Speaker 1: 0:23

Hello everyone, welcome to another episode of OpsCast brought to you by MarketingOpscom, powered by all the MoPros out there. I am your host, michael Hartman, flying solo, which is all good, but I am excited to talk to you with our guest today, yomi Tejemola. I think I already butchered it, yomi, so you'll have to correct me again, but we're going to talk about a topic that is interesting. The evolved marketer Yomi is the founder and CEO of Algo Marketing. Algo Marketing leverages machine learning and automation to empower teams with deeper insights, faster execution and streamlined operations. He has over a decade experience in leading the development and deployment of AI and automated marketing solutions. Yomi is a pioneer and visionary in the field of algorithmic marketing. Yomi has a proven track record of delivering innovative and impactful projects using machine learning and automation, such as recommender systems, predictive modeling and time-series forecasting for Google Ads and Google Cloud. Yomi, thanks for joining today.

Speaker 2: 1:21

Thank you, Michael. Thanks for the intro.

Speaker 1: 1:23

Yeah, All right. So all those times I think I got your name close to right, but I'm always skeptical of my own pronunciation.

Speaker 2: 1:33

As I said, just pretend you're Japanese and you get it right.

Speaker 1: 1:36

All right, there you go, hi, I'll do that, all right. So I mentioned in the intro that we're going to be talking about something called the evolved marketer, but let's start with a maybe definition. What does that mean?

Speaker 2: 1:54

Sure, absolutely. The idea of the evolved marketer represents a new breed of marketing professionals who have fully adopted AI and automation not just as tools to simply speed up existing workflows, but as catalysts for deeper transformation. This transformation comes from the long-term effect of being free from repetitive and mundane tasks to unlock more mental energy for strategic thinking. Unlock more mental energy for strategic thinking, creativity and true innovation. Ultimately, the evolved marketeer isn't just more productive, they're happier and even more fulfilled, and they're better positioned to thrive, both personally and professionally, in this new area of work.

Speaker 1: 2:47

So at the he says I think I missed the phrase completely but it's like fully adopted AI, so does it have to be fully adopted or could it be? You know, could you be like me? Who's like dipping my toes in the water? I've come from being a skeptic to. I'm a believer that potential. I just haven't spent a lot of time actually using some of the tools yeah, absolutely.

Speaker 2: 3:16

I mean it's. It's like evolution, right? Um, there will be different stages of that evolution. Um, a fully evolved marketeer would be someone that has, you know, hit that threshold of fully adopting AI and automation. And adoption is a key term there, in the sense that it's not just about you just dipping your toes or just having a few tools here and there. You've actually adopted it into your daily routine. Rather than it being an extra tool you're using, it is the tool, it is the means by which you actually accomplish your work.

Speaker 1: 3:52

So almost like a habit that just becomes, you know, like it's just memory, like you don't even have to think about it. Is that kind of the goal? Is what you're saying?

Speaker 2: 4:01

Correct, correct and it's being also seamlessly embedded or integrated with your existing workflows, makes it easier to adopt, right and it works hand in hand you adopting it, but also the tool being readily and available there for you to adopt and use. Sure.

Speaker 1: 4:22

So I mean, I'm sure there are people listening to this who are maybe even not just skeptics about AI, but also concerned about AI and you know what its potential impact is to their job. Will they have a job? Things like that. But maybe even go more at an abstract level, like how because this I mean you said evolved marketer. It feels like this is something that could be bigger than just marketers. So how are you seeing, or how have you seen, ai impacting human beings in general?

Speaker 2: 4:57

Yeah, I believe the long-term impact of AI would be to catalyze the next evolution of our species. It is a grand statement to make, but if you just hear me out here, um, I mean, there's a grand statement as we discussed before.

Speaker 2: 5:14

So neuroscience shows that when we engage in repetitive tasks, our brains fall into fixed patterns. Right, um, we're running the same mental algorithms every day. We we're essentially running on, say, mental autopilot. This repetition solidifies neural connections in our brain, which actually limits our thinking and creativity. However, when you throw AI into the mix and AI takes over the mundane, our brains break free from these repetitive loops, our brains begin to rewire new neural pathways, which then sparks fresh ideas, creativity, innovation, innovation, and if you think about this happening over a long period of time, this continuous rewiring, also known as neuroplasticity, yeah, um, can literally reshape our brains. Right, forming, always forming, neural, neural, neural pathway, new ways of connecting, um, the dots, um, this is actually reshaping our brains over a long period of time, which empowers us in going into this new version of human species that we're approaching.

