In this episode of Executive Connect, host Melissa Aarskaug sits down with Daniel Hindi, founder, engineer, and CEO of Gnome.ai, to talk about what AI is really changing in business, leadership, and the future of work. Daniel shares lessons from building and selling multiple companies, and explains why the real power of AI is not just automation, but helping people focus on clearer thinking, better communication, and stronger results. This conversation is for CEOs, founders, operators, and professionals who want to understand how to use AI without losing the human side of work. Daniel explains why many AI projects fail, how employees can use AI to grow their careers, why business owners should start with the problem instead of the tool, and why human craftsmanship may become even more valuable in the years ahead. He also shares why AI should never fully replace human judgment, especially in messaging, branding, and leadership.

Chapters:

(0:28) Why “If AI can replace your job, it should” is the starting point

(1:43) Daniel Hindi’s path from engineer to founder and CEO

(7:30) Why so many AI products fail in the real world

(11:35) How leaders should approach AI by starting with the problem

(15:37) How AI will affect jobs and how people should assess their risk

(19:04) Why meaning, impact, and purpose matter more than task output

(23:52) How employees can turn AI from a threat into leverage

(27:44) Why human craftsmanship could become premium again

(31:28) What a solo builder can realistically do with AI today

(38:32) How AI should shape the way we prepare kids for the future

(43:17) Where AI is still being massively underused in business

(47:49) What should never be fully handed over to AI

Daniel

(0:00) It should never be handed over completely. (0:02) There should always be a human in the loop. (0:05) So, for example, your messaging and branding, you can use AI to help fine-tune it, but AI should not be responsible for that impact.

(0:15) AI should not be responsible to make sure that you’re hitting the goals that you’re trying to hit. (0:22) There must be a human ensuring that your goals are being met.

Melissa

(0:28) Let’s get something uncomfortable out of the way right now. (0:32) If AI can replace your job, it probably should. (0:36) The statement makes a lot of people uneasy, but today’s guest believes it’s actually the most honest starting point for understanding the future of work.

(0:46) Daniel Hindi is a founder, engineer, and operator who has spent the last two decades turning complex technology into real businesses. (0:56) He’s helped build multimillion-dollar products at the intersection of AI, SaaS, and automation, and today he’s the CEO of Gnome.ai, where AI chatbots are transforming how companies communicate with customers across the web, text, voice, and social. (1:18) This episode isn’t about hype, it’s about what AI is actually replacing, what it elevates, and how founders, operators, and even parents should be thinking about work in the AI-first world.

(1:33) Welcome, Daniel.

Daneil

(1:35) Thank you for having me. (1:37) I appreciate it.

Melissa

(1:38) Now, you’ve spent two decades shaping real products, not demos.

Daniel

(1:43) Walk us through your journey from engineering to operating to founding companies like BuildFire and now Gnome.ai. Yeah, so I started off as a software engineer, and what I realized early is that I was lazy, and I know that comes with connotations. (2:03) It was basically seeing inefficiencies in processes, and saying, how can I make this better just by sheer willing to want to reduce my workload? (2:14) So by doing that, I realized this is what business owners do all the time.

(2:18) They look at inefficiencies in our process, make it more efficient so your margins increase, and then your headcount doesn’t have to increase, and your cost stays stable. (2:26) I realized what I was doing is actually optimizing the business, and so I built my first company in the healthcare space aggregating data, and I sort of just fell into that. (2:37) And once I realized imposter syndrome is real, that everybody has self-doubt in the beginning, and says, why me?

(2:44) I’m not anything special, but I think I can do this. (2:47) Once you get over that hump, I basically said, hey, you know what? (2:51) I think I can do this.

(2:52) Sold my first company at 26, built five other successful companies, and exited those. (2:58) I have a graveyard full of bad companies that I’ve created. (3:01) I won’t talk too much about those failures, but currently, after I sold my business, built five in the mobile app space, I’m building Gnome AI.

(3:10) We’ve been doing this for over a year now, basically looking at the findings that I’ve learned over my previous companies, observing other companies where the struggles are, specifically with particular positions and roles that every company has a problem with, and try to solve it at Gnome AI. (3:32) Those two roles specifically are SDR and CS roles. (3:37) We do a lot more than that, but that was the genesis of the company.

(3:40) So if you look at an SDR, so you spend a lot of money on your marketing team to get you these leads, and then you have your sales team try to qualify these leads to close them, and you realize everybody in the sales industry says, okay, I need to pay my dues as an SDR, as a sales development representative, but I don’t want to be here for long. (4:01) Six months to a year, I’ll pay my dues, and then I want to be an account executive, an AE, because that’s where the commissions are at. (4:07) Well, from the business owner’s point of view, I just spent all this time hiring somebody, and we all know nobody has 100% batting rate at hiring.

(4:15) You get some duds, unfortunately, when you hire. (4:17) You finally find somebody, you spend months training them, they spend months getting good at it, and they’re finally good, and they say, well, boss, can I become an AE now? (4:27) Well, your investment as an SDR is now gone.

(4:30) Same thing with your CS team. (4:32) Nobody wants to spend their career answering questions like, how do I reset my password? (4:37) Once they’re good at it, they’re saying, hey, can I go to tier two?

(4:40) Can I become management? (4:42) And so it’s a constant struggle for a lot of businesses, and that’s what we’re trying to solve at Nomai.

Melissa

(4:48) That’s great. (4:49) And first of all, congratulations on knowing your personality. (4:52) I find so many people are trying to figure out who they are and how they function.

(4:56) And as a fellow engineer, I’ve often gotten categorized in that box that I’m only an engineer and I have to exist in that space. (5:07) So when did you realize you’re more than just an engineer?

Daniel

(5:12) I think it was early for me because I didn’t fit in. (5:15) The stereotype that you get with engineering is the guy that’s a super introvert, doesn’t want to talk to anybody, just wants to sit behind a desk, and doesn’t understand really why they’re doing what they’re doing. (5:28) They understand the problem.

