Implementing AI Across Your Company: A Comprehensive Method

Happy 2026. Hey, how's this for a New Year's resolution? How about you master AI marketing? The AI Business Society gives you live training with experts who are getting real results. No hype, no fluff, just proven workflows that work. And you've got access to an incredible library of training from many of the incredible guests that you've heard on this show. So secure your AI transformation by visiting socialmediaexaminer.com AI.

Welcome to the AI Explored podcast, helping you put AI to work. And now, here's your host, Michael Stelzner.

Hello, hello, hello. Thank you so much for joining me for the AI Explored podcast brought to you by Social Media Examiner. I'm your host, Michael Stelzner, and this is the podcast for marketers, creators and business owners who want to know how to use AI. Today, I'm going to be joined by Ashley Gross and we're going to explore how to implement AI across your company. She'll be revealing how to get everybody in your company to use AI for the purposes of the business objectives. I think you're going to love today's episode. If you're new to the show, be sure to follow us on your favorite app. We got some great stuff coming up. Let's transition over to this week's interview with Ashley Gross.

Helping you simplify your AI journey. Here is this week's expert guide.

Today, I'm very excited to be joined by Ashley Gross. If you don't know who Ashley is, she is an AI strategist and founder of the AI Workshop Alliance, a membership designed to help employees and employers become AI enabled. Ashley's course is building a business with AI in 10 days. Ashley, welcome to the show. How you doing today?

Hi. Thanks for having me. I'm great.

Super excited that you're here. Today Ashley and I are going to explore how to train your team to implement AI. Before we get on that fascinating discussion, I'd love to hear a little bit about your backstory. How did you get into AI? Start wherever you want to start.

Absolutely. I'll start in 2020. So I'm a tenured marketer, but in 2020 I found out I was going to be a mom and I did not know much about children, but I thought, oh man, I'm about to have way less time to do all the million and one things that marketers do. So I should probably figure out how to get the same outcome that I can from a 40 hour week, except for a 15 hour week. So I was using Jasper AI to create the brand voice and authority for me so that I could start having a strategic positioning in all of my content. So I wasn just creating content to create content. There was actually a workflow, there was a strategy behind it. I was just using AI in general to hack my time and be productive, but also to spend less time doing tasks that I hated doing so I could spend more time doing the marketing tasks that brought me more joy. And so I was just known as the AI girl in 2020. So still preach chat GPT days I had the CMO approach me and she was like, hey, you know, internally you're known as the AI girl. I know you love this stuff. And she asked me if I wanted to roll out AI to a marketing org of over marketers. And honestly I did not know if I could, but I knew I would figure it out because I recognize that this technology enables you to switch careers and switch industries so much faster than ever before. And so I recognize that this was a rare opportunity to say yes and take the years of practice and turn it into a company wide AI initiative.

Now just to be clear, this is not for Jasper. This is for a company you're working for at the time. Is that correct?

Yes, this was not for Jasper. This was just, Jasper was just the tool that I used back in the day. It was one of the first LLMs out there. And fun fact, if you were using Jasper back then, you know, it was called Jarvis. So everyone was saying Jarvis was, you know, my boyfriend on the side. That was how frequently I was talking about AI.

So you were asked to implement this out for 100 people inside of a big marketing department. What happened?

Yes, and so, I mean I looked at this as a visibility opportunity for me to prove my impact and contribution to the business by, you know, implementing and aligning technology with senior stakeholders. So many cool things happen. To answer your question, you know, I developed over 25 AI use cases solely focused on implementing AI for tasks that my teammates hated doing. And so three months after AI implementation, we overachieved our pipeline target of 90 million and hit 115 million. So if you want to quantify the impact in terms of revenue and overlap that with time, that's a huge roi. And that's kind of when I went, this is bananas.

So at some point along the journey you decided to go off and do this on your own, Right? I mean, tell me, how'd that happen?

I did, but it was really after seeing, you know, I think that the natural evolution of knowledge and sharing it is once you get really good at something, you know, just by practicing over and over again, then it's almost like you can't shut up about it. You're like, oh, my gosh, this is so cool. You have to see this, and you have to see this. And so as I trained other people and as it became more of a movement, not just me, I thought, okay, this is what I want to do. Because I was hired, you know, at that company as a marketer. I was doing AI and running the AI task force on top of my marketing job. And so it was really just like, this is what I want to do. Like, if I'm going to be in the trenches, it needs to be an AI implementation, not necessarily marketing.

What happened next?

Do you want me to take you through how I trained the entire company?

You don't have to go there. But, you know, here's what we know so far in the story. We know that you dabbled with some tools in early days of AI. You were asked to implement it across the company, and the company was very, very successful. What I'm cur about is how did you eventually leverage that into going and building your own thing and tell us that story and then ultimately what you're doing now?

