112: A School Marketer’s Guide to Generative AI with Jordache Johnson

The following content has been automatically generated by an AI system from the audio recording of this podcast. We cannot guarantee the accuracy, or completeness of the information provided, and we apologize for any errors.

 

Aubrey:  Welcome to Mindful School Marketing. I’m Aubrey Burch. 

Tara: And I’m Tara Clays. Today I’m so excited. We’re joined by Jordash Johnson. He’s an AI strategist and founder of Never Tech Behind, who helps organizations successfully integrate AI into their workflows while keeping humans at the center. Through his innovative adapt system, dice method, and AHA framework, George [00:01:00] transforms how companies implement AI to achieve tangible results.

He’s known for making complex AI concepts accessible to everyone, from solo entrepreneurs to executive teams, and we are so excited to dive into this conversation. Welcome George.

\

Jordache: Hey, thank you so much. I’m so excited to be here with you guys. Uh, looking forward to our conversation.

I think it’s gonna be a good one. 

Aubrey: Oh my gosh, I can’t wait to dive in. In fact, we’re just gonna dive in if that’s okay with you. 

Jordache: Let’s do it. Uh, 

Aubrey: let’s do it. Right. So generative AI tools like chat, GPT, which we’re all familiar with, and image gen image generators are everywhere now. You know. As we’re looking at our audience, some people who may not have like used these things, what is generative AI and what do you see it as the most impactful use cases for school marketers and school leaders, and where should they, you know, be a little cautious?

Jordache: Such a good question to start off with. I love this. So, before I dive into like some specifics on this, I always [00:02:00] wanna. You know, give people some context. I always like, could I zoom out before we kind of zoom in? And one of the things I always say is, we’ve been living with AI for decades, like AI’s been around since the fifties, um, in some form or fashion, right?

So if you, and, and, and most of us just experienced it over the last couple decades of if you’ve watched Netflix or if you shop on Amazon. Or if you are underneath the social media platforms, any recommendation saying, Hey, you like this show. You should like this show. Hey, you like, you know this person. Do you know this person?

Hey, somebody bought this. You should also buy this. These is all forms of ai. So the way I say this is generative AI is like a subset of ai and this is what we’ve, we’ve kind of been. Exploding onto the scene since around 2017 when the Google DeepMind paper came out. But one of the things we have to remember is when we, when we speak about AI in general, it’s, it’s a.

Big industry, and this is why I say this is one of the technologies that’s gonna impact almost every aspect of our life as a consumer, as a [00:03:00] user, as a professional, et cetera. Now, so let’s kind of zoom in on Gen AI for a second. So when we talk about generator ai, this is the type of technology that, um, is essentially creative technology because it produces new content, whether that’s images, whether that’s text, whether that’s music, whether that’s audio, et cetera.

Rather than just analyzing information, like we always, we, we joke around the old ai, right? The old AI was all about classifying data and predicting data versus gen AI is all about creating something. So the best way I always talk about this, it’s like having a creative assistance who can write with you, design with you, edit with you, analyze with you, organize with you, brainstorm alongside you, and at its core it’s taking a whole bunch of data.

This is when we talk about being trained on the data and it’s basically predicting or identifying patterns and kind of predicting what it thinks you want next by creating something new. And so this is like when we start talking about like IP and all and the legal side of things. It’s like, is it really.

Copy copyright [00:04:00] infringement. If it’s creating something new, if it’s a derivative. And that’s why, you know, IP lawyers have jobs for decades to come. I always joke around. So, um, so that’s kind of what we think about Gen A. It’s kinda like this creative assistant that helps us create new things based off of the direction that the human gives.

And so when I say direction, that’s when people are talking about prompts, right? Um, or prompt engineer, whatever you wanna say. I always talk about a direction because it’s. A little bit easier to understand of like, if I, if I tell my 4-year-old what to do, I’m giving him directions, he should do that.

Right? Same thing. If I need to tell an AI what to do, I’m the human. I can direct it in whatever, you know, whatever aspect it is. So now when we, when we, the, the second part of your question was like, you know, what are some use cases for school marketers and, you know, and then the cautious side of things.

’cause there’s always, you know, there’s the dark side with any form of technology, it can be good for, you know, used for good or, you know, not so good. So. You know, some of the things that come top of mind when it comes to, um, use cases for, you know, a, uh, school marketers when it comes to AI is one, is content acceleration.

You know, and, and I’m not saying [00:05:00] producing full blown content that’s fully ai and we’ll, I’m sure we’ll we’ll dive into, dive into that later on in this episode, but it’s more so, you know, most. These educators or these school marketers and the people that I’ve, you know, that I know I’ve worked with, they’re, they’re understaffed.

They’re overwhelmed. They gotta create content across all these multiple platforms. They feel like they’re on this constant con or content hamster wheel and where AI can help. And a concept I always talk about is. Let AI create that 80% draft for you. Let it create that 80% draft of a blog post or social content or newsletter, et cetera, while keeping the human in the loop.

We’re not saying, Hey, ai, go do everything for me, but do a little bit. Help me do this because one of the things else, as humans, it’s a lot easier for us to edit. Than it is to create and we can spend more time on the strategy and the refinement of that versus steering at a blank page. So content acceleration’s a big area when it comes to anything marketing, sales related, whether you’re in education or, or not.

Um, but one thing specifically in the school [00:06:00] space is personalization at scale. And what do I mean by that is because without massive time and investment. That’s usually required to like personalize content, personalized communication. We have this amazing technology that can speed that up and, and, and one, like for example, because when you’re thinking about like in a school situation, there’s a lot of stakeholders, right?

There’s a lot of different segments I gotta talk to from perspective families that various. Stages of that decision making process to current parents, to alumnis, to donors, and we can use AI and take our core message and tailor the follow up, tailor the messaging based off of that individual persona.

