How to Turn What You Know Into AI Tools People Will Pay For
47m 27s
The podcast explores how experts can leverage AI to productize their knowledge and scale their businesses. Host Michael Stelzner interviews Kelly Sinclair, an AI strategist, who shares her journey from early skepticism about AI to building tools that extend her expertise. She emphasizes that AI doesn’t devalue human insight—it amplifies it. A key concept is "expert-backed AI," where an expert’s unique experience is embedded into AI tools to provide personalized, actionable guidance. This approach solves common client pain points like lack of momentum, low confidence, and implementation gaps, dramatically improving results and completion rates. Kelly outlines a four-step framework to identify opportunities—repetition, implementation gaps, skip zones, and confidence gaps—before structuring AI tools using the IPO model (input, process, output). She highlights that tools like custom GPTs and cloud skills offer different trade-offs, with cloud skills enabling multi-agent orchestration, portability, and updates, while full product development (like her platform Wave) offers deeper control and scalability. The conversation concludes with a call to action: listeners are encouraged to take a free AI level quiz at socialmediaexaminer.com/aiquiz26 to identify their current AI usage level and receive a personalized improvement plan. The core takeaway is that AI’s true value lies not in replacing human expertise, but in enabling it to scale, deliver results, and create more engaging, sustainable offerings for clients.
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Welcome to the AI Explored Podcast, helping you put AI to work.
And now, here's your host, Michael Stelzner.
Hello, hello, hello, thank you so much for joining me for the AI Explored Podcast.
I'm your host, Michael Stelzner and this is the podcast for marketers, creators and business
owners who want to know how to put AI to work.
Today, we'll explore how to productize your expertise with AI.
My special guest is an AI strategist who helps entrepreneurs leverage their expertise
and AI to grow their businesses.
Her podcast is the entrepreneur school in the AI era.
She's co-founder of W, AIV by Gravia Studio, Kelly Sinclair.
Welcome to the show.
How are you doing today?
So great.
So excited to chat with you, Michael.
Super excited to chat with you as well.
I'd love to hear a little bit about your journey.
How did you get into AI?
So I feel like this is a hilarious turn of events because I am not the typical started
with chat GPT on day one kind of guests that you often have on this show.
For me, I have a background in communications and public relations and I actually the first
time I tried chat GPT, I was like, wow, this is fast, but it's kind of garbage, right?
So as far as the output, I was not very impressed.
But once I learned how to actually train it with brand context, give it what it needs to
know about me in order to produce good results like, you know, who your audience is, how
you like to speak, your style, those kinds of things, then I saw the opportunity that
I could actually use generative AI to help my clients with implementation.
So I was working as a brand and marketing strategist, helping entrepreneurs to get visible, figure
out how to get in front of the right audiences and reach their ideal clients.
And we would build these strategies and then they would just not do anything with them.
So I saw the opportunity to bring Gen AI into the game and actually build tools that would
help them take action.
And so this was a game changer in terms of getting results for my clients and it was a way
that I could help them to just improve and deliver on the strategies that we were creating
together.
I mean, I know since chat GPT came out, we're coming up on four years now.
So just out of curiosity, when did you start integrating these AI services into your client
offerings?
Early 2024, probably.
So yeah, more of a late adopter myself, I'm going to still early, I mean, I ended up
getting to be like a top 1% user of chat GPT once I started diving into it because I was
like, Oh, okay, this has so much potential, especially when we think about how our clients
can actually access and implement and do these things.
So, but then I started running up against some of these hurdles that I know we're going
to talk about later on delivery and how to actually get these tools to clients, which
sent me down this other rabbit trail of having conversations with the local software developer
who long story short is now my business partner.
And I am now an accidental tech co-founder.
And we work with entrepreneurs on productizing their expertise with AI.
I love it.
I love it.
I love it.
Okay, cool.
So such a great journey and so many people I think can relate to this because I mean,
we're going to look back in the future and say we're still really early.
I mean, even people listening to our voices right now, folks, it is really, really early.
Even though the whole world is talking about AI, it is very early days.
I mean, it's kind of like social media in year three and a half, you know what I mean?
Like the whole world is going to change and a pretty dramatic way.
And it's not too late.
And that's why I'm really excited to get you on the show.
All right.
When it comes to actually this concept that we're talking about, which is to somehow take
your insights and knowledge that you have as an expert and leverage AI and some kind of
capacity, what are some of the misconceptions or false beliefs people have when it comes
to leveraging AI in this kind of way?
So Michael, I think that one of the biggest misconceptions is that if I take my expertise
and put it into AI and make a product, that somehow devalues me as the expert.
I think that we can really unpack this way of looking at it because I really believe
it's actually the opposite.
I am a firm believer in human first AI adoption and that we should be incorporating
AI into what we're doing so that we get to extend ourselves and our experience as experts
and really leverage that to allow us to do more of what only the humans can do.
