In the ever-evolving landscape of AI, even the smallest ideas carry the potential to grow into powerful forces of transformation. Noelle Russell, founder and CEO of the AI Leadership Institute and a pioneer behind Amazon Alexa, says that leadership in AI “is about empowering people to dream big — and equipping them with the tools to turn those dreams into reality.” Explore how nurturing early-stage AI initiatives with strategic vision and creativity can lead to groundbreaking innovations that drive significant impact across industries.
In the ever-evolving landscape of AI, even the smallest ideas carry the potential to grow into powerful forces of transformation. Noelle Russell, founder and CEO of the AI Leadership Institute and a pioneer behind Amazon Alexa, says that leadership in AI “is about empowering people to dream big — and equipping them with the tools to turn those dreams into reality.”
Explore how nurturing early-stage AI initiatives with strategic vision and creativity can lead to groundbreaking innovations that drive significant impact across industries.
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Nichol: Welcome to the A IHI project. I'm Nichol Bradford SHRM's, executive and Residence for AI plus. Hi, thanks for joining us This week we're diving into the real world intersection of responsible ai, inclusive innovation and business transformation.
Our guest is Noelle Russell. Founder and chief AI officer of the AI Leadership Institute. She's a [00:01:00] multi-award winning technologist and one of the original team members behind Amazon. Alexa Noelle, welcome to the A IHI project.
Noelle: Hi. So good to be here. Thank you for having me.
Nichol: Thank you for coming. I, I've been following you for a while and I'm so excited to get you on the show. I'm really curious about your journey in ai. What drew you to the intersection of emerging technology, ethical design, and inclusive innovation, and I'd love to hear more about that. Alexa background.
Noelle: Oh my gosh, for sure. Actually, most people are pretty surprised to find out that, you know, I didn't, I wasn't intentional about my AI journey, but my dad certainly was. He actually raised me on the golden age of science fiction, like really long time ago. So I was like in sixth grade and I was reading the foundation series and then moved on to, you know, all the.
All the greats, Arthur C. Clark, and Asimov and Bradberry, like I grew up kind of talking to [00:02:00] robots and books and, and realizing this, just natural symbiosis. And it's funny because most of the time, um, these books don't end well, but usually it's not, you know, machines that are the trouble, it's actually humans.
And I think that's really what kind of sponsored my interest. Um, I ended up, you know, of course actually the, the reason I got into tech at all was because I have a, my. I have six kids. I always like to say that out loud. I have six kiddos, but my very first child, uh, was born with Down Syndrome. And so when he was born, I really, really wanted to kind of change the world for him and make it more available and accessible to him. And so when I got a chance to work on Amazon Alexa, I remember an email coming into my inbox saying, we're gonna build the Star Trek computer. Being a sci-fi person and then also having a very direct purpose like, Hey, how do I make the world available? I realized, oh my gosh, I could put them both together and build a talking machine that could help my son, you know, make the world more accessible.
And that was how it was made.
Nichol: Yeah. You know, [00:03:00] I, I am in science and technology because of Star Trek.
Noelle: Yeah.
Nichol: I, you know, I was a little girl in Houston, Texas. My dad was a plumber. And I, you know, and I would watch Star Trek and see a world where humanity, for the most part was on the same page. We had cleaned up home world and we were about the business of discovery and, and, um, you know, and.
Exploration. And a few years ago when I, when I finally met Michelle Nichols, I could barely speak. I could barely speak because I, I'm in science and technology because of her. And so I really relate with, uh, your story of how you came to it. And then of course, I, you know, I was an early adopter on Amazon Alexa, and so I feel like, you know, your, your.
Uh, your product journey? You know, I, I've had a, a, a taste of, [00:04:00] um, I wanna circle back on something. One of the things I've heard you say is you've described AI as a human enabler, and I think you've told us a little bit about your origin story around that. But for leaders who are. You know, raising the AI literacy in their organization, getting people on board.