Right. And, and, and if you understand, kind of, if you’re picking up kind of what I’m talking about, this is nothing new in marketing. It’s how do I talk to a persona? How do I talk to one person? How do I talk to, that’s something that relates. And I would joke around with my seventh grade speech teachers, like my job as the.

Communicator is to make sure the receiver gets my message. It’s the same thing here. Now we can just do it in [00:07:00] without, with less effort, but actually more effectively ’cause AI can actually start taking our message and you know, translating it to individuals on that. And then the last thing I’ll say, just from a use case is really, is data interpretation.

Analyzing survey responses, appli or, you know, looking at application or trends or engagement metrics to help surface these insights and these patterns that us as humans may have not missed or we may not have been able to connect the dots right out. And that’s what I, you know, when I was saying earlier, the ai, especially gen ai, it’s very, very good at identifying patterns and looking at trends and connecting dots.

And so those are just some of the things top of mind that I can think about when it comes to, you know, use cases. I’m sure we can talk some more specifics later on now. The last part of your question, the cautious side, like what do we need to be concerned with and what are the things, and this is, this is part of being the responsible AI user, the responsible AI implementer, right?

And one thing is, and this is across every business, every business I work with, and this is usually within the top three questions, how do we maintain our authentic voice or our brand voice? Or if it’s an entrepreneur, how do we [00:08:00] make sure that, you know, our, it, it doesn’t sound robotic in a sense. Right?

And, and when it comes to schools like. Your school has a unique culture, has values, and this generic AI output won’t capture it without proper guidance and context, which we can dive in. This is kind of what DICE is about and things along those lines of which we can dive in here in a little bit. Um, but the last two things I would say that are always big when it comes to AI is factual, accuracy and biases.

Um, you gotta understand that AI is confidently wrong. Sometimes. And what do I mean by that? It’s gonna give you an answer. It’s gonna tell you Yes. It’s like, it’s rarely gonna tell you I don’t know that unless it’s against their terms or services. So it’s gonna give you something you as a human gotta say.

Is that right? Does that make sense? Do I need to fact check? Are those numbers correct? Especially when you’re analyzing data and things like that. Right. And then obviously, you know, biases and so like. The way you kinda look at that is like, whatever you’re at, you’re having AI assist you with, you gotta kinda understand the risk associated that When I mean risk, it’s like if I’m, if I’m a, if I’m in the [00:09:00] education space and I’m having AI help with my curriculum or policies or admission requirements, that’s high risk, right?

Like, that’s very like, there, there, there’s some. There’s some big consequences if something goes wrong on that side. So we need to be extra cautious and extra human review or make sure we’re using the right models or biases aren’t in play right. And then the last thing I’ll say cautious is, you know, the transparency side of things Now.

I’m, I’m, you’re gonna hear people from all sides of camps on this, right? Because I’m not the type of person that says, you need to announce of saying, Hey, I wrote this email. This email was drafted with AI assistance in the bottom of my footer. Right? I’ve seen people do that, right? All that my, this was made by human and ai.

It’s like, okay, I don’t go that far because I look, the way I think about this is if I’m writing a newsletter or if I’m writing a blog post and I use spell check, do I have to disclose that? I use spell check or grammar check to help me make this more effectively. Now, if I didn’t, if there the human wasn’t involved at all, there’s some deceiving there.

But I think where, what we’re gonna see a lot of this transparency comes [00:10:00] into play is as we get more of these voice agents, if you’re talking to somebody, is am I talking to human or am I talking to an AI person? Um, and so I, the way that I always kind of judge this is like, how do you just maintain a human connection, your communications, but at the same time.

How do I just be a good person? I know this sounds corny, but it’s like I’m not trying to, I’m not trying to deceive somebody, but if I can communicate, if a, I can make me a better communicator. That makes sense with you like. Me as a receiver wants that. Like, I want you to use any tools at your disposal to help me learn, help me connect or help me, um, educate me on something or connect with me on some, some point.

So that’s when I kind of look at those, those cautions of, you know, it’s, it’s whatever’s makes whatever, makes you comfortable. But you know, on an organizational level, these are the conversations you need to have when it comes to like drafting your AI policies and your governance policies because, you know, you need to make sure your brand’s not at risk with some of this stuff.

Tara: Wow, that is great. So much information. Let’s kind of, go back and [00:11:00] say like, if a school is taking all this in mm-hmm. Where should they start? If they’re just beginning to explore these tools? Just if they’ve just scratched the surface and set up a chat GPT account or something. 

Jordache: Great question. Great question.

So I will say this, when it comes to ai, this is a new skill that you’re learning, right? And, and, and, and this is a, and and you need to, people need to understand this is a new skill and what, what happens when it comes to new skills? I. We typically suck at ’em, right? Like if I pick up a violin, I’m gonna break some screens or strings right now, right?

If I try to play the piano like I had, I don’t have musical building ’cause I never practiced, I never played, I never did this stuff, right? So one thing you always gotta give yourself grace with this, right? And, and, and people, I. There’s ego that comes in blood. There’s a whole psych, psychological and sociology side of things when it comes to adopting technologies that I talk a lot about in my newsletters, which is fun.

But like the, the, there’s, there’s these invisible forces. So make sure you’re aware of some invisible forces that are gonna prevent you from this. Now, when it comes to [00:12:00] using this stuff, I always say, you gotta experiment with this. We have to get back to our mindset of like being curious and experimenting with this.

So if you have chat GPT, or if you have Claude or Gemini or any of these, these main foundational model tools. Start simple and start small, but start looking at things from a, what are things that I do not like doing that I just want to have assistance with? Or on the flip side, what are things that I love doing that I just wanna get more creativity outta this?

