilize it?

Speaker B: Yeah, no, thanks for the question. I think the first thing I'd like to say is AI is an outcome of data capabilities. It's a use case, it's not the journey. Um, and I think a lot of times, especially now with Genai being a big part of our lives in whichever formal shape you use it, it's always assumed to be the entirety of everything, but it's not, it's actually an outcome. And so understanding the use case to deliver AI outcomes is really important. And uh, not doing or delivering AI objectives for the sake of it or just because it's a fad or a trend as you were saying earlier on, but because there is actually a need within the organization for an AI outcome and it's going to deliver better. Ah, better revenue, better deliverables, more efficiency, productivity and automating. It is actually going to help you to focus on better productivity measures, better projects. It's not a one size fits all. You know, Genai is a great subset of AI and it's a great opportunity to start thinking about AI in the organization. But it's not necessarily what every organization needs. So it's again, you know, don't do it for the sake of doing it, but really think about what problem are you solving.

Speaker A: It's a. But it's such a PR statement.

Speaker B: It is a PR statement unfortunately. But the reality is I think it's just been blown out because it's so accessible now. Mhm. But something accessible doesn't mean it needs to be necessarily, uh, embedded into every single organization. Every organization may need AI, but I think it's so specific to the need and the problem you're trying to solve. Right, exactly. Um, but also AI is also about inclusivity at the same time. It's also about diversity, um, and how it applies to the general, uh, workforce and how you want to do business and how you want to be able to resonate and attract your audiences as well.

Speaker A: Yeah, I think there's a lot of confusion around the difference of machine learning versus AI. You know, they're very much overlapping topics. Do you have a perspective on simplifying the noise in that question?

Speaker B: Correct. And I think at the same time, before you start thinking AI strategy, maybe first take a step back and think data strategy. Um, because AI is a part of that data strategy. If you don't have good quality data, you don't have a process around managing, collecting and storing your data, then you're probably not going to have a great AI strategy to drive because they kind of work hand in hand with each other. And it's also all about who are you serving by delivering this outcome. Are you serving the organization, your internal stakeholders, or is it your customers who actually help to drive your revenue feeds?

Speaker A: Mhm. Yeah, that's a really good question. And I think you've seen Elon Musk kind of positioning himself in the way that, you know, Tesla is using AI and his kind of position recently on if you're using an iOS operating device, I'm going to remove you from my X platform because you know that that device now has access to OpenAI and you know he's competing with Sam Altman and you know, there's just, there's so much happening in this space. It's, it feels like the application, like you just said, is it internal use cases or is it external for your customers needs to be created before you start just riding the hype. TR so let's talk through some of the applications of AI. Um, what have you seen as some of the most common applications and use cases versus some of the things that you see are driving some of the biggest and best results?

Speaker B: Um, it's both. Again, it's internal. There's internal applications as well as what your customer or your front end user or your front end, um, person, ah, who actually consumes and pays, pays the bills as such would find. Um, I would say, you know, there's so many applications for it. One is content creation. And I mean, who hasn't tried to get an email to sound better than the way you've written it, Right? So that's one way in which, you know, as consumers, we do it for imaging, for videos, for content creation, for ideas, um, understand the gaps that, you know, possibly you don't have to do by synthesizing all the information on the website yourself. You can now get AI to do that for you, or Gen AI for that matter, to do it for you, which, which is really, um, you know, a really efficient way of managing time, uh, but also getting ideas out there that you may not be able to necessarily do by yourself by creating your own content in a way that's meaningful for your audience. Right, so that's one way. Um, but also I think from a university perspective, the tertiary perspective, there's so many um, students who are now utilizing it to get, garner different ideas but then also be able to implement that into their daily lives as part of their operating rhythm, um, as part of making their work more efficient and building out models that can actually look at innovation, can drive innovation and hopefully solve some problems. I think one of the other applications I've seen it work really well is in climate management. Um, so there's a few organizations out there that are trying to build out models to help resolve some of the climate crisis issues that we've heard about or that we're trying to face through better modeling. Um, and that's a social good aspect of it, right? If we understand our environment from a weather, a climate, uh, or an environmental perspective better, we might be able to mitigate some of those issues. We're not obviously going to solve all the problems, but as people, if that information is provided to us through AI modeling and predictions and forecasting, possibly we can start to take some actions towards that. And that's really important because AI for social good is again about um, making life easier for people without always thinking about the profitability as well as uh, uh, the revenue of it, but really trying to look at ways in which people's lives can be made better and more efficient. If we look at even um, areas around disability, for instance, AI for social good has so many options and opportunities to really enhance the lives of those who are affected, um, by disability situations or situations that are not in their control. I mean can you imagine if you had AI help with caring capabilities, you would um, help to save the economy from a cost point perspective. But at the same time you'll also be providing support for the carers who need to help people in those situations or directly to the person in that situation. Um, and there's ways in which that can really help. Um, the other aspect I also look at it outside of disability is healthcare in general. How many times haven't we heard of hospitals still using fax machines, right, to get information, to share information. Um, that alone is such A great opportunity just from a back office perspective to start using AI to help um, alleviate some of those problems and make it easier for our health care system to handle the challenges that come with patient care, hospital care and administration. Um, and then really focus on those cases of actually improving the quality of people's lives. Um, I think that's some of the things we need to really think about from a front facing perspective. But within the internal organization, whatever organization that may be, um, there's again ways to optimize the way we use our data, manual management of data, the way we collect and having to manually um, look at rows of data to clean that up for meaningful analysis. Or um, being able to assist contact centers to synthesize information in a much more relatable way that can help um, the customers achieve outcomes. You know, if there's a complaint, resolve the complaint. If there's a praise, provide that feedback back to customers service who can really then use that information to make better decisions. Um, so from that perspective there's always ways in which to optimize and really improve the quality of our data, either as a consumer of it or as a back office support, uh, mechanism.

