Tammy Soares: I wish everyone could see us.
Wendy Collins: I know, right?
(Laughter)
Tammy: Like, Wendy's pumping her fist. Like, it's fantastic.
(CATALYST INTRO MUSIC)
Tammy: Welcome to Catalyst, the Launch by NTT Data podcast. Catalyst is an ongoing discussion for digital leaders dissatisfied with the status quo, and optimistic about what's possible through smart technology and great people. I'm your host, Tammy Soares, and I'm the President of Launch. It's my first time hosting our podcast, and I am thrilled to be here. So, at Launch, we deeply believe that technology only reaches its full potential when it's designed around real human needs. And I find too often in the race to implement the latest technologies, sometimes we overlook the very people who will be most affected by these innovations. And it is no different with generative AI. Today, I want to dive into the often overlooked human side of AI. How it impacts the workforce, shapes our roles, and challenges the way we work and think. We're not just talking about AI and automation, we're talking about humans. The employees, the engineers, and the leaders who are living through the transformation. How do we ensure that these innovations truly enhance human capabilities rather than diminish them? How do we capture the wisdom and experience of an aging workforce before it's lost? How do we maintain creativity in the face of automation? And perhaps most importantly, how do we get people to embrace AI tools in ways that empower them and not replace them? So, to explore these questions, I'm joined by two incredible guests and colleagues from NTT Data. First, we have Kim Curley. Kim is the Vice President of People and Organizational Consulting at NTT Data, and I think she's been on here a time or two. Kim has spent her career focused on the human side of business, enabling leaders and organizations to thrive through change. She leads our advisory consultants, who deliver people-centric solutions to help solve complex business challenges. And Kim, it's a pleasure to have you here.
Kim Curley: I am glad to be here, too, on your inaugural hosting job. So, it's going to be a good one.
Tammy: Thanks. We are also joined by Wendy Collins. Wendy is our Chief Generative AI Officer for North America, and she brings over 25 years of experience in AI and advanced analytics, having worked with iconic brands like Delta and Macy's and the US Army. She leads our AI strategy development, and ensures AI delivers competitive advantage while supporting human-centered adoption. Wendy, thanks for being here today.
Wendy: Thank you. I'm thrilled to be here.
Tammy: What a dream team of a podcast, I have to say.
Kim: Woo-hoo! (Laughs)
(Laughing) You both know how excited I am to have you here, for sure. And it was actually a recommendation of one of our clients, that said, it would be great to hear what the three of you think on this topic. So, happy to have you here. So, Wendy, you and I were at our client advisory board event pretty recently, and we were hosting and facilitating a session with a section of CIOs across a few different industries. And we had asked them, you know, what topic did they want to talk about together? And one of the ones that came up the most was the aging workforce. And, it was really interesting to me, because we started with that challenge and it very quickly went to AI and generative AI. And I'm just really curious. I think that's a great way of starting to talk about it, is thinking about the business challenge that the organizations were having. Do you want to talk just a little bit about that, and sort of, where that conversation went?
Wendy: Yeah. For sure. And what's even more fascinating is since that conversation, I've talked with clients from other industries, from healthcare and from life sciences and other spaces where they're experiencing the exact same problem. So it's not unique to manufacturing, or other types of commercial industries. You know, one of the big pressures we're hearing a lot about is that baby boomers are starting to retire. And especially in manufacturing and in healthcare provider settings, some of those employees and people are the most experienced people. On the plant floor, in the doctor's practice. And they carry a wealth of information, experience and knowledge. And unfortunately, because they're about to retire, they're potentially walking out the door with it. So, not only are enterprises struggling with, how do we replace that knowledge? But then they're also being bombarded with other challenges in terms of backfilling those individuals. So, we're seeing a lot of pressure from Gen Z, wanting to, instead of go work for companies the same way that we did or our parents did, they want to work part time. Or they're more interested in the flexibility that the gig economy offers them. And then, when you combine that with the decreased birth rates that we're seeing across a wide variety of countries, there's a lot of pressure for clients across all kinds of industries and areas that struggle with, how do we fill and satisfy the workforce that we need in order to be able to maintain the productivity and the patient care that we want? And so, AI is actually emerging not just as a tool that can assist with that process, but it's almost becoming an urgent need. Because there just aren't enough humans to fulfill the workforce needs to sustain our economy.
