Blessing Richardson: Find the task that you do enough times a week that you're like, this is so repetitive, and if you can just figure out how to ask ChatGPT, or any AI that you have to help you do it right? It's like, okay, great. That's a good place to start because that will build your confidence in just using ai.
Kristiana Corona: Ever feel like everyone else has leadership figured out and you're just making it up as you go? I've been there. I spent two decades leading design and technology teams at Fortune 500 companies, and for years I looked like I had everything pulled together on the outside, but on the inside I felt burned out, overwhelmed, and unworthy of the title leader.
Then a surprise encounter with executive coaching changed my life and dramatically improved my leadership style and my results. Now I help others make that same shift in their leadership. This podcast is where we do the work, building the mindset, the coaching skills, and the confidence to lead with clarity and authenticity, and to finally feel worthy to lead from the inside out.
Hello and welcome to The Worthy to Lead podcast. I'm Kristiana Corona, your host, and I'm so glad that you decided to join us for today. We have a special guest. This is Blessing Richardson. Welcome to the show. Blessing. Hi everyone. And the topic for today that we're really gonna be diving into is something that I hear leaders talking about all the time, which is.
Hey, my boss said Go use AI and make everything faster and more efficient. And you know, what does that even mean? When you are leading a team, you're stretched thin, you just went through layoffs, you are new to navigating ai, and you just haven't been doing this your whole life. What does it even look like to make that a reality?
And so today, blessing is going to join us and really talk through some of this from her experience. She is an AI strategist, she is a former engineer. She's someone who's very familiar with this struggle, and we're gonna hear a little bit more about her thoughts on this topic. We're gonna hear about what she's making that's really cool, and how you can start using some of this AI capability to your advantage to amplify your creativity and capacity not to replace you.
So with that, I'm going to let us just dive right in and let blessing introduce herself. So thanks for being here.
Blessing Richardson: Thank you. Thank you. I am really excited because I spend most of my days working with non-technical founders, helping them adopt AI in their business. And so to be with an audience that is more on the product side, I feel like my inner nerd really wants to geek and we're gonna, we're gonna try and keep that.
Came a little bit, but I'm really excited. I have worn many hats in the digital world. I was fortunate enough to figure out how to code before I was, like, before I graduated high school. And so by the time I graduated, I'd had a graphic design, web design, picked up photography, did a lot of coding through college.
My first job was at a cloud company, right? And so I've been deep in tech and technical development for quite a while As I've moved into consulting, all of my consulting is in software consulting, right? And so I've just lived in this space and have worked with startups and solopreneurs and enterprise teams, trying to help them really adapt technology into their products.
And I've always found that the UX problem exists everywhere I go. There's always a UX element, even being an engineer when it is building solutions for corporate teams, right? Then internal products, your customer is the company, which creates an interesting tension point, right? And it's kind of hard to get people to sit down and actually figure out what they really need from a UX experience.
Everybody's ready to tell you what doesn't work and what they think is the right solution. But it's really hard to get like, okay, what's the why? What we're doing? Right. When I was in DevOps, it was interesting because everybody goes DevOps, people automate all the things and they're super smart. But then like DevOps presents a different UX problem a lot of times because yes, we're helping engineers, but then we're also trying to present information to business leaders.
So when things go down and there's a fire, right? We don't have to run around. How do you empower leaders who don't live in tech to see this deeply technical thing? And so from every facet I can keep going, there's always been this ux, which I think sometimes is used interchangeably to say this really empathetic side of the technical solutions we're making, right?
There's somebody on the other side, on the other side of that, how do we give them a seat at the table so they can show up and be the best, confident and competent selves without having to be as technically competent? As the people who are making these tools,
Kristiana Corona: I'll just say I feel very seen right now. I love that.
And I feel like you are really a translation layer. Like a lot of what you're doing is saying, okay, I understand how all of these things work and how do I take that and turn it into terms that other people can understand, right. And not forget the core of the problem we're trying to solve. Like what is the core problem?
We're not just making shiny new technology for the point of making technology, right. Like it, it actually has to solve a problem. And I feel like that's actually a great segue into the headlines that we're seeing right now, which is, you know, leaders everywhere are saying, let's go invest in ai and. Big changes are happening across the industry.
I mean, there's so many companies that are laying people off for ai making big bets, Amazon Meta, Microsoft, IBM, and then you have consulting firms, you know, like McKinsey and Bain that are investing millions of dollars in ai. And the results that they're getting are just sort of lackluster. They're not getting the returns yet that they wanted to.
And so there's this sort of friction or difference between, hey, this is a shiny technology that's going to change everything. And then over here it's like, well, we're not really seeing that manifest yet. So I'm curious, just from your perspective and the companies that you've been working with, what are you seeing in the field?
Blessing Richardson: The energy around watching. What looks like a whole class of work kind of get burned down because of ai. It is interesting from this perspective 'cause as much as I help people deploy it, I also build these solutions myself. And I use LLMs every day. And so watching the investments and decisions is pretty interesting.
'cause I'm like, so we all know AI can't replace people Like I know. You know what? I know, especially when I look at the tech companies, right? What I really think we're observing are companies that saw the.com bubble and they realized like, oh yeah, people who didn't think that innovation was going to change things dramatically, we don't really talk about them anymore.
And they know that small companies are nimble and are adaptive. And so what I'm seeing is really a preemptive strike in a lot of ways. We need to become more nimble and we are betting that AI will become. So capable that it will replace people, and I think that's the reality of what we're seeing. It's a bet that a lot of companies are making.
I don't think it's gonna pay off probably in the next five or 10 years. I just don't, because of what I see with LLMs and that what's fundamentally missed a little bit is that LLMs are amazing prediction models. They will only predict really well based off of how much information we give them and the quality of the information we give them and how well they are trained.
And because a lot of these general models are trained on like, I don't know, all the world's capable information, I don't even know how to put it. Like we leaders train them on such large datasets, right? You end up with what I like to call a very capable intern. You can do so much. You are young. Your brain fires really fast.
You are so capable. You got energy. Like, if I need you to be in early, you'll be here and you'll stay late if you, if we need you to. Right. You are so capable, but you are untrained in my business, in my customers. And what is our value, unique value proposition? You are untrained in what we have learned through experience and what has made us mature.
Right. And our expertise, which means that you are capable, but you lack deep experience and context, which means that we can't just let you loose. Right? And these organizations are realized, and I imagine with these failed deployments, that you can't just let AI loose the amount of work you have to put in to end it with something that you can literally say, I trust you.
Like I would somebody I hired after 12 to 18 months. That it, it is an amazing amount of work that goes into it, right? And so that, I think that's where that's coming from. But I think it's people though. Rest assured, a lot of people are course correcting and quietly hiring people back. I think as contractors and as advisors, for all reasons set aside, we're not gonna go there, but these jobs are coming back.
I think slowly the reality is, yeah, a number of jobs will be lost to ai, but not nearly from what I think we're seeing. I don't think that's the reality of the situation at all.