Or I want to be able to have it think or like, I always like, one of the way I use AI a lot and in my day to day is a sparring partner. And what do I mean by that? It’s like. Yo tell me, gimme, poke holes in whatever I’m saying, my arguments, my thoughts, my ideas. What am I missing? Like it gives me somebody to bounce ideas off of, or my brainstorms, like, help me organize my thoughts.

I go on walks every morning. I talk to myself in my, in, in my voice memos, and I just, I have, you know, AI tools that will, or prompts and, and my AI tools that will just organize this [00:13:00] stuff for me. So help me organize my thoughts, right? So like, there’s little things like that, that you can start small with.

But then what it really comes down to this is when we’re, when we’re looking at how do we start, I integrating AI into our organizations or on our teams, we have to start looking at our workflows. A lot of people look at tools first versus saying, what are we doing extremely well already, and how can we make it better?

Or, what problems are we facing that maybe AI can help us solve? And so when we look at our workflows. We start looking at mapping out, here are all the steps that it takes me to do A, B, C, D to achieve, you know, achieve d. And then I can start break. When I break that down on a, like on a task level, then I can start identifying, well, hey, can AI help me write this or ideate on this or analyze this or edit this or give me more ideas or spar with me on this, on this specific task.

And then that’s when you kind of find these little pieces in a workflow that will allow you to start. Injecting AI and, [00:14:00] and I always say like, Rome wasn’t built in a day. You’re not, you’re not integrating AI in every aspect of your business in a day. It’s these little small wins that build up your learning and your experience by doing versus just consuming.

And because when you learn ai, unlike a lot of skills, this isn’t a linear learning where I’m just gonna go take a course, I’m gonna be good at ai. It is, you learn it by doing. You learn it by experimenting, you learn it by understanding what models may be better at this. Specific task or other, and then all that knowledge starts getting built up.

And then you start, you’re gonna be able to have this exponential learning curve decrease at the end of the day. So. 

Aubrey: I love the, the way that you talked about it in terms of like, we do a lot of plannings with teams in schools, and I love the idea of looking at your workflow and asking how can AI like make this better, easier?

Yeah. Or you know, I think that is so important for school leaders to consider, um, as they’re like examining their workflows. Like do you see some you know, key tools that are effective in certain workflow areas? Um, I mean, we [00:15:00] all. A lot of us know about Chat g, PT Claw, Gemini, and all those things. Yeah.

But like, do you have any, you know, thoughts around, uh, key tools in certain workflow areas? 

Jordache: Yeah, a hundred percent. So one of the, one of the things that we, we have to like start changing our mindset around is as we start utilizing AI a lot more, we have to, and this goes back to like when I was saying earlier, I, we have to give it direction and it’s not just.

Better prompting it is the context and the information. Because one of the things I always say is AI is very, very smart when it comes to world knowledge, when it comes to, you know, all this has all this data in it, but it’s very, very dumb when it knows that, when it comes to you, your business, your organization, your team, the, the stuff that’s on that, on that nitty gritty level.

Right? And so when we’re looking at, when you’re talking about, you know, when we’re doing planning meetings or et cetera. You need to capture those meetings, right? You have these meeting note takers. You have like, I use Otter all the time. It’s on my phone. It’s in my Zooms. It’s, [00:16:00] it’s when I’m going to events, I’m recording because they always, like, my mind is now trained of.

If you give me a transcript, we can go do some damage. Good damage in, in with ai, right, because, and what does, what does that allow me to do? Especially like when I’m in calls or when I’m in calls with clients or I’m doing planning, or I’m doing workshops or I’m doing keynotes, I can stay present and focused.

1. On the individual and the conversation at hand, because I know it’s being captured in the background. I know I can reference it, I know I can analyze it. I know I can extract things, I can get word for word things from that. So that’s one thing is Otter or there’s Fathom, there’s a couple other, you know, any of these recording tools, you wanna start capturing this stuff.

Okay. Number two other things I would say is Notebook LM by Google is a great, great tool. And it’s great because. When I’m using a chatt or I’m using a Claude, or I’m using a Gemini, et cetera yes, I can, I can put some constraints around it and start, you know, giving it some context and uploading documents and all sorts of stuff.

But it’s still pulling from all its world [00:17:00] knowledge. And so, or all of its other data is what I, what I’m mean by world knowledge. And when we’re using Notebook lm, I can bring in specific sources. That Gemini, it’s using Gemini’s or Google’s model Gemini to be able to just only pull from them. So those sources.

So when I wanna go deep on a topic or when I wanna learn specifically around this, or connect one domain to another domain and start doing some cross domain analysis or whatever it means, or planning the reality that it comes down to that is. I’m able to just use Notebook lm, because I can put a lot of sources in there.

It’s like 300 notebooks that have 500,000 words each. It’s millions of words. I can just build kind of my own little model on that. So that’s one thing. The other thing is within chat, CBT or Claude or whatever, when you’re doing these planning meetings or when you’re doing these things that are repetitive or whether it’s weekly, daily, quarterly, whatever.

Creating projects inside of there. Right. And, and you know, they used to be, uh, you could still build custom gpt. I rarely build custom GPT anymore, but it’s, it’s, you know, chat GPT uh, [00:18:00] projects or clawed projects or whatever where you’re just saying, Hey, for this one specific task, I already have it trained.

I can just go drop my stuff in there and it’s gonna do whatever I want it to do. And I’ll give you like a personal example that is, like I was saying earlier, like I. I, I talk a lot to myself on voice memos. When I’m doing my morning walks, I literally just copy that transcript, drop it into a clawed project, and it analyzes my, in the format that I want and the things that I want, what I want to extract, what no questions, I wanna follow up questions, things like that, because that’s something that I’m doing consistently.