And I'm with you on this for sure.
I've evolved my thinking on this as well in the very early days.
I'm like guard my intellectual capital, my IP, my whatever the heck you call it, my thoughts
like guard a guard a guard it.
But then once I began to understand how AI can be a massive enabler that allows my many
decades of experience, in my particular case, being an entrepreneur and also being someone
who talks a lot about marketing to leverage that in a pretty powerful way, it's an unlock.
So when we actually are able to properly do what we're about to do today, right?
When if people pay real close attention to what we're going to talk about today, what's
the upside?
What are the benefits that are waiting for them?
So two things I really want to talk about here, maybe first we should contextualize this
in the concept of like how things are changing because AI has come into the scene.
So I think for anybody who is a consultant or coach or service provider in any kind of
way, if you're in the knowledge business, as they say, and you teach other people how
to do what you do or you use your brain to help other people, then the game is shifting.
The expectations are changing.
And what we have now is the opportunity of what I like to call expert backed AI.
And I think it's important to differentiate that from generic AI.
So if somebody says, oh, I can just go use AI to do whatever it is you teach me how to
do, the answer is sure, but you don't have the ability to like validate if the results
are any good.
You don't know if that's actually producing something beneficial for the user versus when
you as the expert who has, let's be honest, years, if not decades of experience, of wins,
of losses, of failures, of knowing exactly how things work, what your frameworks are, that
is like hard one expertise.
And that can be embedded into AI tools that actually allow your clients to use that thinking.
So digital courses are like teaching people how to think, but AI tools are allowing people
to use your thinking.
And that's where implementation comes in.
And so this expert backed AI is really the opportunity and how things I'm seeing are
going.
I know there's a second thing that you want to say here, but I want to double down on
this a little bit as you were speaking, I was thinking to myself, okay, anyone can pick
up a camera and take a picture.
But it's people that have been extensively trained on how to frame a picture, how to
get the lighting, how to get the composition, how to get the subject to do the things that
you want the subject to do.
So you could say that the camera is effectively like AI, it's a tool, right?
But when you bring all that unique background and expertise to the table, you're able to
take this very powerful tool and do something with it that others do not know how to do with
it.
I just wanted to throw that over the fence because I thought that was a really good analogy
because I could go up against the best photographers in the world quote unquote, right?
But I'm not going to know what they know, right?
I just have access to the same tools that they have, but what I have is experience and knowledge
and insights that are allowing me to use the tool in a powerful way.
That's why I really like that analogy that you're talking about this expert backed insights,
right?
Taking what you, what you've developed in your own brain and from your own learned experiences
and applying it to AI allows you to do things.
No one else can do.
Oh, I love that analogy.
I'm going to pocket that and use that one because it makes total sense to me.
Absolutely.
Okay.
So the question was, how, what's the upside, what are the benefits here, right?
And you set this groundwork on this expert backed AI concept.
What are some of the benefits?
I know we're going to get into how we do this in just a minute, but I just want to plant
the carrot in front of the horse if you will for us being the horse like, what is waiting
for us on the other side when we get this right?
Big analogy guy, Michael.
I love it.
They're just coming out today, I don't know.
What else can we come up with while we're on this podcast?
So I want to say that like, so we're talking about human first AI adoption.
We're talking about expert backed AI and what does that allow you as the expert to do?
Well, what our clients are finding is it's actually allowing you to go deeper with your
clients to actually leverage and and be more in the expertise that you have.
We're having more nuanced strategy conversation because your clients aren't coming to you with
that.
How do I get started?
They're coming to you much further along where you're able to really apply that outcome
and give them better results.
And I think the other thing here too is to consider the fact that, you know, with digital
courses as an example, I did some research into this and think if it had put out a study
in 2025 where they talk about completion rate of digital courses, right, and it's abysmal
to be honest. 10 to 20% of people will actually finish a course like hands up if you have
a digital course graveyard. That is me too.
Or our hands up if you bought a course and never finished it.
Yeah, exactly. Yeah, never finished it. Maybe didn't even start it. It just felt good
because you bought it. You're like, I have this information now. That's great.
So the completion rate thing is that's not what you're here for. As an expert, you want to
help people. You want them to get results. You want them to have the opportunity to apply
what you know so that they can, you know, you can make an impact in the world. That's really what
a lot of the people that are I'm sure listening to this show and that I circulate with want to do.
And that completion rate when you add AI tools into it, it's sky rockets to 70 to 80%.