Kristiana Corona: I think that's first very reassuring, but then also begs the question, so if you are someone who sees that it's gonna be the pendulum swing back, right?
Like, okay, there's gonna be this boomerang. It's like people are gonna get really excited, they're gonna go all in, they're gonna get disillusioned, they're gonna come back, they're gonna need someone to steer the ship. Like they're gonna realize, oh, I need the strategy. I need the experience level of the person that was there.
So as you think about that, like what are some of the things that leaders should be thinking about then? Knowing that's the case, like where's it smart to invest and where's it smart to not get too concerned or worried or overindexed because the things that matter most are still coming from you.
Blessing Richardson: You know, I feel like I have some of the most lackluster answers to these types of questions, which is where should you have been automating before LLMs came up? Because at the end of the day, the most successful deployments I'm looking at with AI are the ones where we're taking AI capability and putting them into automated workflows.
A lot of the true agented deployments of AI are really struggling because of the technical limitations around agents just doing what we need them to do without us being able to explain every single thing it needs to do, and then it comprehending that, right? And so I think the better question for somebody who's sitting here and kind of middle management, looking at their team going, what do I do with hiring?
How do I manage my team? Now? How do I lead them? Is asking, gosh, what are the things that we should not be doing? And I look at that and go, the only reason why I'm obsessed in my own business about automating things, like putting tasks in my project management tool and putting files on Google Drive is because Google Drive and task management was foisted upon me.
Like if I did not have these tools in my life, I would not be trying to automate them. So where are things in your day to day where you're like, gosh, this is a tool problem. This is a technology problem in and of itself. If we can get in a room and have a conversation about customers having an issue on the product, there's a feature request that comes in, what is the best way to solve this problem?
That's all stuff that can happen in an analog way. It's the stuff after that. So we now came to some conclusion and understanding because we work in a digital and a lot of times asynchronous and remote space, now we have all these tools that are in our communication layer of our business. So how do we take the decisions we make in the meeting and then share them throughout our team for visibility so that the right people know, not just have access to the notes, but have the right context at the right time.
How do we empower them to have that? I think those are the types of things you automate the friction around your work, but not your work. Because I think it takes much more time to build an AI assistant that can actually help you with your work. And I see a lot of what we do in creating product as us being artisans with a digital medium.
So if I consider us as artisans, I don't think many of us are too jazzed about the idea of having an AI that does my job because part of what I do isn't just doing the job. It is the creative nature of what I do. I enjoy this. It is as much of an outlet as it is a means of income. But there are parts around this I don't enjoy.
There are parts around this that maybe I do enjoy, but in the grand scheme of the team and in the organization, we need to find a better way to delegate this and maybe AI is a solution. So that's kind of the way I'd go back and look at it and say, let's kind of move AI out of the forefront and just ask the question, what are things we should be doing really well in an analog fashion?
What are things that we should be delegating extremely well? And will AI help us to do that better?
Kristiana Corona: There's something that comes up when you were talking about. The things that we shouldn't have been doing anyway. I think we have a lot of conversations about prioritization, prioritize your time, do more with less, right?
All of those kinds of things, with or without ai, and the people who really thrive are the ones who can get extremely focused on what it is that they have as a superpower, and eliminating those distractions, eliminating the manual work, the things that is not adding value to what you do as a superpower.
And I think that's what you're calling out, right? Like the artisan in us, the creativity in us, whatever is blocking that, whatever is getting in the way of that, that is. Prime time for ai. Like, let's go take that off your plate, right? Like, oh, summarizing your notes from the meeting and then sending it out to your team, or things like that.
And I think a lot of times we get confused that we have to be doing all of those things or owning all of those things. And I think that, you know, if we see it as an opportunity to really unleash our creativity, to allow ourselves to have that, that focus more, it really does become this interesting opportunity at work that maybe wouldn't have been there before.
So I love that advice, just finding the ways that you can operationally amplify yourself and focus your time differently. And so from that perspective, let's say someone does successfully do that, they find ways to simply use AI in their day-to-day to eliminate some of those tasks. What are you seeing as far as creative uses of AI where it's like helping people to be more innovative or think differently or try new things that maybe they wouldn't have before?
Blessing Richardson: The simplest way. Almost like, it's like tool agnostic, almost like industry and team agnostic is actually asking different questions of ai. So we know that ChatGPT is one of the largest, they have one of the largest user bases. I think that span, not just corporate, but like just consumer, right? Use as a whole.
They're kind of like the ai we almost use it like we use the word Google, right? ChatGPT. And I say that because if you're using ChatGPT, you'll notice that when they rolled out the GPT five, that model, people were mad. People were really frustrated because it changed the interaction. And it wasn't as personalized, right?
You couldn't really get it to be kinda like your best friend or your confident. And I personally never use AI in this way. I treat it and think of it like a robot, but GPT four had a lot of characteristics that made it feel very familiar and there was a lot of pushback around that. And I found that with GPT five and even 5.1.
If I am asking questions that are not so much like, tell me what I want to hear, be my friend, be like my girlfriend, be like, you know, just be something close to me. Versus saying, you're a tool and I'm asking you a direct question. It changes my whole experience with ai. And so if you're using LLMs and Chatbots, and I think we go to them and we ask questions, Hey, help me with this and help me with that.
And I think our engagement starts kind of one sided. We're like, can you do a thing? And then somehow it becomes conversational. I think there's a gap in our maturity when how we use AI to ask it to be a critic. Ask it to kind of asking it to be creative in a way. And yes, I know AI isn't truly creative, but when you ask it to play this kind of counterpart, to be the critic, to be the thing that sees the gaps, there are always opinions and perspectives that kind of come in the conversation that you just might not have naturally seen.
And so I think that's a way that anybody in any discipline or background can go to AI and say, help me think about this differently. When I use any of the code building ai, so I use Cursor to do a lot of my building on the side, and that is IDE that has AI integrated so you can build apps, right?
Again, I do have a bit of engineering me, so I like engineering AI tools. But if you're using things like Lovable and all these kind of other apps that let you build things, you could actually ask it, is there a better way? To build this feature. Is there a way to make this easier? Just asking the question.
Don't have it. Go and write it and design it for you, but just ask the question, is there some other way? And I think whenever I ask lums that question, the way I get better answers is by saying not what I want it to do, but giving it the context around the people I'm trying to solve the problem for and the problem itself.
So taking off the solutioning hat that I'm so good at wearing, because a lot of my job is solutioning. So you know what? I'm not going to prescribe an outcome to you. I'm going to tell you where I am, where I'm stuck. I'm going to tell you where I want to go and I'm gonna ask you, is there a better way to get there?
Kristiana Corona: I love that. And that's reminding me of a couple of conversations I've been having recently with people in a similar way. I'm gonna go back to the thought of the critic, the other voice in the room that maybe you don't represent that voice, but it is the client you are trying to attract. It is the stakeholder you want to win over.
