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Erin. I'm Erin Brinker, and welcome to the hope table. I'm so excited about our next guest, and you're going to love her as much as I do. After a successful career in Austria, where she worked in finance and then later assisted her family business, she became one of the leading IT consultants and Hewlett Packard distributors in Vienna. She returned to the United States in 1997 and founded PC tronics. She surrounded herself with incredible people, engineers from MIT and other places, and she became one of the first engineers to obtain a formal Microsoft certification. Well, one of her engineers was, that's Manfred schmutzer, great Austrian name there, one of the founding members of PC tronics, who's the CTO of the company, but all led by Manal. If you've been in the Inland Empire, you know who she is. Manal Iskander, welcome to the show.
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Thank you for having me. Erin, what a great introduction. Really appreciate that.
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So tell me about who you are, and were you born in Vienna, or did you move from somewhere else to Vienna?
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So basically, I'll give you a little recap, a little bio. I consider myself a renaissance woman. There you go.
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So I was born in Egypt, Alexandria, Egypt. My dad was in the UN my biological father, and basically we moved a lot due to his work assignment. So I had some experience of living in different countries growing up, and eventually landed here in the US, but wanted to further my education back in Europe, so I once again, left the US and went to Austria Vienna to intern and start studying, also in Austria.
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So I had an aunt and uncle who lived there, which helped the journey, and then basically, I went over there, did some education, and then started working in the IT field in Austria in the mid 90s, when Windows and Microsoft was just starting to make everything user friendly, and computers weren't really a major or it didn't really exist back then, yet, it was all kind of brand new and developing, and so I kind of jumped up on that, And my background wasn't in engineering. So what I think makes my story a bit fascinating is my background was in finance.
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So I studied finance and I worked in the markets. I actually got a job when I lived in Austria at the DAX. So I was noticing, like millions, billions of dollars flowing into just technology, the computers, computer chips, building computers, internet, internet startups. So I just saw lots of money moving into that direction. So I kind of know from my background, if you follow the money, you can follow the industry. So I knew that there was going to be a big opportunity in tech, so I started working with my family's company, and they owned a company called the medis, and they were the first Hewlett Packard kind of partnership in Austria, and they kind of got a really good deal, because basically, it's not a monopoly, but it works like that. You get these contracts and they're locked in. So they were pretty much like the sole supplier for Hewlett Packard products in Austria. So they grew really quickly in the tech industry, and I got to see how it was built. And then, like mid to late 90s, around mid 96 or so, I started realizing, like, hey, maybe the US would be a better opportunity. Since, if you've ever lived in Europe, you know, it's not like the best for business owners. It's a lot more bureaucratic, a lot more tax heavy. So I thought maybe I could try my hand here in the US, where building a business is a little bit, let's say, less restrictive. So I came here and decided I was going to try to duplicate that business here in SoCal, in which I did, and it was like instantly successful, and I was right. And money and industry all went into tech. And everybody was needing, you know, an infrastructure, email servers. Everybody was needing everything they needed in order to keep them relevant. And that's kind of how I started long story. But that's it.
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Oh, that's incredible. I'm, I'm struck not only with your ability to adapt to a changing market, and then to seek out what you need. So moving from Austria to the United States, recognizing that it's, it's more business friendly here and it is, that's a huge move. And I wonder, you know you're, you're moving from different countries with your family as you were growing up.
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I'm sure, played a role in your ability to then adapt to a new culture, because Egypt is nothing like United States, which is nothing like Austria, and, you know, kind of, can you talk about those cultural differences and what that was like for you?
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Yeah, basically, I think living abroad does make it's a it's a two fold, right?
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Like one it, I don't get to say I have these childhood friends.
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I have a couple that I met when we moved to the United States that are still my friends today.
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But overall, you know, I can't say I went to kindergarten and lived in the same neighborhood like everybody else, and can talk about those stories. So that's the negative side of it.
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The positive side of it is, is that you are exposed to adapting into a new culture. And I would go to schools that were basically English speaking. So you got to choose when you're in the UN either like English or French, and so everybody's speaking the same language, no matter where they're from. So that's kind of helpful, at least for me there, but I got to live in, like, some fun places and some scary places. I lived in Costa Rica, I've lived in London, I've lived in Marrakech, I've lived in Vienna, I've lived in Paris, I've lived in accent Provence, I've lived in Honolulu. I've lived in Montreal, Canada. So, yeah, we moved around quite a bit. So yeah, it does make it like, not as scary. I guess maybe the biggest takeaway, I could tell you is maybe fear isn't an option, so you just don't even get to think about it.
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It probably didn't occur to you to be afraid, because you didn't have much choice,
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exactly, exactly. So it was kind of a natural thing for me to bump around. And it's actually interesting, because I've lived in the same house now for like, 10 years, and I it's the longest I've ever lived in one place.
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And so I always think maybe this is a sign of age now
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well, and I wonder, you know, as you're moving around, how you grounded yourself. Everybody has to be grounded somewhere, right? And sometimes it's with people, with the family around you, and sometimes it's with place where you're you're grounded. My husband, I joke that he's like an oak tree. He He loves where he grew up. His father was in the Air Force. When they finally landed at Norton Air Force Base.
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They stuck in this area. And, you know, there's, you know, taking him out of our house in a pine box. He'll, we will, he will always live here. And so, so I wonder, Where did you find your grounding growing up?
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So, you know, it's really interesting. I have answered that question before, and what I've noticed about me is that, because I'm new going someplace, you know, I'm the new person in town all the time that it's easier to help people. So I know I have a set of skills that I take with me wherever I go, because I've had exposure to things that most people don't.
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So I can go into a new town or new area and kind of recognize what value I could add. So I usually ground myself by helping people. So it helps, because now I get to teach something, which in turn creates the reciprocity, you know, kind of behavior, and then people didn't want to guide me through the new town, new system, new work environment, whatever it is. So I ground myself really by helping people.
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And it makes me feel more humble. It makes me realize that, you know, we're all in this together, and centers me.
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So that's usually how I get grounded, is, so
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you meet the people and then and then learn about them, and they learn about you, and that then connects you to where you are exactly. I think that's beautiful. Now, when I think about both Egypt and Austria and but also many of the other places that you've lived, they're old cultures with traditions. I mean, Austria was was, you know, 100 and a little over 100 years ago, was the breakup of an Austro Hungarian empire. And there's echoes of that regal culture still there.
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At least there was 30 years ago, or 40 years ago, when I was there. And, you know, Egypt is, I mean, you can't, I don't know that you can get a more famous ancient culture than Egypt, as as a as you move from different cultures and and connected with their history, because that's part of their their present.
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What was that like.
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So I love a lot of tradition. I love old culture. I love countries that grow from their history. You know, unfortunately, I can say with Egypt, not as much. I was really small. I didn't return a lot as an adult, but I learned it through family, right? So basically, in the home, culture is that way. In Austria, I got to visit it firsthand and meet several other families that didn't belong to me, and watch how they incorporate their history and tradition. Is, I think, helps connect people and it helps give. People a commonality right away that makes them feel that they're friends. And I think that's really important. And I think when I come here to the States, or even when I'm traveling and I'm in the States again, I do notice that we have a much more individualistic society where theirs is more collective. And so I find that a little bit difficult when I come here, that it's kind of each out for their own. And so you have to, you have to kind of articulate that.
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You have to build it yourself here. So it's possible, but you have to be the olive branch. You have to be the person that offers first. So I can create that same culture here, but I have to go out and serve, and then what I'm serving, then people see my intention is, like, maybe pure, or I want to elevate them, or I want to help them, and then they create me to be inclusive. So they then invite me into their world, and then we have, like, a shared a shared goal or shared friendship, and it works much better for me that way.
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So, you know, Americans are like that because we don't share a common history, because we come from everywhere, as, you know, as families, as ancestors, some hundreds of years ago, some five minutes ago, and and so we don't have that. We have to create that.
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And we tend to connect and make friends in the United States by doing things together, whether it's playing baseball or volunteering at a cleanup or whatever we we connect by by activities, which I don't think is in every culture. I don't think that's the case.
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Correct, correct? Yeah. I mean, a lot of cultures, it's already just built in. I mean, collectivism is really common, and, you know, we're just a very individualist society. And I know churches and things like that try to, like be the healing source for that dilemma that we have, but overall, I think it's built into our upbringing. So, you know, the nuclear family, not very many people spend time with their extended family and cousins and things like that.
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So, you know, I just noticed that it's different. But I accept all cultures, and I try to like understand them to the best of my ability. And, you know, this country has given me so many opportunities that I like, you know, I can't, I can't stop but be grateful, right?
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So, so let's talk about your American experience.
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Where in the United States have you lived?
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So I've lived in like the SoCal area, so like Huntington Beach, Manhattan Beach area and Riverside San Bernardino areas, and I've lived in Honolulu, and I spend quite a bit of time back east, like DC area. So that's pretty much it.
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I don't have a lot of us experience, but I mean, California, I think is, in a nutshell, the best experience you can have if you're going to live in the US, right? Indeed, we
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certainly have the best weather by far. Yeah. So let's talk about your your PC tronics business. And you know, the 30 years in in the tech industry is like a millennia.
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Things move so fast. So talk about how things have changed.
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What you've learned. Talk about that experience.
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When I came here, I kind of knew very early that this was booming, and I'm going to just bring it up to today, because I definitely, honestly believe that, you know, we can see how industries move by watching where investments take place. So like as today, everyone hears the big buzz word, artificial intelligence.
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And billions, if not trillions, of dollars are moving into artificial intelligence. Ai, the same thing was happening to me in the 90s. I was watching the same type of money roll into tech startups.com, internet was, you know, just booming, and everybody was entering the market? Well, when everybody enters the market, there's not, like, really a set of rules or culture that's really developed, and everybody's just trying to sell you something, and people are buying it, but they really don't even understand what they're buying. So what happened was, I was noticing when I came here to the United States, that the humanity of it was already lost. So I thought, you know, I watched, I worked for another company, first, to try to learn it here, how it worked in the US. And I was noticing that they would go in, sell to a company, set them all up, give them the servers, emails, everything that they needed. And then once they were kind of up and running, they would chase the next new customer. So the maintenance and the kind of customer care was being neglected, so these customers would like put in requests, but not really get good service, which was a really big common thing that happened in the early 2000s late 90s. And so I kind of thought I want to change that. I want. To be the person that takes care of the people that were kind of left behind and make sure that their environments are working and they're staying up to date and making sure that the software or SaaS they add is valuable to them. Because I understood the concept of business, so when I started doing that, I decided I was going to go after companies that were not needing a new setup, but that were feeling more neglected by their IT companies. So that's how I started. So I started by taking companies that didn't feel heard, and then I was giving them a voice, and I was guiding them through the new technology change, how to use the the new tools how to operate, windows, Calendar, Outlook, and I ended up kind of becoming like a strategic partner instead of just an IT firm. And why I like that story is because here I am, 30 years later, and like half of my customers are from that time still, oh, wow, yeah. So if they stayed in business, they grew with me, you know. So basically they grew if they needed new locations, if they needed to upgrade, if they wanted to go to the cloud. Now a lot of them are, like, asking me about AI, and now I can still, like, grow with them. So I tell myself, I know I have to say I'm an IT company, but I'd like to say I'm a strategic partner, because it means more to me that I am the partner that tells you how to use technology to meet your outcome, instead of saying you need to store your data, so you need a you need storage, data, storage, or you need a new backup system. Instead of saying that, I want to be able to say how much data do you need to store, and why are we using it?