So huge improvement in terms of how your clients are actually getting results and that whole ability
of them to use your thinking is a big part of that too. Okay, so a couple of things I want to
clarify just real quick here. We're talking about how basically to get AI to effectively monetize
your expertise. Is that really what we're talking about? Because I want to make sure everybody kind
of knows the direction where we're going with this. Like if that's the case, are we talking about
using AI to effectively create tools that we're going to sell to our clients? Because I think that's
the part. Maybe that we're missing here that if that's part of it. Yeah, absolutely. And that's
why I kind of said the word product ties to and so it almost like giving you the idea of okay,
so if I have a digital course or if I was to teach my expertise through a digital course,
that's what that pathway would look like. But now that we have AI on the scene, we have to think
about how can we actually add AI into our offers. And so there's a hybrid approach where you're
building AI tools that your clients could potentially subscribe to or use. And then potentially,
you're also adding layers of coaching or office hours or things like that to bring the human
component to the table because I think really both are important. And again, we're not just fully
outsourcing to AI. Okay, so I just want to explore this a little bit before we get into this too
deep so people can wrap their heads around this. We all have expertise that we have developed over
many, many, especially those of us that are gray hairs that have been around for a while.
And that expertise might be something that we sell in the form of a course or in the form of a
membership or in the form of consulting strategy sessions, all that kind of stuff. And what I'm
hearing you say is that if you can properly somehow use AI to create some sort of added value
service or software or whatever, right? Something that will allow you to provide even more value
to the people that you're serving and also free your time for the higher order things.
This is where you can actually scale a business. Is that kind of what I'm hearing you say?
Yeah, absolutely. Like I call this bot squads and we're going to talk about like how to actually
build these and what different opportunities there are with the technology that exists right now.
But I mean, I want to like boldly say bot squads are the digital course circa 2026.
Like this is where we're going. This is what people are expecting. Your clients are expecting
to be able to do things with the ease of AI. So this is the opportunity for your monetization to
blend this together and create tools that they can use. And we'll get into exactly how to do that.
Folks that don't know this. I mean, I talked to at least over a hundred different people on
the two different shows that I do. And there's a big trend going on right now where people are
moving away from courses because that knowledge is becoming commoditized, right? And AI is sucking up
all that information. But they're moving towards this new model, which is to have some sort of a
software product that provides super instant access to insights that were uniquely created by
folks like Kelly or me or others that is a reason for them to keep on a subscription model.
Right. So there's a movement towards this concept of a monthly subscription or a membership
for like a better words that doesn't just include access to knowledge and access to live people.
But it also includes access to tools that were specifically designed to solve whatever the problem
is that you ultimately solve. And what's cool about this concept is this is something that's sticky.
What I mean is that a lot of people now are going to keep memberships because they want to lose
access to these tools. Would you agree with that? Oh, absolutely. And you nailed it with the
AI has commoditized knowledge. So it's not the knowledge itself that's valuable anymore. It is
actually your expertise, your lens and the way that you tackle whatever it is that you do. And
that's the thing to figure out how to package that up. Perfect. Okay. So now we're going to transition
into like how to do this. Let's start with the basics. Where do we start? So the first step is
identifying the opportunities within your frameworks and your processes. So I will do a little bit
of a teaser that I'm going to share at the end. A tool that I built to actually help you do this
called the AI tool launch playbook. But I'm going to give you some questions that you can consider
that you can throw into your favorite AI LLM to have a conversation to try and extract this.
Apparently you just need to also talk to Michael because this is a big framework guy. You can pull
this together. I was never really an expert at this, but identifying the pieces of the puzzle is
this the first step. So question number one is around repetition. So where are your clients asking
the same questions over and over that you could create a tool that would help them with that
piece of what it is that you do. The second question is implementation gap. So this is like what I
identified before with my visibility strategies. I saw them not taking action on the visibility
and then I was able to build a tool that would help them with momentum and actually understanding
what they were doing and how that was working and what the ROI was on that. So where do your clients
regularly get stuck that you could provide something that would help them get over that hurdle?
Number three is there a skip zone. So what is the thing that your clients like I don't want to do
that like I'm just not gonna and you know that it's a really important part of your process that
they have to take those actions in order to get the results that you're helping them to achieve.
So what does that skip zone look like and what would really support them and even just sometimes
it's about like getting to a first draft of something where they can't get over that blank
page syndrome that they're having right and they need to take action there. And then the last
one is around a confidence gap. And so this is like mindset to where do they get stuck? Where
are they you know getting all in their head that you could provide some guidance and support
that would make them feel more confident to move forward and take action on the framework that you
have. Okay so let's explore these a little bit. We talked about four things here. First of all
these are all things that anyone who's listening could explore as possible starting places to
potentially develop something with AI. You said repetition. What are the things that people keep
asking you over and over and over again right. And then you said implementation gap whenever you
work with someone or someone works with you. What's the area where they maybe it's a momentum issue.
I always call it a fly will affect where sometimes getting started is the hardest thing right. So
some sort of thing that could help them get off the mark to get that momentum as you refer to it.