(5:29) They understand that I’m here to solve problems, but they have a difficulty translating that into software engineering outside of code. (5:38) When I grab software engineers, even CTOs, and I say, do you understand the problems you solve? (5:42) If you just remove the element of code, this applies to marketing, this applies to operations, this applies to sales.

(5:49) It’s finding the bottlenecks, seeing where the struggle is, and then translating a solution to it. (5:53) And the solution may not be, sometimes it’s code, sometimes it’s not code. (5:57) And just understanding the problem-solving aspect of it and going back to why we’re doing what we’re doing.

(6:04) It is missed in our workforce for, I want to say, at least the past 20, 30 years, where I’m here for the paycheck. (6:12) I don’t know why we’re doing what we’re doing. (6:13) Yeah, I get the mission and vision and goals of the company, but I don’t care.

(6:17) I’m here for the paycheck. (6:18) If you’re able to align, not necessarily agree all the time, just understand, why is it that I’m important in this company? (6:25) How do I measure success?

(6:27) Is it lines of code? (6:28) Is it number of tickets I solve? (6:30) Is it number of boxes I move?

(6:31) No, there’s a real understanding of why I matter that is being lost in our workforce. (6:38) And I think that’s one of the things that as leaders, we need to come back and make sure our entire team understands why they matter. (6:46) And as a software engineer, I noticed that I was different in that sense and was able to build my career around it.

Melissa

(6:53) And I think the one beautiful thing about studying and learning engineering is you think differently than a lot of people. (7:01) And that’s the one thing I love about studying engineering, how my brain works and solves problems is different than a lot of my peers. (7:08) And the beautiful part is if you’re an extrovert, you carry the mind of an engineer, but the personality of a salesperson.

(7:18) And so you said your edge is turning complex technology into clear business outcomes. (7:24) Why does so many AI products fail to cross that gap?

Daniel

(7:30) Ooh, there’s a lot to unpack there. (7:33) So first of all, in my career, I realized some of my peers would take something that I thought was very simple and make it extremely complex to make it sound smarter than what it was. (7:45) And then I realized I was opposite.

(7:47) I was taking things that are extremely complex and be able to translate it in business language where everybody on the team could understand. (7:56) And that also translates to all departments is can you take what you do that could be very complex, could be very nuanced and explain it. (8:06) When you can do that, you can find flaws.

(8:09) You can find where are you not optimizing, where are you failing to see or recognize your blind spots. (8:18) So when you go into AI, what I feel is, is AI is exposing how bad we are at communicating. (8:27) It’s been very enlightening for the past five, 10 years dealing with AI.

(8:33) So we all go to chat GPT and ask a question and you try to one shot it in one sentence. (8:41) You come in, maybe you put in two, three sentences at max and then assume chat GPT is going to come back with your perfect answer, right? (8:47) Something simple in AI, right?

(8:49) We’ve all done that. (8:50) Well, one, you’re trying to one shot it. (8:52) One question, one answer, done.

(8:55) Most complex scenarios requires a little bit of back and forth, appealing back this complex issue. (9:03) And what I’ve realized helping my customers and even in my own communication is how poorly we’ve communicated. (9:10) Let me give you a very simple example.

(9:12) If I said, my wife and I went to a restaurant yesterday, we had this amazing dish. (9:17) It was hot. (9:19) What was hot?

(9:20) The dish? (9:21) The like spicy or the temperature was hot? (9:24) Was the restaurant hot?

(9:25) Was my wife hot? (9:26) What are you saying, right? (9:29) And something as simple as that, I’m just talking about describing my last night’s dinner at this restaurant.

(9:34) And there’s confusion in what we said, what we do the same thing with AI, right? (9:39) When we come in and we do that with our employees as well, right? (9:42) With our staff, with our team members, with our peers, we’re very poor communicators.

(9:46) What AI does is it tries to understand what you’ve said, not what you mean. (9:51) And then when you get subpar results, you understand, oh, I wasn’t clear enough. (9:55) I wasn’t concise enough.

(9:56) I wasn’t comprehensive enough. (9:58) But we’ve been doing that with our team for decades. (10:02) AI just gives us a really quick feedback loop.

(10:05) It says immediately what I said was not enough. (10:08) So I’ve realized like in the past, and I see this with my clients, is that you say something that you think everybody resonates and is on the same page, but it takes them weeks to come back and say, that’s not what I asked for. (10:20) Or that’s not how we measure success.

(10:22) You just ticked off a box. (10:23) You didn’t measure the impact, right? (10:25) What AI is doing for us is it’s exposing that lack of communication, lack of clarity in our instructions quickly, instantly.

(10:36) And so I actually do believe, I know I’m a contrarian saying, hey, if AI can take your job, it should. (10:40) But I also believe AI will bring back humanity in our communication, in our relationships, because we’re realizing how poorly we’ve been communicating. (10:49) Does that make sense?

Melissa

(10:50) It makes perfect sense. (10:52) It’s a fantastic example. (10:54) I’ve seen a lot of AI demos and they look so impressive in the demo.

(11:00) And I’m like, this is solving a real problem. (11:02) But when I see it in production or hear about it in production, it often fails. (11:07) And so I’m curious to get from that context, how can leaders ensure the AI initiatives that they’re hoping to accomplish solve the actual real problem?

(11:20) Ready to lead smarter and invest wiser? (11:24) On the Executive Connect podcast, we unpack executive strategies for wealth and influence. (11:30) Hit the subscribe button now.

(11:33) Don’t just watch, act.

Daniel

(11:35) So you definitely want to start with the problem and not the model to say, I want to start with chat GPT and see what chat GPT can do. (11:41) I want to start with perplexity and see what perplexity can No, no, no. (11:45) Start with the problem you’re trying to solve.

(11:48) Then try to break down, how do you measure success? (11:51) What is the problem? (11:52) Articulate it very clearly.

(11:54) Articulate how you measure success. (11:56) Articulate who you are. (11:58) Am I a person?

(12:00) Am I a business? (12:01) What is it that we do? (12:02) Who’s this audience for?