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Like, what can we do with this information? So now I'm more like with what you have now, how can we make that make sense for you, like, with this information that you have, with this technology, do you have? What do you hope to get from it? And then we try to work backwards, so we're like outcome based.
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I love that. And I worked in tech. During that tech revolution as well. I was working for an application service provider doing project management, and our sales people would sell what I lovingly called a vaporware Sure, Mr.
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Customer, this project product can do whatever you want. It'll mow your lawn, it'll make your beds, it'll do all the things.
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And then the customer would come to me and say, you know, to help me build it my I was a liaison with with the actual engineers.
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And I'm like, well, it won't quite do that, but it will do.
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People were just saying whatever they wanted, and they, you know, and that was, it was the wild, wild west.
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So, so, you know, I'm, like, really determined to be a voice in this field. I'm determined to have women sit at this table and shape the conversation. I believe so adamantly that this has not been written yet, and as it's being written, if we take a seat at the table, we have a opportunity to change what it looks like. We have an opportunity to change the narrative of what AI means in the workforce for us today. So for me, it's really important that we talk about it.
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I agree. And you're, you're like a Sherpa on Everest, because although Everest, I know, is a well a well traveled path now, okay, you're a Sherpa on k2 you know, trying to tend to help people navigate the unnavigable. And I, I'm hearing from different sources, oh AI, it's, it's going to take over the world. It's going to create terminators, and the other side is, oh AI, it's going to be this great tool. And there's a lot of confusion. So let's, let's just what is AI and how is it being used today?
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Okay, so AI is a big buzzword, so we have to be careful. So Artificial intelligence has been around around for a very, very long time. It's just been the last few years, people have been talking about it openly because llms came to the to the surface, which is just a large language model that is a predictive model. So all it is is like an algorithm that predicts the next word by likelihood from all the information that's ever been on the internet. That, to me, is not AI, but it is what has allowed, like a layer of intelligence, intelligent thinking, to be interjected into certain software tools. So right now, people are going into chat.
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GPT, Gemini, Claude, anthropic, whatever your tool may be, or some of the image generators mid journey, nano banana, there's so many out there, and those are tools. And those tools people are just pulling through the markets and just using them.
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Yes, they're great. Yes, they're. But they're also very dangerous. So all of that is true. Whatever you put out there, you don't know what's happening with that information.
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It's being used to train people are taking their customer data, important information, and going into the chat, depositing it to ask a question. What they don't realize is they're putting their their customer or their employer at risk. They're allowing sensitive information to be released to they have no idea where, because once it's been put into the chat, you don't know where exactly that information is going, and nobody can answer that question. So basically, there's some risks involved with all of that, and that's what people are talking about.
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Okay, I'm processing what you're saying.
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So if I'm I have a problem, a business problem, that I need to solve, and I use people's real names, you know, such and such.
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Joe Smith, customer, this is what's happening. I need help.
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And how do I solve this problem?
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Then that I have then made that somehow public 100% 100%
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that information can is now variable, so they can actually query your exact prompt in that answer
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and oh my gosh. So I'm certain nobody knows that.
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That's what I'm saying. It's just like because it's it got introduced to the public massively, without any governance, governance or ethics applied to it, we have now some danger spots for environments like specifically work environments where you're you're having your secretary or your sales person put in a data sheet that they've just dropped into a chat on the way to a customer to get information that is dangerous because your environment isn't secure. Okay?
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So to me, that's one side of AI that we need to be talking about, and we need to be able to create safe environments. So you can create safe environments.
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There are safe ways to download llms or to use llms in an environment they're not trained, and they stay safe within something called your tenant, your work environment. Okay, so to me, like people in my profession, should be talking to everybody about that number one.
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Okay, so that's the safety governance side of it. Now there's another side of it that people just aren't really understanding. Ai having a chat bot that's just like a fun little tool, or creating a picture that's a fun little tool. But what makes artificial intelligence useful as a business is that it can put intelligence in your data. So we have all this data. Why do we have all this data? It must be good for something, right, right? How do we want to use it?
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That is where AI should come in.
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So I'm going to say, I have this little saying that. I say we're customer zero. So I'm just going to give you for my own company, to give you an example, and then we'll think about it for any other company you want. So in my own company, what would be helpful to my customers with the data that I have that I could use AI, I decided, well, we do a security analysis on all customers, so like, if we've gone out to fix their systems manually, remotely, what attacks were attempted, which ones were resolved, which ones, what? What employees had difficulty this month. Just a general executive summary of everything that happened in their environment.
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Well, we have software that gives each different report for each of these things. And if you've ever gotten a security report, it's very blah, it's very dry. It's like reading, like, I don't know, like a thesis, you know, you just don't necessarily want to do right?
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You're not going to sit down, you know, with a cup of tea and enjoy that reading
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Exactly, exactly. So what we did was we created an intelligence layer that told about the customer, so it's the customer profile, so it tells how does the customer like to receive their information.
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What language do they want to have it in? What items are the most important to them, and what do they not care about? Right?
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And then we plug that intelligence layer. So we used, we used a Microsoft product called Azure AI foundry, and I think they've just relabeled it to Microsoft foundry, but basically there's all different types of intelligence layers.
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That's just the one we used, okay? And so basically we created the profile all the instructions in that. And then what we did was we created an automation from another tool. I know these are a lot, but it's called N 8n. Okay. And basically what we did was we told the automation go into each one of our SAS tools, pull out the report, apply this customer profile to it, and now create a user friendly fun infographic, a fun email that lets them know, maybe not fun, depending how they want it read, right, and automatically send that report directly to the. Customer as well as deposit that into our SharePoint as a record. So now, at the end of every month, we don't do anything this automation we created with this intelligence layer goes into all our reporting systems, pulls out all the reports, deposits it into a database. Then it takes a intelligence layer with the customer profile, pulls out the necessary information from each report, puts it in the format the customer wants to have, puts it into a report and an email, and automatically sends it to that customer, as well as sends it to our security analysis analyst, as well as deposits it into SharePoint. Now that is an AI automation that I've used to now make my business more successful. So now customers are getting like a report that tells them, you know, you have this one employee that called in more Help Desk than anyone else, or you have this one aging system, you better be careful the next month it's coming up, so you should be budgeting for it, or this software renewal is going to be up in the next quarter, so now they have this, like, way to read their data that's like, understandable to them. Wow.
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Okay, so I'm processing all of this. It. This is people companies would spend millions of dollars on consultants, you know, from Bain Capital and McKinsey and all of those to do this work, and now you've created a system that will do it for you.
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Yes, anybody can, which is the greatest thing. So this tool is going to level up small and medium sized businesses to be able to work at an enterprise level, because if you can automate. So basically, what you need to know in order for any of this to work is you have to have clean data, and you have to know your work processes, right? So I have a little thing that I call it's called the double diamond theory, and it's discover, define, that's the first diamond, and then design, deliver, that's the second diamond. So the first diamond is discover, define. So we got to go in, and we have to discover your workflows, your friction points, what work is repetitive, that can be automated. What? How do you use your data? What do you want your data to do? What are we trying? What is what are we trying to create here? And then, once we do that, we can define it. We can say, okay, these work processes can be processes can be automated.
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These work processes can be assisted. You know, this is the map or the map flow, or workflow that we can create and make it more efficient. So now we're in a definition process where we can now decide what are all the problems you want to solve? What do the workflows look like? Now we can actually decide design them, and once we design them, then we can deliver them. So basically, anything can be created. Now if you have clean data, everything's AI readable.
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So is it in a safe environment?
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Is the data protected? Can the AI intelligence layer easily access it, read it, identify it, and use it. So for us now, all these small businesses are going to be able to automate things that used to be very, very expensive to do. They would need employees to be able to sort data, send emails, send like I'm going to give you another one we automated. So we automated this blog profile. So basically it was a blog profile for a customer that's in blockchain, and basically they wanted all the latest blockchain news, so we connected it to coin desk. It takes all of the latest news from blockchain, it turns it into a current blog. It goes on to WordPress, it it posts it for them, and then it sends them a copy, and then it gives them analytics at the end of the month. So the whole thing is automated. The whole thing is automated, but it's staying current, so it's exactly what the person would have done. So it's not giving like fake AI information. It's not like fake AI news. It's doing what a human would have done anyway. It would have gone to coin desk, pulled today's latest news and then put it into their blog with today's latest news.
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So for those of you all who are listening and they don't, you don't know what blockchain is. It's Bitcoin, right?
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Yeah, well, Bitcoin was created on blockchain, right? Bitcoin is just a use case scenario. So blockchain is any any data that is you want to put on something that's immutable, and you can't, you can't, you can't delete it, and you can always trace it. So what they did was they created Bitcoin as a proof of concept that blockchain works and blockchain works. So Ethereum is on blockchain. Solana's on the ledger. There's lots of blockchains, and basically it's like a block where you can put data in it, but nobody can access it unless they have keys, and nobody can change the data.
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So this is on a tangent, but I'll give you like a little way I used to help children understand it. You know, when you were a kid, and you would get, like, those paper mache little things, and you would make like a like a ring. You would take a piece of strip of paper, and you would glue it together, and you would make it into like an oval shape, and then you'd add another one to it, and another one to it, and another one to it. Okay, so that's kind of like blockchain.
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If you want to change something in the middle, you can't without destroying the entire chain. So you can't go backwards. You can only go forward. So anything that's ever been created is always discoverable. That's why it's like being used in like the financial worlds, because people can't go back and alter their accounting. So that's a whole nother story. But in any case, my point is, is that you can automate anything, so like the security reporting the you could automate. You know, in my company, we automate, like onboarding new customers.