Speaker 1: 6:55

It's interesting because I'm familiar with neuroplasticity in the context I've usually heard it brought up is actually after a brain injury, right, and yeah, going through, essentially I'll call them in quotes, right exercises that help to either rebuild, not rebuild, the pathways, because once they're kind of broken, they're broken as I understand it in the brain, but they're you like, build back up pathways that have been neglected is the best way I to think of it.

Speaker 1: 7:28

So it's interesting because I've never, I think when you and I talked about the idea of neuroplasticity and a beneficial effect of AI, it caught me off guard because it felt like to me, at least quickly thinking about it, I would have assumed sort of the other effect, right, quickly thinking about it, I would have assumed sort of the other effect, right, ai is going to make it that we don't have to think as much and therefore, like, actually we'll actually reduce some of those pathways in our brain. So to me it's a little bit counterintuitive. I mean, was that relatively easy for you to get to that kind of understanding, or is this something that you struggled with as well?

Speaker 2: 8:09

to get to that kind of understanding, or is this something that you struggled with as well? Yes, actually, in the, in the initial um onset of ai coming in, that was my um concern and understanding onto. We started to experiment and see firsthand how it is actually affecting people, um, especially in our work with our clients, that we started to notice that, oh, there's actually a new trajectory to where AI could take us, right. The initial onset is that you use AI and then you know you throw things in there, you don't have to think much and it gives you outputs and so on, right. However, when you combine ai and automation, right? So there's two key things there. One is the use of ai, the second is the automation part and intelligent automation, um, to help take away those redundant and mundane tasks in our day-to-day life over a consistent period of time.

Speaker 2: 9:14

The absence of those things actually start to make us want to create more, to innovate more. It actually gives us that mental space to do these things. Another aspect actually is that. Another aspect actually is that to be able to be successful in using AI requires you to be creative and imaginative, right? Sure, that is going to become, I guess, the new competitive advantage, if you would like, in the workforce. The new competitive advantage wouldn't be the skill you have in a specific subject, it would be how imaginative you are. How creative are you? How imaginative, how much can you push the boundaries of AI to give you some outputs that has never been seen before? Right, and because of this, that becomes something people want to tap into a lot more. And then you know that neuroplasticity kicks in of wanting to be more imaginative. Use that nascent aspects of our brains a lot more, and, yeah, and.

Speaker 1: 10:24

I think it's. I mean, it's a fascinating idea, like, as I said, like it's no-transcript, so I can only imagine there's lots of potential there. So you talked about this idea that repetitive tasks kind of go away, and one of the things I think is interesting is that I've heard different models for how we perceive our knowledge over time as we become more expert in a task or a subject or something like that. But one of the things is that you said that over time, if we do something on a regular basis, 80% of our lives is work and that if we're doing that on a daily basis, then 80 of that work is is repetitive. So what, like? What is the implications of that? I guess I'm gonna assume that's mostly in your eyes like a net negative, but maybe there's some net positives as well yeah, yeah.

Speaker 2: 11:45

So so the concept comes when you consider that 80% of our conscious adult lives revolve around work ages of 21 to 65, average retirement age and the conscious part of that time, which is the waking hours, 80% of that period revolves around work. And also, when you become, research shows, when you become a seasoned professional in your work or in your art, you spend roughly 80% on the day-to-day on repetitive tasks. Now, the implications of that are profound because over time, this repetition hardwires our brains into fixed neural pathways. Right, having those fixed neural pathways makes us less flexible and adaptable. Now, when we um, grow in quotes, grow old and or, and then we have this uh, you know, our ability to change careers or lifestyle or our mindsets, we say it slows because we're getting old.

Speaker 2: 13:12

It's actually not true. It's not just age that makes us less able or less, uh, flexible, it's the what I call the mental rots in our brains that have been carved by doing the same thing over and over again. Um, that makes us less flexible, not age. So essentially that means, if we, if 80 of our lives is work and 80 of work is repetitive and mundane, essentially that means we've become human robots. Right, we're just running mostly on algorithms, into locked predictable algorithms that limit our capacity for growth and reinvention so it's interesting.