Speaker A: Yeah, I think this way you can unpack this in so many different ways. I think you know, the first kind of application we saw of AI and we ran some research recently with um, with Arctic Fox and uh, Six Degrees recruiting firm. And we asked like, what are the actual applications that people are using AI inside their workforce and where it requires clean data and accurate data you're seeing too hard. But where you're seeing opportunities for what it really is, just operationalizing, um, mundane tasks like writing me an email or you know, aggregating some information for me. Um, you're seeing a lot of adoption in particular content creation. It seems to be the number one use case right now. And the content that we're seeing people create with AI is quite interesting and daunting at the same time. Like I heard of Frank Sinatra song yesterday and it was actually an Eminem M song that they just used Frank Sinatra's voice to sing. And um, it, you know, it feels like there's a lot of gimmick use cases that are being applied, not real business use cases. And I work for a company who obviously is embedded in the AI ecosystem. And you mentioned one of those use cases, right? Like cleansing and analyzing lots of data in a matter of minutes, which would take the human eye a matter of days if not weeks to do. And that in itself allows companies to start using that data in Better ways. So, um, we talk about this big movement in co pilots. You know, you're going to see a lot of companies launching their own, ah, copilot. A version of that copilot is probably like Gemini's AI, right? So Gemini's Google's version of AI where you can put in a statement like create a picture of uh, a bulldog riding a skateboard, um, and it will create an image of a bulldog riding a skateboard. You can then say things like, well, make it more gothic or that bulldog doesn't look quite cool enough. Right, so give it a hat. And it does this stuff in a matter of minutes. I actually used it, I used it for a cd front cover for a personal music, uh, project. And uh, I created this Image in about 10 minutes. And uh, it's quite cool in my opinion. Like everyone's like, wow, your design skills are great. They were. I, uh, don't really use Photoshop anymore. But it was so good, it was so powerful that you're right, like there's use cases of where I don't need to provide IT data, I just need to provide a reason of questioning I can accelerate my output if I know what I'm trying to do with it. Right. That's the, that's the question that you kind of asked before around data strategy. So, uh, it's not just a chatbot that can respond to a couple of customers that are asking questions if that chatbot can actually provide value. And I've heard of some really dark stories of some chatbots that have gone absolute rogue and provided refund policies. It's uh, it's interesting. So, yeah, thanks for that information. Um, and obviously with your, with your history, you know, you've obviously been in positions where you've, you know, studied and used some of these tools. So I'd be interested to ask you the next question around hype versus reality. What have you seen in the lens of people talking about it, as opposed to businesses or colleagues of yours that work in this industry that are actually applying it?