How can leaders reframe the conversation with their teams to sort of reduce the fear and help people see AI as a tool for growth? And as this human enabler, I.
Noelle: I think you're a hundred percent right that a lot of people are afraid that that fear is. Often when I walk into a room and I'm about to talk to them, they often have their arms kind of crossed across their chest. Another AI session, and I, I really am passionate about coming to it at, from a very different perspective.
In my early days at Amazon Alexa, I was a very unique voice in the room. I was the only woman, I was the only Latina, but I was also the only mom. I was the only caregiver taking care of [00:05:00] my dad. Like there were so many things about me that. Made my approach to this technology just different than everyone else's.
And in that the way I was able to achieve success on that team was I became a builder. Now, granted, I'm a builder at heart, but I realized the value in doing versus talking, in showing versus telling. And I do think that that's one of the reasons why. As a complete like novice to the world of AI and jumping into a team like Amazon Alexa, I wasn't really afraid because I was willing to put my fingers on a keyboard and just start building stuff and learn as I built.
And that's, I think, the world we have now. And so I encourage leaders to really think about like. How can you make this a tangible like thing for your, your teams? They won't know what they can do. Like the, the first instinct is for us to be like, don't use that. And really the opportunity we have when you ignorance is, is like, it begets fear, right?
It creates fear when you don't know what you're doing. You don't know what's gonna come. You don't know what this technology [00:06:00] can do. when you actually partner with it, there's a huge opportunity to excite your workforce. And that's what I'm good at, right? I come into organizations, the AI Leadership Institute, that's what we do.
We, we go into companies and we teach, but not just teach, we excite people to realize that this tool, when you actually know how to communicate to it safely, responsibly, it can become one of the most profit generating. Uh, cost optimizing and energy producing like elements of your entire organization, and that's why most people end up realizing that AI is a little bit about tech, but a lot about culture and a lot about, you mentioned inclusive innovation. It's really about how important it is that we all come into the room and feel like there's a part for us to play. And AI really does amplify that. I.
Nichol: Absolutely. And well, you know, one of the reasons why people are afraid is because they're worried about their jobs. They're, they, like you said, they don't really understand yet. What AI [00:07:00] can and cannot do. And so we're in the midst of, you know, jobs being redesigned. You know, two weeks ago, the Spotify CEO said, no headcount, unless you can prove that you couldn't do it with ai.
And last week, the du lingo, CO said the same thing. And so, you know, people are are worried. But you know, the, the part about like understanding job design. So you know, what if you know, for managers who are in these organizations, especially HR leaders, that's our audience, what are some of the ways that they can think through and practically work with teams and managers to identify.
You know, in that context, what actually could be done by AI and, and, and how do they involve employees in that process? Because, you know, I think also the, the definition of inclusiveness, um, it, it also means people in this context, it means it employees all across the organization who have [00:08:00] actually been doing the work.
Uh, and they're the ones who really know what could be automated. So like, what are the practical. Things that an HR leader can do to support a manager who you know has been told no more. No new headcount unless you've tried it with AI first.
Noelle: Well, and, and the reality is for a long time, I've been doing this about a decade and for a long time I've been saying that there are jobs that computers. AI systems, machine learning models are good at, and it just so happens there's a direct correlation to things that humans don't care that much about doing. I'll give you an example. I was over at the Metropolitan Museum of Art a few years ago, and we were commissioned to come in as data scientists. I had about nine data scientists from MIT. We go in and we're like, Hey, metropolitan Museum of Art Curators, we're gonna, tech is gonna fix you all right? And, and they again had that like. What are you doing here? Like, are you gonna, they first they, they made a very big assumption that art and ai or [00:09:00] art and tech, they don't go together. And a lot of people
Nichol: Hmm.
Noelle: this way 'cause they're like, no, no, no. This is uniquely human work. But what we did, and I encourage HR leaders to kind of think about it the same way, is we did what I today call an AI impact assessment. And what we did is we went in and we talked to these organizations very specifically and we said, Hey. gonna build something, just talk me through the way you build this technology and we watch them. We watch the humans do the work. And when they do this work, it's funny, when they get to a certain part in the process, you can see their whole countenance changes.
