A (4:45)

Yeah, I met you at Macon, which is Paul's event this year. You were a speaker, but you were not on staff then. And it's been great for me to watch Paul's journey and all the great stuff that Marketing AI Institute has been accomplishing. I saw you, you were a great communicator. I invited you on the show and now here you are working for Paul's organization, which I think is super cool.

B (5:04)

Yeah, I'm really excited.

A (5:06)

So you've been in the AI space for a while now, maybe longer than most of my guests that are listening to the show. What do you believe is one of the biggest misconceptions that a lot of people face when it comes to AI.

B (5:18)

The default approach to AI in business today is to use it like a shortcut. Everybody is so laser focused on efficiency, using it to produce more more in less time with the least amount of effort to churn more things out. And I do think it is a bit of a short sighted approach. Generative AI, the large language models that power ChatGPT, Gemini, Claude, they are probabilistic. So when they're trained, they take in tons of training data and they identify the patterns in that data. So they're looking for the patterns in emails and marketing content, sales pitch decks, strategy documents, whatever it is. And so this is why AI outputs can feel a little bit predictable on average is because they are, they're by default going to repeat those patterns and produce the most boilerplate version of whatever that thing is. And that is not a huge problem in isolation, but it does become a really big problem at scale when you have everybody on your team using it and you and your top competitor and everybody else in your industry is using AI in largely the same ways, especially as the market really starts to consolidate around a few core frontier models. So my concern is that this race to do less is going to lead a lot of companies straight into the trap of looking and sounding like everybody else. And the reality is that good enough is no longer good enough. Good enough has become so accessible and so attainable that marketing professionals, salespeople, brands, businesses, we actually have to be better in order to stand out. So I'm not opposed to AI. I don't think the goal is to avoid it entirely, but people have to use it more intentionally and strategically in order to produce better work.

A (7:11)

I love that and I double down on that because I agree with you that AI out of the box is going to give you an intelligent, rational and likely correct answer, but it may not give you what you really need. Right? And this is where those of us that are already craftspeople, for example, I'm a writer and a copywriter. When we use AI to make us better, we know what good is and we know what great is and we know what outstanding is, right? So that's why I often tell people, start using AI in your area of expertise so that you can kind of dial it in and make it absolutely better. Because you have discernment to know what good, average and excellent is. But you don't have that discernment if you're using AI in areas where you don't have domain expertise, right? And that's where you have to kind of learn a lot of the things that a lot of my guests have been talking about. So I think there's absolutely something powerful to what you just said. So we're going to talk about AI personalization. We're going to get into what the heck it is. But first I want to talk about the benefits when a business uses this AI personalization. Well, what's the upside? Let's describe the benefits and let's define what the heck it is.

B (8:21)

In short, you start by you can produce better work in less time. I'm not opposed to efficiency. You're going to still be more efficient and more productive, but you will do so while creating higher quality work. And that's because you're going to eliminate the repetitive setup. You're not going to have to keep repeating the same background information about your business and repeating the same instructions over and over again. Second, you're going to produce work that is actually original and authentic to your business. And so that is going to be unique. It's going to, like I said, stand out, especially in a world that is going to become increasingly cluttered with generic AI content. And third is you're going to create work that you can actually trust because AI generated outputs, as we know they have to be validated and fact checked. These models hallucinate, they can make things up. They're also just summarizing the Internet. And not all of that is really high quality to what you just said. Like, some of it is good, not great. So when you can personalize it and you can layer in your company's own knowledge and expertise, you can be a lot more confident that what you're getting out of it is it's current, it's up to date, it's reliable. So that's just going to mean fewer errors and a lot more confidence in the work that you produce. So in short, you're basically just going to produce better work in less time.

A (9:43)

I'll layer in a couple other benefits that I perceive based on what you're saying. Number one is if you do what we're talking about in today's interview, if everyone who's listening does this, they're going to become more valued by whoever they work for. If you work inside of a business and, and you deploy what we're about to talk about today, you're going to go from mediocre to above average to maybe even excellent. And that's going to be more valuable for you because you're going to be perceived as someone worth keeping. You know, when AI starts coming for jobs, because it's going to be coming for jobs. Those that really know how to output exceptionally high quality are going to be the ones that are going to be remaining behind. In addition, for, for those of you that have customers and clients that you're using AI for, you're going to be able to retain those people because they're going to want to continue to work with you because you are producing higher quality, whatever it is, strategy, content, fill in the blank. So this just allows you to become more valued. And this is what we want in an age where AI is a threat to so many of us, is to become more valuable. Thoughts on that?