Tammy: So, what are the kinds of things that we can use generative AI for, or AI for, to solve for it?
Wendy: Well, so, you know, one of the interesting things we've been talking to a lot of manufacturing clients, especially about their soon-to-be retiring employee base is, the majority of the information sits in those employees' brains, and it hasn't been captured in knowledge bases, for example, that can be tapped into, or that you can put generative AI on top of to make it more accessible and readily available. So we're actually talking about, how do you leverage generative AI to extract that information from the workforce that is about to retire? How do you capture the information in a way that can be digitized, and then layer additional levels of generative AI on top of, to make it interactable and usable by other parts of the workforce?
Tammy: So, Kim, how does that work when you've got... We were just talking about this being, you know, an aging population. So, typically they're not engaged with technology and digital. So how do we think about even extracting that information? How do we get them to even do the machine learning, teaching the machines around what they have in their brain? How do we think about that?
Kim: Yeah, there's a couple of things that come right to mind, and a couple of things that I think are pretty exciting. One of those is, with the advancement of all kinds of sensors that we use in a manufacturing environment, we're getting a lot more information around how some of these workers work. We know the movements that they make. We know movements that lead to good outcomes, movements that lead to bad outcomes. You know, we really understand a whole lot better how the humans and machines, for example, on a plant floor are interacting. So, if you can grab some of that information, which, frankly, the worker does not really have to, you know, do anything to help, they just have to do their job, to help gather that information. If you combine that with things like knowledge bases around manuals for particular types of equipment, or maintenance logs for specific pieces of equipment, you get something that's really robust, and a robust set of data that you can use to really help train the next generation. Now, what's really cool is, as some of these individuals are starting to want to retire, they want some flexibility, too. So, they may not want to go from a full-time job to zero. They might want to start stepping down into retirement. Right? So, let's take those big brains that they have and use them as the sniff testers, right? For the outcomes of whatever gen AI is creating for us. So if they create a training module, or a troubleshooting module, or whatever that might be, to help a newer generation in the workforce learn how to do something, wouldn't it be great if we could pair up that training with somebody so that they can troubleshoot, and say, yeah, that's right, or no, that's not right, or gosh, the data behind this is wrong, that feels like a hallucination. Or, you know, gosh, they just got that entirely wrong and we need to rethink how we're gathering those signals for those insights. So I think there's a lot of ways that we can help our, particularly our aging workforce or our workforce nearing retirement, step into that or ease into that, which may ease their financial burden, their concerns. Also gives them an opportunity to be really engaged with the employer, and have a great, positive feeling about the opportunity that that employer is giving them at that stage in their career. So that's one of the creative ways that we ought to be thinking about gathering a lot of information, but then still using that physical brain, while it's on site, to sniff test and help advance that learning even more.
Tammy: I mean, I'm telling you, I like the idea of ramping down. I can see that in my... That's like, in my next ten years, maybe I can talk to our leadership team about sniff testing and letting me do some sniff testing because I...
Kim: (Laughing) There you go.
Tammy: I feel like I could... I'm, like, seeing that, you know? Like, I'm seeing it on the horizon. So, I really like that. I like that idea.
Kim: Well, and with the financials and the economics the way they are, I mean, that whole step down is necessary for a lot of the workforce, not just something that they might look forward to, but it's really necessary to take care of their families. So, I think it's kind of a cool option.
Tammy: Yeah. I mean, how are companies thinking about this? Is it about planning appropriately? Like, I mean, it sounds to me like we would really need to be very proactive and see it coming and really focusing on how to solve for it now before it's too late. So, how are clients or companies thinking about that?