It is the executive leader who you just can't seem to say the right thing when you present your work. I've seen people. Training like a character training characters in their world, varying characters and saying, this is what they care about. This is who they are, this is what they do. And then asking them to play a part and saying, okay, read the post that I just wrote and tell me what's missing.
Tell me what I could have done better. You know, help me to figure out a different way to say this more concisely that you are gonna resonate with. And by the way, this is your experience level in the company. This is the problems that you're, are keeping you up at night. That kind of thing. And I just think that's so brilliant because it is one thing to be able to say, you know, I can bring certain things to the table and I can make myself the smartest in the room, but when you.
Allow yourself to create other characters. And then almost like role play. Role play, what it is like to either share this message or build this for them. Like I'm even imagining the team I used to be on at Amazon where we were delivering app features for drivers who are out on the road and we know they struggle with so many different problems each and every day.
And if the model knew what those problems were and we could show them some of our prototypes and our mockups and have them say, well, what would this driver think is easy and what would they be confused by, or what would they not like? And just get more of that resilient feedback. Like it's super easy to do once you have those characters established.
Blessing Richardson: It is fantastic to do. I see this come up a lot in the kind of micro business. Coaching space where it looks like it's marketing copy. I don't know if you've seen a lot of that space and that offer where people say, Hey, who's your ideal client? You know who your ideal client is, that's fine. Go to Chad jbt and ask it a bunch of questions and then like it'll give you a customer or even a user profile.
And I help some of my clients do that when we're working on their products. You don't have enough to pull from to really build a real user profile. I go, Hey, great. Let's maybe use chat two BT. Here's the thing though. I think about going to AI just saying build a profile. Again, it has this massive knowledge base.
It will pick a bunch of things to put together and sometimes it's great. And I did that myself earlier in my business when I did not know how to take what I already had and make better profiles. I was like, well, I don't know. I'm having this problem. This person's like giving me this feedback. Let me tell Chad chat a little bit about this person and build a profile.
And then when I got to a point where I realized why am I making things up? Like I have transcripts, I have recordings. I have a lot of lived experience with certain people. What if I actually stop, take 20 to 30 minutes, record myself talking about this person? Some of the issues that we had, and again, not necessarily saying like, oh, this person has this characteristic, but again, explaining the history of our relationship and what are the problems that we encountered and what are things that went really well, right?
And then I can go back to AI and say, I need you to make me a really realistic profile that matches this person or these groups of people, and bake a profile for this purpose based off of what I've given you. That has given me far better profiles, right, than just asking it and giving it a blanket persona because now it is.
Tuned and understands my world and my niche and my use cases and the people that I actually work with. And so I think yes, profiles are an amazing tool because you can get AI to simulate and impersonate someone and get you further whenever you're working on, you know, a design or a solution. But the more we put our real experiences in there.
The better those profiles become and the better the interaction and really the simulation becomes.
Kristiana Corona: So for example you're talking about conversations that you've recorded, so maybe meetings that you've recorded where you're talking about things and you're hearing people's questions. Do you make it a standard practice to just record every meeting that you're in now for the purpose of being able to later use it or feed it into something?
Or what are the things that you find the most valuable to keep?
Blessing Richardson: I'm grateful in an environment where I can bring my note taker with me to the vast majority of my calls. And so I do, apple has voice memos and then I use an app as well. That's really helpful for me to do recordings. And I find that if I'm in an environment where I don't want my note taker, I kick it out.
And again, as much as we love ai, sometimes the transcriptions are wrong. Quite often things are wrong and. If it gets critical parts wrong, that changes the whole thing. And so I do have an automation that receives my transcripts. When they're recorded, it creates those notes. It sends me a message in Google Chat, it loads into my project management tool.
It sees if there's certain people on the call and shares with them. It's this whole process. It does this whole bit. It's really great, right? But when it gets it wrong, then I have to step in. And so as I've worked even with clients in building their own, I'm like, lemme take my lessons learned. And here's what I've learned.
I have to be the authoritative person on what is real and what isn't after my meetings. And so as I build solutions like this for clients, we add a human in the loop step that lets somebody interject and say, oh, I'm so glad that you came up with this wonderful, you know, notes for this meeting. But that's wrong.
Like, these are the actual items, these are the actual sentiments that was communicated, which that might sound super cool, but in reality, that's what we were doing before, right? We were just taking our own notes. We had our own insights. I don't think we were as nearly as. Thoughtful about using the data from those things.
Right. I keep it in my head. I scribble it down on your notes, and if you're like me, as digital as I am, I write anything in front of me. So I have numerous papers and notebooks. I'm still taking notes, but it stays there and it doesn't go anywhere. And so I do think there's still actually a lot of power in just making your own notes though, because when you engage in the note taking process, you internalize things.
And I think when we delegate to AI or delegate to technology, a lot of us don't realize how much information context we lose. It's just gone. And so totally was like, oh, the AI has it. So I don't,
Kristiana Corona: I have the transcripts, but I stopped using some of the automated note summaries just because I was like, it's not capturing the right messages.
And so I prefer, what I prefer to do is write a bunch of messy notes and then have it formatted for me, but I, it comes from me first. Like I am the one who is delivering that particular value. And then, you know, I'll link the transcript and other things, but I love that you have all of that automated and I'm sure there are people who are like, Ooh, how do I
Blessing Richardson: do
Kristiana Corona: that?
Blessing Richardson: It is helpful when. Capturing people's real voice is actually the point. And, but when it's not, it's, to me it's just like high level summaries. If we talked about dates or rescheduling things like I need that pulled out of it and then action items and I just filter out things aren't really action items.
But the way I've gotten my business to work really well with that is that, funny enough, we go back to SOPs. I have SOPs that my agents read to know what is important to me based off of what kind of meeting. It's because that's why I have my custom workflow, because just having an AI go, oh, these are your notes.
These are action items. That's not true all the time. Right? And I have different objectives for different context, but you know what is still a lot easier than building all of the automation. And just taking your own notes. I'll be honest. Just go on it and do it A quick voice memo. Right. Taking that and being like, yep.
Here are my notes. And then you share them where you need to share them.
Kristiana Corona: Yeah, sometimes. Yeah. The A to B is so much simpler than we make it. So in your business, in sale I know you're making a lot of custom tools for people. I'm just curious if you have like an example that you can share of something that you're excited about that you've used AI to really change their business.
Like more than note taking or more than organizing a file. It's like actually changing the way that they work in a profound way.
Blessing Richardson: Yes. So note taking. Funny, yes. We're talking about note taking because it's one, I think the first ways that a lot of us interact with ai, having that meeting recorder join the call and do its thing.
But I did a webinar series earlier this year about ai and when I talked about like meeting notes. People really tapped in a way, and I was like, why do we care about that? I was like, wait, like one, I had already automated it, but two. Then I was like, I thought we wanna talk about how AI can show up and do like your expense reporting and how AI can help you do client deliverables and like doing deeper analytics and research and like doing lead research.