(12:03) If I’m doing something as simple as writing a blog, something as simple as that, right? (12:08) Who’s this blog for? (12:10) You said, write a blog about this topic.

(12:12) Great. (12:13) That’s it. (12:14) Just one line.

(12:15) So you want to say, hey, I want to increase my visibility. (12:18) I want to rank for these keywords. (12:20) This blog specifically addresses my audience.

(12:22) Here’s my audience. (12:23) Here’s my differentiator, why I’m different than the competition. (12:27) And this is how I want you to measure success.

(12:29) And here are your constraints. (12:31) Don’t mention this. (12:32) Don’t mention this.

(12:33) The other thing that we fail with specifically large language models, which their entire purpose is to produce text, is when we overemphasize the constraints and not the path forward, where you say all the don’ts, but less of the dos. (12:49) So it starts finding the cracks in your prompt to try to give you an answer because its job is to regurgitate text. (12:58) It’s trying to give you text back.

(13:00) So we say, hey, these are things you’re not supposed to do. (13:02) Stay away from calling my employees contractors because they’re not employees by the state of California. (13:08) I’m not supposed to call them employees.

(13:09) Okay. (13:09) You told them what not to say, but then you should give it examples on what you should say. (13:15) So the tone, the roles, the goals, the expected outcomes, how to measure success, how to measure failure, examples of good examples, bad examples.

(13:24) And then you’ll see the AI sort of fine tune itself to the best results. (13:30) And when you’re not sure, have the AI interview you. (13:35) Say this is what I want.

(13:37) Ask me any questions that you think is missing, ambiguous, and not clear, and interview me before we begin. (13:44) We try to one-shot everything. (13:47) Just one question, one response.

(13:48) Okay. (13:49) Well, here’s my question. (13:50) I put my prompts one page long.

(13:52) Great. (13:52) But that you’re still trying to one-shot. (13:54) It’s iterative.

(13:55) You try to go back and forth with the AI until it’s exactly, okay, we’re on the same page. (14:01) Now go produce. (14:02) And then ask it, how would you make it better?

(14:05) What am I missing here? (14:06) Don’t be my friend here. (14:08) Right.

(14:08) Well, if I go to an LLM, and again, I’m giving very simple examples here, but this translates into the complex. (14:14) If I go to an LLM and I say, why is Canada going to be the major force in the global economy in 2026? (14:23) I sort of baked the answer that Canada will be the global force in 2026.

(14:29) And you’ll realize it will answer. (14:31) It’ll come up with some scenario for you, because I was biased in the way I asked the question. (14:38) Right.

(14:38) So we have to, that’s the other thing that in our communication that has been poor is that we have hidden biases in the way we ask, in the way we prompt, in the way we communicate. (14:48) And some biases, if you’re aware of them and I want to be biased, I want to be biased towards my company. (14:53) Yes.

(14:53) Explain why my company is best for this customer. (14:56) Sure. (14:57) That’s okay.

(14:57) That’s a very obvious bias that you want to bake into your posting, into your blog. (15:04) That’s fine. (15:05) That’s okay.

(15:06) But you’re aware of it. (15:07) It’s the hidden biases that sometimes sabotage our AI projects.

Melissa

(15:13) Yeah, that’s, those are great. (15:15) Those are great explanations. (15:16) And I think the question that everyone is asking is, is my job at risk?

(15:22) And so let’s address that concern and fear directly. (15:25) How will AI disrupt the workforce this year? (15:30) And how should people honestly assess their risk so they can build a plan?

Daniel

(15:37) So very controversial. (15:39) We said at the top, top, and I’ll say it again, if AI can take your job, it should. (15:44) But my question to you is, is your job fingers on keyboard?

(15:48) Is that how you define your job? (15:51) All of us go into a particular career path with a passion, right? (15:55) We go into this and say, I selected this career path because I really enjoy doing X.

(16:00) A few years in, you’re like, I really don’t like my job anymore. (16:03) It’s Monday morning. (16:04) I don’t want to go to it.

(16:05) And if you realize all the things that drain you in your career, if that can be taken away with AI, the task oriented part of your job, the repetitiveness, the, I need to remind people and communicate in a very articulate way. (16:21) And it’s 5 PM and it’s my daughter’s recital in a few hours and I have to jump off and I just, I need this done. (16:28) If we can get AI to take care of that portion for us, what’s left?

(16:35) Well, am I out of a job? (16:36) No, no, no. (16:38) You are still there to coach the AI on why you’re doing what you’re doing, who’s it for, and how do you measure impact?

(16:49) Going back because all of us have team members and you say, you know what? (16:54) I went and I checked off these 10 things you asked me to do. (16:57) Well, did it actually accomplish the goal?

(16:59) I don’t know. (17:00) I just did the 10. (17:01) And what they’re doing is they’re doing this and saying, look, measure the sweat on my brow.

(17:06) That’s not how we run businesses. (17:08) We run businesses based on impact. (17:11) What is the KPI you’re trying to influence?

(17:14) And did you influence it positively or negatively? (17:17) Negatively still has its findings. (17:19) Hey, I tried something and it didn’t work.

(17:21) Great. (17:21) Now, you know, adjust, adjust quickly. (17:24) We always say, if you’re going to fail, fail quickly.

(17:27) So if AI can take your job, it should, but reassess your job. (17:31) Is it fingers on keyboard? (17:34) No, that’s a part of the job you hate.

(17:37) Let AI take that over and go back to deep thought, deep spend. (17:43) We spent so much of our time in shallow thinking, running these simple tasks, but they consume eight hours of our day. (17:52) If I can give you the chance to think deeply, before you take action and the action can be 10 X fold, you know, in terms of speed, in terms of productivity, if you spend time thinking deeply, why I’m doing what I’m doing, who’s it for?

(18:08) And what is the expected outcome, the impact that we’re going to have? (18:12) You’ll actually produce much more. (18:14) You’ll be extremely valuable to your company, whether you’re in leadership or you’re an IC, an individual contributor, right?

(18:22) If you go back to deep thought, why am I here? (18:25) How do I measure success? (18:26) Who is this for?