Speaker B: Yeah, it's a really good question. I think, um, if you're looking at tech driven companies or companies where data is the core of their business, they've probably jumped on that wagon really quickly to enable AI capabilities or enable more sophisticated data capabilities, whereas the broad majority of organizations do not fall in that category. So for them it's a lot of fomo. And for listeners who are not sure with fomo, it's fear of missing out. And so I do see, uh, that is one of the Prompts for organizations now wanting to rush into the AI, um, generation that we're going into, it's not necessarily whether it's a use case that will actually solve business problems. Um, it's nice to have, but again gen AI in general are so dependent on data and the number of organizations that we know of that do not have good data practices are not going to have good AI practices. And I think if we just look at the last 18 months to two years, just in Australia alone, um, and I have spoken about this before in previous, uh, engagements. Just looking at some of the leading companies that have fallen short of their data policies and principles and the breaches that have taken place. Um, I would not trust any of the AI models that would come out of organizations like that. Because if you don't know the lineage of your data, if you don't have a good understanding of the governance of your data, how can we trust the AI models that would come out of that data? Um, but it's also the security of it. How do you know that you've got the best and latest information, um, available to you? Um, but also how are you validating it, how are you monitoring it and uh, working out whether this is the right data set for our need for our use? It'll all come down to the strategy. What are you targeting, what are you focusing on? Um, so yes, I do think there is definitely a lot of hype for the whole feeling of fomo. Um, there are definitely cases where there is reasonable grounds to initiate such initiatives. Uh, but at the same time it'll all go back to your fundamentals. What is your strategy? What is the technology that's going to be managing this entire project? Because yes, technology is an enabler, but the end to end journey is really data. And how have you built the foundation for that? I mean, you know, there's also the huge um, conversation around regulation versus ethics versus responsibility, around AI and all of those are independently different. They are, uh, related, but they're actually different pillars that need to be considered within an organization. Um, and how transparent is that information? Do we have policies that help to garner those key areas? We don't. I mean Australia is still working on the laws, um, that need to help, um, provide those guardrails to us.

Speaker A: 38 new laws passed. We're on the march. 38 new laws passed, but we're on the march. We're still on the way there.

Speaker B: Yep, yeah, exactly. I mean we can't compare ourselves to the eu, right? They have far more robust and stringent policies. I mean GDPR has been around um, within the EU for almost 15 years now, um, 10 to 15 years. And Australia has not taken the full encompassing view of gdpr. So um, there's still a long way to go. And it's not that we're not going to get there, we will get there, but it's again used by use case by use case. Um, and as much as we have the talent to also manage all of that, honestly I would not expect AI to come up with the laws. It'll have to be humans who can help to deliver those policies and laws.

Speaker A: Uh, agreed, agreed.

Speaker B: Yeah. And I think also I go back to what I said earlier on, which is just because we have it, does it really mean we need it? And if you do have it, how are you going to protect it? And I don't know how much of that features in the strategy.

Speaker A: I don't think it does at all. Or it sits in a contract with your vendor and it's the vendor's responsibility and I think that that's, you know, software is, is, is owned by the, both, both the business and the company, the ah, vendor. So anyway, that's a, that's a, I could go through a whole rigmarole of issues with that discussion. Um, but you know, uh, you also spoke about, you um, know, hiring and resourcing this and I just find that it's such, it is still such a hype cycle. Like I spoke to a recruiter recently on a, on a panel and she, she mentioned that there aren't in the jobs that are people are recruiting for nowhere in the skills assessment do they require AI, ah experience. Like it's such a, you know, executive kind of PR statement right now that you need to go out and do these projects for AI. AI built this ad or you know, AI is now the backbone of our customer um, service experience. It is for pr. And even like her comment was, we're not even seeing it in job descriptions at the moment. We're not even companies that users aren't even using it as part of their hiring strategy right now. So it is this kind of uh, mythical discussion still. Have you seen any use cases or models that you've rolled out or you've spoken to people that rolling out that are effective in this space? I have a few examples of what I've seen, very much business focus. But uh, I thought I'd pose that question. Have you seen anything you could talk about?

Speaker B: Yeah, I mean there's quite a few, I mean that I've worked with over the last few years. And they're not necessarily gen AI. So I just want I guess the audience to understand that AI is much larger than just gen AI. And um, there's so many awesome, creative, innovative um, solutions that we can leverage from AI. It may not necessarily be as sophisticated looking um, as gen AI where it's so accessible, but it can make the back office um, so much more efficient. Um, so one that I'll leverage is one uh, that I did a few years ago around employee health and wellbeing. And it was a product um, that we launched um, as part of the organization that I worked in which was really around understanding how do employees interact with each other, manage their workloads, but also care for their well being. And so it was like a program that went into monitoring how often do employees stand up in their meeting or stand up when they're doing work. Which kind of was before the whole smartwatch, um, evolution. Um, so it was actually collecting data on employee behavior where they just always tuned to their screenshots, um, did they have walking meetings or not? Did they do activities outside of two hours perhaps, and monitoring their steps, monitoring the activity level. So there was a whole wealth of data that went into building out these models. Um, and that was a model that we sold back to the client to basically talk to them about how their employees wellbeing was going.