There were some questions, but the meeting thing really piqued people's interest. And finally I was like, okay, there's enough of you that are showing up and engaging that I have to figure out why you care so much about this. And as I began to kind of go through things, I realized meetings create a flywheel of a lot of things, right?
If it's a sales call that I'm having with someone, then there's a lot of insight to understand what helped me land that sale and maybe how I lost that sale, right? And how people were speaking, right? Voice of customer comes out on those calls, support calls. Whenever we are doing, just interviewing customers in a product space, right?
I want to talk to users. What are they actually saying? There's so much in there. I think also as well, there is a lot of thought leadership that happens. Just like we're on this podcast now and we're recording and we're having a conversation. I can get on a call with a friend and we can just shoot the breeze and have a great conversation and we say some great bangers we lost and we're like, man, this should be a podcast.
Maybe we didn't have the conversation with the attention of it being one, but that meeting actually had some really great highlights and quotes that we could have used in our thought leadership and in our content marketing. And so when I start to look at meetings around the objective, around the meeting.
Then there is an amazing flywheel. And so my clients are coming to me saying, Hey, we are on calls because I work, a lot of strategists and consultants, we're on calls all day and the amount of work that we are creating is so high and we're not able to keep track of it. And so funny enough. Solving the meeting problem is one of the things I've been doing for people with custom agents and helping them pull those things out of the flow.
How do we get the agent to draft an email that isn't just like a stuffy draft, but maybe it taps into your C rm? It looks at the history of your relationship with someone or your project to say, you know what? I see that you've been talking about the same thing for the last four months, and this has changed a little bit over time, so let me contextually draft a good draft for you.
Right? And so how do we build more context into the agent to make it more accurate? So those are kind of the things that we're doing around, like from meeting to update the CRM, save the good version of the meeting notes, share those notes. Let's pull the voice of customer insights out of it and really creating all those functionalities because in truth, we do those things at different points, which is not as intentional.
We do it in the debrief, we do it three weeks later when that little comment comes back to us. But it helps to kind of have those things done after certain meetings. It doesn't have to be everyone, but just have it ready for you so that way when you are ready to show up and engage with that perspective from a meeting, like what did they really say?
Like, I know we just did these three things and we made these changes based off what they told us, but what was that sentiment? When you are ready for that feedback, the information is there. So that's literally a whole bucket of implementation I've been doing for people. It's the meeting analyst.
Kristiana Corona: Isn't it funny?
You think like, oh wow, it's gotta be this really big, fantastical, exciting, shiny thing. And really what it comes down to, from what I'm hearing from you, is those micro actions and making the most of those micro actions that we're doing anyway, but not losing the learning and not wasting the time in between.
It's almost like the movies where you have that assistant that is always with you always there right behind you who's like, oh, yep, they want that done. Okay, I am gonna go do it. They want this moved. Oh, you know, I noticed this thing about this person and so maybe next time we should try that.
And it's like all the thoughts, all the things that if you slowed down, you could remember and think about, but you're moving so fast and you're jumping from meeting to meeting that often, you know, it does get lost. And so I love that, like, just not letting those things fall through the cracks, but taking that and evolving and learning from it.
That's fun. So, as you think about leaders who are in this type of environment, they are in those back to back meetings and they're running from thing to thing. Is there simple things that they can start doing that will help them to take advantage of that? You know, or do they need to create some sort of a custom agent that can do all of these things?
Or are there like more manual ways you can use AI to do that today?
Blessing Richardson: The reason why I do that is because I run a micro consultancy, right? I need to do those things. I think if I were to put on my kind of engineering lead hat and go back to my corporate days, unless I was just slammed in middle management, a constant meetings that I didn't have time, I don't think I would invest in that way.
What I would do looking at AI is say, okay, what are the routines of my job? That middle management's fun too because you get asked from executives and more senior leadership to do things that, in my opinion, with ai. Would threaten more of their job security than mine and middle management. Right. It's interesting.
It's like, Hey, can you go pull those reports outta Jira For me, it's like, great, you could click the button to go to Jira yourself. You could, but you needed it from me. Right. And I think those kind of things and those kind of apps I would have, I would spend more time using my tools. So not really even using LLMs if I don't need to, but just using my tools to say, great, when this person asked me for this, we're gonna have the dashboard ready for them.
If I were gonna put an LLM on top of it, I'd ask the question, can a LLM connect to something like a Jira for me? And go pull those reports and then just draft the email and give it back to them. Those are kind of the things I would do. 'cause it's predictable. This is predictable stuff. If I have to stop to do it will break my flow of other things.
And what technically is five minutes to open up to go to Jira and like look at it and then copy paste it. And I, and trust me, I remember the rigamarole, right? You open up the corporate tools and it's never in the format the leader wants it to be, or you need it to be for whatever thing you're presenting.
And so you have to copy paste it and then you have to do all the prettifying of it. And God forbid you're in an organization that wants everything in a deck. Because I think decks are like wild because you go from just presenting information to becoming a designer because now you have to, it's a UX thing, right?
It's how is the person gonna receive this in a more visual way than if it was just a document. And so you have all this stuff to just create this report because it's not just a report anymore. Those are the things that I would try and automate. I've been in a position where I try to automate my standups.
I had a bot kind of come through and say, Hey Tony, do your standup. Yeah, that wasn't great. I don't know, it just, people kind of checked out and it was so automated and predictable that it didn't actually create a readiness from people to engage in that format. And so it didn't really help when we did meet synchronously.
So just from those experiences, I'd say you experiment because at the end of the day, it's not just you using these tools, it's how it affects your teams up and down. And if from an up perspective, if leadership isn't willing to go into the tools and leverage them from, you know, the screens and interfaces they have to receive information, you're probably sending that email anyways.
So we wanna figure out how to automate getting data out and then replying to that email as quickly as possible and getting it to them. I think when it comes to managing down, you wanna try and get as much buy-in, in the handful of tools that you live in. So as a design team, if you live in Figma, buy-in might be.
We do comments and we do testing and feedback and QA and Figma. We do as much as possible in Figma to keep everything in the same place where it needs to be. That's not necessarily using chat BT better, it's just how do we do more within our tools to keep work contextual and remove like, oh, well there's this comment on this like element on this UX thing and I'm sending this in teams over here, and now I'm gonna screenshot it and put it in a teams message.
Okay. That might be a great boundary to have for a high level manager who will never log into Figma. Like, we get why we do that. But when it comes to like my team, right? Maybe we should be working better within our tools. And so that's kind of really where I'd start, make your tools more efficient because by the time you start bringing in, again, yet another app as an orchestrator, now you're dealing with the whole integration game.
Does my ChatGPT really integrate in a way that it becomes a one-stop shop And the unfortunately, the answer for most of these integration tools is that they don't, there's always something missing. There's always something that it can't do really well. And so the best place is to go back and look at our core tools.