Speaker 1: 13:54

So one of the analogies I've heard about this sort of the way in which the the wiring in our brain for lack of a better term, really the pathways happens over time and it starts from when you're an infant all the way through right is that the pathways that get used a lot get built up, so those become the freeways, you know highways in your brain, and the other ones slowly become, you know, dirt roads that have ruts in them and they're not very efficient and when you do try to use them, it's super slow. I think so that it's interesting. You had the ruts analogy.

Speaker 1: 14:30

It feels like to me there's also a bit of a risk on doing these repetitive things is that it also could lead to mistakes or danger and they simply I was just driving. So it's top of mind is every few months I'll be driving, and it's so rote and I do it all the time that I'll forget to do the simple thing like looking over my shoulder before I change lanes. You know what I mean, and then invariably I'm going to have a situation where I forget to do that and somebody's right there, right, and I almost have an accident. Uh, hopefully, almost not have an accident. Do you think that's a potential risk too, of us doing this repetitive stuff? We just sort of stop paying attention to the details in some ways.

Speaker 2: 15:14

Yes, and that's absolutely true to our environment. When our environment changes and we're fixated on a specific pathway or an an algorithm, if the, if that environment in which the algorithm is running changes, then that algorithm algorithm is no more useful or functional for that environment, right? So in your example, the environment uh changed, could have changed, which is it's no longer safe there. There's a cyclist or another car and so on, and with that change, because you're running your loop of algorithms, then you wouldn't pay attention to that or be able to flexibly adapt to this new environment.

Speaker 1: 15:59

Yeah, so I think it's interesting. I think I felt like I was going to come in here and be devil's advocate. I keep finding myself agreeing with you, so I don't know if that's a good thing or a bad thing. So you mentioned the idea that that you know those who adopt AI kind of eliminates it, because in automation, ai and automation it sounds like it's the combination that really is the game changer would enable us to, you know, not spend as much of our time on repetitive tasks and therefore lead to more creative ability. So maybe talk through a little more about like, how how would that mechanism work? How do you, how do you see that playing out? And have you seen it playing out yet?

Speaker 2: 18:02

yeah, so you mean in terms of um how the like I still, I still.

Speaker 1: 18:08

I think there's a step here to me like, okay, I think I bought, I bought into the idea that the repetitive tasks create ruts, automation. Then how does that then generate more creative capability? That's really what I'm trying to understand.

Speaker 2: 18:42

Right, right. So if you imagine I mean what we've seen if you imagine you know you're, you know in in the workplace, you imagine you know in the workplace you have targets or OKRs, or you know KPIs to achieve on a day-to-day. To achieve those targets you know could involve, perhaps, say, repetitive tasks like creating reports or creating your weekly presentations to show you know progress and so on, rather than how to actually find new ways or how to actually thinking about new ways in achieving those targets. Right, when I think so. Coming back to your question, work has become very. Work has become very process oriented, for lack of better terms, in order to get things done, Simple things like hey, as a marketer, I need to launch a campaign targeting this audience. The amount of process involved to get there takes up all the mental energy that would be rather used in finding, you know, creative or innovative ways of launching that campaign. So what people would resort to is just the the repeating what's worked in the past.

Speaker 2: 20:32

Repeating what's worked in the past. What's worked in the past. Okay, I'm just going to do so. Today. We have a set number of channels that we use for campaigns, right, events, um, email, yeah, podcasts, sure, webinars and so on. But then, in this new age, perhaps we can actually start to think about new, a new type of channel, a new way in connecting the dots to, to achieve that campaign. So it's a, it's a. It's a function of capacity, both mental capacity and your capacity to, to achieve things, and, yeah, just the amount of process that's involved in actually achieving or trying to accomplish a task.

Speaker 1: 21:19

Yeah, you know. So it's just. I think what occurs to me now is that really the big differentiator here is time, right? So regardless of whether or not you think it actually has an impact on your brain connections directly, that leads to creativity, you are freeing yourself up for time to do something else. Now I could see one argument where that means you go and you you know it's a brave new world kind of thing, right, you go, you just do crazy stuff. It's not necessarily productive, or you can start to use that time to do to, to look for more insights, come up with new, creative ways of achieving those goals. You know new ways of communicating, like all that kind of stuff, and I think that's so. It feels like, even if you don't necessarily, this is is a message, I guess, for the audience right, even if you're not totally bought in or understand the mechanisms, the way your brain can or would change through this, if nothing else, you're buying back time to have the opportunity to do those things more creatively. Is that a fair statement?