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Instead of having a tech go out there, enter all their information into a CRM then enter it into the PSA, all that's just automated. So if you think about how many hours a week do you spend doing tasks that don't need your judgment or discernment, then should you alleviate those tasks so that your time can be used on things that need your judgment and discernment? Right?
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Exactly, well, and it's something that you would you would have hired somebody before. Somebody before, and you may not be able to that may not be in your budget. This is something that's a lot more streamlined. It takes time upfront, because you have to put together the framework for to teach the AI, what you want it to do. And then once you've done that, then it's automated. Is that correct?
00:31:39.500 --> 00:31:41.259
Well, you don't have to teach, which is great.
00:31:41.259 --> 00:31:54.940
It does learn over time. But like, let's say, I'm just trying to think of what you have. Like, let's say, because you you used to, or, I don't know if you still have hope foundation, but do you still have the that foundation?
00:31:55.240 --> 00:32:04.619
So So I, I am not no longer with the making hope happen foundation. I am doing some independent consulting and then focusing on the Inland Empire children's book project.
00:32:05.160 --> 00:33:12.119
Okay, okay, well, let's I'm just going to use the make it Hope Foundation as an example. But let's say you had a list of investors and donors there, and they get reports on how their money is being utilized, and also maybe reminders to donate or invest again. Okay, so maybe you have all this is where your data has to be clean. So let's say you put all of your monthly updates onto your foundation, into a SharePoint folder, and you also have all your donor information and all of your highlights, whatever it is that you use to give people information, you can then create a workflow, and that's what it would be. It would be like a workflow that says, take the donor information, investment information, etc, etc, create a monthly newsletter letter that has a donate Link button there, and have it sent out to this email address that lets donors know where their money is going, and then it updates automatically every month for you. And basically, you write the intelligence layer telling it what to do with the data.
00:33:09.180 --> 00:33:12.119
That's all you're really doing.
00:33:12.119 --> 00:33:15.119
It's not learning from it.
00:33:12.119 --> 00:33:55.480
You're telling it what to do. So instead of you doing it, you're saying, I want this data to be extracted and given to donors as a recap of where their money is being spent, something like that. And then it would take that data, it would read it, it would recap it, and then it would send it automatically to those donors with maybe a donate Link button. So every month, they get an email that tells them where their money is going and if they want to donate more money, fantastic, a workflow program or workflow process. So you're not necessarily having to teach the intelligence. What you're really having to do is understand your workflows, and that's what people are missing.
00:33:52.119 --> 00:34:17.579
It's not about the intelligence layer. The intelligence layer is the bonus. What it is is, do you understand your workflow like, do you understand what it takes to get your work done on a daily basis, weekly basis and monthly basis, and what do those workflows look like? And if you understand that, then you can make sure those workflows now have readable data. So is it something you're handwriting?
00:34:17.579 --> 00:35:14.699
Well, then that won't work. So how do you now make that digital. Is it something like, let's say it's these podcasts you're doing. What if these podcasts were the transcriptions were automatically taken put into like, these transcriptions are turned into five LinkedIn posts and five blog articles from everything you and I talked about for every guest, fantastic, right? Then that's a workflow. So then you could you're then it would take this transcription, it would download it in a database, and then it would take the intelligence layer of us telling it what we want it to do with these transcriptions. And then it would create your LinkedIn profiles. It would search. Your your photo gallery for applicable pictures, it would you could give it the log on to your WordPress, and it would go inside and log on to your WordPress and and deposit blogs.
00:35:09.239 --> 00:35:26.119
It would go into your LinkedIn and update your in post LinkedIn articles so it could do all of that if you understood the workflow and what you want from it. And that's what I noticed.
00:35:23.539 --> 00:35:28.820
People don't have. They either don't have everything digitized, so they don't have clean data.
00:35:29.179 --> 00:35:32.780
So they're like, Oh, we wrote it down. It's on these posted notes. And somebody
00:35:33.380 --> 00:35:35.059
that doesn't work, no, it doesn't,
00:35:35.599 --> 00:36:14.099
it doesn't work, or they don't know what they want, like, they're like, well, they have this great data tool, but they don't know what they want to do with the data. Like, like, what are you going to do with these great podcasts and all these tidbits of information? What's your goal with it? And then once you can understand that, you can be like, Oh, wow. Like, you know, maybe I could sit take the top 10 founder sayings out of these podcasts, and it'll pull it out, and I'll do a series on the top 10 founder sayings, or whatever it could be. But you now get to be creative right now. Now you get to say, what do I want out of my data? If I could do anything I wanted with it?
00:36:15.119 --> 00:36:20.099
Oh my gosh. It's fantastic. And become a much better producer than I would have been otherwise. Exactly.
00:36:20.099 --> 00:37:18.719
So for me, AI is a buzzword that needs to be grounded, and people need to understand there's like, ethics and governance that we have to take very seriously. And it is true that we are in a very dangerous time of society right now with AI, and there is going to be a lot of data linked. And we do need governance and ethics, and we need security all around people who use AI. And then we also need to be able to lead AI into having it help us like it should be our hands and feet. It should be the busy work. It should be the noise Canceler, and that's what I want it to do, for me, is just cancel out all the noise so I can forge the actually, what I want to do is kind of forge the path to let people understand it, especially women, because I think it's a great opportunity that these new workflows are created with a woman's voice in mind.
00:37:19.619 --> 00:37:37.820
So I definitely want to talk about that, but before, before we do, I want to talk about some of the risks, because the potential for AI to be used for hacking and phishing and otherwise scamming is also very high, and so how do you protect yourselves from that?
00:37:34.579 --> 00:38:14.519
Because, and I'll use this very basic example, you get a spoof email. You know, this is your invoice for whatever, and it comes from an email address you don't recognize, or maybe you do, but the language isn't right, so you know that your colleague didn't actually send it to you because it was written by somebody who doesn't speak English. All of that goes away with AI. And it could very much look like, like, you know, an attachment and email. This is a basic example. Could look like something that is completely legitimate, but it's not you your AI, could you know, once it's infiltrated your system, it can do whatever it wants, or whatever it was designed to do.
00:38:11.400 --> 00:38:14.940
Kind of talk about the risk there.
00:38:18.539 --> 00:38:32.719
Human risk is always going to be a risk. It's something you can't even 100% eliminate. So that's the second part of the story, but there are ways we are working on it. So as well as AI is getting developed.
00:38:28.699 --> 00:38:47.079
So is it in a hacker's world? So our security platforms now all are having built in AI. So how that works is there are lots of security platforms we use, like Huntress Sentinel one guards.
00:38:40.420 --> 00:39:26.780
And the ones that we use have an AI intelligence layer built into the security so the one we use is called like, it has purple AI built in. But basically what it does is it has, like a training, a security awareness training that goes out to the organization. So like, if you hired me, you would have a training that goes out to all of your employees that they have to take, and it then sends me back results, and it's the latest AI hacking phishing attempts. So the security training recognizes the latest ones that are out there. It then purposely pushes them through to your employees in a safe garden environment.
00:39:22.820 --> 00:40:01.320
Then it allows you to see which employees are opening up those unsafe emails them to have further training before they're allowed to be on your environment. Then they pass a training every month that lets us know that they're now being aware as well as us having this AI intelligence layer that's pulling the emails ahead of time, so any of these suspicious emails already being pulled so they never even get to the end user, they're going into a quarantine folder where they're getting analyzed by our team, which is also now art of.
00:39:58.780 --> 00:40:08.219
Artificial Intelligence. So artificial intelligence now analyzes this batch of emails, decides that they were unsafe.
00:40:05.039 --> 00:40:25.219
If they are, they're eliminating them. If they were safe, they're re pushing them through to the end user. So now companies like mine are even more important, because you really do need this layer between us and your employees, because your employees are your greatest risk to your company indeed.
00:40:25.278 --> 00:40:37.458
I mean, you know, from opening that email to finding a finding a flash drive in the parking lot and thinking it's a good idea to plug it in, things have happened. Yes, so you
00:40:38.119 --> 00:40:52.000
we've had a customer do that one time we were let go, and because they thought we were a little expensive. And to be honest, I'm not expensive, so it was quite an insult, but I was gracious enough to just back out kindly.
00:40:52.000 --> 00:41:11.340
And they used a company that did not have the software built in, and they got ransomware for one Bitcoin, which was like just under $100,000 at the time, and they had to pay it to get their data back. And I realized that, you know, you can't. There is no cost to security,
00:41:11.340 --> 00:41:24.139
really, no. Oh, my goodness. Oh my goodness. So you started talking about making sure that women, that female entrepreneurs, are at the forefront of this and that they understand what's going on.
00:41:21.559 --> 00:41:57.219
Let's explore that a little bit, because, and I will tell you that I reached out to you and we have, we have both been in this area for and worked alongside each other for a long time, but I had never interviewed you, despite being on the air for a while and and you wrote an article posted on LinkedIn that just moved me so much about about women being relevant in and staying relevant, especially older women in in the workforce, and how things are changing and that that things are flattening, as far as opportunity is concerned, talk about that a little
00:41:59.619 --> 00:43:36.619
bit like women older than 50 have a really interesting opportunity in today's world. Not only did we forge a place for ourselves in a world that had no place for us, and I find that really fascinating, but now we have a way to basically influence what we look like in this world of AI, how AI is utilized and spoken to. It's very important for us to take a seat at the table, because the industry technology in itself, has been shaped mostly by men, language by men, outcomes by men. And unfortunately, we are a world full of diversity, not just gender, but all types of diversity that is not really being accounted for. And little things I noticed, like I gave you an example earlier about the security reporting, I always say to my team, because I'm a female leader, and I work with a lot of men, and there's often not very many women to hire, which is also very disappointing for me, and indeed, more and more women to enter this field, as well as do I speak to a woman's cybersecurity club, because I really want them to be in this field. To shape the language that security reporting I told you about changed, because the reporting that I was getting from my team prior was very technical reporting. You've got mostly men in that work in tech, and they think like engineers.
00:43:31.699 --> 00:43:55.539
They develop with a very clear, concise outcome, which is great, but the world is filled with color and emotion and connection that gets dropped, you know, and with the security reporting in the intelligence layer, I was able to write a profile that talked about the women leaders.