The skip zone I really like this. There are always going to be things that some people just don't
want to do because maybe they don't they don't know how to do it right and it's like but you know
how to do it and when you teach it you presume they know how to do it but they don't know how to do
it so maybe you could develop something that helps those that don't know how to do it to get over
the next hump. And then this whole confidence gap this mindset concept right which is this belief
system that we have about ourselves that may or may not be accurate. So these are all kind of
places to kind of use the magnifying glass. Is that what I'm here and you say and look into our
experiences and say which one of these things resonates with me the most. Any tips on how to
know where to start here I mean because these are four different things we don't want to tackle them
all presumably in the beginning right. Yeah you could create AI tools for any and we're all of these
pieces honestly but I think what you said there makes me want to remind everyone that when you're
the expert in something you think everybody else understands how to do it and that they also like
oh this is in my brain I forget what it's like to not know how to do this or not understand
where I'm at and that is the opportunity to reflect on for yourself and realize that oh no this
isn't easy for other people and something that's really interesting about some of the clients
that we have right now is what they're doing from a pattern perspective is I feel like they are
creating tools that help their clients perceive these things to be a heavy lift they think that it's
hard to do the thing the expert is teaching them and that is what's keeping them from doing it
altogether and now we're able to present well there's AI involved in this so actually it reduces
that lift it is not as much of an objection to get started anymore because this has been brought
into the process so tell us about how you first did this because I know that there's a story there
what my first AI tool was yeah just so people can wrap their head around an example of how this
might have worked for you sure and I have a couple really great client examples that it can explain
as well but for me the first one I created her name was Valerie the visibility auditor and so what
I noticed is that I could tell my clients you know the best thing for you to do is get on podcasts
or go to networking events or do collaborations with other people and they would still
be like, well, I got a post on social media every day. And so I trained this bot with a framework
that would help to identify what are you doing? Like, let me know what you did this week. Let's go
through that together and kind of grade it on ROI. So if you spent all week, you know, prepping
for this one big huge podcast, hello, it's me, then that is a good use of your time. Good ROI can
come from this versus I spent all week trying to put together one Instagram reel. And I just posted
it, had no engagement, that kind of thing. So my tool was actually evaluating that, helping them to
get momentum and redirect them into how to continue to apply the strategies that we built together.
Okay, perfect. I love it. So at this stage, we're auditing for lack of better words, our processes
to try to find something that we could have AI do for us and maybe even better and faster than
we can do for ourselves. Is there anything else we need to talk about here before we move on to
the next step? Well, I think we can talk about the framework of how to actually structure the tool.
And for me, that's what I call it, the IPO framework. So that is input, process, output.
So what's really interesting here, if you think about it like at a super high level, is that you
can create a user agnostic tool that can still produce user specific customized outputs.
So that's when you break it into those three steps that you can understand that it's the input,
that changes the output. And then the process stays the same for every single one. So that's
where the user agnostic piece comes in. Just explain when you mean input and output and process.
Are you talking about like the person using some sort of tool, their inputting information,
and then there's a process happening and then the output that comes out of it, is that what you mean
by that? Yeah, absolutely. So the input is what does the user have to bring to this conversation,
to this tool in order to get the output that you are trying to help them get. So I'll give you
a specific example just in a second, but maybe we should go through like what would be included
in the process and what kind of output you might want it to have. So for the process, you want to
identify the goal. What is its job? And again, keeping that specific. So like, just like, want to give
a moment to chat GPT and custom GPT's because a lot of people are going custom GPT's are dead. Well,
I don't believe that the concept of a GPT is dead like having a specific goal for a tool helps
it really stay in its lane and do a really good job. So you have that clarity around what its
goal is, then you need to give it instructions on what you want it to do and you need to give it
resources. So what is all the training that it needs to do its job properly? This might look like
transcripts from coaching calls, your all of your content inside of your courses or your modules,
whatever templates or frameworks or worksheets or examples that you have that would help get
the output again. What is it that we're trying to achieve with this tool? What is needed in order
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That's socialmediaexaminer.com/aiquiz26. Okay, so at this point, we have thought about like
some of the structure that you just talked about and we've hopefully identified some sort of
area where I want to develop something. What's the next step? So maybe it'll help just to give
some examples and thinking about this actually. So I want to talk about two that are clients
of mine and they're doing very different things. So I like to show how it can be so different,
but the same process like framework is applied. So one person Nicole, she is a PR coach and a
journalist and her clients want to get media, but they find it very hard to do that. They think,
okay, I've got to do all this research, I've got to write pitches, all this sort of thing.
So she built a bot squad that actually does the intake process. So it asks a bunch of questions
of the user and then it creates a customized media messaging document. So that essentially
becomes the input that is needed for the next bots which do podcast research. So that will apply
the user's information in order to select appropriate podcasts, not just like the top podcasts in
the world, ones that are a fit for the type of business, the type of things that the user actually
talks about. And then it will go to the next bot, which does podcast pitch developments.
We'll actually write that pitch in the voice of the user again because that messaging guide
exists and it can create that customized output no matter what kind of user comes to the table.