B (10:49)

Yeah, I absolutely agree with everything you're saying. I'm thinking about a quote I heard last week. Somebody said about workslop, this idea of AI slop or work slop, where you just get something really generic passed up to a manager and it creates this bottleneck. And they said, yeah, if I wanted to ask ChatGPT, I would have. So I think that's the point is we have to add value above and beyond what our boss or our manager would simply be able to get out of one of these tools. And so that's where you come in and that's where your expertise and like you said, your discernment really comes into play.

A (11:19)

Okay, so just to clarify, in case we have any confusions, we are not talking about using AI to personalize emails to, like, your customers, we're talking about personalizing AI for applying inside of your business. Right. So just in case anybody's had any confusion on that, we're talking about really customizing it in such a way that it's going to be highly valuable for you in your work. So you've got a really cool process. Where do we start? Where do we begin? With this?

B (11:45)

Yeah, so to your point, when we talk about better work, high quality work, I mean more original and authentic to you. So this is how can we bring more of ourselves and more of our own context to these tools in order to get better work out of them. And so it's one of those things that is, it's not easy, but it's simple. Better inputs lead to better outputs. So we have to start with improving the quality of the ingredients that we're working with or feeding into these models. So the first place that I encourage people to start is to capture and collect all that stuff. So you need to collect all the files and the documents that you already have in your business that might be technical support documents, product descriptions, existing marketing content, sales pitch decks, proposals Strategy, documents, whatever it is. I'm sure you have a lot of stuff that already exists, and then you need to capture more of that original insight and wisdom that maybe still just lives in the heads of the people on your team or in your own head and get that down in writing. So as far as what we want to capture, I like to think of it as your company's proprietary data. And I don't mean the structured data like spreadsheets or data lakes or whatever. I mean the unstructured data and an easy way to think about it, data D A T A so D is domain expertise. That is the deep insight you have after spending years in a given field. So some things to think about here are what do experts in your field know that beginners or novices maybe don't? What do outsiders tend to get wrong about your space? How can you tell if a piece of content has been written by a true expert versus an outsider? What are the tells? So those can be some good clues for how you can untangle the more surface level insights. The kind of thing that's going to exist in a model's training data from those really unique insights that you might have approached is the frameworks, methodologies, heuristics you've developed over time. If you are an agency or a consultant, maybe you already have this written down. Maybe you have a documented methodology you follow, but maybe you don't. So think about this might be systems, processes, standard operating procedures, think about checklists you follow, questions you tend to ask when you approach a new project or a new client. The frameworks that you tend to just follow instinctively. That's the kind of stuff that you're going to want to be able to capture and scale using AI. T stands for talent. So that's what are you naturally great at? What do people praise you for? T can also stand for testimonials. So if you're struggling to think of things, go to your testimonials, see what other people have said about their experience working with you or with your business. That's part of your secret recipe that you're going to want to capture. And then the second A is your accomplishments. So that is your real world experience. You know, AI models don't have points of view or perspectives or real world experience. You have successes, failures, lessons learned. So, you know, lean into that. You know, what are the stories that only you can tell? That's the kind of stuff that, like I said, isn't going to exist in any training data. You can't Google it. You can't chat GPT it. That's the stuff that you want to make sure that you capture into your system.

A (15:10)

Okay, I got a bazillion little questions related to this. First of all, let me just tell you what I'm hearing. You say first thing we've got to do is capture and or collect lots of information. I guess my first question is how much information is enough information? Because depending on how long your business has been around, you could have an enormous amount of information. Any thoughts of like, the quantity of information we're really talking about here?

B (15:35)

Well, one of the things that we've seen over the past few years is exploding context windows. So the AI models, kind of like the human brain, of two main types of memory. The original training data is basically the entire Internet. That is its long term memory, its short term memory, or its working memory is the context window. And that has expanded from when ChatGPT first came out. It could only keep track of a few paragraphs of information before it would start from forgetting the earlier things. So it would forget details or start to get confused because it wouldn't be able to keep track of the whole conversation. Now these models like Gemini can handle over a million tokens. So we're talking about like almost a.

A (16:14)

Million words or something like that.