Speaker A: Interesting.

Speaker B: So it was sort of a challenge that um, employees took up to predominantly be a part of the movement of you know, it's not just about eight hours of work, we have to take care of ourselves as well. But it was also an opportunity for the organization to build out a benefits program. So attracting new employees, helping existing employees to take care of themselves and then you know, go out and request for you know, essentially either funding or new ways in which to build out their workforce. And this was a whole load of data that was modeled specifically for an organization that was outlined. And they were a good number of different organizations we were working with at that time. And essentially that eventually led to the evolution of smart watches and wearables. Um, but this is a few years before that. Um, but now all that technology collects this data that as an organization if you wanted to, you could really leverage that to um, prevent turnover, attract new employees and also create a more um, reflective or a more resonating benefits program for employees. Because again the ways of working have changed and needs to change. So there's all these things you need to consider as part of that. So that was a really.

Speaker A: Yeah, or you can publish that you can publish that uh, on a TV screen and get some competition internally on who's the healthiest employee. I'm just joking. I'm so kidding. You don't share that information, guys. That is completely personal. But I'm seen gyms.

Speaker B: You could always have always a pseudonym.

Speaker A: That's it.

Speaker B: Yeah, um, exactly. And so that was one way in which we um, you know, had some really interesting use cases around building out those AI models because you know, there was a lot of automation involved, but there was also a lot of synthesis of data that was involved to present back as a model as something that could be actually used in a meaningful way for the organization. Um, so that was a great story. That's in terms of employee well being.

Speaker A: That's it. There's, there's employee well being and employee productivity. Um, it's such a, um, important measure. And you guys obviously realize that, you know, wealth and knowledge and employee satisfaction. Yeah, better performance and productivity of the organization. So you definitely want to invest in your people. So the data strategy was quite clear what you had to do. And I think of the, you know, just three basic pillars of AI that we use inside our organization is obviously you talk about operationalizing data. Well, we use AI to stitch, uh, large data sets and normalize large data sets to be unified in a way that makes sense and is digestible. We then use AI for being able to predict those insights. So I'm assuming what you're talking about there is both calculating and predicting a value from that data, taking in all those large data sets. And we work with obviously businesses, so we're predicting things like lifetime value or are you going to churn or if you are going to buy, what are you going to buy within a certain subcategory? So you're going to buy a pair of shoes. Are they sporting shoes? Are they dress shoes as an example, and if so, how much do we predict you're going to spend? We need four years worth of data to predict that. Right. That's not like give us six months worth of data. The models are uh, built around a certain threshold of data. Um, and then the third thing that we're trying to help companies do is because we still notice that data literacy is so large inside of organizations, not a lot of people really know how to use the data once they even have it is being able to use natural language to ask the data questions. And that's the next part which is just getting insights from the data, asking it questions like you would ask Google. Um, so do you do you think we are going to see a Chief AI Officer anytime soon, sue, or is that another unicorn?

Speaker B: It's a good question. Um, I think it's evolving in other parts of the world. I haven't really seen that happen a lot in Australia yet, but could come in the next 12 months. Um, but yeah, again, my question is, do you need a Chief AI Officer? Do you need a Chief Data Officer?

Speaker A: You know, it's the same thing, isn't it?

Speaker B: One in the same sometimes, right? Because again, AI is the outcome. Data is not the outcome, it's the journey. Um, so, you know, you really need to think about, you know, having a Chief AI officer. How different is that going to be to a Chief Data Officer? And interestingly enough, a lot of organizations that have a lot of data sometimes don't even have a Chief Data Officer. They have a Chief Product Officer where data is a product. So, um, they sell it to the rest of the organization as a product. Um, and so there's all that sort of concepts and thinking to consider when you're building out that operating model, which of course needs to change as well. Because it's not just about having a chief AI or a chief data or a Chief Technology Officer. It's all about how does that then synergize with the rest of the organization and the objectives. Because we no longer work in a silo. And if you are, the, uh, that silo is going to break very soon because it's very collaborative. We have more than ever matrix reporting lines than we did, you know, in the last ten years. Um, we now have exactly as we were talking about product models. And the operation model is a product, um, and that needs to work in a way that can manage BAU business as usual, um, projects and, you know, transformation projects. And, you know, transformation is now an area that is not just a small stint. It is an ongoing capability. No, it doesn't. And it's even, you know, some organizations that can afford it have a Chief Transformation Officer. So how does all of that then work with the rest of the model? So, yeah, you can have a Chief AI Officer just make sure it's not a Chief Data Officer, because they're kind of going to do the same thing.