So I'm. I'm curious by nature. I always joke with people that curiosity is the backbone of innovation. If you just show up and you're just curious, you're going to be fine. And so really seeing how much AI affected, you know, a business in terms of revenue, in terms of morale, and in terms of job satisfaction, I kind of thought, well, that wasn't that hard, right? Like, I wonder what else we could use AI for. That seems ambiguous and hard. That's maybe not that hard. And so I thought, well, building a business. I've built two businesses in the past, and, you know, I'm sure you can relate to this. When you go online, it's just data and data and data. And unless you kind of know where you're going or you have people that, you know haven't been that far ahead of you, kind of showing you the ropes and guiding you, it's a lot of information, and it's not so much actionable. There's really not a lot of strategy. And so I thought, okay, well, I've taught a whole entire organization on how to use AI for a business, right? Why don't I teach people how to build a business using AI? And so from there, again, like, I just started digging into the research, and the research told me that the three main reasons why businesses fail are either lack of resources and knowledge, a bad business plan, or poor financials and not having the proper structure set up for financial planning. And so I thought, well, that all seems really horrible, right? But if we have an AI first approach to this, and we have everyone kind of take an inventory of their skill set based off of their experiences and kind of identify what they're good at and what they need to improve, and then using AI to fill in those gaps, I wonder how many businesses could get off the ground a lot faster. And lo and behold, the answer to that was mind blowing and astonishing because again, you know, you're just testing these things. No matter how much market research you do, you never 100% can be guaranteed that it's going to stick. And so I've had three cohorts of how to build a business using AI in 10 days. So far, 75 people have taken my course and gone on to create their own businesses. And it's just purely because I took the yellow legal pad, you know, notes of how I built my business using AI in 10 days, and I watered everything down so that in 10 modules over the course of 10 days, they just focus on one task, right? And they use AI to get better at that task. So they start the course with idea generation and validation, and they end it with a full financial projection plan, smart goals, marketing strategies, sales projections. But they'd done it all one day at a time. And in a way that made sense. And it wasn't just kind of lumped on top of them. They had a purpose, a strategy, and they were there taking one specific module at a time. And so I really just kind of unlearned everything I was taught and had an AI first approach of, well, if we're going to do a process more than once, there's an AI tool that could make this easier and just kind of slowly backpedaled from there and made it less ambiguous. I put it in common terms because for whatever reason, when you go to look up anything online, they have a whole other vocabulary that you feel like you need an encyclopedia for in order to understand. And I made it approachable. And I think that's kind of my favorite thing about AI is it can take anything and make it approachable if you use the tools to help you solve the problem.

Tell us a little bit about the AI Workforce Alliance.

So the AI Workforce alliance takes a look at not just upskilling employees, but also upskilling employers. Because I realize that 50% of the reason pilot projects are failing isn't because the employees aren't trying. It's because the employees are trying. And there's usually one solo contributor in that organization that's the champion. And they're fizzling out, they're burning out. They don't have the resources, they don't have a strategy. They're not being told, you know, repetitively from the founders and from senior executives, you know, this is important, this is a priority. Yes, you have all these other tasks that you need to do, but AI implementation is in fact a priority. And we're not just saying that. We have a Slack channel and we're all sharing what we're learning. And better yet, there's a learning and development budget. So it's about looking at the workforce and coming together and not just upskilling employees on how to be AI enabled employees, but also coaching employers on how to be AI enabled employers so that the expectations they're setting, they understand are realistic, they're measurable, and they are reaffirming their own employees and not just kind of giving them the reins and saying like, go do whatever, just use AI. Because it really is about both parties bettering each of themselves respectively in order to actually create meaningful impact and change.

I love this. And so much of what we have covered on this show, with a few exceptions, has been here's how you as a marketer or a creator, and in some cases an entrepreneur, can apply AI to a very specific task. Now what I'm excited to explore with you is the concept of applying AI to kind of uplevel an entire company or an entire division, depending on how big of a company you happen to be listening. So there might be people that are listening right now that are early in their journey in the AI discovery and application in the workplace. So why is getting the whole company on board with AI so important? What are the benefits?

So many. But to name a few. Once you as an individual contributor and educator see the impact that you can make and, and you're so much stronger and more strategic, it only makes sense to incorporate the rest of your team, right, to get more done better. So then that teamwork and specifically automating a lot of the tasks that you know they hated doing comes back to the business. Because when our employees are happy, now we're creating these amazing customer experiences and now we're churning less and our upsells are higher, they're at an all time high. When one person can share their knowledge and not gatekeep it, that is when the business as a whole benefits. Because now you have hundreds of individuals realizing their potential and they get their spark back. And now it's a movement as opposed to a silo contribution. And so the ROI now looks like less company turnover, larger learning and development budgets, more internal promotions. There's more to give and improve once you realize the path. Words improvement isn't really that scary or hard. And you only have the company culture that's going to benefit from it. Right. Like everybody knows the ROI of a good company culture. That's a good company culture when you make it all about everyone else and not just about you, by sharing the knowledge that you learn.