B (16:15)

Yes. And I have tried to capture what this would be in terms of. It's the entire Harry Potter series, the entire work of Sherlock Holmes, War and Peace. Like you could stack all of those up and you wouldn't reach the context window. So I guess the answer is the limit almost doesn't exist. I mean, most businesses could probably gather up everything and it would still be able to fit within the context window. But as far as what is useful, I mean that really just comes down to a personal decision of your business, your size, what you're using AI to do, what your goal is.

A (16:47)

You talked about this data thing which was domain expertise. Right. What do experts know that beginners do? Not, I would imagine this is more like we're customizing this for each person. Right. So when we talk about domain expertise, we mean Taylor's domain expertise or Mike's domain expertise. Right. Is that what we're talking about when we're talking about, when we're developing a personalized version of this for ourself or are we talking about domain expertise for kind of a career or job? What's your thoughts on that?

B (17:13)

So you could either do it for yourself, for you personally as a professional. Absolutely. For your own tools and your own use. I also would Encourage companies to think about this as well, because especially like I've spent a lot of time in B2B marketing, one of the big challenges in B2B is the technical complexity of what you're selling. You have to take all this stuff and you have to jump in and be able to market it or to sell it. And so having easy access to those subject matter experts, that can be a real challenge. So that's where that kind of corporate brain comes into play, is if you can create a kind of corporate brain that anybody can tap into, then everyone can benefit from that knowledge and everyone's work gets better. You eliminate those silos of information and expertise.

A (17:58)

Okay, so how do we get stuff out of people's brains? Because obviously that's not easy Right now.

B (18:02)

With AI, I think whenever possible you want to use voice. We speak three to four times faster than we write. I think this is part of the reason why you're seeing this explosion of voice, Voice mode and ChatGPT and Dictation and meeting transcription tools. So anytime you can use voice, you're not only going to be able to capture it a lot more efficiently, but you are also going to capture language that is going to be a lot more natural and authentic to you. You can use meeting transcription tools for internal meetings, for interviewing subject matter experts, like I said, interviewing your executives. You can use it for sales calls.

A (18:41)

So you could get into a Google Meet, presumably with one of your staffers, and have AI come up with a series of questions that I should ask this person to kind of extract some of their expertise, something like that.

B (18:53)

Absolutely. And again, go through the data framework, kind of build some questions off of that, try and draw all that information out. Again, do this stuff, you know, with permission to record, but if you're able to record, it's going to give you those word for word transcripts, which is really useful. So a few tools that can help you do that. Otter really popular reading transcription tool. So again, you're going to get that word for word summary of what happened. You can also with Otter, add your own custom vocabulary, which is cool. So if you have internal acronyms, product names, brand terms, industry jargon, you can add that as custom vocabulary and make sure it gets recorded accurately. Because if you're talking about, you know, hours and hours of audio, you're going to have thousands and thousands of words. And you don't want to have to go through and be able to clean that up in order to be able to put it into an AI model and use it. Fireflies is another one that I personally like and really use often. You can have it automatically join Zoom meetings, so it's very easy. It has a chatbot named Fred that you can use to ask questions of your meetings, pull insights out of it. It can also keep track of how frequently certain keywords come up. It can even report on speaker sentiment. So not only are you getting the content of the conversation, but you're also getting these kind of insights and analytics around what is being said, which can be kind of cool for things like lead or customer conversations, for example. And the other one to your point about having these deeper dives is descript. So a lot of people like Descript for its editing little different. You can take audio and video files, it will transcribe them and then you can edit them really easily so you can cut, copy, paste, trim that audio or video just by editing the transcript. So a lot of applications there. But personally I use it a ton to capture. So if I have a particular piece of content that I'm working on or I'm trying to document my perspective, my point of view on a topic, I will actually just turn on descript, hit the record button and just talk through my ideas. Kind of stream of consciousness Again. You could use this, go through the data framework, go through those questions. I use this with writing my newsletter. I can just talk and get all my ideas out and I wind up with a ton of content that I can then refine and use.

A (21:14)

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 D is domain, A as approach, T is talent and A as accomplishments. There's probably a lot of great insights that are locked up inside people's like if they're using Google, their Google ecosystem, or other kinds of things. Like, do you recommend people just go find a bunch of stuff that they've already created in the past and just put it into this collection, if you will, or not. What's your thoughts on that?