Wendy: Well, I hope that they are thinking about it now, and not just thinking, but actually making plans. Because the worst case scenario would be, do nothing until the big brains, to use Kim's words, which I love, walk out the door for the last day...
(Laughter)
Wendy: ...And then you realize that there is no going back. Kim and I were just at the Manufacturing Institute's Workforce Summit last week, and we heard a couple of really interesting ideas, where clients are taking what would ordinarily be a fixed, you know, eight, ten, 12 hour shift, depending on what their plant looks like, and breaking them into smaller bits to enable activating and allowing that aging workforce to step down. You could also break the shift into smaller bits, and have on-shop floor time be part of it. But then the other bit be capturing information, capturing knowledge. Doing exactly what Kim just described, doing the sniff testing. And I think, you know, one of the things that is one of my strong hypotheses is that an older workforce that can be technology-resistant, if you involve them in creating the technology and leveraging the things that they do really well to create the technology, that will then empower younger workers or more junior workers. That's a space where that group of employees can shine, and can feel pride of ownership in the creation, not in the receipt of technology, or feeling like technology was pushed upon them, but instead feeling empowered to help create it.
Kim: Yeah, you are spot on, and you are now officially an honorary member of the People and Organization consulting team. Because you absolutely nailed it, Wendy.
Wendy: Woo-hoo!
Kim: The very best way to decrease resistance is to engage the employees in the creation of the future. Whether that's the future of the company that they've cared so much about, that they've spent their entire career there, or whether it's the future of the person who's just starting out and we want to create that legacy from day one. So, that engagement is abso-, abso-abso-absolutely critical.
Tammy: I think on that note of the technology being pushed on someone, at the same event that we were talking about earlier, we were hearing a story from one of our clients about how they created a new tool for physicians to use and ordered to... And it was leveraging generative AI, and it was to improve the amount of time they could spend with a patient and really reduce the amount of time that they were going to spend in the evenings. He coined it pajama time, which I love. And so, when they created this tool and they went back to the physicians, they actually talked about it being, this is a tool you can use that's going to reduce pajama time, and enable you to spend more time with the patient. And the adoption rate was very high, because it wasn't a tool that was pushed upon them, like we've developed this new tool that's going to increase your productivity. It was about, like, showing with, sharing with them, like, the impact it was going to have into their daily lives. I think the great part about it was, the adoption was really high, and then an unintended consequence was that it freed up time even in the room where they could actually spend more time with the patient, understanding the patient needs. And so, the satisfaction rate went up with the patients. And it just is a... It was a good reminder for me to think about, when we do take a human-centered approach, or actually using technology behind the scenes, sometimes even unseen, to solve for a very real, whatever it is, consumer issue, patient issue, employee issue. And so, have you... have either of you seen any, can you give more examples of that? Or how you're thinking about it from each of your different areas?
Wendy: Well, I remember that conversation, Tammy. And I remember asking him after he told that story, So, do you think you would have gotten the same adoption if you had forced it upon your physicians? And his answer was a resounding "NO." Right?
(Laughter)
Wendy: So, the option of choice, and the other pattern that I'm seeing that's working with other clients is identifying pockets of early adopters and leaning in there in smaller areas to develop, almost, your own internal change champions. So, rather than trying to do blanket, and drive blanket adoption across an enterprise or across a large portion of the workforce, pick those that you think are likely to respond positively, and then spin those positive responses into stories that they tell on your behalf to drive the change even further.
Kim: I couldn't agree more. And I'll add in there, I think one of the things that's going to be a little different about generative AI in the workforce is the way we teach it is going to be... Or the way people learn, is perhaps what I should say, is going to be much more about peer-to-peer learning, than it is monolithic learning that is sent down through specific training cases. Now, that's not to say we don't need and shouldn't have and won't always have, you know, canned training or more generic training on what it is and how to use a specific tool. But what I'm really talking about is, I didn't start using AI and get better at it until there were others around me who do work that I do telling me how they had used it to decrease the amount of time that they spent on something, or to increase the quality of something they were creating for a client or for a project or whatever that might be. So that peer-to-peer learning capability, I think, is going to show up really big here. This is not a case of, if you build it, they will come. If you build it, they will come for gen AI outside the workplace, where the stakes are lower. Inside the workplace, the stakes are higher, and so that peer-to-peer learning is going to drive it faster than, accompanied by, hey, let's actually give people a tool that does something they need to get done, as opposed to just here's a tool for a tool's sake, right? So, number one, make it outcome-driven. Human-centric, outcome-driven. And number two, invest in that peer-to-peer learning capability. I think that's going to be really important in that community that supports everybody's learning.