What is the workflow we can set up before we bring in an integrator, an integration product, and then only integrate what you need to from there.
Kristiana Corona: I think that's really wise advice because we can really go down a rabbit hole in all of those scenarios and waste our time and end up wasting so much time trying to make whatever that thing is efficient that it doesn't actually get the job done.
But I think there's an opportunity here for a lot of people to think proactively. So again, it comes back to thinking strategically about what your leader needs, what your team is going to need, what is needed in the next quarter. And is this helping you to pull the documents, pull the data, put the story together?
Take the time to tell that story and think differently compared to what you would have before. Like I think there's a lot of. Really wonderful things that can happen when we think about it in that way. And so it's less about automating in the second that someone asks for something, but it's about, well, did could I just do that two weeks earlier and do it on my own time and have thoughtful thinking time associated to that?
And then bring something that looks like I spent a lot of time on it to the table when we know that they're gonna ask.
Blessing Richardson: Oh yeah. I love thinking too on the weekly, monthly, kind of quarterly, like what are those things that I'm doing? Because in reality you lose a lot of like the fidelity of the decisions that are made in the moment, the kind of the emotional 'cause there.
There's an EQ thing to what we do, particularly in design that we have to pay attention to. Right. What's the sentiment around things? You're kind of losing all that as you go. And if I'm sitting in a position where I know in three months I'm gonna have to tell the story about why we've put our energy, where we put it, and why the product looks the way that it does now I'm probably, even if it's not, you know, like a recorded thing necessarily from like my transcripts, I might start doing because I like voice a lot, so I might start doing a voice log.
At least once a week. I'm gonna log and just kind of answer the same questions about like, what are the decisions that we made? What was the impact? What was the positive feedback, the negative feedback? What are things that we changed? Why did we do that? Make that kind of log, because again, that information's gonna be the freshest and most accurate, the closest you are to it.
And when you sit down at the end of your, you know, your three months to write that report, you can take those audios and transcripts and then put that into a chat. You don't even need automation. You can just say, Hey, here are all the insights, right? I need to create this report. This is the format of the report.
Go ahead and write me the first draft and the fidelity of that report, just how rich it will be on that first pass. And of course, you're gonna pull things out. You're, oh, I need that, but you didn't miss things. You know what I mean? And so I've been told that I am the queen of creating metadata around what I do, like metadata on metadata, and that's why I like to start, like what is the goal here?
Okay, I might not know how to like make that thing that I'm trying to do in three months, but I have a thought about the bits of things that I need to get there. What are the building blocks I need in place to do that fairly well? Let me see what I can do to passively start putting those blocks away because AI's gonna help me aggregate that right?
In a way that's much faster. If I then have to be the person who went back through 60 audio notes, that's not gonna happen, but AI can.
Kristiana Corona: I love the ability for us to just be messy and show up exactly as we are, say what's in our head at that moment, extract it, and then be able to say at some future date, this will all make sense.
This will all have order to it, it will all be formatted, but I don't need to spend the extra energy right now to do any of that. We can be humans and
Blessing Richardson: we can be messy. Isn't that funny? Because we're talking about AI and we're like, no, we can be more human and we can be less organized and more unstructured and just our messy selves and worry about bringing the polished version later.
Kristiana Corona: How wonderful. What a release, how much pressure we put on ourselves to show up perfect and polished and have everything done at the beginning. And we don't have to.
Blessing Richardson: We don't. We really don't.
Kristiana Corona: You also touched on another topic, which was the, what I would like to call the design waste, where we spend all this time screenshotting stuff, formatting stuff, just to present to somebody.
I mean, things that are like. Extraneous tasks that don't add to the value of the final solution. So I'm curious, have you found anything that has really helped from a design perspective or a UX perspective, make things faster, easier, reduce that design waste? I know like working directly in Figma is a really great one, and I think that's one that we used to talk about a lot with our teams.
Like how do we train people to be in the tools that we want them to be in and just give the same feedback, would give it in the most convenient place. But from a standpoint of design, it feels like there's just so many things out there that are not great and they're just creating junk and not creating the type of quality design solutions that we would want them to.
So I'm curious, anything there that you're seeing?
Blessing Richardson: The way I solve this in my day to day is that when I present, whether it's a custom GPT or it's an agent solution to a client, I want feedback. Because I built into products already exist. I've kind of eliminated a lot of the ui, ux and a number of the things that I'm doing.
However, I still need to know, well, the ui, but I still have this ux. How are they experiencing what I built? And this is where I go a little bit away from what people might think. And I do calls, I've gone back to doing calls because if I am trying to solicit insight or feedback for somebody who doesn't know how to look past the initial responses, right?
The initial things like, you know, the equivalent might be, I'm gonna show you a screen and you're fixating on the blue button that's not, doesn't have your logo in it. And I'm like, wow, that's just, that's not the thing right now, me giving you that. Designed to go review and not being there to guide it and even remind you, Hey, this is where we are, right?
These are things that really matter. Great. I hear that feedback. That is we, yes, we can fix that. Totally easy to fix. Let's go back to these things that we said, right? And the redirect is what is the end goal? What is experience that we all agree that we're trying to create? If I can't be on there to coach that out of them then, or really coach, I'd say, if I can't be with them to coach them through that review process, then the ask for the review doesn't really make sense anyways.
And so that's where I give my time. I remember that in this interaction, this person is not an expert in the solution. They don't know what we can do, what we can't do, how much time it takes to do something or not, right? They're just giving their gut reaction because I probably just ask for a gut reaction.
So asking for, I think a bit more qualified questions that help them give more contextual answers. It makes everybody happy. 'cause at the end of the day, they're gonna get what they need and what they want and I'll be able to give it to them. And so that's where I found Just get all the call. Yeah. Yeah.
It's not, I don't think tech can solve that really well when we're professionals and we all kind of understand, like in this space, we know how to do this thing really well. The async tools are the, that's what we're we look to then, because you know what to look for when I ask for a review, but when my client doesn't or their team doesn't, I have to be the one to guide them through that process, and I need to be willing to do it.
Kristiana Corona: I think that's a great example of where quality can fall off a cliff when we're inserting AI at the wrong moment instead of the human intelligence. Negotiating. What is a good answer? What is a sufficient answer? Where do I need to push back? Where do I need to challenge my client or the stakeholder that I'm working with and say, actually, I don't think that's gonna accomplish the result for what you want.
No.
Blessing Richardson: And if anything, because a lot of the AI we use is not self-learning, right? It's not in the moment gonna be pretty good at figuring it out. Maybe I'm asking them the wrong question. That is a human intelligence. Thing. And that's what I actually think UX people are amazing at, or actually asking the question, are we asking the right questions?
Are we solving the right problems? And AI just does not have the capacity to do that. And the cost of making self-learning AI is outrageous. So we're gonna need a lot more people who ask better questions.
Kristiana Corona: So when it comes to product and design leaders. Who maybe are trying to come up with the next best way to innovate within their space.