B (22:35)

So the nice thing is there's also a lot of tools out there that are making it easier than ever to bring all this stuff together. It doesn't all have to be in a single platform. So basically this is knowledge management, which isn't new, but AI is making knowledge management a lot more accessible and powerful. And also it can be really, really affordable. So there are some dedicated platforms that can help with this. You might also see it called Enterprise Search, but Guru, Glean, Coveyo are three that we can touch on. How they all basically work at a high level is you connect all of those tools to your point, you can connect it to Google Drive, to your Slack channels, to your support tickets, to all these different places. Because my guess is most people in their companies, yeah, they have all these ideas and they have all these insights and they're scattered all over the place. And so with this kind of AI powered knowledge management or enterprise search, you can put in a query, a question, a prompt, it will write, generate an answer drawing from all of those different sources. And then below that answer it will provide the actual sources of where it actually pulled that information from. And then you can click in and you can go to those specific files or conversations and dig in deeper. So that's how they kind of all work at a high level. And to your point, whenever something's buried in an email or a wiki or whatever, this brings it all together and makes it more accessible.

A (24:04)

And a little tip for those of you that are small businesses that are on the Google ecosystem, with Gemini, you can tag in Drive and Asana if you're using Asana, which I used both of them and I tagged them both in and I asked it to help me find everything under this certain topic and it did and it hot linked to it. Right. So there are some pretty sophisticated things that you can already do out of the box if you know that there's like something in Asana or in Google Drive or Email somewhere on this thing, but you just can't find it. Some of these cool things that are coming from Google are pretty impressive and I imagine the other AI models would do this as well. Okay, so we've spent some time talking about the first part of the process of customizing AI for you and your business. So that you get something that's not mediocre. And we've been talking about capturing and collecting information. Now there's more. What's next?

B (24:54)

Yeah, so now you want to make all that information work for you. And so you want to do that by building custom AI assistants around different common tasks or common roles within your company. So these are your custom GPTs, your gems. Guru has not only an enterprise search function, but also you can create knowledge agents. So there's a lot of different ways that you can go with this.

A (25:16)

Talk to us about Guru. Cause we've never talked about that on the show, I don't think.

B (25:19)

Sure. Yeah, Guru is very cool. Like I said, it has an enterprise search function so you can ask questions, it'll query across all these different platforms and provide an answer for you with its sources. You can also create knowledge agents which kind of act like custom GPTs or gems. They have a specific function, maybe specific behavior might be trained on a specific knowledge base. So for example, you might have like an HR buddy or a customer support assistant, all these different functions within your business, but then you can manage all of them from the AI agent center. So as your team is interacting with all these different agents for these different specific use cases, all of those conversations, the questions that they're being asked and the answer that was generated gets put into a single view. And then verifiers, which might be subject matter experts, it might be admins, can go in and actually review, edit and verify those responses. So basically this creates a self improving system that gets smarter the more your team uses it, because it can go in, someone can go in and say, okay, that answer was good, but it could be a little bit better. And the next time somebody asks a similar question, your system is basically getting smarter. So there's kind of a verification layer that's built right into, which is really cool.

A (26:39)

This is interesting because I'm looking at their website while we're talking. Get guru.com and it looks like it starts at $25 per person per month, but it looks like it integrates with just about everything. So what I like about what I think I'm hearing is that it's going to learn as more information comes into these various sources. Right. That is really intriguing. Is that what I'm hearing you say?

B (27:00)

Yeah, it's basically it's going to improve those answers. It can create these knowledge cards that might be built around specific pieces of information in your company, projects, products, services, whatever. And you can also set up recurring verification schedules. So let's Say you have a big, you know, your go to products or a big project that your team is working on. You can set up a verification schedule for three months or six months. And that assigned verifier, which again is the person who knows the most about that thing, will get a notification to come in and double check that that information is still correct. Because that's going to really be the next thing that companies are going to need to grapple with, is making sure that all that, that knowledge is current and up to date. Because if it's not, your team is going to quickly lose trust in any AI generated responses. So I love that that's like built in as a product feature.

A (27:52)

Yeah, that's a really big thing that we got to pause on for a second because so many of us are creating cloud projects or custom GPTs or Google gems. Right. And we're uploading data sources to them, right. Generally as PDFs or something along those lines. And things change and when they change, we don't think to update the data source. Right. And this is like one of the fundamental challenges that I think we're facing right now. Now I do know, at least with gems, I know that they're dynamic. I know when you attach like a sheet or a doc and you update the sheet or the dock, it updates dynamically. And I think I've been told that it's also true with Claude. But this is a really good point. Like, if you're going to customize this, you gotta make sure your data sources are accurate because this is the same thing with humans. If they're operating off an old script and something's changed, then you're gonna have nothing but problems. Do you think this is gonna be a big part of the future where it's gonna be like data management is gonna be huge?