That's really well said. And for those that are listening, do not have hundreds of employees. I can speak for my company, which has dozens implementing AI across. Not everybody obviously is going to embrace it in the same way because you've got different kinds of people. But it's crazy how much it has allowed us to get more creative faster and to improve things more easily. And all that stuff does output greater quality, persuasive copy from a marketing perspective. It allows the sales team to be equipped to be able to better deal with objections. There's just so many great things that you can do with it. And I'm a huge proponent of it. And I'm sure you in the near future, it's not going to be a question of if everyone in the company is going to get trained on AI. It's going to be more of a question of how is everyone in the company going to be get trained on AI. So let's transition to the first part of your process. Let's assume everyone listening has the ear of the C suite or the executives, or maybe they're the founder. What's the first part of the process when it comes to training a workplace workforce, if you will, to implement AI?

Yes, ID the problem. And actually to your point, I'll even add this disclaimer in here. Cause you brought up a great point. The smaller your team is, the faster you can innovate. It's just the reality of the hand that you're dealt. The smaller you are, the less silos you have, the faster you can go and lean into that curiosity. So if you do have a smaller organization, not hundreds of employees, you should be even more excited because you will go way further just because of the way that your team is built and how scrappy you have to be when you're smaller. And so I would say find the problem, but don't just find the business problem. Find a painful business problem. And here's a few that I constantly run into. And I like to name these because I've never run into an organization that didn't have at least one of these problems. So a lack of insight into your customer's journey over reliance on measuring all metrics instead of honing in on intent. Let's see. Lack of clean data. These are issues that businesses have had for years. So pick one of them and then go have a one on one with the founder and hold that problem in your pocket and just ask them what they think the problem is.

Before we go there, I want to ask some clarifying questions. So just to be crystal clear, you're proposing that we find one problem, not five problems. Right. And I would imagine whoever's listening to this is going to look through a different lens. Like the, the problems we just shared are obviously problems that a marketer would see. Right? Like not understanding the intent of the prospect who wants to buy, or not fully understanding the customer journey. But depending on if you look through a sales lens, you know, a salesperson might say, I don't have enough qualified leads. Or they might say something along those lines. Right. So I'd imagine the problem.

The problem, what you're going to spoil it? This is where I'm going.

Oh, okay. But we should pick a problem, one problem. Right. And I understand that there's a process here, but is your advice that they pick a problem, whoever is listening, that they pick a problem that they believe is important from their seat in the company? Is that kind of where we're coming at this? Yes, because I just want to make sure that that part's clear. So keep going. Okay, perfect.

No, thank you. Yes. Pick it from where you're sitting. Because this is, you know, the point of learning isn't just for you to teach. You need to be learning why you're teaching, because you need to go in with the assumption that you could be wrong. That's a very important part of this. So here's why I like to do this. If there's a lack of alignment and communication pre AI, then it's only going to muddy up the post AI progress. Right. So I want to look for the issues, the bottlenecks, the silos. So I actively listen to the founder nine times out of 10, and this is where I was going, they tell me lack of revenue is the problem this quarter. So right away I know they see a surface pain and it is a real pain. I'm not saying it's not, but there is an underlying problem to that pain that is actually what we should be addressing. But you know, if you think about this is to be expected because they have been out of the trenches for a while. So I don't expect them to know on a deep level where the issues are. Right. I would be shocked if they could actually identify specifically what that issue is. That is the reason behind the lack of revenue. So then what happens next is you get all these executives together in front of the founder without any of the employees they manage.

Wait real quick. Before you go into the next thing, I just want to make sure I totally understand this beginning part of the process. So I have identified what I believe to be the problem. As the director of marketing or whatever role I happen to be in, I'm going to the founder, the CEO, the president, whoever leads the company. What am I asking them exactly? Just to be clear, I want to equip people with the right question to ask. Do I say, hey, do you agree with me that this is the problem or do I say something completely different?

You actively listen, which is really, really hard for a lot of people. You just say, what do you think the problem is with the business this quarter? What is the main problem that you think? Right, because the whole idea is this is like multiple layers, right? We are coming in it from our perspective. But how silly would we look if we said, hey, I think this is the business's problem to the founder and they didn't agree because maybe we don't know everything. We're coming from one perspective. They're coming from a way different perspective where they don't have the in the trenches practitioner everyday experience. So we're both trying, we're kind of trying to come from different perspectives and figure out where we can meet in the middle. The idea though is you have ideas in your head of what the problem is and you're actively listening to them talk about what they think the problem is. And that's all we're doing for this step.

Okay, so you're just going to hear what they say, echo it back to them. Do I have it right? And you're going to document it. Okay, perfect. Yep. What's the next, what's the next part of the process?