They're like. Ugh. Like their shoulders droop, they get frustrated. This little spot shows up right between their eyebrows. Like they're like, you could tell they got to a part in the process. They don't like to do, they don't want to do that is old. That is, um, redundant. Those are the opportunities. Like we're literally watching human behavior and as HR leaders, this is our specialty. We watch human behavior [00:10:00] and find opportunities. This is why I actually think it's great that Spotify did this. I don't know that they educated people enough on why they did it, but you actually don't want humans doing things that a machine can do. Humans don't enjoy that work. It's mundane and monotonous.
Like why would you want a human to read a spreadsheet? they could take the output of a spreadsheet and invent something, or create something or design something like that is true human enjoyment. And so watching humans, you can, you could just see, it's almost like being behind the FBI mirror. You can watch them and be like, oh, I see right there.
That moment you did not like what you were doing. What was that? What did that look like? we go in and we find out, oh, that was a time when they were connecting two ERP systems together. We can use AI for that or, oh, that was the time where we were
Nichol: It's not even my job, and I'm like, uh,
Noelle: scraping metadata, right? Like
Nichol: yeah.
Noelle: wants to scrape metadata for a living.
Like there, there's easy ways, but here's the [00:11:00] trick is that you do have to be a leader that cares enough to look at your humans and see that pain and then listen to them. And that is one of the tricks that I, I mean, I uncovered when I was talking to the Metropolitan Museum of Art Leaders, but then went on to use it in high fashion and in agriculture and all these places where tech isn't like infused the way it is in high tech companies that I've
Nichol: Okay.
Noelle: But I went in and realized like, they just wanna be heard. They wanna know like, Hey, I hate this part. Can, can you do something about that? And no one is afraid. Of me taking that part of their job, they actually cry. Little tears of joy, like, please take that. I don't wanna do that. I never wanna build another PowerPoint if I can help it.
I never wanna submit another service ticket if I could help it. Like if I don't have to do that, I would gladly give that work away so I can work on higher value tasks. And I think, I think that's kind of the. You know, the, the strategy that most leaders need to take is how do I look at my humans, figure out what they're great at, what they don't wanna do, and create a [00:12:00] symbiosis, right?
Create a symbi symbiotic relationship with AI systems to help amplify human ingenuity instead of take away from it.
Nichol: Wow. I, I, I love this simplicity of, of saying look for the, uh, and, and, and it's so, it's very, um, it's very tangible because I. You know, you, you're the only person that I've talked to who's described it like that. And as soon as you said it, I knew exactly what tasks you were talking about. And I think, you know, with dealing with the fear that people are experiencing, that's a really great way to bring them in. So you've helped launch some of the most influential AI products of our time. What did those experiences teach you about the role of human leadership in shaping AI outcomes?
Because you've talked a little bit about it's a social, cultural, behavioral thing. It's not a type upgrade. Like how do leaders need to be different for this change?
Noelle: Well, [00:13:00] I I love that you mentioned like leadership. That is really, I mean, it's, the reason I created my company was that I realized that we needed different leaders and that in the world of artificial intelligence in a world with de. Democratized ai, meaning everyone has access. You know? Now I go into a room and if I'm speaking to a thousand people, I might just ask them, how many of you've used GPT Chat, GPT or a variance?
And 90% of that room goes up. It took me five years to get half the room to say they've used Alexa, and then before people started putting them in their closets. But that was. A really interesting moment for me to go, oh my gosh, everyone's using this. And not a lot of people know what they're doing. They don't know, you know, what's coming into their world. And I went from Amazon to Microsoft and so I got a really, you know, I went to Microsoft because they wanted to do a similar thing. They wanted to bring an AI model, actually a collection of models to market. I'm really good at that. I'm good at taking models and helping people adopt [00:14:00] them and use them and create energy and excitement. one of the things that I realized while I was doing that, it's actually become an analogy, um, as a matter of fact, if you look at my book, the the Scaling Responsible AI book, it has a tiger right on the cover. And the reason that I have this baby tiger, you can probably see one behind me,
Nichol: Yeah.