Speaker 2: 22:30

Yeah, that's a fair statement, but I would also add to that, and just to add to why it's not just time right, there is a time element, but it's also. It's not just repetitive tasks, but also mundane tasks, and mundane tasks are those tasks that are boring, or I think the brain finds boring.

Speaker 1: 22:49

I have such a hard time just getting started on those and they're part of day-to-day work. I get it, but it's so hard for me yeah, and, and it's, it's taking that away.

Speaker 2: 23:03

Um, even if some of those mundane tasks are not time consuming, um the mental energy and, uh, the mental drain that it sucks away from you from doing mundane tasks, even if it's not time consuming, um freeze your brain up for creativity.

Speaker 2: 23:22

For, because it frees your brain up for creativity, because you are happier, right, if you don't have any mundane tasks in your day, you would generally be happier to get through your day and do work, and those energy levels of happiness or serotonin you have leads to things like creativity or imagination and these other elements of how we think right, rather than just thinking linearly.

Speaker 1: 23:55

No, I think that energy one is an important one. I don't know the number off the top of my head, but I do know that the brain consumes a disproportionate number of calories and energy from what our body generates than anything else, and so if you're using it up with mundane tasks, it just leaves. If you assume that your energy levels have more or less a fixed level, right amount in a given period of time, then you you're taking that away from other potential uses, right. So it's, yeah, that's a good point about the mundane test too. So, um, so one of the things you shared with me the mundane tasks too so, um, so one of the things you shared with me before also is that at algo marketing, you're doing research, as with your workforce, on your workforce, uh, and, if I understood it right, you are evaluating the impact of on productivity and stress levels, which is interesting when mundane tasks are automated. So, getting back to that mundane test, so, first off, did I understand that right? And then, what have you learned from this research that you're doing?

Speaker 2: 25:00

Yep, you're right, absolutely right.

Speaker 2: 25:03

So we are evolving our workforce by doing this research on the impacts of implementing AI and automation on productivity and stress levels.

Speaker 2: 25:14

So we started with our client services team because they tend to have they would be the team that we saw that would tend to have a lot of different types of tasks and interaction, both internally and on the client level. It's also one of the teams where we have the most individuals in, so it was a good, I guess, research slash testing place to do the test and research. So what we did is worked with each member of this team to evaluate and categorize daily tasks. We did it. We categorized them across two axes. One axis, say, you have the Y axis being the level of repetition or mundaneness of the task, and then on the X axis, you have the level of impact Level of impact in the sense of how impactful that task is to a KPI or a performance objective. Okay, but then once, when we did that mapping and we then in terms of priority in what types of problems or tasks to automate, we then focused on the tasks on the upper right, quadrant right.

Speaker 2: 26:30

So in that axis, if you split it into four quadrants, the ones at the upper right are the ones with high impacts and also a high level of mundane repetition okay the ones on the upper left but slightly closer to the middle were also very interesting as well, because that would have um, that would usually have um high mundaneness and repetition not as much impact.

Speaker 2: 26:59

But we found that those were also like low-hanging fruits, right okay um, there will be tasks such as, you know, emails, like things you do around communication, so emails, reports and so on. Now, in implementing and automating these tasks, what we've learned so far one key thing we've learned is around adoption and adoption of these tools or solutions that we implemented, now AI. Just the fact that user adoption is the key driver to success in any type of AI implementation was a resounding factor in what we've learned, and that's because AI predominantly relies on learning.

Speaker 1: 27:54

Right Makes sense.

Speaker 2: 27:56

You sell any AI tool that you launch you know would seldom come out perfect, right. It's just like a baby. The initial results of any tool would come out speaking gibberish Right and gibberish right, and it takes a while, with a lot of training and learning, for that AI to start to spit out relevant results or results that are useful to the users.