Speaker A: Yeah, so that's my job. Hang on a second. That's my job.

Speaker B: Exactly. There you go. And then that's when, you know, all the, the cards will fall down. You know, that's my job. Why are you doing it? You know, so. Exactly. So again, you can have a Chief AI Officer. I'm not against it, but just be clear about what the objectives are and whether you need both a chief data and a chief tech and a chief AI officer.

Speaker A: The irony, the irony of this discussion is we had to put AI on our website because, you know, if we didn't have it on our website, people just assumed that we didn't have any AI capabilities. That's most ridiculous thing. But sometimes you just need to promote yourself because, you know, you can get lost in the uh, in the ether. But, um, you know, now rounding off this conversation, I think it's important to start talking about, you know, we spoke briefly on this around ethics and regulations. What are you seeing in the market around, you know, either backlash or, um, ethical challenges and like, you know, deep fakes and things that are starting to really become quite sinister in this market.

Speaker B: You know, it's, it's a black hole. It's m. A black hole. And the reason why I say that is I think, you know, only when you start imposing fines and penalties and laws either on organizations or people, will they take it seriously. And up until then, basically there's a loophole. And the loophole is do whatever you want. So there's a law. Um, and that basically means you give a lot of opportunity to the sinister, dark, um, data and dark Internet world out there to take advantage of the situation until there's laws in place. And that's the challenge, is that the laws are not catching up fast enough and we don't have um, enough people to break away, um, from identifying when something sinister or dark is going to happen. It's almost like it has to happen and then we'll do something about it. Um, and that's actually the challenge. And I think one of the other challenges are, ah, as there are more laws in place, as there are more penalties in place, organizations will have less control over how much they can do with the data. With this whole, um, one of the examples is this whole cookie list world that we're entering. That's right. It takes away a lot of control from organizations which means they can do a lot less, but they can do things more with innovation. And that's one of the things that has to change is you need to find more innovative ways, um, to support customers or support your initiatives whilst protecting your customer. Yeah, so it's that transparency of that

Speaker A: relationship is so important. You know, if a customer is. And one of the things that are, it is in the current, uh, privacy. It passes the privacy laws for Australia was around automated decision making, um, and having transparency. How those AI models are, uh, making decisions on your customer's benefit. For your customer's benefit. Right. So uploading my driver's license and there's some AI bot that's scanning it and making a decision based on, I don't know, race, gender, a certain set of outcomes. Well, you need to make that clear now. It's not going to be buried in some T's and C's. It might be. Um, but like you said before around the cookie deprecation, that's been kind of coming to a point now for so long purely because of customers not clearly understanding these free platforms are collecting and on selling my data and on selling it to people that they just shouldn't be like, you know, you're collecting it, doing something with it. I should, I should know that, um, it's this form of transparency that's being intersected, um, at that kind of handshake between customer and brand or customer and platform. Um, super interesting to see it kind of start to just become normal practice for businesses. Now I'm in a seat where I see this all the time. Businesses are really starting to figure out how to, how to do this and balance this. So many other initiatives you mentioned last time we spoke around this $200 million Hong Kong cybercrime, and it was, it, uh, kind of blew my mind. But did you want to kind of illustrate just how powerful some of these, you know, these, these tools can be at being sinister?

Speaker B: Yeah, I mean, that was, you know, basically a big bank in Hong Kong losing, I think over $200 million, um, to basically deep fake. And the deep fake was so good, um, that it was able to, you know, get one of their chief financial officers to sign off a massive check, when in reality, uh, none of those people were the real employees of the organization. Um, but, you know, of course, definitely go and investigate that case, read a bit more about it. But it's becoming dangerously close to home. It is almost impersonating a person when they're not there. And that is very scary.

Speaker A: Voice expressions.