Noelle: this, this like. is a really good kind of image for what I felt like I was surrounding myself in. At Microsoft there were like 17 of these little baby models, little baby tigers running around. No one was asking in that excitement, right? Nobody was saying like, Hey, baby tiger. Like, look at your paws. How big are you gonna be?
Or, wow, you've got pretty sharp teeth. What are you going to eat? Where are you gonna live? Like if you think about a model when it starts, everyone's super enamored with it. Like you're like, oh my God, this is amazing. It's so fluffy and cute and I wanna be part of it. thinking about like, what happens when this thing gets older, right?
What happens? When it grows up
Nichol: Yeah.
Noelle: that is where I'm very [00:15:00] concerned. And here's the cool thing about the world, that human resources leaders like the human resource leaders of the world, you literally own the humans that are going to now need to not just know how to lead humans, but they need to be even better leaders to know how to lead machines.
And that was a big aha moment where I was like, oh my gosh, humans are the ones who are gonna raise these baby tigers. And my. Right now, they don't even know that they've adopted them. They don't even know the level of responsibility that has come in to to their world. And so that's why I got very passionate about teaching kind of everyone that everyone's a leader in the world of ai. Because even if you don't lead humans, you are leading machines. So how do you. The, the number one reason most AI systems failed was because we didn't lack clarity of thought in, in designing them. We kind of were like, on this rollercoaster, oh,
Nichol: Mm-hmm.
Noelle: let's build this. Um, and what we realized was like, oh no, we need to, someone in the room needs to slow down and ask these questions.
And many [00:16:00] times, the, the human resources or human capital organizations, right, like many times. It is those people who ask those questions, or at least they know how to get that to happen within teams and within organizations. And that's why I do a lot of work with HR because I feel like they are my, my hidden strength inside organizations to start asking questions that maybe we weren't really ready to ask five years ago.
Nichol: Absolutely. I, I, I agree with you completely because you know the. One of the things that we've seen, so with SHRM, you know, we have the vantage point to look across industries, look across organizations from, you know, mid-size, small, mid-size, all the way up to the very large organizations. Some of the things we're seeing, it's a success.
It's are the people who realize it's a social, cultural, behavioral change that comes along with the technology. And also the other thing is like, when this first started a couple of years ago, um, people weren't certain. You know, people outside of HR, were not [00:17:00] certain that HR absolutely has to be in the room on these because it's where the technology touches the people.
So I, I just love that you say, you were saying that, um, you know, many people in HR are what people call non. Technical. Um, though there's a lot of HR tech coming in and, and you know, we're seeing software in every task that's related to HR coming in. But, you know, for introducing AI into everyday workflows for non-technical teams, what, what works?
Because to your point, it's like we, you know, HR ask the right questions. For what's happening globally with the organization, but also, you know, the HR teams are coming up to speed. So what really works for non-technical leaders or non-technical teams to get the AI into their everyday workflows? How did you do that?
Noelle: Yes. So the, the one i I, and it might surprise, it'll, it [00:18:00] won't surprise you now 'cause I've kind of led a little bit earlier, uh, on this topic. But the most important skillset you can give people is the ability to talk to a machine teach them their, the ability they have to control that machine with their voice, with their words. And so I use it, um, we have a, a, a session we call it. AI agent building, right? Build an agent, and this agent can be very sim, very simple, but anyone can build an agent like anyone, because today you can go into Chachi PT and use GPT Builder. You can go to, Instagram and use AI studio. I mean like it's available to anyone, but there is a way to do it.