So I, instead of me having to open up a tool like that, I can just use that over and over and over again. So those would be, I would say, notebook, a lamb, otter setting up projects. And then, um. Those would probably be the three that I would recommend. Right now, just thinking through from a, those, you can do a lot of damage with those three tools within a workflow.

Um, at this point in time, data analysis is a little bit differently, but those, you know, from a planning, organizing, analyzing, creation standpoint, those would be the go-to. 

Tara: I’m curious ’cause I’ve, I’ve basically [00:19:00] just a chat GPT user. Mm-hmm. That’s the one I’ve, mm-hmm. Become familiar with and have a paid membership too.

So I haven’t really played with Claude. My developer uses Claude Aubrey and I were talking about Gemini the other day. So I think those three right, are the, am I right those are the top three sort of gen, like general chat g AI tools that are used. And I was gonna see if, if so. Compare them for us.

Like, what’s your question? Favorite, or what’s the, what are the differences? Why would I use one versus the other? I feel now, like I’ve invested in chat GBT not just financially, but like it knows me now. But I’m curious, am I, I’m a shiny object syndrome. I. Sufferer. So, you know, like, tell me, tell me, tell me what I’m missing and what, yeah.

Yeah. 

Jordache: Great. And, and, and actually this is a very relevant question to ask now, and I’m gonna, I, I’ll, I’ll point out why, what I mean by that in a second. So, um, what we’re talking about here, we call ’em foundational models, right? These are the, the, the models that come out of these AI labs. So, like you [00:20:00] said, Gemini.

Claude, uh, from Anthropic Open AI or chat piti from Open ai. You also have Lama from Meta, AKA, Facebook. Um, you have minstrel from, I can’t remember the company out of France. Um, and you have gr from, from X or from Twitter, et cetera. Those are kind of the, the core, um, models right now. Um, and so when it comes to, and this, this goes back to kind of the workflow question, like you wanna understand like what is the best tool to use for the workflow or the task at hand and.

Before I get into specifics, one thing I will say, and, and, and, and one of the things you were, you just kind of mentioned was these, all, all these tools can do things pretty well, to be honest with you. Like, but like some of ’em do things a little bit better and why? What I, why I say that is because if you’re comfortable and you’re using one of these tools, like you don’t always have to have the latest and greatest.

A little bit of strategic AI can do a lot of, add a lot of value in your life right now. One thing I will say though is when these AI models are trained on all that data, there’s [00:21:00] like two parts of the training. There’s a pre-train, which is they’re ingesting all this data to basically kind of build out their models that they’re building on.

And then there’s the post-training aspect. And that’s kind of when they fine tune it or they add a little bit more personality or they add a little bit more contact, things like that. Right. And that’s kind of an important step because what I found when I test things out. Claude Anthropic. I use that maybe 90% of my time when I’m creating things because it has more personality, it has a little bit more humanness when it comes to how it writes and creates content, and it’s very, very good at coding as well.

So that’s one that I’ve typically found myself using when I’m creating, you know, helping AI assist me with emails or, you know, content or whatever it is. When it comes to, you know, chat t one thing that, and this is why I was saying right before you, after you asked this question, this is a very relevant thing, is because one thing that chat t recently announced is this.

Kind of ongoing memory, meaning now it reme, it’s starting to remember [00:22:00] across all of your chats. Previously it only, it like every new chat you would start within one of these models, it would know, it would have this context window, right? How much does it remember in that chat for it to kind of reference and use this.

And those, these context windows get bigger and bigger and bigger. Every model release, it’s kind of like an iPhone. There’s a new and better camera, a megapixel every release, and, but when it comes down to what Chad GBT recently released is now it’s like, hey. We’re looking at all your chats, we’re, we’re bringing in all this knowledge and this context around this chat.

So it’s, it’s kind of because, and, and this is where a lot of ’em are going, and this is why it’s relevant, is because they’re trying to lock us into these models. If you think about this, because the switching costs of me going from chat GBT to Claude, I, it’s $20 a month. I, it is death by $20 a month.

Subscriptions we always joke about, but like, there’s nothing holding me to that. But if I’m, if, if I’m using a model enough and now it has. All this context about me and all my writing, et cetera, that’s a, that’s a bigger shift, right? So this is what I’m saying is use what’s comfortable. So, you know, I’ll use chat GPTA lot when it [00:23:00] comes to like, I feel like their reasoning models, their thinking models when I need to do like a, from some complex questions.

I feel like they’re oh four and oh three models are a little bit better than Sauna 3.7, which is, you know, you know, Claude’s latest model. Um, data analysis I usually find using, I use chat GPTA little bit more when it comes to that as well. When, now when it comes to like big context windows, meaning like what models can take in the most information?

If I’m, if I’m dropping in like, let’s say four p four PDFs of books and, you know, all this other information. Gemini and Google has the biggest context windows out there, right? There are a million, 2 million tokens is what they call, which is about, whenever you hear tokens like it’s 75 per rule of thumb is about 75% of the token number is the amount of words.

So if it’s a million tokens, it can remember 750,000 words, give or take, you know, here and there. And so like that is like when you want to ingest a lot of data or a lot of information. Gemini is usually one that is, I’m gonna go to for [00:24:00] the first step of the analysis. Because it can handle a lot of that context.

But then, like, I usually pull that over into Claude if I’m trying to like, you know, write the analysis or do things like that. So I’m using Claude a lot more from writing chat GT’s a little bit more from the data analysis side of things and Gemini’s more of the big context window side of things. But one thing I will mention though is before, before we move on to this, is this is where experimentation comes into play.