00:43:55.539 --> 00:45:31.219
I have two or three companies with women leaders. I'm speaking to women, not men. I know how they want their material. I know how they would like to be spoken to. I know how they would enjoy seeing their highlights, or their team highlighted, not just the things that were fixed. So I was able to use my voice to create these security reports that now have such a different feel than the ones before, and that's because we as women now have are touching these fields like we are going to be able to shape how information is going to be transferred from company to company, human to human, how the language models when you go into chat, GPT or Gemini, how it's going to deliver information to you. So for me, we have a very large opportunity now to take our seats at the table. We shouldn't be waiting for permission. We need to speak up, we need to write, we need to post. We need to figure out. Our workflows are, and make sure we write the intelligence layers that show how that workflow gets automated, how that workflow gets actualized, right? So for me, it's really important that we let our 50 years or more of judgment and discernment, our wisdom, our ability to nurture our ability to recognize human connection, allow all those things to take part in what we're building right now. It's really important to me to see women do that.
00:45:31.219 --> 00:45:43.119
You know, it's interesting because, you know, because it's a computer and because it's, you know, it's based on logic models. It's, there is no human element in AI.
00:45:43.360 --> 00:45:55.119
So if you want a human element, you have to put it there, and you have to recognize that it's missing. And I, and I think that that's more than likely that that it would seem that that would come from women more than from men.
00:45:56.199 --> 00:46:27.320
Yes, exactly, you know. And you know, we are in a position where we are now kind of redefining what work looks like, right? So right now, in this time, like no other time before, there's going to be a new definition of work. I've just told you about all these automations. So think about a year from now, how many jobs will be replaced by these automations. Okay? So if these jobs are replaced, what happens to those work? That workforce?
00:46:24.320 --> 00:47:04.320
It doesn't disappear. It gets redefined right like now, humans are going to be used to do other types of work. What does that look like now we sit at the forefront where we get to define that. To me, it's really important to decide, you know, we're still relevant. How do we fit in? How do we use the skills we've crafted to create new ways for us to contribute to this new definition of work? Right? Like, I definitely think you're going to see the whole definition of work be redefined within the next one year to 18 months, that
00:47:05.039 --> 00:47:17.579
wow and it, you know, think about, we talked about the the the internet boom, and in the late 90s and early 2000s we thought that moved quickly. This dwarfs that exactly.
00:47:18.119 --> 00:48:10.199
It's going to happen so fast. I have been speaking at these panels across the world. I went to Amsterdam and spoke last October, and I was in Colorado and spoke a couple months ago, and I've been just traveling and speaking on this topic. And interesting enough, I'm going to speak next summer at a big AI conference in Utah called Beyond 2026 and I was trying to like, put together what my breakout room is going to look like, like what I'm going to be teaching people. And I realized that whatever I put together today may be obsolete by June or maybe so widely known already that it will be not obsolete, but be too familiar.
00:48:05.099 --> 00:48:46.840
So I'm realizing that, like what I'm talking to about now, like taking a work workflow, automating it with an intelligence layer, you know, using these in SAS tools, it will probably be common knowledge by March already. So information is going so fast that even trying to find things to talk about, I have to get to the podium quickly, like I'm like, I can give you this, this speech on AI, but we have to do this like now, because it is moving so quickly that I can't even tell you that some of the things we're talking about now will just be very common knowledge six months from
00:48:47.920 --> 00:49:06.539
now, wow, it'll be common knowledge. However, it won't be, because I think for those people who are not living in a tech world, they're the small business, they're running a bakery, they're running a preschool, they're doing whatever it is that they do, they're being told, AI is great, Ai, you need AI, you need AI.
00:49:03.719 --> 00:49:15.539
And they're like, I can't even get my brain around it, and I read something and it's completely contradictory to something else. And what do I use? Which system do I use?
00:49:12.539 --> 00:49:29.539
Gemini? Do you use chat? You know, does it have the capacity to do all the things I don't even know? You know where this fits, and I'm overwhelmed, as I think that this is moving so fast that, you know, if the tech people are feeling exhausted, everybody else is lost,
00:49:32.840 --> 00:50:59.199
same time, you're not going to be able to function without it. AI is now available to the small and medium business, and because it's not going to have a price tag attached to it like it did two years ago or a year ago, it's it's going to make a lot of sense I'm going to give you because you know this person, so Kim Calvin, for example, owns a coma unity organization Learning Center and. She has been operating in this NGO space with you know how everyone else has been doing their best, trying to survive, trying to get the data organized, etc. I went and spoke with her, and I'm like, Listen, you are super primed to be able to get an AI ready, readiness grant where you are able to organize your data to be aI retrievable, so AI systems can sit on top of any NGO and be able to scale it. And we went in, and she's eligible for like 95% of the grants out there. And oh my gosh, that's great. $1 billion worth of grants out there to help companies become AI ready. So what I want you to hear, it may be overwhelming, I hear that, but you've got companies like open AI Gemini, who have spent billions, if not trillions, of dollars, developing this, and now they need people to use it safely.
00:50:59.199 --> 00:51:02.159
How are they going to do that?
00:50:59.199 --> 00:51:54.340
If it's not working the way they want it to, they're putting grants and money out there so organizations can adapt, adapt it correctly, so that organizations will start to use and be aI ready, AI first. So basically, these people, every massive enterprise organization that's dealing with AI, needs your data to be aI ready so they can access it, so they can build even further. So a company like Google needs every small and medium business to be aI ready, so that their data is AI readable, so that they can build it better. So they're going to encourage companies like yours, mine, anybody, to be able to to have this learning curve be not so difficult. So I'm telling you it's going to go out there by getting companies, foundations, their digital readiness first.
00:51:54.340 --> 00:52:23.480
And that's what you're going to see, a big, massive push towards digital readiness. AI ready data, and there'll be grants and money out there to help this happen, and there'll be companies like me that are going to be hired to help companies do that. And that's what I'm that's what I'm doing. I'm hoping to get grants for companies who want to hire me so that I can help them organize their workflow and then automate it and put intelligence layers so that they can be outcome based.
00:52:24.438 --> 00:52:38.119
Wow, wow. It's, that's, that's fantastic. And and the next question, then this, all of this, begs the question, do we have the energy to power all of this as a global community?
00:52:39.199 --> 00:54:24.860
So obviously, that is the big buzz word and environmental harm and everything else. So I'm an optimist, so I believe that AI, the usage of AI, is going to help solve a lot of our AI issues. So I feel like it's a tool that helps us connect areas that we don't see connected yet, information that we don't see that we're not able to see, because we broadly can't see everything at once. So I am a huge optimist, optimist that believe that this issue will be solved through AI, and that AI will eventually help us become carbon negative, not just carbon zero. So I am a huge optimist. I am not a typical voice out there, but I have watched industries grow and seen doom and gloom, and you're as old as I am, so I'm sure you remember y 2k where we thought at the year 2000 everything was going to go and then nothing. Then nothing happened, and everything was fine. I kind of feel like as humans, we catastrophize things that we don't know. And I do not believe that this is something that is going to be a war of AI, robots, destruction. I don't believe that. I think this is going to be our our way, to allow humans to be redefined, for us to have more creativity, for us to undo the damage we've done, for us to be able to be more self regulated. So I see this is a big upgrade to humanity, and we're just going to go through chaos for the next five years until the upgrade is completed, if that can make sense.
00:54:24.920 --> 00:54:36.079
But well, Manel Iskander, we are completely out of time. How do people find find you on social media, if you're on social media, and more importantly, how do they connect with you at PC tronics
00:54:36.679 --> 00:54:39.380
appreciate that.
00:54:36.679 --> 00:55:02.280
So I'm mostly on LinkedIn. I don't do a whole lot of social media. So LinkedIn, Manel Iskander, president and founder of PC tronics and Fugazi, which is a marketing company, and I am at PC tronics.us, so you can do I have like a reach out to me on that. So you can reach out to me either by LinkedIn or www.
00:54:57.099 --> 00:55:02.280
Seatronics.us on my website.
00:55:02.280 --> 00:55:04.199
There is a way to contact me there.
00:55:04.920 --> 00:55:08.760
Well, what a treat this has been. Manaliskander, thank you so much for joining me today.
00:55:09.179 --> 00:55:12.599
Thank you so much, Erin. I appreciate having you having me on today.
00:55:13.440 --> 00:55:24.079
Well, that is all we have time for today. Thank you so much for spending this time with me. And I hope you learned a lot. I know I did. I'm Erin Brinker, and you've been listening to the hope table.
00:55:21.800 --> 00:55:24.079
I'll see you next week.
00:55:34.880 --> 00:56:11.579
This holiday season, while many children unwrap toys and treats, some will unwrap something far more powerful their very first book at the Inland Empire children's book project, we believe every child deserves the joy of reading the kind that sparks imagination, builds confidence and opens the door to a brighter future. But right now, more than 75% of low income students aren't reading at grade level, and many have no books at home at all. You can change that with your support, we're putting books directly into the hands of children, one gift, one child, one story at a time. Join us in giving the gift of literacy.
00:56:11.760 --> 00:56:29.960
Visit Inland Empire children's book project.org. To donate or get involved. That's Inland Empire children's book project.org. We're a 501, c3, non profit organization and 100% volunteer. Give a book. Give hope, give the gift of literacy.
00:56:30.139 --> 00:56:33.260
At Inland Empire children's book project.org, you.
00:00:00.000 --> 00:00:55.719
Erin. I'm Erin Brinker, and welcome to the hope table. I'm so excited about our next guest, and you're going to love her as much as I do. After a successful career in Austria, where she worked in finance and then later assisted her family business, she became one of the leading IT consultants and Hewlett Packard distributors in Vienna. She returned to the United States in 1997 and founded PC tronics. She surrounded herself with incredible people, engineers from MIT and other places, and she became one of the first engineers to obtain a formal Microsoft certification. Well, one of her engineers was, that's Manfred schmutzer, great Austrian name there, one of the founding members of PC tronics, who's the CTO of the company, but all led by Manal. If you've been in the Inland Empire, you know who she is. Manal Iskander, welcome to the show.
00:00:56.560 --> 00:01:00.659
Thank you for having me. Erin, what a great introduction. Really appreciate that.
00:01:01.079 --> 00:01:08.040
So tell me about who you are, and were you born in Vienna, or did you move from somewhere else to Vienna?
00:01:08.879 --> 00:01:15.480
So basically, I'll give you a little recap, a little bio. I consider myself a renaissance woman. There you go.
00:01:16.439 --> 00:01:50.319
So I was born in Egypt, Alexandria, Egypt. My dad was in the UN my biological father, and basically we moved a lot due to his work assignment. So I had some experience of living in different countries growing up, and eventually landed here in the US, but wanted to further my education back in Europe, so I once again, left the US and went to Austria Vienna to intern and start studying, also in Austria.