Okay, so then you bring in the executives, the senior, most executives and the founder, nobody else. Because the senior members almost take on this personality of early managers, middle managers, just less senior managers in general is what I'm getting at. Because they don't want to be wrong. They don't want to take a chance at guessing incorrectly. So they're not raising their hands they're not as confident, they're not as outspoken as they usually are. And we kind of want them to be that way because we want them to have almost like a clean palette where they're open to new ideas. And we can't do that unless we make them feel a little bit vulnerable and make them feel like, well, maybe there's not a right or wrong answer. Maybe it's about figuring it all out together. Right.

What are we asking when we bring these pe, Are we bringing these people in immediately or are you scheduling a separate meeting?

No, I would definitely schedule a separate meeting. Because most of the time after you have that initial conversation with the founder, they might change their mind, you know, after a couple days when they've actually thought about it. Cause you intentionally put that thought into their head that no one else has probably asked them. So they might come back to you, you know, a day or two later. So you want to leave a little bit of room before you bring in the executives.

Let's talk about filling the gap between AI potential and AI results. I'm sure you've tried ChatGPT. Are you seeing real ROI? Most marketers are barely scratching the surface of what's possible with AI. And it's because they're missing the training and the frameworks that will allow them to be very successful in their careers and in their future. AI Business World allows you to get that training. When you attend, you get two full days and 20 expert practitioners, many of the people that you've heard on this very show, that will teach you exactly what you need to know to build the workflows and to succeed as an AI enhanced marketer. It takes place April 29th and 30th in Anaheim, right alongside Social Media Marketing World. And here's the good news. You can save $300 when you register by January 16th. Head to AIbusinessWorld live right now. This discount ends soon. So we've got everybody either physically or virtually together in a meeting. And what exactly are you asking of the executives and what are you asking of the founder during that time?

So the founder, I let them know, listen, we're going to ask your executives now. I don't want you to tell them what you think it is. I don't want you to agree. I don't want you to do anything. I want you to observe them discussing, right? I. I just want you to watch them discuss this. And I set a timer. Usually it's just 10 minutes. I might even give like a few prompts, right? A few conversation starters. Just to get the ball rolling of does anyone have any ideas of what the main problem is this quarter that we should focus on? And then like maybe some conversation starters are, you know, I have the win loss reviews from the quarter before and I'm saying, well, according to the win loss reviews, X, Y, Z, right? Like maybe I'm giving them some prompts, but it's usually, you know, only one conversation starter that I have to give them before someone's brave enough to raise their hand. And nobody validates them, right? Because whenever everyone's kind of nervous and they're feeling vulnerable, people raise their hand. All of a sudden you have senior executives, like typing in the zoom chat instead of like saying the answer out loud. They're just all kind of like, they want to be right, they want to have input, but they don't want to be wrong and they don't want to disappoint. And I need them to understand how uncomfortable that feels because I don't want them to do that to the employees they're managing, right? So I want them to sit in that uncomfortability and realize that there's not going to be a right or wrong answer here. And I have to get them to feel like, okay, we need to experiment. Experimentation is important at some point in time, right? They, they communicate, they disagree respectfully. You have to get past the surface level finger pointing and, and blaming and you cannot point fingers if the founder is in the call, right, hopefully, and, and not revealing what they think the problem is. So everyone's actively listening and discussing and eventually the founder, after 10 minutes, you know, says that they agree with the problem. So now recap. They have agreed on the problem. Let's just say for conversation purposes, the problem started initially with the founder as a revenue problem. And now everyone is in agreement that the quality of leads being targeted isn't great quality. So there's. Right, we're like going a little bit, a little bit deeper than the revenue problem. So after a few more minutes, a few members from marketing talk about how, you know, maybe their CRMs are scoring leads. And sales says, well, that's not how we've been scoring leads. So we find our first silo, right? Our first barrier. We're finding it right away. Great, now what? Well, now the executives say, okay, so the quality of leads on our CRM isn't great. What have we done in the past that we have measured with a timeline and we know what the expected ROI is. Would we have had this issue in the past? So I'll Do a recap, right? Founder agreed on a problem. We were surface level, no longer saying revenue is a problem. We've identified that the quality of leads is what we think the problem is. And so now we're saying, okay, well, in the past, you know, maybe when we were doing a migration, something in the past, right, We've had this issue before where we all agreed that the quality of leads was not good. So now we're all going around, all the senior executives and the founders going around and thinking, okay, well what did we do the last time that we diagnosed this problem? Like, did, have we tried anything in the past? Do we have metrics? Have we had a timeline attached to those metrics where we looked at what we've done to solve this issue? You know, sales might say they didn't rely on lead scoring and they planned their own top 100 accounts, right? Marketing says, okay, short explainer videos, they tend to convert really well. And if we stick, I don't know, an embed code into these short videos, when someone clicks on them, we can actually see their data and who they are and where they work. And we don't have to guess if our prospects are interested. And you just keep going around and around from one department to another. But the important thing is you found the problem behind the lack of revenue and you're talking about past experiments that you have measured and know to work to fix that problem. These are good indicators that we are starting to narrow down our AI use cases because we know pre AI how well they perform and we all align on those metrics. Okay, so the third step is now that the senior leaders and the founder have aligned, it's time for those leaders to go back to their teams and break down those use cases. So if we're still going with the problem of lack of good data leading to lack of good leads, maybe we have the marketing org saying, okay, we have 10 AI use cases team. Out of these 10 use cases, which ones do you hate doing?