So, believe it or not, I’m, I’m one of these, oh, it is kind of part of my job, but like I pay for all these tools, right? Like, so I’m paying, I don’t even look at my monthly subscriptions anymore. It’s like it is what it is. I have all these tools. I. But like when I’m, when I’m doing things, if I’m, like, even when I’m preparing like, like what?

I do a lot of keynote talks, right? So like when I’m doing the research on the industry and, and, and figuring out how to tie in my messaging and doing all that stuff, like, I use AI a lot to help me kind of craft my story, craft my throughput line, all the other things and do research. But I’m also like, when I’m doing that, I might have Gemini pulled up with.

Cha BT asking the same exact prompt or Claude bring asking the, because they’re all gonna [00:25:00] gimme little things a little bit differently. And I’m pulling, I’m like, oh, I like what that said over there. Let me pull this over here. Right? So I’m kind of like frankensteining the, the outputs a little bit and learning about what it is.

And the reason why I’m telling you to like start doing that, it’s because you’ll start picking up what are the differences, what are the little nuances? And this is just part of that AI literacy, AI learning, and AI skill building. So you can start identifying, oh. If I need to solve this task, here’s probably the model I’m gonna start with.

’cause based off past experiences, it’s usually giving me better data analysis or beta or better writing or better, you know, whatever the output may be. So. 

Aubrey: This is fascinating. I’m so grateful for you for sharing all that information. That is amazing. Um, I do have kind of a follow up question regarding Gemini or just in general.

Like a lot of schools use Google Workspace. Like they’ve got their, their housing, all their Google Docs or using sheets. They’re all the, all the stuff, you know. Is it with Gemini tied to Google? Like is that a better route to go if you’re trying to kind of set up systems [00:26:00] within your workspace? Um, or is it a combination?

Like what are your thoughts on that? 

Jordache: Great question. So, and this, this is, these are the conversations I’m having a lot of times on with the leadership teams and the executive teams, um, that I, that I work with. Because there’s things, a few things you have to keep in mind, like. When it comes to data and data security, like if you’re, like, a lot of these organizations that I work with, like they already vetted in, they’ve already gone through the whole procurement process and our data stored on either AWS or, you know, Google Sir or Google Cloud or whatever like that.

And so it makes sense to kind of keep that data siloed into there, right. And, and, and keeping that so, you know, it, so it’s, it’s, that’s one part of it, right? To thinking, thinking through of like, where’s my data being stored and my, do I already trust ’em? Et cetera, et cetera. Now the other thing I will add though is like with Gemini specifically, and this, this is something we see like a lot in corporate as well, like with Microsoft copilot, is they’re, they’re kind of attempt at this.

You’re, you’re starting to see it [00:27:00] injected in all their different other tools, right? You start seeing like, Hey, analyze your email, or Hey, look, use Gemini to see what’s in his Google Drive folder, et cetera. Personally, I. I typically don’t use those like little add-on. Like I always say like, if you remember like, I’m gonna, I’m gonna age myself, but like remember in the early Microsoft days, a little clippy, a little clip, like, you know, little annoying little paper clip that used to sit on Microsoft documents, say, Hey, I’m just get a little, like, that’s what it reminds me of and it just drives me crazy.

Um, because I don’t feel like it’s nearly as effective. ’cause it’s just like shortcuts in a sense, right? It can be valuable. So, so like, but. Where I do see value when it comes to Gemini, et cetera, is they’re catching up and, and, and, and because all these tech companies, all these big tech companies, meta, Google, Amazon, all these guys, they got caught, I always say, with their pants down when Chat GBT dropped.

Google’s been working on AI for two decades, but they weren’t working. And, and they created the, the freaking research paper of that, that was the foundation of GPTs, right? In 2017, they released it, but what [00:28:00] t brought to us. Was that consumer experience, it was the first time on a mass scale that us as an individual, us as consumers, could tap into this generative AI technology.

And they, and they weren’t ready for that. And so Google’s been playing catch up and they’re getting better and their models are getting better. And they’re, they’re, they are. It is a good place to start if this is the environment you’re already. You know, you’re already working it and you know, obviously like notebook ln, if you’re in Google workspace, you get notebook LN for free with a pro version.

So there’s a lot of value that’s already kind of in that ecosystem. So it’s a good place to start. Now what I would then though, like what I, as AI starts becoming more and more prevalent within an organizational, within a school, then you start looking at it on a team level. Because if I’m a marketer and I’m creating communications and content, yes I can use Gemini and, and eventually help me do that, but there’s probably a.

Better use of my time to start getting Claude involved at, at some aspect of that, right? And so that’s where you start looking at it on a case by case basis. But once again, the tool [00:29:00] has to be vetted by the organization, it, legal, et cetera. Start looking at the terms, how’s the data? And this is like, this is where I work with organizations like developing these AI policies and these governance policies.

Because if you don’t do that, believe it or not, your your, your employees are using it one way or the other, whether you, whether you, you, you wanna believe it or not. So it’s like. Stop putting your organization at risk because, and, and put some, you know, guardrails around what data can be used, especially like in schools.

Like can student data be used? Should we be sending student data to servers or do we need to have an uh, uh, an open source model that’s offline, but we can still tap into AI using some stuff like that. So that’s where all these questions are. But to answer your question, if the ecosystem’s already built around Google workspace, people are comfortable with it.

Um, it is a good place to start. Um, and then as you kind of get more mature with the adoption of AI in your organization, you can start finding different use cases to start potentially bringing in some other tools. 

Tara: Thank you. This is so helpful. I have been so excited to be using these tools and saving so much time.

I was telling you [00:30:00] when we started that I was using it for code and um, you know, it’s just been a game changer for me. In many ways. I even have had some like personal chats with it, which you know. Like, uh, it’s, it’s become really a invaluable tool. Before I ask my next question, though, this is a quick one.