We have huge amount of research now over the last two and a half years since this product. Chat GBT was launched. 3 million weekly users. Like, it's amazing how many people are using this technology and don't truly understand how it, how it's used. So the best thing you can do to encourage people to use it every day is to take, take a minute. Like don't just throw [00:19:00] copilot in their lap and be like, good luck. I hope it works out. this is where failure begins instead. I mean, we are. Oftentimes we own learning and development. This is a perfect opportunity to take learning and development into the hands of the people in our organizations and teach them how to build a system, a responsible AI system end to end.
It takes about four hours at least the workshop I deliver does. Sometimes I've done it in an hour, especially for executives 'cause they have less time and think they need to do it fast. But the goal of the pro of the workshop. It's just to go in and build a system. And the funny thing about this is that now you might have heard the term vibe coding.
It's probably one of my favorite things to say,
Nichol: Yeah, I was about to ask you about that because you started in voice too, you know, so
Noelle: right.
Nichol: it's.
Noelle: coding. It's meant for people like you and me. Like, we're like, wait, what? It's totally a vibe. But when you build vibe coding, or in this case just building a system with your words, you [00:20:00] realize, number one, how very important your words are. number two, you also learn what is truth? Why does the, why does the machine lie? Why doesn't it say the right thing? Why? Why did it answer a question I didn't tell her to answer? And then you also learn a machine learning principle. You, you learn the concept of reinforcement learning with human feedback. You realize that it's your job to tell a machine when it's right and wrong, so that it knows to correct its behavior. And most organizations, they hand you an AI model, baby tiger mode and go, I hope it works out. Good luck. It's in a cardboard box and you're like on your own. But that's not really like, this is more
Nichol: Yeah.
Noelle: ever before because now you don't have to know any tech to build an AI system.
You don't have to know any tech to configure a system that'll talk to your customers. But you should know how to build an inclusive, responsible, safe system. It'll still generate profit for you, but it won't hurt your brand or hurt your customers down the road. The first year of an AI system is the best year of its [00:21:00] life, just like babies. from a baby's perspective, everything's kind of done for them But once it goes out of the door, once it gets into production,
that's usually when bad things start to happen. Security, accuracy, fairness. All the worst stories in AI happen when people prematurely and with. Little training, push something
out into the world that they don't really know what it can do. And that's what I say, every tiger baby tigers become big tigers, every single one of them. They all grow up and it's our responsibility to know how to take care of them. And I do think that that's, honestly, it's human resources responsibility to ensure we're building a culture of people that care about that problem, that care about building. Inclusive, safe systems. And you know, I always hear people talk about like Black mirror and the
Nichol: Yeah.
Noelle: that'll happen. I'm like, not if we're in charge, like not if we're in charge
Nichol: Yeah. One of the things that I, I share with, um, HR leaders when I'm speaking with them is sort of like the, [00:22:00] you know, no matter what, uh, Microsoft or open AI, or, you know, any anthropic, any of them build, it doesn't start creating value until it's within the context, culture and customer set of any given organization.
And, uh, you know, and, and within that, there actually, there are no experts because everyone's learning how to do this at the same time, um, in terms of AI within a particular company's culture, context, and customer. So I say to them like, there are no excerpts. It might as well be you like, just, it's you, you know?
So, you know, jump in. And then also, because I think because. HR actually owns the employee lifecycle. Um, they actually, you know, from sourcing all the way through to, um, you know, every step, everything that an employee touches, even though those employees might have managers here or there, HR owns the whole lifecycle.
And because of that, I believe that responsible ai. [00:23:00] Internally is going to migrate into HR, uh, because they're the ones that are asking the questions like, what about the people? And, and people are asking them, is it fair, is it transparent? If it's in performance, am I being evaluated? How am I being evaluated?
Um, so I just resonate and, and with everything you say, what I'd love to know is. You know, what do you see the future of HR being, you know, because you, you know, you've pointed out several places where it's really clear that HR is in the room. They have something special to contribute. So for our audience who, you know, will, you know, hear this conversation or, uh, for our audience for this conversation, you know, what does the future of HR look like when they're fully empowered?