Where are these use cases coming from? Because we went from like one thing to ten things real quick there.

So when we were in that conversation, right, with the founder and all those executives, and we were talking about in the past when we thought our, our lead quality was bad. What have we tried in the past? So we've had, we have a bunch of things that different departments are saying, oh, we did this, we did this. We just have a list of proven ways and metrics and timelines for how we have partially or fully solved. In the past, having bad leads got.

It so that list becomes the foundation on which you can apply AI. Okay, keep going.

Yes. So then the executives all go back to their team with this long list and they say, out of these things, what do, what do you all hate doing? What takes the most time? Because we want to choose an AI use case that we know how to measure pre AI. But I also don't want to threaten anyone's safety of job security. So I want to find the sweet spot of the use cases that will still help solve the problem, but that people really hate doing. So things like competitive analysis, creating messaging and positioning framework and testing IT tasks like these. But it's during this third phase when at the end of it, you want to have your use cases and have them ready to go because then you're almost ready to hit the ground running and you've aligned on use cases that you already know how to measure, you already know how to track as far as time goes. And your employees hate doing them. So they're going to be more open minded at using this new technology to automate the tasks that they don't enjoy doing. You're not threatening their sense of security, you're not threatening things that they love doing. You're not taking joy away from them, you're adding joy to them. That's very important for step three. But that's, that's it. That's step three.

Okay. So just to be clear for everybody who's listening, we've started with the founder/CEO, identified what he or she believes is the big problem. We brought the leadership team together. He remained in the background and listened to what they thought the problem was, eventually worked out, a consensus and then identified. How do we solve this problem? In the past came up with a short list of, you know, maybe five or ten different things. Right. And then the various departments that are relevant to executing that task, those department heads went back to the department, talked to their staff and said, hey, let's explore these things. Okay. That we've identified. Because what we want to do is we want to identify ones that we can basically apply AI to. Right? And, and the ones we're going to start with are the ones that are the most challenging for you to, to accomplish or the ones that you just, for whatever reason, dislike the most. Am I hearing correctly? Is that, is that accurate?

It's perfect.

Okay, cool.

Absolutely.

So what's next?

Okay, so fourth step spine. This is the motivation. So you motivate by leading by example. Right? So it's really hard for me to get my team Excited and aligned on AI being a priority. If I never talk about it, I never tell them what I'm learning. There's no action. So maybe we have a few interdepartmental task force calls. So it's those department heads, those senior leaders with their teams only. Okay, so task force calls where, you know, maybe we talk about those few tasks that we hate doing. Are they saving us time? Are they making us enjoy our jobs more? It's more. So just. Okay, has anyone tried to start doing any of these tasks? What are you doing? Right? You want to leave a little bit of room for individualization and curiosity because you've honed them in. You've given them the use cases, you've given them the metrics and the timeline for them. You just kind of want to frame this as a motivational conversation, right? It's a community centric sharing approach, type of office hours, if you will.

Just real quick, at this point, are they already starting to use AI or have they not yet used AI?

So this is the tricky thing. Back in 2022, a lot of people weren't using AI. Now it's going to be really rare that you get an organization that hasn't started dabbling in around it. So I would say it's safe to assume that 50% of your organization has already started using AI. Maybe they're not being productive with it, but they're playing around with it. Absolutely.

So in this stage, in this four step, are we identifying, in these interdepartmental calls that you're talking about, are we identifying specific applications where AI can be put to work, or are we already using the AI? And we're debriefing on the challenges and stuff that have happened since we've been using AI. That's where I'm a little confused.

A little bit of both. So we're not diving deep into our use cases, right? Like we've diagnosed them. I'll say it like this. If we're in a meeting, right? And there's 10 other people in that meeting, and I say, okay, team, we have a board meeting next week. Okay, no, no rush, but you got to have your slides in by the end of the week because we really have to prepare for that meeting. It's going to take a lot of data and a lot of time. No one is going to, like, synchronously go into that slide deck on Thursday evening. You're going to have some people that want to overachieve and get it done right away, and they want to start playing around A little bit. You're going to have some people that wait till the last minute. So the point is, we didn't say, all right, we're going right, like the gun went off, the race is on, let's roll. We're just saying we've. We have our use cases, we've diagnosed them. Has anyone started to play around with AI? Has anyone played around the time saving? We want to learn what motivates people. How people learn the best is having fun. They. They need to want to learn. So it's more so about, you know, we have a structure, we have a strategy. We haven't started that strategy yet, but we're also not penalizing anyone that's starting to dabble around in it because we want to motivate and intrinsically award that type of curiosity. Does that make more sense?