Very cool. And in this particular case, this is not something that Nicole created for herself
or is it something she created for herself or is it something she created for her client so
they could just autonomously go do it without her. I'm just curious. Both actually, she uses it
for herself to get media because a lot of people who are experts, they're like, here's what I do
and here's how you can do it too, right? So that fits there. And she actually told me she had
reached out to a media, like a TV outlet. So she also has like a TV segment one that does this too.
And the producer was like, this is the best pitch I've ever received, which is incredible because
she's like, that's the part where your ego gets hit a little bit as the x where you're like, oh,
I'm the one who is supposed to be really good at this and the bots are doing such a good job.
And that's great. And the way that she does it with her clients is she actually gives them access
to this tool and they can use it regularly and they get some touch points with her as well to,
you know, review things maybe before sending pitches if they want to just to keep the human
in the loop, which I think is really important. I love it. You said you had a second example.
Yeah. So I also have an example of Michelle and she is a messaging strategist. She says one of
the biggest objections she has in her own sales process is that people are like, oh, messaging,
like that's gonna take months to get that right. I better make sure I don't have any other projects
on the go. I need to be able to like clear the decks and have all this time. And she's like, well,
now we actually have the ability to use AI to help with this. So the first step in her process is
to do voice of customer research. So she has her clients do the research. They bring it to her
bot squad, which she calls Moxie. And Moxie will analyze that and pull out all of the key pieces
and turn it into the types of marketing messaging that her clients can now use and know that is
actually going to be effective, which I think is really cool because she's like, I have a doctorate
in communications. My clients don't want to learn how to do research analysis. They don't want
you and they shouldn't have to. But I can give them a tool that will do that in the way that I'm
trained on it. Love it. Okay. Now there's some people listening that are like, okay, I want to do
that just so I don't have to do that part of my job anymore. These examples that Kelly just
shared here, you could design these and just use them internally to free up your time. But what I
like about what Kelly's recommending here is actually we productize these into an actual thing
that we can make money on, which I think is really cool. So it does beg the next question,
which is, how do we actually build these kind of things? Because it sounds awesome and it sounds
hard. So let's talk a little bit about that because I'm sure people are really excited. Like a
lot of people like have trained up, you know, cloud projects or custom GPTs on their insights,
but they have no idea how to actually turn that into something that they can sell. So let's talk
a little bit about that part of the process because I'm very fascinated about that. Yeah. And this
is where I'm like, oh, if only there was like something really great, which is something that I'm
working on for sure. But I will say like the whole thing about AI is that it's always evolving.
There's always new tools. There's always new capabilities within the tools that we have. So let's
talk about like the maybe three core ones that you might actually use to execute this or at the
very least to create like a proof of concept for yourself using the framework, the IPO framework
that we just talked about. So the first one that would be a custom GPT. So building a custom GPT,
using that framework with the thinking about what the user has to bring in, thinking about the
process that that GPT should run them through. And then thinking about what the output looks like.
So building a custom GPT,
GPT is very easy to do with chat GPT
because it will walk you through exactly how to do it,
just have a conversation with it,
apply the frameworks from this conversation
and you can get a bot out of that.
The thing I don't love about that is that you get one bot
that does one job really well.
And that means that there's no connectivity.
If you have a multi-step process
and you want to bring AI into a lot of different elements
of your process,
and I'm seeing some of our clients,
they're like have 17 different bots
and what they're ending up doing is making a PDF
with a bunch of links that are saying,
go to this one for this and then take the output
and go to this one for that
and do the next thing and follow it along.
So that's one of the issues with chat GPT,
the other one being security when you're actually sharing it.
So right now you can send a link, you can make a public,
you can allow people to use your GPT,
but you don't have the ability to gate that,
turn off access if you're actually wanting
to monetize this kind of more like on a subscription
or something like that.
- Yeah, and before we move on to the next thing,
I do want to say that we've interviewed a lot of people
on the show, this is episode 115, I believe.
And there have been people who have talked about how,
I think they've just taken a link, right?
And they shared it in their members area, right?
And they just kind of say please don't share it, right?
That's kind of the hope.
But the problem is if somebody leaves the membership,
they're still gonna be able to potentially use
this custom GPT.
So I see the problem, this is not gonna be something
that's gonna get someone to necessarily want to stick
with you month after month after month.
And also with custom GPTs, I've heard lots of stories
about sometimes they break, you know what I mean?
Because like the models shift and they change
and all of a sudden like your custom GPT
doesn't work the way you thought it worked.
And I also know that OpenAI was thinking about
building a great marketplace for these kind of things.
But it seems as if they've put their attention elsewhere.
So it used to be like the only option in the early days.
But it seems like now things are changing.
Would that be fair in your professional opinion?
- Yeah, absolutely.
I think you nailed kind of all of the same frustrations
that I was having when I first started as well.