Do you say thank you and please 

Jordache: gotta, gotta live the internet with the rumors? Yeah. Um, you know. There was a time that there were actually, people have done studies that you, sometimes you will get better. You know, it, it will give you better responses from this. I, you know, the joke around is like, I’m always gonna say please and thank you.

’cause when they, you know, take over the human race, they’re gonna member who’s kind to you. I, I, I used to like do it a lot more, but like, I also just find myself like, that’s just like how I talk a lot. Like, same I say, please thank you. Like, I appreciate you, thanks for that. Like, you know. Yeah. But you know.

When you’re, one thing I was, I will say though, is when it comes to like, specific language that you’re [00:31:00] using when you’re, when you’re, you know, given ai these directions. If you really want to emphasize something, you really want to, and one of the, like, one part of these directions we talk about is like putting constraints of like, what do I not want it to do?

What do I not want it to? You know, say, do not use the word revolutionary. Do not use the word game changing. ’cause it loves to use these kind of, you know, very, you know, fluffy words when it comes to content and writing. And so like, you can give it, you can direct it where it’s. You can quote, unquote punish it.

If you wanna say like, if you use this word, you are going to X, Y, Z, or I’m gonna reward you with a million dollars if you do not use this word. Things like little, little things like that just to kind of play and experiment with it. You’ll start seeing like if I, and you don’t have to go to that extent, but like how can you be stern?

And I honestly like as a. Relatively new parent. I have a 4-year-old and a four month old. It’s like, like, how can I communicate it with like, to make sure that you truly understand what I’m saying here. Right. And that’s like, [00:32:00] that’s the, that’s the thing where people like, I gotta understand like we are having a conversation with a child in a sense of like, I gotta be a very good communicator to make sure I’m articulating exactly what I want.

And that’s why a lot of people struggle. ’cause a lot of people struggle with, you know, effective communication. And the other thing too is a lot of people struggle with. Being a manager and AI is basically making us all managers, right? And leadership, and how do I effectively communicate? How do I make sure I give it enough information and context?

It’s like, oh, so I say all that because I don’t say thank you much anymore, but I’m making, I’m, I’m very cognizant of the language and the, the, the words that I’m using to make sure I’m setting, I’m setting it up to give the re the results that I wanted to give. But yeah, the whole. Please. Thank you. I don’t, you know, 

Tara: so interesting.

I’m gonna take my time to analyze the 

Jordache: data if it, if it makes sense. 

Tara: I just do it just out habit and so I crazy. I talk to her like it’s a 

Jordache: human, right? Like, I’m literally talking to like, you’re my best friend sitting next to me and helping me. All my And it back to 

Tara: you like that too, right? Yeah. It, it’s.

[00:33:00] It’s crazy. Alright, let me ask you about, you know, I, I, when I introduced you, I mentioned all these different platforms that you have. So Adapt system and the DICE method. And you also mentioned, we also mentioned aha. Yeah. So these are things that can help organizations implement AI in a structured way.

Yep. Can you walk us through these programs a little bit and how a school might apply one of these frameworks to a marketing or admissions challenge? 

Jordache: Yeah. Yeah. Great question. So I’ll start with adapt. So adapts kind of like the, one of the things I always talk about is like organizations and leaders and, and, and doesn’t matter what industry you’re in, we have to start.

Building a culture and ecosystem to have like an AI first culture. And I’m not, when I say AI first, I’m not saying remove the humans and lay people off and that like, that’s so short term thinking, but it’s more so of how do I create an ecosystem that allows this technology to thrive and scale and actually unlock the value that it actually can provide.

So what ADAPT stands for [00:34:00] is align. Develop, analyze, plan, train. So let me break these down real quick. So, and, and this is what I, this is really from a, like a leadership level, whether it’s a team lead or a organization lead, et cetera. But like, this is where with Align, you have to align your AI strategy with your business goals.

And typically what that starts with is building down an internal AI council. And I don’t care if you’re a five person team, I don’t care if you’re a 5,000 person team, there needs to be an AI council that’s gonna kind of take the lead and, and, and be the guiding force and to, to kind of allow or improve.

The chances of AI adoption in your organization and the larger your organization is, they also kind of become the. Leaders where your team members and your employees can come to or bring their ideas for, and they kind of help shape the strategy and align the goals with the business, with the leadership team, with the AI strategy, and kinda do this.

So, and AI council is like a vital, vital, vital piece within an organization. Um, the d develop is when we start talking about AI governance and policies, I kind of touched on that before. What data can go in? What tools are approved? Um, how are we [00:35:00] able to use it? Like what’s been embedded? What, because, you know, the worst thing you can do is start putting in, you know.

Private data into, uh, something that doesn’t have the right terms of service. And then, you know, you just put your whole organization at risk. So that’s why putting these guardrails is in place and this is where, you know, legal can come into play and make sure in it and all that other stuff. The A in adapt for is analyze use cases and launch pilots.

And this is what I always talk about of like. You have to start small. The best AI implementation and the the highest level of success is when you start small and we start identifying these use cases, whether it’s on an individual level or a team level. I do a lot of workshops with organizations, just helping them identify use cases specifically for their teams or the organizations, and then launching these little.

Pilots, and what I mean by pilot, 30 days, 60 days, 90 days, proven the value, proven the use case. And this is what also allows you to vet the vendors where you’re not signing up for, you know, year long contracts. Next thing you know, no one’s using it three months in because there wasn’t appropriate training.

There wasn’t, they didn’t do what it, you know, [00:36:00] every, every tool launch today or a tool, it has ai, AI power this, ai this, AI this. And you have to vet them because, just because you slap chat GPT inside of your tool, like. Is that really the, like, am I really getting value from that? So there’s like a whole, you know, vendor analysis, things like that.