I think I understand, and I think I can give an application here. I started in the company here at Social Media examiner with AI and then eventually Editorial department was one of the first departments to begin adopting AI because it made so much sense for the kind of work that they're doing with our newsletter and article creations from podcasts and stuff, and then eventually the sales department, and then eventually the marketing department and other departments. And before you know it, I've got everybody doing different things, but they're all doing it in a little vacuum. And what you're saying is, hey, get some of these people together and kind of share little tips and tidbits about how they're using the AI. Is that kind of what I'm hearing you say? Or is that not exactly right?

No, you got it exactly right. Because externally, I'm getting them motivated, right? I'm. I'm showing them that I'm open to curiosity because I'm having my interdepartmental meeting, and I'm saying, like, has anyone tried around with this? This is a community office hours, safe space. Let me know. But I'm also identifying my champions, just like you said, you know, you've got people in the organization. They might not be the people you think they are, but there are people in the organization that are ready to go. They want to embrace this technology. They want to figure it out. You need, as a leader to identify those people. So this is the spot where you're going to do that. They might not know it, though.

It's almost like putting together a little task force of people that are interested in AI, which is where we started. So we started with saying, we emailed the whole company, if anybody's interested in being on this little committee that we're putting together, let me know that kind of thing. And these were the ones in the company that were most interested in kind of the deep. The people that love research, the people that love deep diving and experimenting were kind of the initial crew. Is that kind of what we're talking about here?

Exactly, yes.

Okay.

So what I'm explicitly telling them to do in these task force calls is I'm giving them little fun use cases that we can all do together within like a 30 minute period. So maybe, you know, our fun little task is what recipes can you make in, you know, out of your fridge? So in ChatGPT, all of us right now, let's take the next 90 seconds and type in a couple ingredients that you have sitting in your fridge and ask Chat GPT what recipe you can make. Is it contributing anything to those use cases we identified? Absolutely not. But again, as a leader, I'm identifying and trying to find those champions, and I'm making it fun for everyone else while I'm still trying to figure out how everyone is intrinsically motivated to learn AI. So that's the first use case. It takes 90 seconds. We're all doing it together. The one important thing that I would emphasize in this is, you know, you're doing a lot of things. You're like kind of doing mental jumping jacks as a leader in your head. You're looking for your champions, you're intrinsically motivating them. And the whole point of these use cases that I'm about to, you know, talk about in a second are you're working your way up. We live in an instant gratification society. So you have to think in your head. Okay? I don't want them to think. This is hard. So 90 seconds. What can you make with the ingredients that are in your fridge? Instant roi. You're solving a problem. They're getting excited, right? The goal isn't to be serious. The goal is to show them how fast they can see the ROI that is beneficial to them. So then the next use case comes around and I choose something that takes a little bit longer than planning a recipe. Maybe I asked ChatGPT to recite a line in Spanish, right? We want to go for the sweet spot of, you know, we went from 90 second ROI use case to building it up to 3 to 7 minute AI use case. I'm building up the patience of my teammates, if you will, because we have all been dampened by this instant gratification world we live in. And learning AI is Not hard. It just takes time. And no one believes they have time, but they do. So you're making them realize they have time. Okay, we're at the third use case. This is where we get a little bit personal. This is the Ask chatgpt how you reorganize your email inbox by prioritizing just the email subject line and it saves you hours. There's no data risk if done right. Again, you can do it all together during those 30 minute task force calls. But it provides relief to everyone doing that use case because I don't know one person, correct me if I'm wrong, but I don't know one soul who enjoys answering 200 emails every morning.

So wait real quick on this one. And just to clarify, we are onto our fifth step here, right? We just didn't say we were onto our fifth step.

Yes.

So just to clarify, the difference between the fourth step and the fifth step was just some sort of a conversational get together to kind of identify who wants to be part of this experimental thing. And the fifth step is really this training exercise stuff that you've been talking about. Now, specifically to your more challenging task of asking ChatGPT to reorganize your inbox, are you giving it a screenshot so can just see the subject lines? What are you providing to ChatGPT in order for it to be able to do that exercise?

Just the email subject lines. I'm just copying and pasting the email subject lines.

So you're literally just. That's it, 200 of them, though. How do you do that? People are like, I don't even know how to do that. I mean, I know how to. The good news is you can't put screenshots in there, right? I mean, for people that don't know how to do that, you, you can.

No.

And I know you can because yes, you can, because ChatGPT can read text.

Well, now you can off of an image.

You didn't know that?

Now you can.

Yeah, yeah.

It was very different back in 2022, so there's more security risks. But yeah, yes.