B (28:51)

Yeah, I definitely think so. To your point, that's one of the reasons that gems are great is because the attached knowledge can be a Google Doc, Google Sheet. So it's dynamic. You don't have this static file that then you have to remember to update. So yes, that is a great reason to use a gem because it takes away some of that concern. But part of it is going to be you're going to need to have to create a single source of truth for your data, for your information. I think, you know, you need to have those verification processes and protocols in place. You have to start thinking about, yeah, how often do we need to check in on this information and make sure it's updated? Who is responsible for updating these things similar to how we're building processes around things like prompt libraries. It's like who is allowed to manage this stuff? And I actually think there's going to be. There's some tools that are coming out called memory Ops, which is this idea of this kind of corporate memory layer.

A (29:41)

Was that memory ops or memory ops? I just wanted to clarify that Ops.

B (29:44)

Like ops like a memory operating system. Because I mean, you're going to need to have someone or some platform to manage all of this. Another big thing is with the agencies. I've had agencies ask me about managing their saved memories and their personalization across different clients. You have to kind of be able to silo that information off because as an agency, you know, you're context switching between different businesses and industries. So how you manage all of that I think is going to be a huge. We're not there maybe yet in a lot of businesses, but it's certainly something to be starting to think about.

A (30:17)

When we were prepping for this, you mentioned notebook. I would love to hear what your thoughts are on that particular tool and maybe how someone would use that.

B (30:25)

Yeah, for sure. That is another one that's kind of in the understand category. It can really aggregate and help you make sense of all of your data. So for example, example, I had a client who runs an interior design firm, high end interior design. And so she had created a ton of great resources for her business like we talked about. She had standard operating procedures, she had checklists for her teams, she had samples of great email templates, how she wanted them to communicate with these high end clients. But she was struggling to get her team to actually adopt and use all of these resources. So we threw all of them into NotebookLM, which if people are unfamiliar, it is put out by Google. And it kind of went viral because of the podcast style interviews. But the real value is it grounds all of its answers in the sources you provide. So similar to that kind of knowledge management that I talked about earlier. But instead of pulling across all of these systems, you are assigning specific sources to a notebook and the answers are going to be grounded in those specific files that you have designated. So in this case with this interior design firm, now she has this interactive hub that her team can now use for just general questions, self servicing things that they would maybe come to her for a lot of questions. What is our policy on this? And also for client communication to standardize it so they can go in and say, what is our return policy? How do we handle installation day? And it's going to again provide that answer. And not only will it provide the source, but you can actually hover over it and see the exact excerpt that it pulled it from and again confirm that the information's correct. And then you can take it even further and you can actually start doing things. So you could just simply copy and paste a client email in there. Let's say somebody asked about the timeline for a new build. Paste it in there, reply to this email. The NotebookLM notebook will look at their standard operating procedures, how they handle new builds. It will look at those email templates, how they tend to like to communicate with clients, the tone, and it would just write the email for you. So it's another way to take things a step further, make it reduce that friction that I think can kind of happen with adopting AI. You have to go to these different platforms and it's like multiple steps. This is all in one place. It's so simple. And again, you can trust that the information it's pulling is specific to your business.

A (32:49)

I love this. First of all, I use Gemini, I use Claude, And I use ChatGPT. I use them all. And I don't use Notebook LM as much as others do. And in my head, I'm trying to rationalize what's the difference between taking this exact same set of data and putting it into a custom GPT or a cloud project or a Google gem versus putting it into Notebook lm. Like, why Notebook LLM? I think that's the part that I would love to just explore a little bit.

B (33:16)

Yeah, no, that's. That's a great question. I think part of it is, like we were saying, the context windows. So you can upload. I don't remember offhand how many sources you can upload to custom GPTs or gems, but there's a limit.

A (33:26)

It's like 40 or something like that.