Noelle: I, I think it starts with this concept we mentioned at the beginning, inclusive innovation. Like it's, we're branding a lot of our innovation with the words AI now, but soon AI will be. So infused into [00:24:00] everything. It really is about how are we doing things in a new way with the latest technology available to us?
And we now also know that it's not gonna be necessarily all defined and built and designed by technologists that business leaders are gonna be doing this work. So the job of a human resources organization is gonna be to make sure. That those teams are well equipped with not only, you know, the frameworks that are useful in building out these systems.
You know, I always think about, you know, there, there's what's called a, an AI safety system. When you build an AI system, there's like four levels of it. One, the very first level. the most important level is called the human AI experience, that someone needs to design ahead of time. How is the human and the AI gonna talk to each other?
Like how is
Nichol: Mm-hmm.
Noelle: happen? And that is intentional design, meaning in every role, someone, I mean, it's just like what we did maybe 20 years ago when the internet and you know, computers and cloud. [00:25:00] Like we had to rethink a lot of our roles and our descriptions of how work was done. That reinvention of work in a world where AI is gonna do AI things, humans are gonna need that. Redesign and that work redesign and it only happens. It's not hrs job to do that work. It's their job to facilitate those conversations with the people that know that work. Just like you said, maybe they don't become experts, but they will definitely become the domain like domain experts. be the critical commodity, right?
People who have done this work for years, either in a line of business, finance, healthcare, retail, like everyone has had 20 years that that has been in this field for a while, has had decades of experience in something. It probably wasn't ai, but that's the good news, is that we don't need you to have experience in tech. We need you to have experience in the work that you do. And I think human resources organizations need to now figure out, like that's the work they need to do is build a way to shepherd all of these new [00:26:00] humans that wanna do higher value work. Who better than HR to help them craft those new roles? Amplify their human experience on the job. And that's, that's definitely, it's already happening. So I wanna, I wanna say this, this is not like future speaking, like it's happening in large organizations now. Specifically in organizations like Anthropic and Meta and Right, and OpenAI, like they're already redesigning their workforce.
So follow those examples. Maybe not exactly. be, be sure that you are not the only one doing this work. And that's what I would encourage you to do is that there are a few people that are very good at this at scale. Obviously that's why we're having this conversation. They're all, I would find people that you could talk to, to give you just, you know, don't do this alone. Community is everything. Find a few people in your industry or across industries, but. Do it together. 'cause there's so much power in actually joining forces inside an industry or across industry verticals where we can build things and learn from each other. And I don't wanna [00:27:00] repeat some of the mistakes we made 15 years ago in social media where we all created little fiefdoms. Like we
Nichol: Yeah.
Noelle: to actually break those walls down. And again, HR organizations, you're the ones who are gonna be able to do that.
Nichol: what does the actual process of redesigning work. Like, what are, what would you say are like the three or four steps that go into that? Because you know, I, I, I think that where people get a little anxious is that they know that they have to redesign the workflows. Um, they're not yet, you know, they may or may not yet be fluent with ai.
And so there's just sort of like this, this question. So what is the advice that you would give to them about, okay, here's the 1, 2, 3, 4 thing that you need to do. To work with the organization to redesign the workflows to be AI compatible.
Noelle: Yes, I love this question. It's my what I do every day, but the first thing goes back to what we could just kind of pull all the threads [00:28:00] together, right? The first thing you have to do is understand the work. Many of us. Just, let's face it over time, if kind of copied and pasted job descriptions and we've kind of got a little bit of ambiguity in what people actually do.
Like they get a job and they're like, wait, what am I supposed to do? Like they don't, there's not a lot of clarity, so it allows us to go in and look at the work that's being done and analyze that work. And if you already have this analysis, great. You get to move on to step two. But the first thing is understanding the work and then understanding the human AI experience.