00:01:45.939 --> 00:02:27.860
So I had an aunt and uncle who lived there, which helped the journey, and then basically, I went over there, did some education, and then started working in the IT field in Austria in the mid 90s, when Windows and Microsoft was just starting to make everything user friendly, and computers weren't really a major or it didn't really exist back then, yet, it was all kind of brand new and developing, and so I kind of jumped up on that, And my background wasn't in engineering. So what I think makes my story a bit fascinating is my background was in finance.
00:02:27.860 --> 00:04:36.079
So I studied finance and I worked in the markets. I actually got a job when I lived in Austria at the DAX. So I was noticing, like millions, billions of dollars flowing into just technology, the computers, computer chips, building computers, internet, internet startups. So I just saw lots of money moving into that direction. So I kind of know from my background, if you follow the money, you can follow the industry. So I knew that there was going to be a big opportunity in tech, so I started working with my family's company, and they owned a company called the medis, and they were the first Hewlett Packard kind of partnership in Austria, and they kind of got a really good deal, because basically, it's not a monopoly, but it works like that. You get these contracts and they're locked in. So they were pretty much like the sole supplier for Hewlett Packard products in Austria. So they grew really quickly in the tech industry, and I got to see how it was built. And then, like mid to late 90s, around mid 96 or so, I started realizing, like, hey, maybe the US would be a better opportunity. Since, if you've ever lived in Europe, you know, it's not like the best for business owners. It's a lot more bureaucratic, a lot more tax heavy. So I thought maybe I could try my hand here in the US, where building a business is a little bit, let's say, less restrictive. So I came here and decided I was going to try to duplicate that business here in SoCal, in which I did, and it was like instantly successful, and I was right. And money and industry all went into tech. And everybody was needing, you know, an infrastructure, email servers. Everybody was needing everything they needed in order to keep them relevant. And that's kind of how I started long story. But that's it.
00:04:36.259 --> 00:05:02.040
Oh, that's incredible. I'm, I'm struck not only with your ability to adapt to a changing market, and then to seek out what you need. So moving from Austria to the United States, recognizing that it's, it's more business friendly here and it is, that's a huge move. And I wonder, you know you're, you're moving from different countries with your family as you were growing up.
00:04:58.240 --> 00:05:13.680
I'm sure, played a role in your ability to then adapt to a new culture, because Egypt is nothing like United States, which is nothing like Austria, and, you know, kind of, can you talk about those cultural differences and what that was like for you?
00:05:13.680 --> 00:05:22.579
Yeah, basically, I think living abroad does make it's a it's a two fold, right?
00:05:18.779 --> 00:05:25.459
Like one it, I don't get to say I have these childhood friends.
00:05:22.579 --> 00:05:28.100
I have a couple that I met when we moved to the United States that are still my friends today.
00:05:28.819 --> 00:05:38.360
But overall, you know, I can't say I went to kindergarten and lived in the same neighborhood like everybody else, and can talk about those stories. So that's the negative side of it.
00:05:38.360 --> 00:06:32.839
The positive side of it is, is that you are exposed to adapting into a new culture. And I would go to schools that were basically English speaking. So you got to choose when you're in the UN either like English or French, and so everybody's speaking the same language, no matter where they're from. So that's kind of helpful, at least for me there, but I got to live in, like, some fun places and some scary places. I lived in Costa Rica, I've lived in London, I've lived in Marrakech, I've lived in Vienna, I've lived in Paris, I've lived in accent Provence, I've lived in Honolulu. I've lived in Montreal, Canada. So, yeah, we moved around quite a bit. So yeah, it does make it like, not as scary. I guess maybe the biggest takeaway, I could tell you is maybe fear isn't an option, so you just don't even get to think about it.
00:06:33.500 --> 00:06:37.459
It probably didn't occur to you to be afraid, because you didn't have much choice,
00:06:37.759 --> 00:06:52.000
exactly, exactly. So it was kind of a natural thing for me to bump around. And it's actually interesting, because I've lived in the same house now for like, 10 years, and I it's the longest I've ever lived in one place.
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And so I always think maybe this is a sign of age now
00:06:55.660 --> 00:07:17.699
well, and I wonder, you know, as you're moving around, how you grounded yourself. Everybody has to be grounded somewhere, right? And sometimes it's with people, with the family around you, and sometimes it's with place where you're you're grounded. My husband, I joke that he's like an oak tree. He He loves where he grew up. His father was in the Air Force. When they finally landed at Norton Air Force Base.
00:07:18.120 --> 00:07:30.920
They stuck in this area. And, you know, there's, you know, taking him out of our house in a pine box. He'll, we will, he will always live here. And so, so I wonder, Where did you find your grounding growing up?
00:07:31.459 --> 00:07:50.920
So, you know, it's really interesting. I have answered that question before, and what I've noticed about me is that, because I'm new going someplace, you know, I'm the new person in town all the time that it's easier to help people. So I know I have a set of skills that I take with me wherever I go, because I've had exposure to things that most people don't.
00:07:51.399 --> 00:08:18.600
So I can go into a new town or new area and kind of recognize what value I could add. So I usually ground myself by helping people. So it helps, because now I get to teach something, which in turn creates the reciprocity, you know, kind of behavior, and then people didn't want to guide me through the new town, new system, new work environment, whatever it is. So I ground myself really by helping people.
00:08:15.899 --> 00:08:24.560
And it makes me feel more humble. It makes me realize that, you know, we're all in this together, and centers me.
00:08:21.019 --> 00:08:25.519
So that's usually how I get grounded, is, so
00:08:25.519 --> 00:08:58.240
you meet the people and then and then learn about them, and they learn about you, and that then connects you to where you are exactly. I think that's beautiful. Now, when I think about both Egypt and Austria and but also many of the other places that you've lived, they're old cultures with traditions. I mean, Austria was was, you know, 100 and a little over 100 years ago, was the breakup of an Austro Hungarian empire. And there's echoes of that regal culture still there.
00:08:58.779 --> 00:09:19.320
At least there was 30 years ago, or 40 years ago, when I was there. And, you know, Egypt is, I mean, you can't, I don't know that you can get a more famous ancient culture than Egypt, as as a as you move from different cultures and and connected with their history, because that's part of their their present.
00:09:19.620 --> 00:09:20.659
What was that like.
00:09:21.860 --> 00:10:31.279
So I love a lot of tradition. I love old culture. I love countries that grow from their history. You know, unfortunately, I can say with Egypt, not as much. I was really small. I didn't return a lot as an adult, but I learned it through family, right? So basically, in the home, culture is that way. In Austria, I got to visit it firsthand and meet several other families that didn't belong to me, and watch how they incorporate their history and tradition. Is, I think, helps connect people and it helps give. People a commonality right away that makes them feel that they're friends. And I think that's really important. And I think when I come here to the States, or even when I'm traveling and I'm in the States again, I do notice that we have a much more individualistic society where theirs is more collective. And so I find that a little bit difficult when I come here, that it's kind of each out for their own. And so you have to, you have to kind of articulate that.
00:10:28.039 --> 00:11:03.299
You have to build it yourself here. So it's possible, but you have to be the olive branch. You have to be the person that offers first. So I can create that same culture here, but I have to go out and serve, and then what I'm serving, then people see my intention is, like, maybe pure, or I want to elevate them, or I want to help them, and then they create me to be inclusive. So they then invite me into their world, and then we have, like, a shared a shared goal or shared friendship, and it works much better for me that way.
00:11:03.299 --> 00:11:19.620
So, you know, Americans are like that because we don't share a common history, because we come from everywhere, as, you know, as families, as ancestors, some hundreds of years ago, some five minutes ago, and and so we don't have that. We have to create that.
00:11:16.379 --> 00:11:33.259
And we tend to connect and make friends in the United States by doing things together, whether it's playing baseball or volunteering at a cleanup or whatever we we connect by by activities, which I don't think is in every culture. I don't think that's the case.
00:11:34.099 --> 00:12:01.198
Correct, correct? Yeah. I mean, a lot of cultures, it's already just built in. I mean, collectivism is really common, and, you know, we're just a very individualist society. And I know churches and things like that try to, like be the healing source for that dilemma that we have, but overall, I think it's built into our upbringing. So, you know, the nuclear family, not very many people spend time with their extended family and cousins and things like that.
00:12:01.198 --> 00:12:16.918
So, you know, I just noticed that it's different. But I accept all cultures, and I try to like understand them to the best of my ability. And, you know, this country has given me so many opportunities that I like, you know, I can't, I can't stop but be grateful, right?
00:12:17.940 --> 00:12:20.600
So, so let's talk about your American experience.
00:12:20.720 --> 00:12:22.580
Where in the United States have you lived?
00:12:23.779 --> 00:12:43.179
So I've lived in like the SoCal area, so like Huntington Beach, Manhattan Beach area and Riverside San Bernardino areas, and I've lived in Honolulu, and I spend quite a bit of time back east, like DC area. So that's pretty much it.
00:12:43.179 --> 00:12:53.019
I don't have a lot of us experience, but I mean, California, I think is, in a nutshell, the best experience you can have if you're going to live in the US, right? Indeed, we
00:12:53.078 --> 00:13:10.318
certainly have the best weather by far. Yeah. So let's talk about your your PC tronics business. And you know, the 30 years in in the tech industry is like a millennia.
00:13:10.318 --> 00:13:13.918
Things move so fast. So talk about how things have changed.
00:13:13.918 --> 00:13:16.019
What you've learned. Talk about that experience.
00:13:18.539 --> 00:13:44.080
When I came here, I kind of knew very early that this was booming, and I'm going to just bring it up to today, because I definitely, honestly believe that, you know, we can see how industries move by watching where investments take place. So like as today, everyone hears the big buzz word, artificial intelligence.