Wendy: Yeah. I mean, a very simple example was one that Kim and I tag-teamed. Kim, you want to tell the story about the prompt that we did?
Kim: Sure.
Wendy: In the Workforce summit. Yeah. Go ahead.
Kim: Yeah. So one of the things that we found really interesting in the conversation leading up to our presentation was, I think people have this conceptual understanding of how to use gen AI, but maybe don't have and aren't getting those real life examples of how to make it really sing for them. So on stage, Wendy and I said, we're going to show you how understanding how to write a really great prompt can completely change your experience with gen AI. So, Wendy went first. She was awesome. And her prompt... Wendy, I think your prompt was something like, I have free time in Minneapolis, what should I do? And Wendy got 15 scrolls on a cell phone worth of non-specific, everything on the planet that you can do in the city of Minneapolis. So, helpful, maybe, but not specifically helpful. How much time did it really save her? Probably didn't save her any time. My prompt said, I'm in Minneapolis on business for three days. I have two free hours one evening, and two free hours one afternoon. I'm staying at the Hyatt Regency. What can I do that's within walking distance? And gen AI, in splits of a second, sent back, here's two itineraries. Here are the two walks you should take. You should take this one in the evening and you should take this one in the afternoon. Here are the stops that you should make along the way. Each one of these should take about two hours, and they all start close to your hotel. Everybody in the audience was like, wow, I never thought about the fact that I need to learn how to write prompts in order for this tool to be better for me. But it's like using any other tool. You've got to learn what it can do for you and how to make it sing.
Tammy: So, what I love about that is, a couple of things. One is that, Kim, it sounded like yours was like, if you were going to ask someone, a person, a real life person who lives there, what to do, I feel like it would be similar to what you said, right? You gave a lot of context. You gave a lot more details. Whereas, Wendy, it sounded like yours was more of, like, the Google search prompt, right? So I think there's a very different way of, we sort of, when we have those text boxes, we're sort of thinking about the old way of doing search, and you're trying just to, you know, get the return search list and then you can parse it. Whereas with the generative AI, with large language models and those prompts, you can, the more context you give it, the more detailed and more powerful it can be, it sounds like. It seems like. Now, did you do that on purpose, or were those really, like... Are those the ways your brains worked in order to... I'm really curious about this. Like, did you do it on purpose, or, to illustrate? Or was it, this is how you would have done it. Come on, tell the truth, ladies.
Kim: (Laughs) We definitely did it on purpose to illustrate.
Tammy: I just wanted to make sure, because Wendy, I was a little disappointed that that was...
Kim: (Laughs) Yeah. Yeah, yeah. I'd be willing to bet that Wendy's prompt would have been a lot closer to my prompt. And my prompt might have been closer to Wendy's prompt, if we were out in the wild, actually.
Tammy: I had to illustrate that for the audience, because...
Kim: I haven't been at it for 25 years.
Wendy: But I love the word that you chose, though, Tammy. You chose context. And that is the... I think that's the crux of it, right? Our intent, my intent in asking the question that was more like a Google search, and Kim's intent of asking her question that was more like a conversation, was the same, right? We both wanted the same outcome. But the context that we gave is what mattered in terms of getting the best response from generative AI. And so, the more context you can give, and even that you can give about yourself, right? So one of the things that I often do when I write a prompt in ChatGPT, or whatever the tool is that I'm using, is, I say, "I am a X." And maybe, in that moment, so, for example, my daughter is in Girl Scouts. And so, I was planning a Girl Scout event, and I said, I am a fourth grader. (Laughs) And I am going to learn about turkey vultures. You know, describe to me the environment in which a turkey vulture lives. Or whatever it is, right? So, you don't even have to have your own persona. You can project. And that's what's kind of fun about gen AI and conversational AI, right? Is that you can pretend to be somebody, and have a completely different conversation and response from generative AI than you would normally from a regular conversation.