(18:27) I think AI will unlock meaning back into our day-to-day work.

Melissa

(18:35) Yeah, definitely. (18:37) It’s so true. (18:38) I think measuring your job past what you actually do, the task itself is so key and people often measure their value by the output they’re producing, whether that be, you know, reports or administrative items or engineering, but you argue it should be measured by something deeper.

(19:00) The why, the who, and the impact. (19:02) Can you break that down for us?

Daniel

(19:04) Yeah. (19:05) So I’m going to give you the most menial example here, right? (19:08) If I load boxes into a truck every day, that is my job.

(19:14) AI and robotics is going to come and take over my job. (19:17) My quota every day is to put a hundred boxes on this truck and then I can clock out and leave. (19:22) The most menial job you can think of.

(19:24) You know what? (19:25) This is a robot can do my job. (19:27) Well, a robot is probably going to do your job, right?

(19:30) Now, if I just changed one thing and brought back meaning, I said, in these boxes, what do we send? (19:35) Oh, I think there’s food in the boxes. (19:37) No, no, what is it that we send?

(19:39) It’s food. (19:40) What is it that we send? (19:43) Meals.

(19:43) Meals for who? (19:45) We’re a nonprofit. (19:46) It’s for hungry families.

(19:48) So when you put 99 boxes and not the hundredth box, there was a family that didn’t eat that night. (19:54) Okay. (19:55) That’s different.

(19:56) Same boxes. (19:58) Nothing’s changed in terms of the task, but the meaning behind it changed. (20:03) The meaning behind, wow, I feed hungry.

(20:06) I load boxes into this truck, but yes, one Friday at 4 PM, when the guys are all off to the bar early, you know, starting the weekend early and you didn’t put that last box on. (20:17) There was somebody that didn’t eat that night. (20:20) Now you understand, no, there’s meaning to my job.

(20:24) I understand who this is for, why I’m doing it, the impact I have. (20:28) And if we don’t provide that to our employees now, yes, not all of us are feeding hungry families, right? (20:35) But if we are unlocking success for our customers, if our customers don’t succeed, we don’t succeed.

(20:40) Be passionate about that. (20:42) And if you’re not passionate about that, reassess, are you in the right company or not? (20:46) Because we all build companies where we want everybody to be as passionate as we are.

(20:50) That’s a high bar. (20:51) We are the owners of the company. (20:52) We manage the company.

(20:53) Sure. (20:53) That’s a high bar. (20:54) At some ratio of that, we need our team to be bought into our mission.

(21:00) And if we, I think if with the age of AI, with us being better communicators, with us spending more time, understanding the meaning behind what we’re doing, translating that to our entire workforce will actually impact us positively. (21:15) That doesn’t mean you lose your job. (21:17) It means now instead of you doing a hundred boxes, you’re doing 500 boxes because you are now the manager of these robots, right?

(21:25) Make sure that the robots don’t know and don’t understand why they, they, they put on you do right. (21:30) And the robots don’t understand that, Hey, it’s actually this, this box is in the sun and it has food in it. (21:36) Right.

(21:36) You need to move on. (21:37) Again, I’m using a very menial example here, but it translates to all of us going back to why, who, and the impact and going back to measuring your KPIs and the meaning of what you do. (21:50) Does that make sense?

Melissa

(21:51) No, it makes perfect sense. (21:53) I think it’s a great example because everything’s been kind of doom and gloom, right? (21:58) With, you know, some companies have let go people and we leverage, you know, that news heading to really dictate how we feel about a situation that we may not have done any research or any introspective thinking about what we can do or skill sets we can go get, or maybe that gives us the opportunity to decide, Hey, I’m tired of, you know, boxing things outdoors.

(22:20) I’d rather take an inside job. (22:22) What can I do inside that translates from outdoor to indoor? (22:26) And so I think you’re spot on with people need to start understanding the, what that they do.

(22:31) And if it’s something that they enjoy doing, if you hate putting boxes on a truck and you are, you know, getting older and it’s hard on your body, maybe you think about what you can do or a skill you can to learn to go inside. (22:45) And so I think, you know, right now AI appears to favor businesses and business owners for someone who doesn’t own one yet. (22:56) How do they flip AI from threat to leverage?

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Daniel

(23:52) So tying that into the previous, previous question, one, if you’re not, you’re not going to be replaced by AI, you’re going to be replaced with somebody who’s good at AI. (24:01) Right? (24:01) So that’s, that’s a part that you need to take a look at.

(24:05) The other part is if I am trying to learn AI, I can give you 50 masterclasses, 50 books about swimming. (24:13) Until you jump into the pool, doesn’t matter. (24:17) Until you get your feet wet, doesn’t matter.

(24:19) Right? (24:20) Same thing with AI. (24:21) Stop being afraid of AI, embrace it.

(24:24) What, what companies like Gnome AI do is reduce the barrier to entry, make it very simple, prompt engineering. (24:30) That sounds complicated. (24:31) It really isn’t.

(24:32) It really isn’t. (24:33) There is good prompt engineering and bad prompt engineering. (24:36) Sure.

(24:37) But get started. (24:38) The other part that I think people either starting their career or towards the, the sunset of their career, or, I mean, 80% of our graduates don’t work in their field. (24:50) What does that mean?

(24:51) A lot of people are not passionate about that. (24:53) I had to pick something and, you know, I started off with mathematics and I moved to economics and it was just a job I don’t care about. (24:59) You never really found your passion or you found your passion and didn’t realize that.

(25:05) I don’t know how to make money with it. (25:07) AI is reducing the barrier to entry for you to start your own business. (25:11) You can build a website with a few prompts.

(25:15) If you’ve got a replica or, or, you know, lovable, you can just start talking to AI and it will build your product for you. (25:22) Here’s the other side. (25:24) I do believe craftsmanship is going to have its renaissance again.

(25:31) Yes, we all see AI slop out there. (25:33) Not all AI slop, meaningless AI slop, right? (25:38) If you said, Hey, you know, I have this painting behind me.

(25:43) AI did that. (25:43) Great. (25:44) Good for you.