And what I see some of my clients doing,
which is like, you know, they're password protecting
their GPTs.
So this is one kind of hack that you can do.
But then you have to like remember to change the password
and share the password again.
And so just becomes like a management challenge as well.
- Okay, so what are the other options?
- So what we love about CloudSkills.
So move over custom GPTs.
CloudSkills are here and everyone's thinking,
this is the new hot thing and I love it.
I love it because it operates differently.
In terms of it creates what nerdy AI version of me now says,
which is multi agent orchestration.
And that means that a lot of things can be happening
at the same time, that connectivity issue
that you have with custom GPTs being siloed individuals,
changes with a CloudSkill.
That can be multiple steps happening
and accessing information from different places
within somebody's Cloud account.
So you can apply this to a CloudSkill.
You can sell that CloudSkill.
And so lots of people are doing that too.
They're just, here's a product, here's a CloudSkill.
You now take that and you plug it into your own Cloud account.
And you can now use my expertise throughout your own Cloud.
- Okay, that's fascinating.
Let's explore this a little bit.
The benefit of a skill is, and I've learned this mostly
because of all the guests I've had on the show,
as it's portable, which is really cool.
So you could take a skill and Cloud was the first company
to come out with skills.
And then everybody else now seems to support skills.
Chat GPT, Gemini, and many others, everyone is embracing skills.
So they're portable, meaning you can take them,
like if you don't use Cloud in your Gemini house or whatever,
you can take them over.
And they're fundamentally very similar,
which I think is awesome.
So I guess the one advantage to going with skills
is that you're gonna update and change the skills,
which might be a reason why somebody might want to maintain
some sort of a subscription model,
is because as your quote unquote skill evolves, right?
You might have new versions of a skill, kind of like,
you know, on the olden days, you would pay one price
for a piece of software.
Do you remember those days?
Like you buy Photoshop and then it would last you for years
until the new version came out.
So I think we're moving towards an era
where there's gonna be versions of skills.
And if you want to get all the newest versions of the skills,
you're gonna have to subscribe, right?
And there's a lot of people going towards this route
with software, right?
So it's a good model.
We're like, hey, you pay an annual fee,
and you're gonna get all the updates that I make to the skill.
So this is almost rethinking, like in the beginning,
when we start creating skills, it might be very simple,
but we could evolve them and make them
more sophisticated over time.
And therefore, if we keep upgrading skills,
there might be a reason to keep people on a membership.
I'm just free-flown with you a little bit,
but is this where you see it going potentially?
- Yeah, for sure.
And also the capabilities within side,
the quad interface and connectivity,
I would say this is like next level,
where skills can actually pull on other tools.
So if you're a business coach who's teaching people
how to create digital products or something like that with AI,
you might also be like,
you want the tool to pull in your air table database
and do something with your email service provider
and do something else with your video creator.
All of those things can happen.
And as AI evolves, it gives you the opportunity
to continue to upgrade that and evolve it with it.
I will say though, the one thing I don't love about skills
is again, it's like handing over a zip folder
of your expertise.
So this comes the conversation around your IP
and how that really is protected
and wanting to keep that locked down
or deciding that maybe it doesn't matter.
- Yeah, it's really interesting.
I love it.
Okay, cool.
So we've talked about custom GPTs.
We've talked about cloud skills.
What's the next level?
I know we're leveling it up each time, right?
- Leveling that?
Well, I mean, for anybody who has started,
I'm laughing because like this is me, I guess.
But anybody who has started like really going deep into AI
can see how it actually maybe isn't as difficult
as we thought to vibe code something
to create our own platform that could replace other things
that we could build a kind of digital product interface
that's specific to the tools that are built on our own
IP and frameworks.
And that is the next level of thing.
But if you want to start doing that,
which may not seem like a big lift,
the problem becomes what kind of business are you in?
Are do you want to be a software business?
'Cause high, that's what happened to me
because that's what we're doing.
I know it's happened to you too, with no go, Michael.
So is that what you're doing?
Or do you want to be the expert?
So do you want to teach people how to use AI?
Do you want to have AI help them learn what you do?
Or do you want to just like become
an entirely a software company?
'Cause there's a lot of things under the hood
that are a lot more complicated than you may think
to get started with that.
- Well, and this is important, right?
Because folks, the kind of things we're coming back to
with this is access control, right?
And ideally, if we're going to use AI tools
and we want them to just be available to our clients
or our students or our members or whatever you call it,
we have to figure out how to number one,
build these things and number two,
how to control access.
And that is like the next layer,
'cause if for anybody who ever gets in the software,
you start to realize, okay, there's a database involved, right?
And there's access management and all these kind of things
that you've learned and that I've learned as well.
So let's just start with the very basic levels.
How could someone, quote unquote, very easily vibe code,
if you will, some sort of a tool that does some of these things?
What are some of the tools that basically you might recommend?