Speaker B: Yeah, correct. And it's getting so good that, you know, it could easily be you and me. It could easily be any one of us. Right. And you wouldn't know. And I was going to say earlier on, you know, I think there's at least two or three times more the number of mobile phones in the world than there is actually people. And can you imagine the amount of data you are sharing on a daily basis to the hour, to the second, with anyone, um, in the virtual space that can access that information? Basically everything you do can be created into a personality. And that could be a deep fake of any individual person. And that is when it can get really sinister and really dark. Because, you know, it could be a real profile of you or it could be an absolutely incorrect profile of you, but mimicking everything from your voice to your face to your, to your skin color, whatever it may be. And we don't have any laws or control over that. Um, and you know, we don't have a safe cyber environment where we can even report this information and it can actually be dealt with. There is really no serious laws on cyber crime yet, because people are getting away with it yet. Yeah, yet being the keyword yet.

Speaker A: It has to happen. It's so important.

Speaker B: It does. And yes, as much as it means it takes away some control from organizations, the reality is each and every one of us, whether, uh, we are employees or we are senior managers, whatever that may be, at the end of the day, we are all consumers and we are all a, uh, target for really dark, sinister activities in the dark web.

Speaker A: And just to be clear, and I want to make sure people listening understand this data isn't just a line of data in a database. That's one thing. But if you think about, uh, just unstructured data, things like audio, right, that can be turned into a line of data into a database, things like an image that actually is a digital image that actually has data behind it. And there are technologies today that can scan those images, live video, and start to really understand the makeup of that video. Uh, is there a lamp in the background? You know, what's that postal address where that photo was taken? And it starts to become a little bit like, you're right, uh, sue, what do we allow versus what don't we allow? And I know there's tools out there that are starting to try and become available to block things like stealing someone's digital art. If I'm a photographer and I put all my image online, what's stopping OpenAI to crawl and steal my library and use it? Um, so, you know, there's some really exciting, innovative brands and startups that are starting to try and fight this problem. Um, it's just another exciting time to be in tech. Right? Never stops.

Speaker B: It is. And that's so true because whilst we have automation and we have, you know, the fear of AI stealing our jobs, it actually opens up many other opportunities around social management, around, um, you know, regulation and responsibility. Seriously, we cannot rely on an AI to do that for us. That is not the job of AI. Um, it is our job as humans, and that is why being part of the human in a loop, in the loop sort of phenomenon is so critical. Because as human beings, as much as we love tech and we love the sophistication and advances of our world, we need humans to help manage that circle, manage that ecosystem, um, so that we don't end up having dodgy AI or that we don't end up having AI that goes rogue. So I would say it's really, again, important that we are a part of the system, not outside of the system, and we shouldn't be living in that fear that AI is going to take over roles. Um, the jobs of yesterday may not be here tomorrow, but there'll be other jobs. It's also, again, about staying relevant so that you can always be a part of the advances of technology, data, and AI.

Speaker A: Yep, yep. There's, uh, there's roles that are going to be around prompt creation. Google that if you don't know what that is. But it's basically being able to write languages for AI to perform certain tasks. Who would have thought? Um, so. So, sue, thank you so much, uh, for having this chat. I've got two final questions for you, and this is standard for all podcasts. What is the one skill based on our discussion today or. Or one thought that you think people need to take away based on what we discussed?

Speaker B: Yeah, good one. It's, uh, a good segue to what I just said earlier on M. I think one of the key things is we have to manage change. Change is inevitable, but, uh, part of that change means to be able to continuously learn. Um, I think if we're worried about jobs being stolen by AI, then just continue learning, because there's always a job out there, um, to stay relevant. Um, so my biggest, um, interest and something that I do love doing is I love learning. Um, what I knew last year is very different to what I know this year, sometimes better. So my encouragement is to keep learning.

Speaker A: Awesome. And, uh, avid listener to the podcast, I assume. Um, and, uh, last question. Buzzword. Bingo. What's your favorite buzzword of 2024?

Speaker B: Goodness. There were so many, but I'll stick with this one. Um, Synergy. I think that's my buzzword for 2024.

Speaker A: Synergy. Where have you heard that?

Speaker B: You hear that a lot with AI. I hear that a lot, but I also use it a lot.

Speaker A: Synergy. I use it a lot, too. Now that you've said it, I'm like, geez, I think I've got three slides that has the word synergy on it in my next presentation I'm giving.

Speaker B: There you go.

Speaker A: Awesome.

Speaker B: Amazing.

Speaker A: Uh, I'm sure we could pick this discussion back up in six months and have it all over again, because things would have changed so dramatically by then. Both in regulation and both in applications.

Speaker B: Awes you so much, Billy. Great conversation. And, uh, looking forward to collaborating. Collaborating with you again.

Speaker A: Cheers, Sue. Uh, appreciate it.

Speaker B: Thank you.