(25:44) It looks beautiful painting. (25:45) If I told you somebody paint, hand painted it for me, I believe that’s going to be in the next few years going back to a premium. (25:53) It’s like, Hey, my kitchen, I, I built my kitchen from Ikea or I had a carpenter customize this kitchen for me.

(26:02) So the craftsmanship in the arts, in the application of our hands will become a premium because it will become rare and not everybody can, can afford it. (26:14) I’m old enough to remember like craftsmanship got cheap, right? (26:20) It was, it was because there was many craft craftsmen out there and you know, it became a commodity.

(26:25) And so we devalued it, right? (26:27) Tell today there’s still, Hey, this plumber, you know, that plumber makes more than you, right? (26:32) That electrician makes more than you.

(26:33) He works with his hands. (26:34) He just replaced a few chandeliers at this high ceiling made more than you. (26:39) Oh my God.

(26:40) Anybody who owns a home realizes there’s a tax besides property tax that you pay on your home, right? (26:45) Every little thing that you try to maintain in your house is getting super expensive. (26:49) That will come back.

(26:51) There’s certain jobs AI won’t do and won’t do as well as a human’s craftsmanship and the premium of knowing a human did this for me. (27:00) So you can embrace AI, add that to creating your next business, increasing your position and your, your career, or unlocking what you were meant to do, going back and say, yes, I want to go back to craftsmanship. (27:14) And now there’s a market for this and there’s a premium for it.

(27:18) And I can create this other company that is not necessarily an AI. (27:22) It is actually the opposite of, you know, 10 X seeing my, my productivity. (27:28) No, this is a hand painted a portrait of my wife and I, this is a handmade table.

(27:35) This is a hat. (27:36) And you’ll realize in the next few years, you’re going to see this as my prediction that craftsmanship will have its Renaissance.

Melissa

(27:44) Yeah, I love that. (27:45) So how can employees start thinking like owners and turn, and then kind of the second piece of that for those people that are employees today, how can individuals turn AI tools into career accelerators?

Daniel

(28:02) Have an honest discussion with your management and say, Hey, I know I’m, I’m here and I’m supposed to create these reports. (28:10) Can we just sit down just for 30 minutes and explain to me the impact of these reports? (28:15) I do it for our clients and I just run this report.

(28:18) I make it pretty. (28:19) I check it and then I send it off. (28:21) What’s the real impact?

(28:22) What is my KPI? (28:24) Now, a lot of managers will say, well, how have you been here for 10 years and not know what your KPI is? (28:29) Right?

(28:29) Well, the truth is it’s poor management. (28:32) Management has not been communicating that, but take the time to understand how do you measure success? (28:38) Who is this for?

(28:39) And what makes us different? (28:41) Then go back to AI and, and explain it, create a project. (28:45) It’s important when we’re, when we’re creating AI models to help us understand things, to give it enough context, and you don’t want to keep on restating the context.

(28:53) So create a project, say, this is the company I work for. (28:56) This is the career. (28:57) This, this is the, the, the mission.

(28:59) This is the vision. (28:59) This is, this is my KPI and this is my role in the company. (29:03) Okay.

(29:03) I’ve given it context, so I don’t have to constantly reiterate. (29:06) I need to use AI, ask AI. (29:09) I need to use AI to accelerate my career.

(29:11) But now that I’ve given it enough context, it will then break it down. (29:15) And if you’re in marketing, there are a ton of products for you that you can start using and adopting, and you can be the early adopter in your department to show how you can accelerate it. (29:25) Sales, support, engineering, whatever it may be, you can have AI help assist and coach you.

(29:33) Where is that unlock for you? (29:35) Where is that bottleneck? (29:36) And then you will discover one step at a time, start small.

(29:40) Don’t, don’t be too lofty with your goal. (29:42) Initially, you know, walk before you run, start small, and then you’ll realize the tasks that are on your plate get reduced because AI will take it over. (29:51) It is not one tool.

(29:53) It’s maybe 10 tools that you use, but don’t start with the tool. (29:57) Start with understanding your KPI. (29:59) What is your key performance indicator?

(30:02) Understand your differentiators and who you are and have AI coach you on what you do. (30:08) But there are a plethora of products out there that can help you in your department. (30:12) Not my department.

(30:13) Yes, your department too, right? (30:15) Nobody’s going to be unscathed by this. (30:18) And early adopters, if you know what, my manager is sort of resisting or whatever, there are super low barrier to entry products out there that you could just try and bring a prototype to your manager and say, Hey, I think we can do this.

(30:30) Don’t come at it out of fear to say, Oh, if I provide this to my manager, I may get fired. (30:36) Right? (30:36) Do you think that low of yourself?

(30:39) Do you think that’s how little you do for the company? (30:43) If you come in and you say, Hey, it’s going to happen, whether it’s your, it’s your colleague doing it or your manager discovering, if you come in and you say, I am, I am the innovator. (30:51) I’m trying to do what’s right for the company.

(30:53) They’re going to say, great. (30:55) I’m not going to fire you. (30:57) I’m just going to expect more out of you now that you have this tool.

(31:00) And because I expect more out of you, you’re more valuable. (31:03) And because you now produce more, I can give you that race. (31:07) Does that make sense?

Melissa

(31:09) Yeah, it makes perfect sense. (31:10) And when I think about, you know, AI as a co-founder, you know, it can do research and marketing and design and customer success and delivery. (31:21) What’s realistically possible for a single builder?

Daniel

(31:28) So for a single builder, the easiest thing is prototyping, right? (31:33) I have an idea. (31:34) I’m not sure if it works or not.

(31:37) Okay. (31:37) So you start off with AI to build a product, you know, go to lovable, go to a replit and just build something. (31:46) Then go to an LLM and say, this is what I’m thinking about doing.

(31:50) Here’s the product that I’ve created. (31:52) Poke holes in it. (31:52) You are not my friend.

(31:53) Give me productive criticism, right? (31:55) It’s very important to tell LLMs that you’re not my friend. (31:59) I don’t want you to agree with me.