- Well, some that I've played around with lovable
for sure before I actually ended up vibe coding myself
a family command center to help manage my life
during my daughter's softball seasons and the end of school.
And how do we eat and where do we go?
And because the reason I chose that process was
because it had to integrate with other tools.
Like I wanted it to have access to my calendar
and be able to look at Google Maps to determine
how long it would take me to get wherever in the world
I had to drive for a softball game that night.
So that's one tool that if you basically are just,
actually just like maybe a high level thing.
I think to be good at AI,
you have to be good at two things primarily.
And that is communication and organization.
So if you can communicate and articulate
what it is you're trying to do,
you could talk to any model, any product, any tool
and get it to essentially do what you want
'cause you have that clarity.
And if you have the organization skills,
you're able to see like in steps like this.
And I never used to be kind of a structured thinker
or linear thinker, I don't think.
But since I started using generative AI
that has become so important to be able to help guide a tool
to essentially give it a job description
till it exactly what you want it to do
and what you want it to come out with on the other end.
- So just had a curiosity unlovable, I've never used it,
but does lovable effectively handle the whole darn thing
for you?
Does it help create that thing and host the thing
and access management, all that kind of stuff in one tool?
- Yeah, and it does like security checks
for you things that you don't think.
that you need to do but do need to do. I feel like one of the biggest things that I haven't,
I'm like considering whether I should, you know, make this a tool that I sell also. But one of the
biggest things, if you want multiple people to be able to access your thing, you need to have
what's called multi-tenancy availability. So you need to be able to ensure that one user is going
to log in and all their data stays in their lane versus it's not like getting mixed up with
everybody else who's maybe using your tool. And that's why I have a software developer who does
these kinds of things and builds our platform thinking about these things and I just know that it's
something that I would have never thought about before. There are other things we need to be thinking
about because at this point we talked about custom GPTs and we talked about like the pros and cons
of that. We talked about cloud skills. Cloud skills are amazing when you have complete division over
it but the idea of giving away your cloud skill is scarier for people. And then we've talked about the
next level which is to develop your own product. And I know you have a product that does this for
people and we're going to get into that. But before we get to that, is there anything people
need to be thinking about when they're actually designing something that is intended to have multiple
users and have access just any any kind of things people need to be thinking about other than what
you just mentioned. I mean, I think the other thing would just be testing like that's something we
also don't really think about in AI is what you call non deterministic. So it's different. You're
going to get a different output from a custom GPT from even just even if you just put the exact
same prompt into your cloud twice. You're going to get a different output. So what you think about
when multiple users are coming is they're going to approach this differently. They're going to
have a different input into the process. And the output is going to be different no matter what
I put in the middle to filter that. So we have to do as much like guard railing I guess in that
middle piece of the the P so that the I and the O can be as close as possible and not like wonky
different results. So testing is annoying and important. One other tool that we didn't mention
but I've had plenty of guests on the show talk about is cloud code and cloud code is more than
just a coding tool, but it is the coding tool. There's also open AI codex both those tools are very
good and very easy to use. You just kind of talk to it and we'll create whatever the heck you want.
But the problem with both these tools is it's just going to give you the code. You're going to need
to take it somewhere. And that's where it gets complicated, right? And that's where, for example,
in our case, you know, cloud code is like our tool of choice to develop not go, you know, and it's
ridiculously powerful and almost every major company in the world that has coders are using cloud
code. It's ridiculous. But at the same time, complicated, right? Because you're dealing with
something that feels super, super technical. But like Kelly said, there are people out there
you can partner with, right? Who are technical to take this to the next level? But there are also our
tools that people have built that help with this. And I believe you built such a tool. So maybe
you could just share a little bit about that. Yeah, sure. Thanks for the opportunity, Michael.
All of these things were frustrating to me. And so I was like, well, I started having these
coffee dates with Andrew, who is my now business partner and he's a software developer. And I was
like, I wish that custom GPTs could connect to each other so that you could actually go through a
whole process of multiple steps without having to copy and paste and copy and paste and copy and
paste all over the place. And I wish that you could share them without being worried that someone's
just going to forward that link. And all of your like hard earned energy and efforts are just like
being shared around like a real on Instagram. And he said, well, let's fix it. And I was like, oh,
really? And here we are on this journey. And we now built a platform called Wave, W-A-I-V. And that
actually provides the infrastructure for creators to actually implement this. So build your AI tools,
you're, we call them bot squads, because I think that's cute. And multiple steps can happen in
the same interface. You can have multiple squads. You send your clients one link. You can see the
access that they have. You can deactivate them if they are no longer part of your program. And all
of those things are now kind of being solved with this interface that we created. And the other
thing that's cool about it is that you can use any LLM. So we thought it was important to have
that portability, to have that flexibility when the LLMs change, when new ones launch every
friggin three days at this rate. New ones like things are coming in there. You use the right one
for the job. And it doesn't have to be like all, you know, an opus 4.8. That's like a Ph.D. doing
what's maybe an interns job that you could use like a Claude Haiku for, for example. And don't
worry, I'm saying all these things, but our platform helps you to make these decisions. And you
don't need to understand all of the details behind them either. Awesome. Tell everybody where they can
go check it out and also explain to everybody if they want to connect with you on the socials,
where they could go as well. Yeah. So if you want to start playing with building your own bot squad,
I built a tool called the AI tool launch playbook. And so our company is called Gravia Studio. And we
make a platform called Wave. And I made a special link for your listeners to go and grab that for
free. So it's graviestudio.com/sme. And you can find me on Instagram @kellymakeswaves. And I just
look forward to nerding out more about this. Please send me a DM. I just like want to hear all of the
ideas that have come up for you and explore those and help you build your bot squad.