They want to try a lot of things, they wanna experiment. I think that's one of the beautiful things is when you get into a company that gives you the space to go and try things and fail and experiment, you wanna be really proficient with how you create those prototypes. How you create those scrappy first things to learn from.
And I'm curious, from your perspective, do you do any of that type of prototyping, like rapid prototyping or putting concepts in front of customers where you're trying a lot of different things before you get to that sort of final finessing or here's our final design system we're working within, or the constraints.
So before the constraints have been created, when you're looking in a lot of different directions, is there anything that's helping make that faster? Or do you feel like you're still, like the human is really the necessary thing within that process that you can't replace with any?
Blessing Richardson: I think it's to prototype Well, with the tools, it's a collaborative experience right now, right?
Because AI is gonna be really good at taking a thought and an idea. And I know you've seen the number of demos of like, I want an app that does these three things and it spits out a whole app, right? And it's like, okay, like that's an app. Mm-hmm. But like, what about everything else that makes a business work with an app, right?
And if I'm prototyping with ai, it's really helpful to prototype. But I could imagine within an established product, you prototyping with AI and it doesn't understand your UX guidelines, it doesn't use your components, right? It's not adhering to all the rules and guidelines that you put in place, doesn't know what to do with that kind of, you know, like a guidebook or your brand assets.
That's kind of pointless. 'cause now you're getting things that might functionally represent what you're trying to create, but now you have to go back and you have to make it adapt to everything else that you have in place. And so when it comes to using AI to rapid prototype, I think the value is gained.
When you rely on AI more, the more context AI has to do it like you and adhere to your guidelines, right? You're in an environment where it's not doing that well and a lot of times I'm not, right? I'm just prototyping simple things. From a UX perspective, you gotta use tools that just have AI FE functionality.
To prototype and I'd say don't be so in your head about it that you don't play. That's the thing. 'cause like these tools can seem intimidating and if you've been in the industry for a while and you have a lot of experience, it might be very offput to hear that. You can just say whatever and the AI will go do a thing.
It can be very offputting. You should do it because after like four times you'll realize the limitations, but then you'll begin to see the benefits of speed. And a lot of it, it really is, it's speed. It's speed to getting something tangible. And when our clients and customers can see things, then it becomes real to them.
We can imagine them because this is the medium we deal with. They can't. And so that's the benefit of actually creating your tool set, which is something that I think is left out of a lot of conversation with ai. When you use ai, you end up creating your own workflow with your tools. You end up knowing how to talk to that AI yourself.
'cause every model essentially is different enough where you have to learn how to talk to it to get what you want, right? And so when you end up picking an environment to say, I'm gonna figure out how to use Google's new product for doing prototypes, right? I'm gonna use that. That's what we're gonna use.
Try that for about a month. Figure out if you can get it to create your prototypes the way that you need. But you're gonna learn how to talk to that model and decide whether or not this is worth keeping around. But you gotta be willing to play. And so the first thing I would do is not really try to use it in a production environment.
I would try to use it for just this idea that I have, this thing that I wish would be better because that gives you that incentive to play and be curious and the pressure off of, I have this deadline and now I'm stressing out all this time trying to figure out how to use the AI to get it to do the thing.
Take that off the table because if you can just play with it and learn how to use the blocks of ai, then your Lego project becomes not just a cool thing, but it's like, oh no, like we used our Legos to make like this, like you know, this grand model of this real world structure that where you know, we're gonna now have engineers build people go, how'd you go get Legos to do that?
Well, you paid enough to understand. What you could do. And then it became real. And I'd say I'd take that same approach.
Kristiana Corona: The thing that I think people get challenged with is they learn how to make a prototype really fast and they can make it do like one thing or two things or something like that.
But then when it comes back to, okay, great, now it looks like it's done, so let's deliver it the gap between what it is. And then, oh, but we haven't thought about the 37 other use cases and what happens when this error happens And oh, it doesn't actually click back to the other page that we needed it to.
And you know, all those micro decisions that then have to be tuned in or dialed in. And then it's like, in some cases, people have to start all over 'cause it's like, oh, I can't. Adapt the AI to do the thing that I wanted it to do. And therefore it's kind of like we're starting over from scratch and then actually building the real thing anyway.
Like, are you seeing ways of people being able to do the rapid prototyping and then actually take that all the way to delivery effectively
Blessing Richardson: to delivery of a finished design or delivery of a finish, like develop product?
Kristiana Corona: Both.
Blessing Richardson: Oh, absolutely not. The models are good enough. I just, I have built so many of my own fun little agent apps, like apps that help build agents and I tried using AI code editors to do it and I went all in on vibe coding for like two weeks and finally I said, if you had to employ me.
To take over a product, somebody I coded, I'd probably say no. I don't know if you could hire me at this point to take over a totally vibe coded product because of the amount of technical debt in the products. Right. The models are not, they're not like our brains. Funny enough, we have really interesting brains for all of the experiences that we have as humans, the things that are seared in our memory because of emotional and traumatic or joyous moments, right?
Our brains have an interesting way of doing recall, but they are fairly reliable when they work. Right. And even when they don't work really well, you still get really interesting things outta our brains. Models have a context window, which means in a really technical computer way, there's only so many lines in a file that this thing can remember and then just starts to forget.
And it in no way compares to what our brains can do. Why does this matter? 'cause when I'm working on a product, I'm not just working on it in a series of, you know, one hour sessions over like 20 hours. This product has a life, right? It is this weird inanimate yet living thing that we always update. We always iterate on, we improve.
And so if you have a product that has a company and that product has aged around the age of your company, where's the AI model that's gonna somehow understand, you know, at minimum, let's say five years of product development and growth of learnings, of feedbacks, of just iteration and improvement of dead code, legacy code, things that we did and we remembered why we didn't do them right.
All that tribal knowledge, there is no model. Today that can comprehend and sit at the table like an actual person and bring their tribal knowledge and lived experience to make that small change. When you say, Hey, we wanna move the sidebar to the right side, and it's like, I know the right way to do that.
No, there isn't one. And so this is the reason why many prototype apps today, they are just prototypes and there's so much more to building a product than just the UI or just the ux or just the data in the background and the integration layer. There's so much more that models don't have a deep understanding of, and to be frank, models don't actually have understanding, they're predicting what to say.
They don't have this thing inside of them that is turning this into some sort of meaning for them. And then the model says, I know what this app means to me. It doesn't have that. Right. And so, yes, anything that you're prototyping, whether it is on the front end, the back end, or whatever they're, you're gonna hit.
This point where the models become insufficient to maintain and develop it into maturity because it just does not have the capability to do so.
Kristiana Corona: From that perspective, if you're thinking about process, so product development process inside of a company and your leader and you're tasked with, okay, my team is coming up with all of these different prototypes.