(32:00) Tell me what I need to hear. (32:03) Poke holes in it. (32:04) What am I missing?

(32:05) What is the value proposition? (32:06) What is my niche? (32:07) Who are my competitors?

(32:09) Right? (32:09) And basically, instead of saying, hey, I don’t have money to hire a bunch of these people. (32:13) I don’t have money to hire creative marketing team.

(32:16) I don’t have money to hire engineers to create my first website. (32:21) Right? (32:21) AI will help you with all of this.

(32:22) Now, you may plateau at a certain point where you do need to hire, but you were able to create a business that you proved was viable. (32:30) And then you have some profits to now spend on actual specialists that come in and optimize your AI or augment your AI. (32:37) But whether it’s marketing, we have AI that’ll create the ads for you, even video ads.

(32:43) We have AI that’ll manage your ads for you. (32:46) So instead of spending four or $5,000 a month, a marketing team to do that for you. (32:51) Now, they may, you may plateau and the marketing team may have you break through that plateau, but let’s get to the first $100,000 in revenue.

(32:59) Right? (33:00) You could totally do that with AI. (33:02) I don’t have support.

(33:03) Well, let me, I see these chatbots out there. (33:05) Let’s, let’s, let’s get a chatbot for support because I don’t want to be the one answering questions in Portuguese at Saturday evening. (33:12) Right?

(33:12) Build that product, prototype, figure out. (33:15) It helps us fail quickly. (33:17) Failure is only on the way to success.

(33:20) It’s a milestone. (33:21) We’ve all failed. (33:23) Anybody who succeeds, do you think I got it right the first time?

(33:27) Melissa, you have so many answers for me. (33:29) You know, I ask you a question, you know, the answer, how do you know the answer so quickly? (33:32) Because I made the mistake before you, that’s all.

(33:34) You know, I’ve made that mistake before. (33:37) I just, I’ve, you know, you’re early in your career and I’ve, I’ve done it. (33:39) It’s not that I didn’t make that mistake.

(33:41) I made the mistake and learned from it. (33:43) Right. (33:43) And so AI can help us iterate much quickly, much more quickly at a much more cost effective point that anybody can build their own business, but you don’t want to build a business just to create a business.

(33:56) You want to create a business to solve a problem, find the problem that is not being addressed right now, the niche in that you don’t want to be great at many things because you can’t be great at many things. (34:05) You’re going to be okay at many things, find your niche and be the best at it. (34:11) AI will help you get, get there until you can hire your team.

Melissa

(34:15) And that’s such great advice. (34:17) I think, you know, the human factor is, is a really important piece of this as well. (34:23) I feel like I’ve gotten a lot of great feedback from the AI tools, but also from my peers, my colleagues and asking them, what do I do differently?

(34:32) What do you see that I’m unique at and getting that from people as well. (34:37) So the second path you describe is pretty fascinating. (34:40) A future where human craftsmanship becomes premium again.

(34:44) So walk me through a little bit about why you believe that and that some of the actual dead jobs we were just talking about may actually end up coming back. (34:54) Money Ripples is on a mission to help professionals just like you get their money working harder for you. (35:00) Their clients free an average of $35,000 their very first year without having to work extra hours to show you how they’ve put together a powerful training called Cashflow Secrets and the listeners of the Executive Connect podcast can get it completely free.

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Daniel

(35:25) So we’re seeing it already with blue collar jobs, right? (35:29) Undersea welder, sorry AI is not going to do that for you, may have some robotics, but when you have an oil tanker, you know how much those guys get paid? (35:36) They get paid pretty well.

Melissa

(35:38) Yes.

Daniel

(35:38) My biggest unlock there is basically understanding the failure we’ve had in our educational system and this ties into our kids as well, is why is it that 80% of graduates don’t work in their degree? (35:52) Why did you select that career path that you weren’t passionate about? (35:56) Finding your passion again.

(35:58) I’m in my 40s now, it’s too late. (36:00) It’s not too late. (36:01) It’s not too late.

(36:03) If AI can automate a lot of your job right now, that unlocks deep thought and part of that deep thought is personal deep thought. (36:10) Say, what is it that I want to do in the next 5-10 years? (36:13) Do I want to continue in my job?

(36:14) How do I do that in the best way I can? (36:17) Or do I want to change my career path? (36:21) Is this something that I’m passionate about?

(36:25) What is it I’m passionate about? (36:26) I’m passionate about working with my hands. (36:29) Okay, great.

(36:31) If you’re passionate about working with your hands, what is the art that you are passionate about? (36:38) What is the skill set that you want to develop that people find value in? (36:44) Again, I have somebody a few blocks away from me that quit his job and just builds artisan tables.

(36:52) Not even furniture, just tables. (36:54) That’s all he does, makes a killing. (36:57) But he’s very passionate.

(36:58) He’s passionate about the type of wood that’s being used. (37:01) He’s passionate about the type of paint and stain on the table. (37:06) When people come over my house and see one of the tables, I know about all the furniture companies in San Diego.

(37:12) I’ve never seen that table before. (37:14) Yes, this was handmade. (37:16) Bringing pride back into handmade craftsmanship and again, with disposable income, with people who stick with AI, making more money with AI, that’s great, but they’re going to spend their money somewhere.

(37:27) I guarantee you the most people pushing AI, I am one of them, put the highest premium on handmade products. (37:35) It’s odd. (37:42) It’s odd.

(37:43) iPhones and cell phones, if you say, hey, do you let your kids use this technology? (37:50) No, I don’t want my kids to use this technology. (37:52) Why?

(37:53) Because there’s a tax to it. (37:55) Part of that translates into their growth and we can talk about our children and the next generation coming. (38:01) But it also says, hey, I do value going out and touching grass.

(38:04) I do value working in the sun. (38:06) I do value the craftsmanship on what we produce. (38:08) I do value art, real authentic art and not mimicking art.

Melissa

(38:14) I’m glad you brought up the kids because I’d like to get your insights on what career paths make sense to our kids in a world of AI and technology. (38:23) I know all of my children work from their computers now. (38:27) It’s less writing and more computer.