What does it look like? Here's the good news. You can actually ask a. Internal model that you've created. Most of us have one now, but if you don't, that would be the first step. An internal model that you can ask about your work, your workflows that you ask it. What would be the best way for humans and AI to collaborate on this? Like it's okay to build a model to support your. Inquisition about this rework, like these models are trained on trillions of data points. Leverage that. Don't leverage it in a public chat, GPT, like don't [00:29:00] leverage it in a model that everyone else uses. Create your own model and train it on the way you work and partner with it to answer these very questions. most important thing to do is to create systems. This is like the third thing is to create systems for decision making and a, and be as inclusive as possible. And I don't just mean gender and ethnicity, but yeah, do that. I mean like people that are almost never called at the beginning.
Security, legal, finance, compliance, um, of course, human resources, you'll be leading the charge, but bringing in this. Old, like ultimate inclusive group, we often will call them a center of excellence or a responsible AI advers, you know, advisory council. But you need a group of people that are gonna look at each one of these new definitions and go, I see what you're doing there.
Yeah, I agree with that. Or no, I don't. But it becomes, and this is why we need to be there, like anyone who's in the HR space that is leading this space. You do need to become aware of how these systems are created. I call them, I [00:30:00] call like AI solutions architecture. Maybe that's the, the fourth thing is understanding. You don't have to be technical, but you do need to understand how AI systems connect because it is in that connection. It used to be, I'm gonna build a team of humans. Now you're gonna build a team of humans and machines, and so the more you understand about those machines, the easier it'll be for you to actually connect them together. Again, we don't have to do the connecting. We could just be the facilitators of bringing together an ultra inclusive group of people across all lines of business, across all different types of educational backgrounds, and helping them pull together, um, a, a team, an advisory board. And then the last thing I'll mention is that there is a new capability and it's called an AI red team.
It's actually a verb more than a noun, but it is the team and another inclusive group of people that are gonna test this model before it gets to production. So there are new roles in your organization. Again, not. Not necessarily technical roles, but there are new roles that you're gonna need to define to [00:31:00] manage the accuracy of models, to manage the fairness of those models, and to manage the security of those models.
At least those are like the first three. so just, you'll wanna be thinking and maybe partnering with someone like myself who can help you see past, how do we just use a little agent right now to do this little thing? And how does our entire org chart change when there is at least one robot? human,
Nichol: Mm-hmm.
Noelle: that is a design thinking session waiting to happen.
Nichol: Wow, wow. So amazing. I, I really appreciate how you just laid out, you know, the, all the steps and, and also like what, what the first 90 days are. Um, so that's amazing. So one last question we always like to end with is, you know, for the person who is in our audience today. They, you know, they, they watch or hear this podcast.
Um, what is the one thing that after they listen to us, that they should go back and do on [00:32:00] Monday? I.
Noelle: My biggest suggestion is to custom build. A model, A GPT. I don't mean going to GitHub and learning how to code. I mean go. You can either, we have a a online on demand Bot Builder bootcamp. You can go through. I have a community, it's called I Love ai. You could just Google it. more importantly, you could just go into Instagram, into AI studio or into Meta Studio on WhatsApp. Like go in and create a custom GPT forever. Anything, doesn't matter what it is, it's actually a bit addicting, so I dare you to do just one, like a la potato chip. Um, but I do think that the most important thing you can do is actually build something for yourself, using just your words to clearly define its behavior. And in doing that, you'll get very excited about the opportunity. And you'll understand Unequivocably, you'll understand how necessary you are in this process, that that machine doesn't do anything unless you have the clarity of thought to describe what it does and have a metric to measure its success.[00:33:00]
So put your fingers on a keyboard and go build something awesome.
Nichol: Fantastic. Wow. And so that's it for this week's episode. A big thank you to Noelle Russell for sharing your experiences and insights with us. And so, so practically speaking, I love it. And uh, I'd love the baby tiger. So before we wrap up. I encourage everyone to follow the A IHI project wherever you enjoy your podcast.
And if you enjoy today's episode, please take a moment to comment, leave a review, and help spread the word. And finally, you can find all of our episodes on our website at SHRM dot org slash a ihi. Thanks for joining the conversation and we'll catch you next time.
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