00:13:40.360 --> 00:16:52.419
And billions, if not trillions, of dollars are moving into artificial intelligence. Ai, the same thing was happening to me in the 90s. I was watching the same type of money roll into tech startups.com, internet was, you know, just booming, and everybody was entering the market? Well, when everybody enters the market, there's not, like, really a set of rules or culture that's really developed, and everybody's just trying to sell you something, and people are buying it, but they really don't even understand what they're buying. So what happened was, I was noticing when I came here to the United States, that the humanity of it was already lost. So I thought, you know, I watched, I worked for another company, first, to try to learn it here, how it worked in the US. And I was noticing that they would go in, sell to a company, set them all up, give them the servers, emails, everything that they needed. And then once they were kind of up and running, they would chase the next new customer. So the maintenance and the kind of customer care was being neglected, so these customers would like put in requests, but not really get good service, which was a really big common thing that happened in the early 2000s late 90s. And so I kind of thought I want to change that. I want. To be the person that takes care of the people that were kind of left behind and make sure that their environments are working and they're staying up to date and making sure that the software or SaaS they add is valuable to them. Because I understood the concept of business, so when I started doing that, I decided I was going to go after companies that were not needing a new setup, but that were feeling more neglected by their IT companies. So that's how I started. So I started by taking companies that didn't feel heard, and then I was giving them a voice, and I was guiding them through the new technology change, how to use the the new tools how to operate, windows, Calendar, Outlook, and I ended up kind of becoming like a strategic partner instead of just an IT firm. And why I like that story is because here I am, 30 years later, and like half of my customers are from that time still, oh, wow, yeah. So if they stayed in business, they grew with me, you know. So basically they grew if they needed new locations, if they needed to upgrade, if they wanted to go to the cloud. Now a lot of them are, like, asking me about AI, and now I can still, like, grow with them. So I tell myself, I know I have to say I'm an IT company, but I'd like to say I'm a strategic partner, because it means more to me that I am the partner that tells you how to use technology to meet your outcome, instead of saying you need to store your data, so you need a you need storage, data, storage, or you need a new backup system. Instead of saying that, I want to be able to say how much data do you need to store, and why are we using it?
00:16:52.419 --> 00:17:09.359
Like, what can we do with this information? So now I'm more like with what you have now, how can we make that make sense for you, like, with this information that you have, with this technology, do you have? What do you hope to get from it? And then we try to work backwards, so we're like outcome based.
00:17:10.200 --> 00:17:24.140
I love that. And I worked in tech. During that tech revolution as well. I was working for an application service provider doing project management, and our sales people would sell what I lovingly called a vaporware Sure, Mr.
00:17:24.140 --> 00:17:32.119
Customer, this project product can do whatever you want. It'll mow your lawn, it'll make your beds, it'll do all the things.
00:17:32.420 --> 00:17:38.720
And then the customer would come to me and say, you know, to help me build it my I was a liaison with with the actual engineers.
00:17:38.839 --> 00:17:41.740
And I'm like, well, it won't quite do that, but it will do.
00:17:43.299 --> 00:17:48.700
People were just saying whatever they wanted, and they, you know, and that was, it was the wild, wild west.
00:17:49.059 --> 00:18:25.640
So, so, you know, I'm, like, really determined to be a voice in this field. I'm determined to have women sit at this table and shape the conversation. I believe so adamantly that this has not been written yet, and as it's being written, if we take a seat at the table, we have a opportunity to change what it looks like. We have an opportunity to change the narrative of what AI means in the workforce for us today. So for me, it's really important that we talk about it.
00:18:25.819 --> 00:18:56.500
I agree. And you're, you're like a Sherpa on Everest, because although Everest, I know, is a well a well traveled path now, okay, you're a Sherpa on k2 you know, trying to tend to help people navigate the unnavigable. And I, I'm hearing from different sources, oh AI, it's, it's going to take over the world. It's going to create terminators, and the other side is, oh AI, it's going to be this great tool. And there's a lot of confusion. So let's, let's just what is AI and how is it being used today?
00:18:57.579 --> 00:19:40.420
Okay, so AI is a big buzzword, so we have to be careful. So Artificial intelligence has been around around for a very, very long time. It's just been the last few years, people have been talking about it openly because llms came to the to the surface, which is just a large language model that is a predictive model. So all it is is like an algorithm that predicts the next word by likelihood from all the information that's ever been on the internet. That, to me, is not AI, but it is what has allowed, like a layer of intelligence, intelligent thinking, to be interjected into certain software tools. So right now, people are going into chat.
00:19:40.420 --> 00:19:57.039
GPT, Gemini, Claude, anthropic, whatever your tool may be, or some of the image generators mid journey, nano banana, there's so many out there, and those are tools. And those tools people are just pulling through the markets and just using them.
00:19:58.240 --> 00:20:06.420
Yes, they're great. Yes, they're. But they're also very dangerous. So all of that is true. Whatever you put out there, you don't know what's happening with that information.
00:20:06.420 --> 00:20:43.660
It's being used to train people are taking their customer data, important information, and going into the chat, depositing it to ask a question. What they don't realize is they're putting their their customer or their employer at risk. They're allowing sensitive information to be released to they have no idea where, because once it's been put into the chat, you don't know where exactly that information is going, and nobody can answer that question. So basically, there's some risks involved with all of that, and that's what people are talking about.
00:20:43.898 --> 00:20:45.759
Okay, I'm processing what you're saying.
00:20:45.759 --> 00:20:52.838
So if I'm I have a problem, a business problem, that I need to solve, and I use people's real names, you know, such and such.
00:20:52.838 --> 00:20:56.499
Joe Smith, customer, this is what's happening. I need help.
00:20:56.499 --> 00:21:00.398
And how do I solve this problem?
00:20:56.499 --> 00:21:02.939
Then that I have then made that somehow public 100% 100%
00:21:04.200 --> 00:21:12.059
that information can is now variable, so they can actually query your exact prompt in that answer
00:21:12.119 --> 00:21:15.480
and oh my gosh. So I'm certain nobody knows that.
00:21:16.140 --> 00:21:45.700
That's what I'm saying. It's just like because it's it got introduced to the public massively, without any governance, governance or ethics applied to it, we have now some danger spots for environments like specifically work environments where you're you're having your secretary or your sales person put in a data sheet that they've just dropped into a chat on the way to a customer to get information that is dangerous because your environment isn't secure. Okay?
00:21:45.940 --> 00:21:58.000
So to me, that's one side of AI that we need to be talking about, and we need to be able to create safe environments. So you can create safe environments.
00:21:53.859 --> 00:22:13.140
There are safe ways to download llms or to use llms in an environment they're not trained, and they stay safe within something called your tenant, your work environment. Okay, so to me, like people in my profession, should be talking to everybody about that number one.
00:22:13.259 --> 00:22:43.539
Okay, so that's the safety governance side of it. Now there's another side of it that people just aren't really understanding. Ai having a chat bot that's just like a fun little tool, or creating a picture that's a fun little tool. But what makes artificial intelligence useful as a business is that it can put intelligence in your data. So we have all this data. Why do we have all this data? It must be good for something, right, right? How do we want to use it?
00:22:43.539 --> 00:22:46.960
That is where AI should come in.
00:22:43.539 --> 00:23:29.119
So I'm going to say, I have this little saying that. I say we're customer zero. So I'm just going to give you for my own company, to give you an example, and then we'll think about it for any other company you want. So in my own company, what would be helpful to my customers with the data that I have that I could use AI, I decided, well, we do a security analysis on all customers, so like, if we've gone out to fix their systems manually, remotely, what attacks were attempted, which ones were resolved, which ones, what? What employees had difficulty this month. Just a general executive summary of everything that happened in their environment.
00:23:29.660 --> 00:23:46.720
Well, we have software that gives each different report for each of these things. And if you've ever gotten a security report, it's very blah, it's very dry. It's like reading, like, I don't know, like a thesis, you know, you just don't necessarily want to do right?
00:23:46.720 --> 00:23:50.559
You're not going to sit down, you know, with a cup of tea and enjoy that reading
00:23:51.039 --> 00:24:03.119
Exactly, exactly. So what we did was we created an intelligence layer that told about the customer, so it's the customer profile, so it tells how does the customer like to receive their information.
00:24:00.660 --> 00:24:10.200
What language do they want to have it in? What items are the most important to them, and what do they not care about? Right?
00:24:06.359 --> 00:24:23.000
And then we plug that intelligence layer. So we used, we used a Microsoft product called Azure AI foundry, and I think they've just relabeled it to Microsoft foundry, but basically there's all different types of intelligence layers.
00:24:20.220 --> 00:26:12.839
That's just the one we used, okay? And so basically we created the profile all the instructions in that. And then what we did was we created an automation from another tool. I know these are a lot, but it's called N 8n. Okay. And basically what we did was we told the automation go into each one of our SAS tools, pull out the report, apply this customer profile to it, and now create a user friendly fun infographic, a fun email that lets them know, maybe not fun, depending how they want it read, right, and automatically send that report directly to the. Customer as well as deposit that into our SharePoint as a record. So now, at the end of every month, we don't do anything this automation we created with this intelligence layer goes into all our reporting systems, pulls out all the reports, deposits it into a database. Then it takes a intelligence layer with the customer profile, pulls out the necessary information from each report, puts it in the format the customer wants to have, puts it into a report and an email, and automatically sends it to that customer, as well as sends it to our security analysis analyst, as well as deposits it into SharePoint. Now that is an AI automation that I've used to now make my business more successful. So now customers are getting like a report that tells them, you know, you have this one employee that called in more Help Desk than anyone else, or you have this one aging system, you better be careful the next month it's coming up, so you should be budgeting for it, or this software renewal is going to be up in the next quarter, so now they have this, like, way to read their data that's like, understandable to them. Wow.
00:26:12.900 --> 00:26:26.779
Okay, so I'm processing all of this. It. This is people companies would spend millions of dollars on consultants, you know, from Bain Capital and McKinsey and all of those to do this work, and now you've created a system that will do it for you.
00:26:27.319 --> 00:27:25.160
Yes, anybody can, which is the greatest thing. So this tool is going to level up small and medium sized businesses to be able to work at an enterprise level, because if you can automate. So basically, what you need to know in order for any of this to work is you have to have clean data, and you have to know your work processes, right? So I have a little thing that I call it's called the double diamond theory, and it's discover, define, that's the first diamond, and then design, deliver, that's the second diamond. So the first diamond is discover, define. So we got to go in, and we have to discover your workflows, your friction points, what work is repetitive, that can be automated. What? How do you use your data? What do you want your data to do? What are we trying? What is what are we trying to create here? And then, once we do that, we can define it. We can say, okay, these work processes can be processes can be automated.
00:27:25.339 --> 00:27:56.200
These work processes can be assisted. You know, this is the map or the map flow, or workflow that we can create and make it more efficient. So now we're in a definition process where we can now decide what are all the problems you want to solve? What do the workflows look like? Now we can actually decide design them, and once we design them, then we can deliver them. So basically, anything can be created. Now if you have clean data, everything's AI readable.
00:27:52.000 --> 00:27:56.200
So is it in a safe environment?