We found some things that are really resonating, but. Within our product development process, we still need to account for a pretty sufficient chunk of build time to go from prototype to final deliverable that we feel good about that AC accomplishes all the use cases that has the context of the last five to 10 years of what we've done and what's important to the business and all of that inner knowledge.
Like you can't skip it sounds like what you're saying is we don't have anything today that allows us to skip that step and just go, cool, hit publish. And we're pretty far away from that. We
Blessing Richardson: do. That's called people. So like that's it. And even as you know, being on the dev side of it, right. What I found is that I've had to learn to design and write my PRDs, my product requirements documents differently for AI to be more effective.
And so what you're gonna end up with, I think, across the whole product development chain are individuals who have learned how to maximize ai that step. That's actually how you're gonna shorten that timeline and you're gonna bring it in, is that people know how to use the AI tools effectively for their phase of the process, and it makes it short.
So the handoff to development to me would be like, great, I now understand the UI you spec. I see what we're trying to do. Awesome. How do I then translate this into the right spec for the agent to go build? Because if I'm now delegating the actual writing of the code to the agent, there does become a question of do I also let the agents write the ui ux or, sorry, not the ui, ux, the unit test, the functional test, right?
Do I let agents test and actually develop, probably shouldn't be the same agent, just like it shouldn't be the same people technically. But do I do that or do I jump back in? And how do I create guardrails in the definition of the agent and how we set it up so that way it does understand and have just enough context to do the task that it's given, right?
And so that has more to do with me knowing how to speak AI and agent. It does with me being a good developer, right? And so that's the skill that we're gonna have to pick up across the product development stack and with people and with tools. How do we speak to ai?
Kristiana Corona: So when you think about that particular challenge of learning to speak AI and write really strong prompts and create really great agents, if someone is not a technical person and they're not used to thinking in those terms, like where do they go?
What do they do? Is there a place that you know, you would recommend them going to learn how to do that?
Blessing Richardson: There are apps like, again, lovable. And the good thing for what we do in product is that the big AI companies who are developing these models like Claude and you know, and the GPT models, right? In Gemini, they're all product companies themselves.
And so all of their agents are getting special functionality knowing how to code and technically everything that we do. All comes down to code, right? And so I still think the best thing to do is gonna go into apps like a lovable into an environment where you are learning how to use the malls and how to talk to it.
Because it's one skill to say, okay, go use like these assets I've made. Right? But it's another thing to just know in general how to talk to ai. It does really come back to play. Can I make this thing? If you are in a environment where you're in a product company, you are developing a product for external users, then I would flip that and say, what is like the meta feature or the internal product that you wish you had?
Go build that. There's an app that I like, especially with internal products because there's so much that just you don't have to deal with when you're building internal tooling. And there is a product I like to use and they've launched a feature that lets you build internal apps called Zeit. Now I mentioned lovable.
Lovable is great for CAN for building consumer facing products. Zeit is specialized in creating internal products, so they solve things like user login through SSO and all this stuff. They kind of solve all that stuff for you. Usually it's to build the internal tool, taking that smaller problem. Let's say it's as simple as, you know, we have this product and we need to manage users with password resets for whatever res reason.
Our password reset process is like, it just sucks. Users get stuck. We dunno how to fix it at any given point. Somebody said they wanna be able to go in and see and create a dashboard so that way our person who does support one, when a user comes in and says that they can't do a password reset, there's a quick dashboard for them to go to and trigger a password reset.
That's an internal tool alongside a consumer facing product. Go build that bit, right? Go build. Go build that small bit. And we know this is not. Because corporations are just notorious for taking that side project and then making it a thing, right? Let's not do that. Let's not magically promote that side tool into a formal tool, but let's look at this as an experiment to say, can you go vibe co a solution for that?
What would it take for us to actually connect something like that to our product in a way that's safe and then create an interface to kind of run these support and these maintenance processes? And I think the thing there is, yes, have a product person participate, but I think it's even more powerful to say, what about our support team, our customer success teams, can they actually produce that internal product without engineering stepping in?
And if engineering has to step in, is it as easy as making the data available but not actually designing the product? Because there are a couple tools in the space. Zeit and Retool will be some of the tools that help you really build robust internal products. And I think that's litmus test. Can we give 'em the licenses, set them up to do it, and empower these teams with the data to go build their own solutions?
Kristiana Corona: I love that so much because I think there are certain areas of the business that just aren't gonna get the technology investment the same way. They're not gonna get prioritized, and they're the ones who probably need it the most and. What I'm hearing you say, and I just wanna make sure that I'm framing this up correctly, is if we think about tackling AI tools in a, like identify a single task, identify a small bit, build a bunch of those small bits and make sure they're reliable, repeatable, we get it right every time.
Then later you can take those and aggregate those and turn them into a more complex tool. But rather than tackling something that's gonna have a lot of moving parts and a lot of assumptions and a lot of things like breaking it down to the simplest Lego blocks, I guess, and trying to get each individual Lego right, is the type of experimentation that you think teams should engage in doing
Blessing Richardson: every day.
And you know, I did, I know I just kind of painted the picture of building an interface, new password resets, right? Because I think that feels good to us, but. 'cause I always brought an engineering spin to what I did and I kind of worked in the middle. When I was in corporate, I would first just make like a fun little command line program with no interface and I think it's through awful lot of people, but I'm like, there's no UX problem to solve.
I don't have to now maintain another interface for an internal product. It's a functional one Today, if I were to do that, it would be an agent. It would be how can I, let's say my organization lives in ChatGPT, right? How can I make a custom GPT that has secure access to go and do a password reset for a user and does it when I ask them to do it?
That's one small building block so that looks like there's our product have an admin API that we can remotely initiate a re a password reset. Right? Great. What do we need to do to make that accessible to our internal team members inside of the chat tool that we're using? Maybe we don't use ChatGPT, it could be teams.
Can we initiate a password reset from teams using an agent? Using an integration. Then building the integration is a really small ask. If you already have a product, it already has the functionality. How do we expose that? And then instead of trying to create this massive process around, we're gonna make a whole process and a tool to create a password reset thing and then get three layers to sign off.
I don't know why you would, but if you would do that, right? How do we give those team members that little functionality to do the reset and then we let them build the best way to integrate it into their own team tool stack. And then we see that result in leadership, upper leadership going, man, we don't see the red indicators on the dashboard telling us anymore that customers are mad because they can't reset their password.
As a matter of fact, we've been able to circumvent it. How do we circumvent it? Well, the customer support team made a tool to do it. That's fantastic. Should they be doing that? You know what? Let's talk about that and put that in the backlog as a story. Right? So we solve two things. We identify the problem, we sense the problem, we came up with a short-term solution, and now we can still escalate it to a long-term solution and improve the customer experience as a whole.
But we didn't get lost and stuck in the middle for weeks and months, and now are dealing with user acquisition problems.
Kristiana Corona: Yeah, that's brilliant. And I think it helps at least prove out the value to your point. Like you can make something really fast, really simple to prove out the value and then maybe you'll know, is that actually as valuable as we thought?