Speaker 2: 28:25

So if adoption isn't there, adoption is the driver of learning right for the AI. So adoption is actually the key ingredient for an AI success to learn successfully. And if adoption isn't there, then, yeah, that tool would fall into the category of yeah, this is just another tool that we have to use.

Speaker 2: 28:50

Stop being adopted and it will just continue on that sort of gibberish path for a long period of time. So what we have to do is really find creative ways in um increasing user adoption. Um, rather than just no matter how amazing the tool is, you have to find creative ways to get user adoption, and one one key thing is how do you ensure that you implement it in a way that it seamlessly integrates with the existing workflows, where they do not have to go away from how they're currently doing what they're doing, where it's actually embedded and really integrated in the existing workflow. So that was a key lesson there, in which way we had to re-engineer the solutions to ensure it was properly embedded.

Speaker 2: 29:45

Sorry, did you have something like a question?

Speaker 1: 29:50

So I'm not sure if you were getting there or not. I'm not sure if you were getting there or not, so I can see how although not totally easy but doable to measure productivity or outcomes of some sort. So, first off, I'm trying to put myself in the shoes of someone in the team who's being asked to go through this. You have to have a baseline for productivity and output, but also you mentioned the stress level. I'm trying to put myself in the shoes of someone in the team who's being asked to go through this. You have to have a baseline for productivity and output, but also you mentioned the stress level. So I think I might feel a little bit I don't know what the right word is trepidatious about sharing with my employer how stressed I am. So I'm curious. I mean, one is like how did you even go about assessing that? Or did you even initially? Or and if you did like, how was that received?

Speaker 2: 30:43

yeah. So on the productivity side, one of the things we measured was how email follow-ups, how email follow-ups right, and how quickly people were responding or following up for you know, responding to other types of communications. And we did see an improvement there in the email follow-up slash response times. In general, people were responding much quicker, which led to more efficiency, more productivity, because it means people are actually moving at a quicker pace. We're able to measure this. Even in your email clients you can see how quickly, or the response times between emails and parties. So that's one of the key things we found in our initial research as improvement.

Speaker 2: 31:38

On the stress level side, what we did was we had a questionnaire not a direct questionnaire asking people how stressed they are. We had to work with a behavior a human behavior specialist or psychologist to create this questionnaire which, um, it's like a stress level indicator or marker. Um. So it's. These are leading questions and you combine everything. It has a score that it gives you and then the score gives you the level of you know, change in in the person's um, uh, not necessary stress level, but in in their kind of like, their no like the indicators of stress yeah, indicators of stress, also indicators of um, of drive or satisfaction.

Speaker 2: 32:33

They are yeah, yeah okay, so so it was more on the. It's harder to or you wouldn't. What were your advices? Don't create something that measures stress.

Speaker 1: 32:44

Create something that measures the opposite, which get or would ultimately I was just like my head was just there, like that's like you there, Like that's like you don't. Yeah, okay, that's the positive question, not the negative question.

Speaker 2: 32:56

Exactly so we're able to see the improvements of those positive markers over time. We had, before the tools were implemented, we had a questionnaire sent on a weekly basis, four weeks, and then, after implementation. Then we have this questionnaire sent that they fill out over four weeks on a weekly basis and then measure the impact of those.

Speaker 1: 33:22

And yeah, we did see positive movements and positive trends in people being happier, more satisfied with their work that's no, I think it's great, um, so another, another thing that you we talked about is that, um, automating some of the mundane and repeatable tasks, and it also will enable leaders to I think the word you used is orchestrate, uh, their teams and their workforce more effectively. So what is that? Maybe describe what that is or what it looks like at, either at Elba Marketing or with your clients.

Speaker 2: 34:07

Yeah, absolutely so. This concept comes when we think about the evolved workforce. Right, and the evolved workforce goes beyond the impact on the individual worker. It's the impact it has on the team or an organization which is actually more profound when you have that at a combination level. So at Algorand Marketing, we created an AI-driven next best action solution, and what this does is uses historical data on actions and the results of those actions to predict what the next best action is to drive a specific outcome. It could be to drive conversions, or to create more opportunities, or upsell, and so on. So we use the solution to provide marketers with next best actions recommendations at an individual level. So, for example, a partner, marketing manager within a specific region or territory, who manager within a specific region, a territory, who was responsible for a specific segment of the audience, a SMB, would be able to get recommendations on what the next sequence of marketing actions in the form of campaigns, outreach or even to to send out that will drive conversion rates or that will drive um. You know, whatever the target is for that individual it could be um. I need x number of opportunity, um or mqls this quarter, and so on.