And by the way, I was going to say, even for the refrigerator thing, for those that are really sophisticated, you can bring ingredients out on your counter, take a picture of it.

Yes, that's true.

And literally upload an image into ChatGPT and ask it, what do you see? And ask it to make recipes. I mean, the visual side of it blows your mind. But okay, so in this more sophisticated case, if you're not taking a screenshot of your Inbox. You're literally. What are you just going. And literally copying. That's. That sounds a little complicated, right?

No, it's real. You just copy it.

Okay.

You paste it and you throw it in. And the whole point is, again, we're doing this in our community office hours. It takes 10 minutes and it's probably going to take another five. Right. For us to actually verify that the email subject lines, you know, it didn't hallucinate. There's not random ones in there that we didn't actually put in there. But again, so we're at 15 minutes, right. We've built up a threshold is the important part. And we've done it together. And I'm throwing a little bit of like data security and privacy in there. But I'm not making it a monotone, boring type of thing. I'm just saying, hey, we're just going to do this way. And I'm modeling it so that they're just going to repeat what I'm doing. Because if I show them and I'm the teacher, they're going to just do what I'm doing. They're going to try and go reinvent the wheel. So I'm dabbling in like the security portion. Right. This is the fifth step. We've found out who our champions are in our departments. We figured out how to intrinsically motivate them and more importantly, what part of this motivates them because everyone's different. And now we are actually applying it to a professional goal where we're all getting relief right now. We don't have to wake up with panic attacks. Cause we have 200 emails. We've solved a business problem. We are ready to take those use cases that we identified in our third step and go.

Okay, so just a couple questions, right? At this stage, when we are essentially in this training stage, this fifth step in your process, these examples that we're giving are just training examples that start very, very simple and get a little bit more complex. Can you explain why we're doing that and can. And then how we pivot over to actually work in the real problem?

Yeah. If you think about it, you know, this is technology, right? So one one HubSpot and Salesforce and Tableau and, you know, all of these wonderful tools that we all use every single day. At one point in time, we didn't use them and they were new and it was egregious to learn them if you had never looked at it before and how you learn and who teaches you and how much fun and and how much applicability it is really shapes your journey of how easy or hard you think this technology is. So this is where there is guardrails. Like, I am here. I'm like a hoverboard at this point in time. Like, I want to know people are having fun, because if they're having fun and they're using this and they're seeing good results and good roi, then I know that I have kind of like this benchmark set of AI capabilities on my team. And I know where everyone kind of lies, you know, virtually on this line. So that when we go to town on this use case and we're ready and we're gonna go, you know, knock it out in this next step. I know. Okay, well, if we're here with these use cases, here's the team members that are a little bit ahead. So they should be ahead when we do this use case. And here's the ones where I might need to spend a little bit more time with them, maybe as a leader. And this has nothing to do with AI, but this is the kind of. The point is, maybe as a leader, I recognize that I haven't seen. Spent a lot of time with a couple of these team members, right? And I don't know a lot about them. And as a leader, that's a gray area. That's a crack, and AI is an amplifier. So if there's already a gap there, AI is going to amplify that gap even more. I need to spend some time working with these people and making sure that I understand why they're here and what they want to learn and how to get them motivated to keep learning and have a good attitude about it. Because if there's one bad apple or one bad mentality, it's going to split, spread like wildfire, and it's game over before it even started.

What I really love about this concept conceptually, is you are. And anybody who's going to model this, you want to basically start a little easy, right? Because people are learning to use, like you said, technology that will eventually be very integral to their career and their job. But for many people, this is going to be their first go at using generative AI at all. So if you're really smart about the three tasks or the different levels, something really simple and something a little bit more complicated, and then something way more complicated, and you're smart about the way you select those actions, you're going to allow everybody to be trained kind of off the same baseline, right? And then out of that, you're going to have people that are going to go off on their own and watch YouTube videos or listen to podcasts like this podcast and learn how to do more advanced things and eventually bring that back to the team. But I really love the, the structure in which you're doing this. So let's just say that we've completed this first initial training. Do you have another meeting? Typically then. And then it's like, all right, we've got this big mission now that's coming down from the executive team. Let's figure out how to make this work. What's the next part there?

Yeah, so you said it you. Exactly what you just said. So we've been having these task force meetings, right? I'm, I've already normalized all of us getting together. I've already normalized the open forum community style office hours where we all contribute. There's no right or wrong answer. We can disagree respectfully and we can experiment. Right? We don't have to completely agree on one thing at the end of the call. There should be room for experimentation in this. I've already normalized all those things. So now the office hours are just changing. And instead of them being, okay, everyone, like, bring your ideas, tell me what you've been working on. It's. Here's our use case. We identified it. I know pre AI, how to measure it and how to put it on a timeline with metrics and actually look at the ROI of it. We have our use case. Let's go. And the exact same task force calls we've been using are now used for those specific use cases.