(38:29) What are your thoughts on that?

Daniel

(38:32) There’s two sides of that. (38:33) There is their development, their cognitive abilities, how they’re developing that in the age of cell phones and now AI, and then their career paths. (38:46) So when we’re looking at our kids, we know screen time is an issue, right?

(38:51) How much are they on screens? (38:52) But if we go back to, you know, I’m in my mid to late 40s. (38:57) I remember the time where, you know, pre Google, the people advancing their careers were the people who had knowledge that weren’t able to, that didn’t want to share it with everybody.

(39:07) Because if you had the same knowledge as I did, then, you know, I may be put at risk. (39:11) And then Google came around, you know, with the internet, we were able to have access to all the answers. (39:19) But now what we wanted to do is be really good researchers, right?

(39:24) Find the answer. (39:25) This reduced a little bit of our critical thinking, where if uncle Google doesn’t have the answer, the answer doesn’t exist. (39:33) No, the answer, sometimes your question is nuanced, and Google doesn’t have the answer, you need to think critically about it.

(39:39) Well, now with the age of AI, we are now the research part is gone, the critical thinking is gone. (39:46) And now it’s, you know, my son wants to write an essay about World War Two, and just ask chat GPTA, can you write an essay about World War Two. (39:55) So their cognitive ability will decline if they don’t know what to put this knowledge as tool to use with, right.

(40:04) So again, our employees, I guarantee you, at least 80% of everybody listening to this will say I’ve received an email from one of my team members from a colleague from from a customer, even I can tell they didn’t write this, the em dashes are everywhere you chat GPT this and this, did you at least proofread this email before you sent it to me? (40:25) Did you at least proofread it? (40:27) Right?

(40:27) So with our kids, they need to be more critical thinkers, we need to bring back critical thinking to say you have the tools, the knowledge is out there, but we need to bring back critical thinking. (40:37) But the critical thinking isn’t solving small problems, problems, it’s solving big problems. (40:42) This is where you’re going to see a lot more business owners come out.

(40:46) And even within an organization, you need to operate as a subsidiary of that organization, you are a small business within a business, right? (40:57) You are a marketing team. (40:58) Well, if you didn’t exist here, what would you do?

(40:59) You’re the marketing team, what would you do? (41:01) Well, I’d have a marketing agency. (41:02) Well, you are a marketing agency with a client of one, act like a marketing agency.

(41:07) Right? (41:08) Now, if we get our kids to think that way, if we get our teams to think our 20 year old hires to think that way, you are a company within a company. (41:17) I need you to think critically, not of the small things, of the big things, of the big picture.

(41:23) Now they can leverage AI to pick the right career, to think what can I do with this and not, hey, I have an essay and I just want to get it done. (41:31) No, what can you do? (41:33) This is a tool.

(41:34) You’re 12, you could run a small business. (41:37) Why not? (41:38) Encourage your kids to try something out.

(41:40) Well, dad, I don’t know how to create a website. (41:43) Well, AI can help you. (41:44) Let’s start early.

(41:45) And a lot of the reasons why our kids pick the wrong career paths is it depends on their environment. (41:50) How many careers have they been? (41:51) I grew up where you’re either a teacher, a lawyer, a doctor, an engineer.

(41:56) Those are your four career paths. (41:59) That’s it. (41:59) Really?

(42:00) That’s it? (42:00) That’s all that exists? (42:01) That’s all that exists in the world, right?

(42:03) And three of those will make your parents proud and one of them may not. (42:08) That’s very limiting. (42:10) We will see, just like if I told my grandfather, you know what?

(42:13) Your grandson is going to make more money than all your grandkids combined, putting his finger on this little thing called a keyboard. (42:21) But does he plow land? (42:23) No.

(42:23) Does he plug fruit? (42:24) No. (42:24) How is he making money?

(42:26) Nope. (42:27) These career paths, we’re going to have new careers and new career paths that didn’t exist before. (42:32) We’re just operating out of fear.

(42:34) It’s when Photoshop first came out, all the photographers said, oh, shoot, I’m going to lose my job. (42:40) No, you’re not. (42:41) You’re going to get better at your job.

(42:43) You’re going to be producing higher quality images at a higher capacity. (42:47) When WordPress came out, all the web developers said, oh, shoot, I just lost my job. (42:51) No, you didn’t.

(42:52) Now we have WordPress developers, right? (42:54) Same thing with AI. (42:55) Nothing’s different.

(42:56) It’s just because we lack the imagination to see the new careers that will be created.

Melissa

(43:02) Now, where are people, and maybe the better question is this, where do you see massive underutilization of AI, and maybe where can people start using it right now?

Daniel

(43:17) I see it massively underutilized everywhere. (43:20) The biggest impact is not necessarily the production, for sure. (43:26) Everybody can start using ChatGPT today.

(43:29) Everybody can start using tools today to make your job output much, much quicker. (43:35) But the biggest impact that I think people are underutilizing is helping AI analyze your data. (43:42) Let me give you an example.

(43:44) A lot of our customers are Shopify stores. (43:47) It’s not exclusive to Shopify stores, but a lot of our customers have Shopify stores. (43:51) What they do at Gnome AI is that we help build a concierge chatbot for them, not just a chatbot that replies back with FAQs, but an actual customizing the experience to find the right product and service for this user.

(44:05) Great, that’s AI, and everybody can connect the dots there. (44:09) That’s not an issue. (44:10) But the real value that they discover is the AI sentiment analysis that says, since you’re AI first, and I’ve monitored all your conversations, here are your gaps.

(44:20) This is the product that you’re missing. (44:22) People are asking you about this product, and you’re not answering because you say, sorry, I don’t have this product. (44:27) But it’s not being leveled up to the executives to say, you should offer this product or not.

(44:33) This is a gap, the knowledge gap that your team has. (44:36) What is stopping you from increasing your NPS or CSAT score, the satisfaction of your customers, and where are the business opportunities that I’m missing? (44:48) Let me give you an example.