But identifying these use cases and launching pilots is big. And then from the p and adapt is planning and, uh, your AI roadmap and scaling, right? And this is like when we start looking out six months, 12 months, 18 months. Okay. Now we have some pilots in there. How do we start, you know. Building out a roadmap to start basically integrating AI and other aspects, other workflows and things along those lines.

This is where the AI council comes into play. This is where the, a AI strategy, all these things work. But the last part is T, which is training and upscaling your team. The biggest risk in most organizations right now is AI literacy. People, just, the, the team doesn’t, they, they, there, there’s not enough, you know.

Support on training, bringing in outside people to come in, doing internal trainings, whatever it may be. Training and upskilling your team is gonna be one of the most vital things to do over the next, you know, 6, 12, [00:37:00] 18 months. Um, because one or two things is gonna happen. Either your team’s not gonna be skilled and they’re gonna be left behind, quote unquote.

Or the people that are skilled, if they don’t have the support to continue to upskill them, they’re gonna leave to find organizations that do. And that’s, that’s where it comes to cut, you know, you know, talent retention and all these other things as well. So that’s kind of what adapt is. So like. That’s really the, like the overarching, how do all these pieces build out this kind of ecosystem in there.

Now, when it comes to specifically Dyson, this is where I want to kind of dive into some specific use cases because I think this is probably a little bit more applicable to individuals and, and, and smaller teams. So DICE is, I always talk about it’s the system before is the system. So it’s, before I delegate, I need a document.

And what do I mean that before I delegate a task to ai, I need to have some documents or some data. Information context examples, that’s what DICE stands for. Data information context examples, to be able to give it that specific thing. This goes back to what I was saying before. AI is very, very smart around things, but it’s very, very dumb when it comes to our own specific [00:38:00] situations.

Our schools, our specific, um, students, our, whatever it is in our own little kind of in world, we have to educate the AI system on that. So. When are we talking about data? What are we talking about here? Enrollment, statistics, um, your in inquiry to enrollment, conversion rates, your demographic information, your survey results, website analytics, social, and all these things that we already have data on.

This is the gold. I always say we all organizations are sitting on gold. They’re just not mining it right now for when it comes to this AI world. They’re in this AI gold rush. So we have to look at this data because this data is gonna be. What’s gonna set us apart in this AI driven world? ’cause no one else has this data, no one else has this information besides the organization.

And, and, and when it comes to like the information side of things, it’s really, you know, taking that data and then telling a story around that, right? So really, like if you’re looking at it from a school standpoint it may be taking some survey information and truly understanding what program was the most interest to certain family demographics and things like that.

And I’m saying all this stuff right now, and I, and I promise you it’ll make sense [00:39:00] because. If we have this stuff readily available to us and we have access to this in a Google Drive, in a, in a Google Doc, somewhere, in a Google sheet, we can then feed that into ai. And we just gave it a lot more context to get a lot more personalized results around us, around the task that we’re gonna delegate to that.

Right. But the big one I would say is the C part of dice, which is context. Okay. Especially in the school, because this is what I was saying earlier, like. Schools have a amazing advantage when it comes to AI because you think about all the unique aspects of a school. They have stories, they have history, they have special traditions, they have community values.

They have, you know, their founding story, their teaching methodologies, all these unique things that make a school unique, but. I think AI doesn’t know anything about that. So if we have that captured somewhere in a document we have that captured somewhere, and there’s easy ways to do this with the help of AI actually to capture this stuff, then I can feed that into.

[00:40:00] Ai, Claude, whatever it may be, to help me write emails that will connect with the right individuals. And when I can combine that with, you know, the student personas or the parent personas, that’s one. It’s kind of like you’re building a recipe. All those little things together are, are specific, are um, are kind of basically come together and, um, feed off each other.

And then the last thing is the examples. And this is when we start talking about brand voice, when we talk about brand style, when we talk about, you know. How does it, how do we help AI sound like us? And it’s through examples, things that we’ve already written. We’ve al, we’re al we’ve already created emails that we know work.

We already, we’ve already created social content that we know that works. We’ve already created marketing material that, that from, from an admission standpoint that we know attracts people. We’ll give AI examples of what success looks like in your world and let that be the foundation that AI can be built upon.

So. When we’re thinking about when, and so the last part of your question was like, you know, how do you, how does this work? Like in, in specific situations, like for like enrollment. Think about like, if I think about enrollment for a second here, right? [00:41:00] There’s a pipeline, it’s a sales pipeline, right? Like, and if you look at it a bit, it’s a, it’s a marketing pipeline, a sales pipeline, enrollment slap, whatever it is that I’m trying to take somebody along a customer journey, right?

And the key is I gotta nurture those people depending on what stage they’re at. This is the literally customer journey 1 0 1, and. If I can understand, if I’m communicating with the person that with an individual at the right stage of their journey and it’s personalized to them. There’s gonna be building a connection, right?

So when we think about that, and you look at to, to kind of how DICE comes into play with that is we need to know kind of what are the conversion rates at, at each step of this enrollment, uh, enrollment journey in a sense, right? What’s, what is our baseline that we’re building off of? That’s data that we would be able to share with ai.

What’s working, where’s the gaps, what it is. And, and humans can look at that and say, oh, there’s a drop here. There’s a drop here, great, but I need to tell ai. So it’s aware of that as we’re starting to kind of. Improve our enrollment pipeline. The information layer of the, [00:42:00] the i in this, in this specific situation is what does successful enrollments look like?

If you do surveys and you ask parents, why did you choose us? Look at those responses and if you have that, that survey data of why they chose our school, there’s probably, there’s probably distinct patterns that AI can pick up saying, well, looking at this, the top three things that keeps getting mentioned over and over is this.