B (33:28)

Yeah. Notebook LM, it's 300. And again, this is. It's built on Gemini. It's powered by Gemini, which has that huge context window of like a million tokens. So it can handle a lot more files and a lot more information than maybe a custom GPT or a gem. You can also easily spin off. I mean, there's a lot of features they're rolling out with it where you can, like, spin off FAQs, you can save pieces of information. So I like to think of it when you want more of a resource hub. And you're also starting to see more source grounding in custom GPTs or gems. And by that I mean within the answer There's a footnote that shows you where it came from. With NotebookLM, that's always there. It is not drafting an idea of a sales email based on its training data. It is specifically pulling from your sources and your sources only. And if you actually don't have something in your attached sources, it will say so. So I think that's part of it too.

A (34:25)

I like that. Right. Because it's not going to make something up, right. Where, where all the others are going to try to just fulfill the role and responsibility. But with Notebook, you just give it data and then you query the data. That's the difference. Right. You're not giving it a role necessarily in a gem. Am I right? Or I mean, I mean in a notebook. In a notebook. I'm sorry, I'm getting these things mixed up.

B (34:43)

Yeah, there's some personalization they're adding now within terms of the type of response you want. So do you want a more analytical response? Do you want something more casual? Things like that. But yeah, you're not shaping custom instructions in the same way as like a custom GPT or a gem. It is more about specifically pulling from the files and the sources that you have used. The, the other use is it's, it's a. No, it's a research tool and it's a learning platform. So that's the other thing is if you want to learn, that's why it has all these features of. These are the sources that I'm specifically focused on and then these are the outputs based on those sources alone.

A (35:20)

The other cool thing, especially if you have a workspace account, is that it can be locked down and it can be shared with your staff. Right. So this means that for example, if you had a customer service department, right. You could put all of the information in there about all the use cases and then this thing could just be there at the computer. And they, anybody who, even if they're not in customer service, could go to this and get the answers to the questions that they want. Right. I mean, is that effectively what your client was doing here? They were allowing people to either get trained up on stuff if they're a new employee or to just get access to like this insight and knowledge about the standard operating procedures for their internal design firm.

B (35:58)

Yeah, absolutely. Yeah. So it was used for. Yeah, internal training of new staff to make sure that they could get those resources, self servicing questions, common questions, common FAQs. And then, yeah, as far as customer service, client support, client communications, it just made it that much easier to rely on these chosen templates they have and then also quickly reference answers to things.

A (36:24)

Where do bots fit into all this? Do you understand what I mean by bots? I'm talking about like these little things you put on your website that might be could query these very same things because I don't know what your thoughts are like if there's internal bots that people are creating that are just like asking a question kind of thing. Just curious what your insights are on using bots for these kinds of things.

B (36:44)

Yeah, absolutely. Because yeah, you could certainly take this information and turn it outward. So yeah, I would use like a notebook LM or like a guru knowledge agent for that internal. So that is I think one thing for people to think about with customer service and support is you can also just empower your team to respond to maybe a live chat. And so you actually have a person checking and validating and responding. But they can be made so much more efficient with these internal facing tools. But then Kobeo I mentioned earlier is more of an enterprise grade knowledge management and enterprise search platform. It encrypts data, it's HIPAA compliant, all of that. But it also has capabilities for what you're talking about like an external chat. And so that's exactly the point is you can take all this knowledge and use it as a tool to empower your internal users and your staff, but you can also put it on your website and allow people to ask questions on a product page or self service and answer to a support question and things like that.

A (37:44)

Yeah, or maybe even like get access to virtual experts inside of a paid community or something like that. Right. Where there's insights and wisdom and you could ask the virtual Paul Racer, you know, if the real Paul Racer isn't available. Right. So okay, we've talked about capture and collect, we've talked about out understand and there's one more step to this process. Right.

B (38:02)

Well so now yeah, we've captured everything, we've collected it, we've made it more accessible, we've got these AI assistants. Now there's of course the challenge for your to do list. You still have work to get done and so we actually have to do and create with all of these things. There's a tons of options here. It really depends on what you're trying to do and what you're trying to create. This again is where those frontier models are great all purpose tools. You can use custom GPTs and gems for things to scale your own expertise to your point to write content. I use them with creation of things like newsletter writing or LinkedIn content and things like that. And then there are also specialty tools out there too. So Grammarly, we all know Grammarly, it checks spelling and grammar, but it also has inline tone and style validation. So Grammarly kind of follows you across whatever platform you're using. And as you're writing, it can give you little prompts to make sure that you are writing in the style of your brand. It also has a knowledge share, so if you reference a product, a project, an acronym, you'll actually be able to hover over it, get a little summary about it and see some sources where you can click to learn more. So it's another way to make all of that knowledge you've collected more actionable and actually use it when you're producing work. Then of course there's the side of automation and workflows. So things like writer can power sophisticated multi step workflows for creating content. There's now Google Workspace Studio which just came out.