Speaker 2: 35:57

Now, on a team level, that's when you then have the next best actions.

Speaker 2: 36:07

When you incorporate team level objectives like for this team or for this organization this is the goal, for these are the OKRs, or these are the targets You're then able to ensure that those next best actions that are sent to the individual members all align to the team objectives as well.

Speaker 2: 36:33

So for marketing leaders, this gives them a much more effective way to orchestrate their team to a unified goal. Knowing that all these recommended actions not only drive results at an individual level, but also you have the team's OKR and targets and objectives baked in the Nextpress Actions is able to drive those recommended actions based off of those, but also based on the more information you give the AI where we test it is, for example, if you connect it to Workday as a platform where Workday has things like people's time offs and their certifications and their general skill sets, those Lexbox actions are also able to be customized, or it's able to distribute the actions appropriately to the right person based on their skill sets, based on their persona or even based on the time off when they're likely to be off, and so on.

Speaker 1: 37:46

So you could train this to take advantage of constraints like time off or things like that, as well as individual people's strengths on the team. So if you have a particular task and one of two people are available to do it, but one tends to be stronger in whatever that activity is, you could route it to that person as opposed to the other person, assuming all those things being equal. I mean oversimplifying, I'm quite sure, but you're nodding, so I'm going to take that as affirmative.

Speaker 2: 38:16

Yeah, correct. And if you think about it, it also gives a less invasive way of distributing tasks based on people's strengths and weaknesses that we've mentioned right strengths and weaknesses that we've mentioned right. If that, in a traditional sense, if that had to be, if a manager had various tasks to apportion to the teams, and the direct apportionment of those tasks to say, hey, you do this task because you're, you know, strong at this and weak at this and so on.

Speaker 2: 38:53

Is you know, having a machine do that is less invasive, right? Because you're not directly pointing out those things.

Speaker 1: 39:07

Yeah, so the reason I've got this look on my face here is that I could see that as a way of abdicating responsibility as a leader though, too, but at the same time and maybe this is more of a question so oftentimes as leaders, right, we've got people who are maybe skilled at one thing but have want to grow in some other skill or experience. Skill or experience. So could an engine like this also be trained or superseded in some way to accommodate that growth? Uh, for individuals, not just based on who's going to be best at doing something, based on past performance or availability or whatever yeah, that's definitely a good concept, baking in those additional constraints as to what.

Speaker 2: 39:59

don't just send the tasks to the best person, right, so accommodate for growth. These tasks would be good for this person because they can grow well in that area. And you can add all these different constraints into it as well. Another thing is that it also adds that level of, or reduces, subjectivity in apportioning tasks. Right, it's slightly more objective, and so you don't get you know, you get a more equal.

Speaker 1: 40:34

Everyone gets equal opportunity in the team yeah, yeah, I can see that, right, there's less potential maybe for favoritism or or the opposite of favoritism, right, um, so okay, so this is all really interesting. Uh, you might some of this may not surprise you, based on what um, uh, some of my questions already, but what? So? I guess two-part questions like what, what do you see happening in the near near, not too too distant future in terms of this being adopted and incorporated into organizations? And then, do you see any uh, potential risks with things like privacy and things like that?

Speaker 1: 41:16

risks with things like privacy and things like that.

Speaker 2: 41:21

So a lot of this stuff is monitoring what I'm doing, what I'm saying, my behavior yeah, I mean for the first question on the not-so-distant switch or what we could see happening, is the concept of digital twins being applying to humans? Right, we've had this digital twin concepts, that that has applied to um, physical assets and and so on, um, but now we would be able to get a human digital twin, um, where you'd have an AI version of your work self. So being able to train an AI in how you do your work, the way your skill sets, your expertise and so on, would become one of the key things in the near future, and that's something you'd be able to take, for example, from job to job. Right, it's not?

Speaker 1: 42:24

just you. I was going to ask you is this something that's going to end up following? Because I heard talk about AI at an engineering school where I'm on an advisory board, and somebody was talking about AI. They talked about it in the context of students having a, a AI, digital version of themselves that would go along with them through their education, and so that would have been my next like that's. The next obvious evolution is that it goes along with them in their careers.