(44:50) If I go into a CS team, and I say, hey, team, how are things going? (44:55) They’re going to tell me one of two things. (44:57) They’re going to say, everything’s fine.

(44:58) Really? (45:00) Everything’s fine? (45:00) Nobody calls CS to say, hey, great, you’re doing a great job.

(45:03) They call you with problems. (45:05) That’s why they call the CS team. (45:06) Or they’ll tell you, hey, remember John from last week?

(45:11) He’s now threatening to sue us over $50. (45:13) Now, you’re just telling me the squeakiest wheel. (45:16) That’s not indicative.

(45:16) Yes, I should know that, but it’s not indicative of the state of the department. (45:22) Let me give you a simpler example. (45:25) We are a pizzeria, and I tell my waiting staff, hey, we sell pizzas.

(45:31) We don’t sell anything else. (45:32) We sell pizzas. (45:34) They’re getting feedback from our customers that sit on the table like, hey, my kid doesn’t eat pizzas.

(45:39) Do you guys offer sushi? (45:42) No, we don’t offer sushi. (45:44) Do you offer subs like a meatball sub or a chicken parm sub or something like that?

(45:49) Oh, no. (45:51) Those two face value sound like the same feedback. (45:54) They’re asking for a product that we don’t sell.

(45:57) But sushi for an Italian restaurant is very different than an Italian sub. (46:03) The owner would have said, yeah, I just throw some meatballs on a baguette, and now I have a sub. (46:09) Yes.

(46:10) Well, why didn’t you tell me that? (46:11) Well, boss, you said we don’t sell subs. (46:12) We only sell pizzas.

(46:13) But there’s some feedback that’s good feedback. (46:16) There’s some feedback that’s bad feedback. (46:17) So using AI to be able to differentiate good feedback from bad feedback, giving you the state of affairs, analyzing your data, and giving me a 30- and 60- and 90-day plan is what really unlocks.

(46:31) It’s giving it this massive amount of information. (46:33) I’m not going to go through the 3,000 tickets. (46:34) It’s way too many.

(46:35) If I go into Zendesk and go, I’m not reading them all. (46:38) I can’t. (46:39) But running that through AI and say, give me the net net.

(46:43) You are the COO of this company. (46:45) You are the CPO of this company. (46:47) Analyze this data and tell me what product and feature gaps, service gaps that we have so we can offer it, so I can increase it at a margin.

(46:55) Tell me why I should select this feature versus that feature because of the margins, because of the opportunity costs, and so on. (47:01) And that’s where I think it is extremely underutilized. (47:04) And the example I gave you is anywhere from a Shopify store to a pizzeria.

(47:10) But all of them can actually use AI to analyze the feedback that they’re getting to create an action plan, to create a roadmap for the next 30, 60, 90 days.

Melissa

(47:21) That’s fantastic. (47:24) Just quickly before we close up, I want to touch on two areas. (47:29) I think in this world of AI, we’re handing over a lot of tasks now to AI, whether it be chatbots, or organize my emails, or calendaring things.

(47:41) But before we wrap, I want to talk a little bit about things you believe that AI should never be handed over.

Daniel

(47:49) It should never be handed over complete. (47:51) There should always be a human in the loop. (47:54) So for example, your messaging and branding, you can use AI to help fine tune it.

(48:01) But AI should not be responsible for that impact. (48:06) AI should not be responsible to make sure that you’re hitting the goals that you’re trying to hit. (48:13) There must be a human ensuring that your goals are being met.

(48:20) AI should unlock humans from your management, your sales team, your support team, and so on, to spend more quality time internally, externally with your customers. (48:35) To say, I’ve just unlocked so much of your time. (48:38) That doesn’t negate the human interaction that you have with your customers.

(48:45) Now, increase the, hey, I used to be able to only spend half an hour. (48:48) Make it an hour. (48:49) That’s okay.

(48:50) Now, spend the extra time getting to know your customers in a more deep level because all the shallow work has now been taken away from you. (48:58) Do not make AI remove the humanity. (49:02) Have it unlock the humanity that you have.

(49:04) If you’re a manager that you were bogged down with these reports that now AI are doing, now spend time, more time with your team. (49:11) Spend time understanding, hey, my team member here seems disconnected. (49:14) Why?

(49:16) Are they trying to slowly exit the company? (49:19) Well, if I know that, I can help you do that in a way that’s not damaging to you or the company. (49:23) Is there somebody feeling neglected in the company?

(49:26) Is there somebody saying, hey, I feel like you’re spending time with John, but not with Jane. (49:30) Now, you have the opportunity to be the best manager. (49:34) Now, you have the opportunity to be the best customer success, not customer support manager.

(49:39) Spend time with the humans. (49:41) Don’t have AI unlock your time just to waste it, just to squander it. (49:46) Spend that time increasing the humanity that we’ve been missing in our businesses and not reducing it.

Melissa

(49:54) That’s great. (49:55) Kind of in closing here, I want to get any final thoughts you want to leave with the listeners or an idea that we didn’t touch on and then share a little bit about the best way to connect with you and learn more about the good work you’re doing.

Daniel

(50:08) Sure. (50:08) Do not hesitate with AI. (50:10) Do not fight AI.

(50:12) Don’t use it just to use it and don’t fight it just to fight it. (50:16) Start with, it’s just like swimming. (50:19) Get your feet wet.

(50:20) You can read a hundred books, watch a hundred podcasts about swimming until you start. (50:24) It doesn’t matter. (50:26) Start with a simple tool.

(50:28) Start with a very defined goal. (50:30) Keep it simple and then iterate. (50:32) Measure success on a 30 day iterations and see the impact in your company.

(50:38) To contact me, feel free to reach out on Gnome AI or at LinkedIn, Daniel Hindi. (50:43) You’re not going to find many Daniel Hindis on LinkedIn. (50:46) Find me and if you have any need for your business to accelerate using AI, check out Gnome AI and contact me.

(50:52) We’ll be happy to help.

Melissa

(50:55) That’s great. (50:56) Thank you so much for being here today, Daniel. (50:58) That’s the Executive Connect podcast.