A (39:37)

Yes, I'm very excited about this.

B (39:38)

Yeah, yeah. So Google had released Opal previously as a experimental tool to create custom AI mini apps. So with that you could describe a workflow and it could actually create an automated workflow for you. So instead of writing social media copy every time you publish a new blog post, you could simply describe that workflow and it would go ahead and build the if this, then that step by step workflow for you and then you could share that. So Google Workspace Studio is brand new. Haven't been able to test it out yet. It seems like it's at capacity, but it seems like a very similar sort of capability where you can build these.

A (40:14)

Really we're using it internally and I can tell you that it's pretty freaking cool.

B (40:18)

Is it good?

A (40:18)

Yeah, it's really good because I think it's a little different than Opal. I don't know if they'll completely get rid of Opal, but basically it's a very simple and I wrote about this on the social platforms. What's really cool about it is like you can just start with a generic prompt and it will try to kind of build the workflow for you just like Opal will. But if you use any Google stuff, it's powerful. But it also works with Salesforce and Asana, which is really good. So I think you're going to see a lot more integrations. But I think some of the ninja things that it does that a lot of people don't understand is it integrates with chat, it integrates with Google Meet it Integrates with email, obviously, docs and sheets. So if you, for a lot of people, if you have a sheet like we do for this podcast episode, we're creating a workflow that is basically going to replace our make.com automations where when someone adds a new column to a sheet where it says Taylor Rady and then it says like the episode number, go ahead and create the folder, create the subfolders, monitor the folders. When the new files are dropped in there, trigger the emails. I mean, all this kind of stuff is now possible with Google Workspace Studio. And it's exciting because everybody can create their own. We don't really know yet if they're totally shareable. That part is like so new because literally we're talking mere days since it launched. And I know Paul recently recorded a podcast where he couldn't get access to it because everybody's using it. I think it's extremely exciting because especially for small business, which is kind of my core audience, we kind of take for granted all these darn tools that Google has access to. And if you can manager it, like imagine if you get a ping in chat when something's ready. I mean that's cool. You know what I mean? You just start to think about and I'm sure Slack integration and all this stuff is coming. But yeah, it's very exciting. I mean, I don't know what's your take on where you think it could go?

B (42:09)

Yeah, I absolutely think to your point, the way it's overlaid with workspace is definitely the most exciting part of it. And yeah, that is something worth saying. You know, maybe you've said this before previously, but if you use Google Workspace, Gemini is built in. It is not an add on product. There's so much of Gemini and of Google's AI that is getting added to Google Workspace business plans. So Gemini and Google Workspace across all of the tools across Google Drives, Notebook LM is included. You might need to just check and see if you need to maybe go into admin panel and turn that on. But that is included. And again, so it is protected, managed, shared. The data is protected in the same way as all of your other data within Google. So that is definitely something to think about in terms of privacy, which we haven't talked about, but you know, protecting all this information and now that they're adding more of this workflow and automation to it, I think that's fantastic because again, you're already trusting Google with your business data and it's already tied into all of these tools and so that's just going to make it so much more seamless.

A (43:09)

Taylor, this has been absolutely fascinating. If people want to connect with you on the socials, do you have a preferred platform? And if they want to check out your newsletter and or the stuff you're doing at a marketing AI institute, where do you want to send them?

B (43:22)

Yeah, absolutely. So yeah, you can find me on LinkedIn. Taylor Rady, you can find me SmartRx AI to find out what more about the AI Academy and the Mastery Membership. So I'll be creating courses and deep dives and things for both of those. And then for my newsletter you can go to taylorrady.com SME and I will have my newsletter and some other resources and things there as well for you to subscribe and to learn more.

A (43:48)

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

B (43:51)

Yeah, thank you so much for having me.

A (43:52)

Hey, if you missed anything, we took all the notes for you over@socialmediaexaminer.com A88 be sure to follow this show on your favorite podcasting app and if you've been a listener for a little while, we would love a review. Also, let your friends know about this show. You can tag me on Facebook, LinkedIn and or X. 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 time week. I hope you make the best out of your day and may AI help you become more successful.

B (44:25)

The AI Explored podcast is a production of Social Media Examiner.

A (44:33)

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