Are you recommending that people use a teams account with chat, GPT or clot? Yes, Claude has an equivalent. I don't remember what they call it, but is that kind of what recommending is? Everybody has a teams account because then they can see each other's work if they share it. Is that kind of how that works?

Not for that reason, because there are still capabilities that you have to turn on to make that happen. I recommend teams especially for ChatGPT, because one ChatGPT is one of the most popular LLMs that everyone's already using. But actually I recommend it for the data privacy because if you have an individual account for ChatGPT, it automatically comes toggled on that they are going to use your data to train their model. So unless you're reading the privacy policy, which very few people do, you have to manually go in there and be aware of these things to toggle them off. I don't want to Even put that on the table. My job as a leader is to assess the risks and mitigate them. And as I'm training up my team, I'm gradually going to, you know, sprinkle in there some things that they need to watch out for. But I'm not just going to dump all these risks on them and tell them the worst things that can happen at the end of the day. The budget and who owns the budget, which is most of the time, the department leads are who's signing off on those tools. Therefore they own those tools and they need to solely be responsible for them. That's the real reason why I recommend the teams version, because that takes the data privacy and having to talk a bunch of employees into how to toggle it off and how to make sure everything's uniform out of the equation.

Let's talk about the next step in your process.

Okay, so this is the AI roi. And when we look at AI roi, there's impact and there's outcome impact might look like less customers turned, there were more upsells and cross sells, there was less employee turnover. But the outcome is 115 million in pipeline that we created so we can quantify and qualify the outcomes. This is the part that I preface everyone with because remember those first few calls, we aligned on a leadership and a founder level what those metrics look like and what those timelines were. But when you're making the AI ROI case, you need to look at what are the impacts that lead to the outcome and can you quantify and qualify both the impact and the outcome? Because now you're getting your teammates to not only prove the experimentation works with using AI for these use cases, you're getting them to actually qualify and quantify the impacts and the outcomes of them so that they can communicate them back to you. So I'm slowly releasing my control and trusting my teammates more because I'm training them how to communicate back and forth with me in order to make them have more control and power. Does that make sense?

Yeah. Some people are going to ask how in the world do I quantify this stuff though? Maybe they don't understand it because maybe they don't come at it from the lens that you come at it at.

Yes. And this is why I said like when we overachieved our pipeline of 90 million and hit 115, I can see that. Right? But maybe the quantifying, qualifying for other people, again looks like less employee turnover and more upsells. But then the long term outcome of it, you know, you pick Something that you can qualify and quantify. And that's why it's so important from a senior level to identify pre AI metrics that are quantifiable and qualifiable. Because if you've never quantified and qualified metrics prei, then you're going in blind and you don't actually know, you know prei and post AI, how much time you're saving, how much money you're saving. You don't have a clear ROI picture. So you need to pick use cases that you already know the metrics of and you've already done this with so that you can actually just add in AI and look at it holistically and see if it worked.

What I really enjoy about this process is how you could, in a matter of months, implement this, measure it, and then come up with new objectives each quarter. Right. And just all of a sudden, what might have taken you a year to do, you can get done in a quarter. Right? And then all of a sudden, you're starting to see massive change inside the business because people are moving faster with the back end of AI and it all becomes very magical, right?

Exactly.

Ashley, if people want to discover more about what you got going on, what's the preferred social channel for them to connect with you on? And then if they want to learn more about all the great services you have to offer, where do you want to send them?

Go to LinkedIn. It is a community, so they are very vocal. You will very quickly see that the only thing that everyone has in common is they are eager to lean into AI and figure out how to make this work and embrace it. So you can head to aiworkforcealliance.com to specifically learn more about the community, but head to the LinkedIn. I encourage you to interact with the community members. You know, they're average people like you and I, and they're really, really kind. And I think that sometimes before jumping into a community, it's always nice to kind of look at the menu, right? Find something that you like, find something that you're comfortable with so that it's. It's more familiar when you actually enter, enter it.

And if they want to connect with you on LinkedIn, do they just look for Ashley Gross?

Is that Ashley Gross? Okay, you got it.

Ashley, thank you so much for sharing your insights with us today.

You're welcome.

Hey, if you missed anything, we took all the notes for you over@social mediaexaminer.com A21. Be sure to follow this show on your favorite podcast app and Would you share this with a friend? If you enjoy the show, you can tag me. I'm Elsner on Facebook, I'm at Stelzner on LinkedIn, and I'm ikestelsner on X. And do check out our other shows, the Social Media Marketing Podcast and the Social Media Marketing Talk Show. This brings us to the end of the AI Explored Podcast. I'm your host, Michael Stelzner. I'll be back with you next week. I hope you make the best out of your day and may AI help you become more successful.

The AI Explored podcast is a production of social media examiner.

AI Business World, early bird pricing ends on January 16th. You can save $300 on your tickets. Register at AI Business World Live today.