Well, we need to extract that out. If, if that’s worked in the past, we needed to let AI know that this was, this has worked in the past. Make sure we keep this in, in an in, in account as we continue to, you know, write emails or create social content and create marketing material to move somebody from, you know, step B to step C in the journey.

Right? And then the other, the last thing, the context and examples in this pipeline, and this is where I think. A lot of people this, it, it’s, it’s so simple that I think a lot of people miss it. But this is also where AI can give us time to start doing more things like this. You know, I was working with, I was coaching somebody, uh, she is, she was a, a school leader in the, um, [00:43:00] in a monastery school.

Right. And this, this 1, 1, 1, 1, 1 of my one-on-one coaching clients at the time. And one of the things that we were doing with her was. We were making it easy, and this is kinda like where Otter comes into play or voice memos, but like record voice notes of after meaningful family conversations or after something happened on campus that was like, oh, this, that’s, that’s a cool, fun story.

That’s a fun thing. And just capturing that information and start building an example library that AI can then reference to maintain that kind of warm personal tone when scaling communications or pulling personal stories that, or things that have happened that, you know. You probably forgot by the end of the day, that would probably be a great story to tell in an email to move somebody from coin.

So like that’s, that’s that context and examples of being able to kind of build all this information, this data beforehand and building these libraries up. So when we are ready to use ai, we have all these different, I call ’em assets that we can pull from. And then we can give to them. And, and when I say assets, like [00:44:00] literally I use Google Drive.

So all my, like, I call ’em dice reports. So I have a dice report around who my, uh, I call ’em. ICA ideal client avatar. Like I have di I know their pains, their problems, their desires, their goals, marketing 1 0 1, but I have it documented so I can, when I’m writing an email and I want to talk to that one individual, I can say.

Hey, ai, here’s who I’m talking to. Here’s the problems they’re facing. Here’s all the other things I’m facing. Here’s some personalized stories that I’ve captured along the way. Can you write me an email? Help me write a draft of an email that tells a story that would make sense to them that would solve a problem.

That’s a lot of brain power. I used to have to do beforehand, copywriting. Now I can just bring these pieces together, mix it in the mixing bowl, which is kind of the ai. Give me the gimme the cake afterwards, and I can put the frost in on afterwards. Right? And so that’s kind of when we think about dice is how do we set up these.

Capturing mechanisms to keep this information organized and stored and, and what kind of things should we be capturing because these are all little assets that we can use for, to help us personalize the ai. 

Aubrey: Oh my gosh, there’s so [00:45:00] much, so much to unpack there. Thank you so much for, for sharing that. I know our audience is going to enjoy listening to that and thinking about that.

There’s just so much that you can do with AI and especially with the systems you mentioned here today. I’m gonna switch kind of our, uh, focus right now and switch us to our rapid fires. Are you ready? Ready for rapid fire? Quick question, quick answer Session. 

Jordache: Oh, quick is you put a microphone in front me.

You can’t tell. Quick is tough sometimes. I know. 

Aubrey: Yeah, we’re running outta time. Alright, quick. I’m sorry. Let’s go. You go, let’s go. Alright. If you could put one book as mandatory reading in your high, the high school curriculum, what would it be? 

Jordache: Mindset. Carol Dweck. Um, all right. Fundamentally change how, how I think about learning growth, which is very applicable with what we’re talking about today.

Tara: Awesome. All right. Next question. Quick answer. What is one app you could not live without? 

Jordache: I do two. I’m gonna do two. Perplex the Otter. 

Aubrey: Love it. Uh, what are you reading right now? Oh, 

Jordache: [00:46:00] I, quick, quick, quick. I have like four books going right now. Um, I would say one right now is I’m, I’m going back through to kinda gimme some balance in this AI tech world, Atlas of the Heart by Brene Brown.

And I, I’m, I’m approaching reading a whole different way with AI now, which is very fascinating. So. 

Tara: That’s awesome. My gosh, that sounds like another episode right there. Okay. We, 

Jordache: I’m, we, there’s probably four episodes that we could go deep in on, on all these. I know things right here, 

Tara: so thank you so much.

All right, one last question. What is one great piece of advice that you can leave us with? 

Jordache: There’s like four that I’m thinking through right now. Start capturing you and your organization’s brilliance today. I think capturing capturing is, is, is what’s going to set us apart. And, and it’s going to allow you to personalize AI and even how we use AI now and as we move into AI agents and as this technology evolves, that that first person data is gonna be important.

So, capture, capture, capture, capture. 

Tara: Awesome. Thank you so much. This has been just jam packed and we are so grateful for your time and all of the information. Where can people find you [00:47:00] online? DoorDash. 

Jordache: Yeah, you know, LinkedIn’s the best place to find me. DoorDash Johnson. I’m an eighties baby, and my mother loved the brand of jeans.

Spelled the same way. But the other thing what I’ll do is for your audience, ’cause I know we, we kind of touched on dice I’m putting together a free email course that we’ll dive deep into dice. So, um, we’ll put the link in the show notes, but it will probably be DoorDash Live slash mindful, um, and they can get access to that and get on my email list.

And one thing about my email list. Hit reply, ask me questions. I reply. I’m a human. I actually don’t use AI in my email. So, um, if there’s something that you wanna follow up with on that we talked about today, do not hesitate to hit reply. 

Tara: Oh my gosh, this is awesome. Thank you. Yeah, thank you so much. Wait, we’ll put that in the show notes for 

Jordache: sure.

Perfect. 

Tara: Thank you again for Thank you. 

Jordache: Thank you so much. It’s been a pleasure. 

Tara: Have a great day.