Speaker 2: 42:53

So I mean, are you seeing?

Speaker 1: 42:54

that kind of stuff happening already.

Speaker 2: 43:07

I am seeing that kind of stuff happening already. I I am seeing that I'm seeing people um really creating, using um gpt, creating custom gpts of their persona, um for various reasons. One um to respond to emails and communications in the way they would. That's like the first part. You would see people that will create presentations and so on create a custom GPT to interact or create or do work in the way they would do work, not just in the way, in general, llm would do it. So you've definitely seen that and I think that concept has already become grander and grander. People are showing in for video, for audio as well, and then you have yeah, I mean, the deepfake thing worries me a little bit, but okay.

Speaker 2: 43:59

But yeah, I think I mean, at the very least, we're definitely seeing, you know, on communication side, emails, follow-ups, creating reports and on yeah, the second question you talked about was around privacy and monitoring. Yeah, it is I. Privacy and monitoring. Yeah, it is I mean, yeah, it is um, I mean the way we. There is already so much data um on an organization level that could be collect or that can be used right for to create or train an ai model um that people would have opted in, like data from you know, salesforce or workday and the marketing campaigns and and so on um. But the concern comes where you started to look into things like you know, email data, meetings and that sort of interaction level data and then using that data to train a model right.

Speaker 2: 45:05

But what I see happening is that people will be able to opt in to that data to not be used for training and in order to do that, they would need to have a. Each person would need to have their own private kind of like a private AI training cloud right, and hopefully we get to the point where computational power doesn't become, it becomes cheaper, becomes less expensive for individuals to actually have their own AI locally rather than being run on the cloud.

Speaker 1: 45:45

Yeah, yeah, I mean, I think it's going to be a complicated thing to figure out. I mean, the sort of maybe not quite analogous situation is salespeople who move from place to place, right, and you know, um, I would argue, part of why b2b crm data is bad is because salespeople, they want to hoard the information that they think is their competitive advantage. So if they go somewhere else, right, they're with the old, what used to be called their rolodex, right, their trusted contacts, things like that. So, um, and that's a value point. So I can see where this is going to come to like. We're going to have to figure that out. I don't know what the answer is.

Speaker 2: 46:26

I'm just, uh, there's a lot of people out there smarter than me that probably figured this out um so yummy this has been for example go ahead I'm just gonna say, for example, apple um, their, apple intelligence, their, I mean they coined I can't remember what word they use something around personal um intelligence or something around the lines where you're. It's training. It's taking the data, your interaction data, on the phone. It's being used to train the model, but it's training that model is run locally on your device or on a private cloud that only you have access to.

Speaker 1: 47:09

Yeah, Still requires a bit of trust, right? I mean, at the end of the the day, these are all trade-offs, right? Um, is the benefit? Is a benefit to any one person worth the trade-off of exposing some data or activity that you might not otherwise want to, right? I think that's everyone's tolerance, is it's like risk, right, everyone's tolerance for that. It's going to be different. Fascinating stuff. My mind is like still racing and I wish we had more time, but we're going to have to wrap it up. If folks want to follow up on this conversation or something with you or the folks at Algo Marketing, what's the best way for them to do that.

Speaker 2: 47:53

You can reach out to us via our website. Our website is algomarketingcom. Um. Either on our website, where you can find us on linkedin as well, um, but yeah, just type search for algomarketing, anywhere you are, you'll definitely find us sounds good.

Speaker 1: 48:12

Yeah, I mean, thank you so much. This is, I said. My mind is full of all kinds of stuff now, so this is the kind of conversation I love and hate at the same time, because it will be hard to concentrate the rest of the day.

Speaker 2: 48:25

Well, yeah, it was lovely having this conversation with you, Michael.

Speaker 1: 48:29

I really enjoyed my time. All right, well, thank you, yeah, and thanks again to our audience for continuing to support us. All right, well, thank you, yeah, and thanks again to our audience for continuing to support us. If you have subjects you want to hear about or guests you want us to talk to, or want to be a guest, feel free to reach out to Naomi, mike or me, and we'd be happy to talk to you about that. Until next time, bye, everybody.