00:27:56.200 --> 00:29:10.380
Is the data protected? Can the AI intelligence layer easily access it, read it, identify it, and use it. So for us now, all these small businesses are going to be able to automate things that used to be very, very expensive to do. They would need employees to be able to sort data, send emails, send like I'm going to give you another one we automated. So we automated this blog profile. So basically it was a blog profile for a customer that's in blockchain, and basically they wanted all the latest blockchain news, so we connected it to coin desk. It takes all of the latest news from blockchain, it turns it into a current blog. It goes on to WordPress, it it posts it for them, and then it sends them a copy, and then it gives them analytics at the end of the month. So the whole thing is automated. The whole thing is automated, but it's staying current, so it's exactly what the person would have done. So it's not giving like fake AI information. It's not like fake AI news. It's doing what a human would have done anyway. It would have gone to coin desk, pulled today's latest news and then put it into their blog with today's latest news.
00:29:10.619 --> 00:29:16.140
So for those of you all who are listening and they don't, you don't know what blockchain is. It's Bitcoin, right?
00:29:16.200 --> 00:29:53.500
Yeah, well, Bitcoin was created on blockchain, right? Bitcoin is just a use case scenario. So blockchain is any any data that is you want to put on something that's immutable, and you can't, you can't, you can't delete it, and you can always trace it. So what they did was they created Bitcoin as a proof of concept that blockchain works and blockchain works. So Ethereum is on blockchain. Solana's on the ledger. There's lots of blockchains, and basically it's like a block where you can put data in it, but nobody can access it unless they have keys, and nobody can change the data.
00:29:53.859 --> 00:30:18.240
So this is on a tangent, but I'll give you like a little way I used to help children understand it. You know, when you were a kid, and you would get, like, those paper mache little things, and you would make like a like a ring. You would take a piece of strip of paper, and you would glue it together, and you would make it into like an oval shape, and then you'd add another one to it, and another one to it, and another one to it. Okay, so that's kind of like blockchain.
00:30:18.240 --> 00:30:53.140
If you want to change something in the middle, you can't without destroying the entire chain. So you can't go backwards. You can only go forward. So anything that's ever been created is always discoverable. That's why it's like being used in like the financial worlds, because people can't go back and alter their accounting. So that's a whole nother story. But in any case, my point is, is that you can automate anything, so like the security reporting the you could automate. You know, in my company, we automate, like onboarding new customers.
00:30:53.140 --> 00:31:19.259
Instead of having a tech go out there, enter all their information into a CRM then enter it into the PSA, all that's just automated. So if you think about how many hours a week do you spend doing tasks that don't need your judgment or discernment, then should you alleviate those tasks so that your time can be used on things that need your judgment and discernment? Right?
00:31:19.619 --> 00:31:39.079
Exactly, well, and it's something that you would you would have hired somebody before. Somebody before, and you may not be able to that may not be in your budget. This is something that's a lot more streamlined. It takes time upfront, because you have to put together the framework for to teach the AI, what you want it to do. And then once you've done that, then it's automated. Is that correct?
00:31:39.500 --> 00:31:41.259
Well, you don't have to teach, which is great.
00:31:41.259 --> 00:31:54.940
It does learn over time. But like, let's say, I'm just trying to think of what you have. Like, let's say, because you you used to, or, I don't know if you still have hope foundation, but do you still have the that foundation?
00:31:55.240 --> 00:32:04.619
So So I, I am not no longer with the making hope happen foundation. I am doing some independent consulting and then focusing on the Inland Empire children's book project.
00:32:05.160 --> 00:33:12.119
Okay, okay, well, let's I'm just going to use the make it Hope Foundation as an example. But let's say you had a list of investors and donors there, and they get reports on how their money is being utilized, and also maybe reminders to donate or invest again. Okay, so maybe you have all this is where your data has to be clean. So let's say you put all of your monthly updates onto your foundation, into a SharePoint folder, and you also have all your donor information and all of your highlights, whatever it is that you use to give people information, you can then create a workflow, and that's what it would be. It would be like a workflow that says, take the donor information, investment information, etc, etc, create a monthly newsletter letter that has a donate Link button there, and have it sent out to this email address that lets donors know where their money is going, and then it updates automatically every month for you. And basically, you write the intelligence layer telling it what to do with the data.
00:33:09.180 --> 00:33:12.119
That's all you're really doing.
00:33:12.119 --> 00:33:15.119
It's not learning from it.
00:33:12.119 --> 00:33:55.480
You're telling it what to do. So instead of you doing it, you're saying, I want this data to be extracted and given to donors as a recap of where their money is being spent, something like that. And then it would take that data, it would read it, it would recap it, and then it would send it automatically to those donors with maybe a donate Link button. So every month, they get an email that tells them where their money is going and if they want to donate more money, fantastic, a workflow program or workflow process. So you're not necessarily having to teach the intelligence. What you're really having to do is understand your workflows, and that's what people are missing.
00:33:52.119 --> 00:34:17.579
It's not about the intelligence layer. The intelligence layer is the bonus. What it is is, do you understand your workflow like, do you understand what it takes to get your work done on a daily basis, weekly basis and monthly basis, and what do those workflows look like? And if you understand that, then you can make sure those workflows now have readable data. So is it something you're handwriting?
00:34:17.579 --> 00:35:14.699
Well, then that won't work. So how do you now make that digital. Is it something like, let's say it's these podcasts you're doing. What if these podcasts were the transcriptions were automatically taken put into like, these transcriptions are turned into five LinkedIn posts and five blog articles from everything you and I talked about for every guest, fantastic, right? Then that's a workflow. So then you could you're then it would take this transcription, it would download it in a database, and then it would take the intelligence layer of us telling it what we want it to do with these transcriptions. And then it would create your LinkedIn profiles. It would search. Your your photo gallery for applicable pictures, it would you could give it the log on to your WordPress, and it would go inside and log on to your WordPress and and deposit blogs.
00:35:09.239 --> 00:35:26.119
It would go into your LinkedIn and update your in post LinkedIn articles so it could do all of that if you understood the workflow and what you want from it. And that's what I noticed.
00:35:23.539 --> 00:35:28.820
People don't have. They either don't have everything digitized, so they don't have clean data.
00:35:29.179 --> 00:35:32.780
So they're like, Oh, we wrote it down. It's on these posted notes. And somebody
00:35:33.380 --> 00:35:35.059
that doesn't work, no, it doesn't,
00:35:35.599 --> 00:36:14.099
it doesn't work, or they don't know what they want, like, they're like, well, they have this great data tool, but they don't know what they want to do with the data. Like, like, what are you going to do with these great podcasts and all these tidbits of information? What's your goal with it? And then once you can understand that, you can be like, Oh, wow. Like, you know, maybe I could sit take the top 10 founder sayings out of these podcasts, and it'll pull it out, and I'll do a series on the top 10 founder sayings, or whatever it could be. But you now get to be creative right now. Now you get to say, what do I want out of my data? If I could do anything I wanted with it?
00:36:15.119 --> 00:36:20.099
Oh my gosh. It's fantastic. And become a much better producer than I would have been otherwise. Exactly.
00:36:20.099 --> 00:37:18.719
So for me, AI is a buzzword that needs to be grounded, and people need to understand there's like, ethics and governance that we have to take very seriously. And it is true that we are in a very dangerous time of society right now with AI, and there is going to be a lot of data linked. And we do need governance and ethics, and we need security all around people who use AI. And then we also need to be able to lead AI into having it help us like it should be our hands and feet. It should be the busy work. It should be the noise Canceler, and that's what I want it to do, for me, is just cancel out all the noise so I can forge the actually, what I want to do is kind of forge the path to let people understand it, especially women, because I think it's a great opportunity that these new workflows are created with a woman's voice in mind.
00:37:19.619 --> 00:37:37.820
So I definitely want to talk about that, but before, before we do, I want to talk about some of the risks, because the potential for AI to be used for hacking and phishing and otherwise scamming is also very high, and so how do you protect yourselves from that?
00:37:34.579 --> 00:38:14.519
Because, and I'll use this very basic example, you get a spoof email. You know, this is your invoice for whatever, and it comes from an email address you don't recognize, or maybe you do, but the language isn't right, so you know that your colleague didn't actually send it to you because it was written by somebody who doesn't speak English. All of that goes away with AI. And it could very much look like, like, you know, an attachment and email. This is a basic example. Could look like something that is completely legitimate, but it's not you your AI, could you know, once it's infiltrated your system, it can do whatever it wants, or whatever it was designed to do.
00:38:11.400 --> 00:38:14.940
Kind of talk about the risk there.
00:38:18.539 --> 00:38:32.719
Human risk is always going to be a risk. It's something you can't even 100% eliminate. So that's the second part of the story, but there are ways we are working on it. So as well as AI is getting developed.
00:38:28.699 --> 00:38:47.079
So is it in a hacker's world? So our security platforms now all are having built in AI. So how that works is there are lots of security platforms we use, like Huntress Sentinel one guards.
00:38:40.420 --> 00:39:26.780
And the ones that we use have an AI intelligence layer built into the security so the one we use is called like, it has purple AI built in. But basically what it does is it has, like a training, a security awareness training that goes out to the organization. So like, if you hired me, you would have a training that goes out to all of your employees that they have to take, and it then sends me back results, and it's the latest AI hacking phishing attempts. So the security training recognizes the latest ones that are out there. It then purposely pushes them through to your employees in a safe garden environment.
00:39:22.820 --> 00:40:01.320
Then it allows you to see which employees are opening up those unsafe emails them to have further training before they're allowed to be on your environment. Then they pass a training every month that lets us know that they're now being aware as well as us having this AI intelligence layer that's pulling the emails ahead of time, so any of these suspicious emails already being pulled so they never even get to the end user, they're going into a quarantine folder where they're getting analyzed by our team, which is also now art of.
00:39:58.780 --> 00:40:08.219
Artificial Intelligence. So artificial intelligence now analyzes this batch of emails, decides that they were unsafe.
00:40:05.039 --> 00:40:25.219
If they are, they're eliminating them. If they were safe, they're re pushing them through to the end user. So now companies like mine are even more important, because you really do need this layer between us and your employees, because your employees are your greatest risk to your company indeed.
00:40:25.278 --> 00:40:37.458
I mean, you know, from opening that email to finding a finding a flash drive in the parking lot and thinking it's a good idea to plug it in, things have happened. Yes, so you
00:40:38.119 --> 00:40:52.000
we've had a customer do that one time we were let go, and because they thought we were a little expensive. And to be honest, I'm not expensive, so it was quite an insult, but I was gracious enough to just back out kindly.