It was Kelly makes Wave, not waves, right? Or was it waves? Well, it's Wave, W-A-I-B.
Oh, W-A. Okay, that's important. Kelly makes W-A-I-V, which is the name of your software. Kelly,
Sinclair, thank you so much for sharing all your wisdom and hopefully inspiring people to go
ahead and create their own cool things with AI. Thanks so much for having me, Michael.
Hey, if you missed anything, we took all the notes for you over at socialmediaxameter.com/a115.
Be sure to follow this show on your favorite podcasting app. And if you've been a listener for a while,
we would love a review on whatever platform you're listening on. And do check out our other show,
the social media marketing podcast. This brings us to the end of the AI Explored podcast.
I'm your host, Michael Stelzen, and I'll be back with you next week. I hope you make the best out of
your day and may AI help you become more successful. The AI Explored podcast is a production of social media
examiner. Do you want to go deeper in your understanding of AI? You've been listening to this
podcast for a while, but did you know that we have a membership with lots and lots of marketers,
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Podcast Summary
Key Points:
Expert-backed AI leverages a human expert’s deep knowledge and experience to create tools that deliver actionable, personalized results, rather than replacing human expertise.
AI tools increase client engagement and results—especially in areas like implementation, confidence, and overcoming mental blocks—raising completion rates from 10–20% to 70–80% by making expert insights accessible and actionable.
The shift from digital courses to AI-powered subscription models reflects a move toward "productizing expertise," where clients access ongoing, automated tools that reflect a unique, personalized expert lens, making the offering more sticky and scalable.
Summary:
The podcast explores how experts can leverage AI to productize their knowledge and scale their businesses. Host Michael Stelzner interviews Kelly Sinclair, an AI strategist, who shares her journey from early skepticism about AI to building tools that extend her expertise. She emphasizes that AI doesn’t devalue human insight—it amplifies it.
A key concept is "expert-backed AI," where an expert’s unique experience is embedded into AI tools to provide personalized, actionable guidance. This approach solves common client pain points like lack of momentum, low confidence, and implementation gaps, dramatically improving results and completion rates. Kelly outlines a four-step framework to identify opportunities—repetition, implementation gaps, skip zones, and confidence gaps—before structuring AI tools using the IPO model (input, process, output).
She highlights that tools like custom GPTs and cloud skills offer different trade-offs, with cloud skills enabling multi-agent orchestration, portability, and updates, while full product development (like her platform Wave) offers deeper control and scalability. com/aiquiz26 to identify their current AI usage level and receive a personalized improvement plan. The core takeaway is that AI’s true value lies not in replacing human expertise, but in enabling it to scale, deliver results, and create more engaging, sustainable offerings for clients.
FAQs
Expert-backed AI uses your unique knowledge, experience, and frameworks to guide AI tools, ensuring high-quality, relevant outputs. Unlike generic AI, which lacks context and personalization, expert-backed AI embeds your specific expertise to deliver actionable insights tailored to your clients' needs.
AI tools can significantly boost completion rates—from 10–20% in traditional courses to 70–80% with AI support. By automating tasks and providing immediate, personalized feedback, clients feel more confident and motivated to apply what they’ve learned, leading to better real-world results.
Look for patterns in client repetition, implementation gaps (like momentum issues), skip zones (where clients avoid action), and confidence gaps (mindset barriers). These insights help identify where AI can provide the most value by removing friction and increasing engagement.
The IPO framework (Input, Process, Output) defines how an AI tool works: the user inputs data, the tool processes it using your expertise, and delivers a customized output. This structure allows for user-agnostic tools that remain consistent while producing personalized results.
Custom GPTs are siloed, lack connectivity between steps, and offer no access control or subscription model. They also break with model updates and can’t be securely shared, making them unsuitable for monetization or multi-user access.
A Cloud Skill is a portable, multi-agent AI tool that can connect across services and evolve over time. Unlike custom GPTs, it supports updates, versioning, and access control, making it ideal for subscription-based models and long-term client engagement.
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