It's ensuring that we don't have so much waste when it comes to engineering and time and process and all of that to release tools that people don't want. And I think that is one of the biggest blockers for people who are starting, whether they're inside a corporation or outside, is assuming that what you're building is valuable and that people are gonna love it and need it.
And sometimes they don't, isn't assumption. And so this, there's much less risk,
Blessing Richardson: which is like, I think it would make everyone's life better if this thing did this. And it's like maybe there's like all of five of you, but you no longer have to say no. Go build a thing for the five of you and we'll see what it does.
Kristiana Corona: That's right. I love it. You all get your shiny little, your little thing and then you can tell me later like, oh, actually I still had to interject myself and finish that process. Anyway. Okay. This has been such an amazing conversation. I feel like I could just talk to you all day and I probably have 25 more questions that I could ask, but just wanna wrap things up here.
If there's just one piece of advice that you would give leaders who are maybe a little overwhelmed by ai, who just, you know, wanna get started, wanna become fluent, but it's just all so much right now. Like, what would be a simple thing that they could start doing that's gonna help 'em out?
Blessing Richardson: Find the task that you do enough times a week that you're like, this is so repetitive.
So repetitive. And if you can just figure out how to ask ChatGPT or any AI that you have to help you do it right. It's like, okay, great. That's a good place to start. 'cause that will build your confidence in just using ai. That is it. Just build your confidence in using ai. And then I think the next kind of, if you wanna jump from like, I use AI to like, no, I really understand ai, right?
If you're willing to endure a little bit of friction, this is it. Ask AI to help you with something that you are fairly competent at doing, right? And not necessarily producing an output, like a design or something, but something text-based, right? Give me the summary, gimme this report, reorganize this, pull these insights from this.
If you give it that task and you ask it to do something that you're good at, well you begin to spot or the limitations very quickly. You start to know where it's kind of hallucinating and making things up. And then you realize, well, okay, since the whole point is to get AI to do this, right, what information do I have to give it to reduce the hallucinations?
How do I need to give it the information? Just by going through that cycle for a little bit, you'll then end up becoming a prompt engineer. I guarantee it. You'll be more a prompt engineer than anyone else, right? Because you have successfully figured out what information I have to give and how do I have to ask it to get a fairly consistent and accurate output every time.
And that is what you're looking for. And so it really is something that you're comfortable doing, something that you're pretty good at and figure out what it takes to get an AI to do it well. You go through that one, two or three times, you'll be so much more proficient with just about any time you see an LLM pop up.
Kristiana Corona: So you're not saying the answer here is to go to magical AI school. You're saying the answer is to play.
Blessing Richardson: It's to play. I mean, you can go to like a lot of the providers, so again, Google OpenAI, they have these academies. Where you can go in and learn like the one two threes. And if you're like an academic learner, like you actually thrive, then yeah, go do that.
'cause a plane doesn't work without having fundamentals first. You're gonna wanna go look for their academies and go through there and they have specific versions for business. So don't get lost in like the developer engineering side doc. Don't do that. Go to the parts for business where it talks to you how to use AI for business.
But if you're the kind of person who's like, I just need this to be real, start with play.
Kristiana Corona: I love that. I feel like that's a perfect way to end off and it makes me excited. Like, I wanna go dive in, I wanna go spend more time. So just like dedicating that time on your calendar the way that you would learning Any other thing just.
Put that on the calendar, make it a priority and yeah. We'll see what happens. And I know that for this audience you had put together a little something special. So do you wanna talk a little bit about the offer that you have for my audience?
Blessing Richardson: Yes. One of the questions I do get a lot is where do I start?
And again, ChatGPT is kinda like a juggernaut, at least a name of. Right. And so what I've put together is a resource called About Me GPT and you can get that at http://syl.to/worthytolead. And what this GPT will do is that it'll interview you. It'll interview you about your name, what computer you use, what kind of apps do you use, some of your preferences.
And the whole point is that it gives you a profile. So a file that you can upload back to any AI that you use. And it just kind of instantly tells it about you because it gets over the frustration of feeling like, I don't feel like AI remembers me. Yeah. Memory is funny with ai, so it's, it kind of is, but really isn't.
And so you gotta be able to just tell it like, oh, this is me. So if you already have that file on hand, you just upload it. And it knows everything it needs to know about you. And then it will also coach you through how to put your settings back into ChatGPT. So that's really helpful. So go ahead, grab that.
It's free, get using it. Because if you find that you're always telling ChatGPT or Claude, like This is who I am, these are my links, right? This is my website and you're just sick of it. This will help you get over that.
Kristiana Corona: I love that. And I feel like there's a lot of people who don't necessarily know all of the pieces that need to go into that when they're feeding and about profile.
So that's beautiful. And then you also have kind of an upgrade to that,
Blessing Richardson: right? I do. So for anyone who's. Sitting here going, well, I am a business owner. I'm a leader where I have all of this work and I work with specific customer bases and groups of people, and I just really want AI to know that, so I don't have to keep telling it over and over again About My Brand GPT.
That is an upgrade, and it will let you just talk to it about. All the things. So I use it myself in my business. I told it about my background, my journey through tech and my career. Why do what I do with clients? And you kind of just pour your heart out. It guides you through all these questions and it gives you two profiles.
It gives you that standard, like this is about my brand, all my links and all my stuff, everything about me, my mission, my value, my mission, like your value proposition. It does that whole kind of interview. But on the other hand, it gives you a file that takes your backstory, the why and the who behind what it is that you do.
And it gives you a file to upload that. 'cause I find that when my AI know why I am engaging with it and it knows who I'm trying to serve, it then has the context to actually give me things that make sense. And since I work with a lot of non-technical people, if I just go and I wax poetic on tech, it does not land.
And so I use a quite a bit to say, oh wait, this is my audience today. Give me a different version of this. And it's always ready to do that because it knows who it is I serve.
Kristiana Corona: I love that. I'm gonna, I'm gonna go use it today. So this upgrade is $14. All of, we'll put the links in the show notes for people so that they can access it.
And then if they wanna just. Connect with you. Where's the best place to connect?
Blessing Richardson: You can find me on threads, LinkedIn and Substack. My substack is blessing effect.com. That is blessing E-F-F-E-C t.com. It's like a blog slash podcast. We're trying to figure it out over there, but it is where I share a little bit of what's going on in my tech journey, what I'm doing, and then threads.
I prefer threads over Instagram, so if I'm posting at all casually, it will be threads and then of course LinkedIn.
Kristiana Corona: Perfect. Thank you so much. Really appreciate your time today and all your insights, and I feel like there are just so many ways that we can take some of these things and apply them tomorrow to our work.
We'll probably have to do like a, another part two of this at some point in the future and see where we're at with AI and how much has changed by then. Probably in, you know, five months it'll be completely different. But really appreciate your time. Thank you for coming on.
Blessing Richardson: Thank you. Thank you.