00:40:52.000 --> 00:41:11.340
And they used a company that did not have the software built in, and they got ransomware for one Bitcoin, which was like just under $100,000 at the time, and they had to pay it to get their data back. And I realized that, you know, you can't. There is no cost to security,
00:41:11.340 --> 00:41:24.139
really, no. Oh, my goodness. Oh my goodness. So you started talking about making sure that women, that female entrepreneurs, are at the forefront of this and that they understand what's going on.
00:41:21.559 --> 00:41:57.219
Let's explore that a little bit, because, and I will tell you that I reached out to you and we have, we have both been in this area for and worked alongside each other for a long time, but I had never interviewed you, despite being on the air for a while and and you wrote an article posted on LinkedIn that just moved me so much about about women being relevant in and staying relevant, especially older women in in the workforce, and how things are changing and that that things are flattening, as far as opportunity is concerned, talk about that a little
00:41:59.619 --> 00:43:36.619
bit like women older than 50 have a really interesting opportunity in today's world. Not only did we forge a place for ourselves in a world that had no place for us, and I find that really fascinating, but now we have a way to basically influence what we look like in this world of AI, how AI is utilized and spoken to. It's very important for us to take a seat at the table, because the industry technology in itself, has been shaped mostly by men, language by men, outcomes by men. And unfortunately, we are a world full of diversity, not just gender, but all types of diversity that is not really being accounted for. And little things I noticed, like I gave you an example earlier about the security reporting, I always say to my team, because I'm a female leader, and I work with a lot of men, and there's often not very many women to hire, which is also very disappointing for me, and indeed, more and more women to enter this field, as well as do I speak to a woman's cybersecurity club, because I really want them to be in this field. To shape the language that security reporting I told you about changed, because the reporting that I was getting from my team prior was very technical reporting. You've got mostly men in that work in tech, and they think like engineers.
00:43:31.699 --> 00:43:55.539
They develop with a very clear, concise outcome, which is great, but the world is filled with color and emotion and connection that gets dropped, you know, and with the security reporting in the intelligence layer, I was able to write a profile that talked about the women leaders.
00:43:55.539 --> 00:45:31.219
I have two or three companies with women leaders. I'm speaking to women, not men. I know how they want their material. I know how they would like to be spoken to. I know how they would enjoy seeing their highlights, or their team highlighted, not just the things that were fixed. So I was able to use my voice to create these security reports that now have such a different feel than the ones before, and that's because we as women now have are touching these fields like we are going to be able to shape how information is going to be transferred from company to company, human to human, how the language models when you go into chat, GPT or Gemini, how it's going to deliver information to you. So for me, we have a very large opportunity now to take our seats at the table. We shouldn't be waiting for permission. We need to speak up, we need to write, we need to post. We need to figure out. Our workflows are, and make sure we write the intelligence layers that show how that workflow gets automated, how that workflow gets actualized, right? So for me, it's really important that we let our 50 years or more of judgment and discernment, our wisdom, our ability to nurture our ability to recognize human connection, allow all those things to take part in what we're building right now. It's really important to me to see women do that.
00:45:31.219 --> 00:45:43.119
You know, it's interesting because, you know, because it's a computer and because it's, you know, it's based on logic models. It's, there is no human element in AI.
00:45:43.360 --> 00:45:55.119
So if you want a human element, you have to put it there, and you have to recognize that it's missing. And I, and I think that that's more than likely that that it would seem that that would come from women more than from men.
00:45:56.199 --> 00:46:27.320
Yes, exactly, you know. And you know, we are in a position where we are now kind of redefining what work looks like, right? So right now, in this time, like no other time before, there's going to be a new definition of work. I've just told you about all these automations. So think about a year from now, how many jobs will be replaced by these automations. Okay? So if these jobs are replaced, what happens to those work? That workforce?
00:46:24.320 --> 00:47:04.320
It doesn't disappear. It gets redefined right like now, humans are going to be used to do other types of work. What does that look like now we sit at the forefront where we get to define that. To me, it's really important to decide, you know, we're still relevant. How do we fit in? How do we use the skills we've crafted to create new ways for us to contribute to this new definition of work? Right? Like, I definitely think you're going to see the whole definition of work be redefined within the next one year to 18 months, that
00:47:05.039 --> 00:47:17.579
wow and it, you know, think about, we talked about the the the internet boom, and in the late 90s and early 2000s we thought that moved quickly. This dwarfs that exactly.
00:47:18.119 --> 00:48:10.199
It's going to happen so fast. I have been speaking at these panels across the world. I went to Amsterdam and spoke last October, and I was in Colorado and spoke a couple months ago, and I've been just traveling and speaking on this topic. And interesting enough, I'm going to speak next summer at a big AI conference in Utah called Beyond 2026 and I was trying to like, put together what my breakout room is going to look like, like what I'm going to be teaching people. And I realized that whatever I put together today may be obsolete by June or maybe so widely known already that it will be not obsolete, but be too familiar.
00:48:05.099 --> 00:48:46.840
So I'm realizing that, like what I'm talking to about now, like taking a work workflow, automating it with an intelligence layer, you know, using these in SAS tools, it will probably be common knowledge by March already. So information is going so fast that even trying to find things to talk about, I have to get to the podium quickly, like I'm like, I can give you this, this speech on AI, but we have to do this like now, because it is moving so quickly that I can't even tell you that some of the things we're talking about now will just be very common knowledge six months from
00:48:47.920 --> 00:49:06.539
now, wow, it'll be common knowledge. However, it won't be, because I think for those people who are not living in a tech world, they're the small business, they're running a bakery, they're running a preschool, they're doing whatever it is that they do, they're being told, AI is great, Ai, you need AI, you need AI.
00:49:03.719 --> 00:49:15.539
And they're like, I can't even get my brain around it, and I read something and it's completely contradictory to something else. And what do I use? Which system do I use?
00:49:12.539 --> 00:49:29.539
Gemini? Do you use chat? You know, does it have the capacity to do all the things I don't even know? You know where this fits, and I'm overwhelmed, as I think that this is moving so fast that, you know, if the tech people are feeling exhausted, everybody else is lost,
00:49:32.840 --> 00:50:59.199
same time, you're not going to be able to function without it. AI is now available to the small and medium business, and because it's not going to have a price tag attached to it like it did two years ago or a year ago, it's it's going to make a lot of sense I'm going to give you because you know this person, so Kim Calvin, for example, owns a coma unity organization Learning Center and. She has been operating in this NGO space with you know how everyone else has been doing their best, trying to survive, trying to get the data organized, etc. I went and spoke with her, and I'm like, Listen, you are super primed to be able to get an AI ready, readiness grant where you are able to organize your data to be aI retrievable, so AI systems can sit on top of any NGO and be able to scale it. And we went in, and she's eligible for like 95% of the grants out there. And oh my gosh, that's great. $1 billion worth of grants out there to help companies become AI ready. So what I want you to hear, it may be overwhelming, I hear that, but you've got companies like open AI Gemini, who have spent billions, if not trillions, of dollars, developing this, and now they need people to use it safely.
00:50:59.199 --> 00:51:02.159
How are they going to do that?
00:50:59.199 --> 00:51:54.340
If it's not working the way they want it to, they're putting grants and money out there so organizations can adapt, adapt it correctly, so that organizations will start to use and be aI ready, AI first. So basically, these people, every massive enterprise organization that's dealing with AI, needs your data to be aI ready so they can access it, so they can build even further. So a company like Google needs every small and medium business to be aI ready, so that their data is AI readable, so that they can build it better. So they're going to encourage companies like yours, mine, anybody, to be able to to have this learning curve be not so difficult. So I'm telling you it's going to go out there by getting companies, foundations, their digital readiness first.
00:51:54.340 --> 00:52:23.480
And that's what you're going to see, a big, massive push towards digital readiness. AI ready data, and there'll be grants and money out there to help this happen, and there'll be companies like me that are going to be hired to help companies do that. And that's what I'm that's what I'm doing. I'm hoping to get grants for companies who want to hire me so that I can help them organize their workflow and then automate it and put intelligence layers so that they can be outcome based.
00:52:24.438 --> 00:52:38.119
Wow, wow. It's, that's, that's fantastic. And and the next question, then this, all of this, begs the question, do we have the energy to power all of this as a global community?
00:52:39.199 --> 00:54:24.860
So obviously, that is the big buzz word and environmental harm and everything else. So I'm an optimist, so I believe that AI, the usage of AI, is going to help solve a lot of our AI issues. So I feel like it's a tool that helps us connect areas that we don't see connected yet, information that we don't see that we're not able to see, because we broadly can't see everything at once. So I am a huge optimist, optimist that believe that this issue will be solved through AI, and that AI will eventually help us become carbon negative, not just carbon zero. So I am a huge optimist. I am not a typical voice out there, but I have watched industries grow and seen doom and gloom, and you're as old as I am, so I'm sure you remember y 2k where we thought at the year 2000 everything was going to go and then nothing. Then nothing happened, and everything was fine. I kind of feel like as humans, we catastrophize things that we don't know. And I do not believe that this is something that is going to be a war of AI, robots, destruction. I don't believe that. I think this is going to be our our way, to allow humans to be redefined, for us to have more creativity, for us to undo the damage we've done, for us to be able to be more self regulated. So I see this is a big upgrade to humanity, and we're just going to go through chaos for the next five years until the upgrade is completed, if that can make sense.
00:54:24.920 --> 00:54:36.079
But well, Manel Iskander, we are completely out of time. How do people find find you on social media, if you're on social media, and more importantly, how do they connect with you at PC tronics
00:54:36.679 --> 00:54:39.380
appreciate that.
00:54:36.679 --> 00:55:02.280
So I'm mostly on LinkedIn. I don't do a whole lot of social media. So LinkedIn, Manel Iskander, president and founder of PC tronics and Fugazi, which is a marketing company, and I am at PC tronics.us, so you can do I have like a reach out to me on that. So you can reach out to me either by LinkedIn or www.
00:54:57.099 --> 00:55:02.280
Seatronics.us on my website.
00:55:02.280 --> 00:55:04.199
There is a way to contact me there.
00:55:04.920 --> 00:55:08.760
Well, what a treat this has been. Manaliskander, thank you so much for joining me today.
00:55:09.179 --> 00:55:12.599
Thank you so much, Erin. I appreciate having you having me on today.
00:55:13.440 --> 00:55:24.079
Well, that is all we have time for today. Thank you so much for spending this time with me. And I hope you learned a lot. I know I did. I'm Erin Brinker, and you've been listening to the hope table.
00:55:21.800 --> 00:55:24.079
I'll see you next week.
00:55:34.880 --> 00:56:11.579
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