Kim: I was just going to say, talk about the ability to create empathy using technology.
Wendy: Absolutely.
Kim: So if you can put yourself in somebody else's shoes and say, I'm all of these things and I have time in Minneapolis and I use a wheelchair. Or, you know, so many different ways that you could create empathy with the experience another person might have by using that context.
Tammy: Prior to joining NTT Data, I did a brief stint at a startup called Soul Machines, and they created digital people who were autonomously animated. And prior to ChatGPT coming on the scene, you would have to actually write the conversations, which, by the way, is very difficult to do. To write like a human speaks is very, very difficult. But putting that aside, one of the clients that we were working with was talking about, had implemented this digital person externally to their audience. And it's a financial planning organization. And so, he shared a story with me that, when their customers were talking to the digital people, they sounded a lot different than when they would interact with the text box. And the text box was still connected to the same LLM and the same Rag model. Everything was the same, it's just one was typing and one was speaking. And so, when someone was typing, they would say, I need a financial plan for next year, where do I start? Right? But when they were speaking, they would say, I'm going through a divorce and I am concerned about, da da da da da da. I need a financial plan. And so it was really interesting to me. So back to that natural human language, we speak more naturally, actually, than we type. But it was just a really, it's a really interesting sort of human dynamic around that context and that empathy that you have, when you can express yourself... Like, from a human level, which I think is sort of what we're talking about, versus just trying to get at the information that you want. Right?
Wendy: Absolutely. I love it. It's having a conversation instead of entering search results requests. Right?
Tammy: Yeah.
Wendy: Absolutely.
Tammy: I feel like... I mean, we all said, Wendy, I said in your intro, 25 years, I say over 25 years. Kim, I'm sure you say over 25 years in digital as well. So we all have this, in 25 years we've all seen all these new technologies come on the market. Some really consumer-facing, some maybe not so much. But I think that, what I've seen in my experience is when that happens, the tech-first approach always is what happens, right? Like, I have this new tech, what can it solve? What can it solve, what can it solve? And when it hits the consumer in the way that the ChatGPT or generative AI has, or when it was mobile devices and the things you could do with your mobile device, or before that, the internet, like, the things that are deeply consumer-focused. I feel like that experimentation phase is even more. Like, there's just a lot of, I got the tech, I got the tech, and I feel like I'm seeing that more in generative AI than I have in a really long time. Like, normal people don't get access to blockchain, right?
(Laughter)
Tammy: I guess, are you seeing that same thing? Like, from your points of view? And I feel like there's some pretty big challenges associated with that.
Wendy: I think... The short answer to your question is yes. What I find fascinating is I often get folks who are somewhat anti-generative AI, and that genuinely believe that this is just a fad. And we'll say, this is no different than the metaverse or blockchain. Tell me why this is, once the hype cycle dies, this is not going to go the way of the dodo the way those two. And there are, I would say, two key differences. Number one, and you alluded to this, Tammy: blockchain and Meta were technologies in search of problems to solve. Where generative AI, it's not a technology in search of a problem to solve. It is a means by which to solve problems. Right? And then, I would say the other reason that I think that we will see and are seeing generative AI survive the first round of hype cycle. And make no mistake, there will be multiple, right? This is just the first one. Is because, in many cases, for example, with the metaverse, it was very localized to one technology company. But generative AI has spread so far and so broadly across so many different companies, that the likelihood that it will last beyond a single hype cycle is almost darn near certain. Right? Because it is such a breadth of coverage that I don't see it disappearing for lack of success for one large technology company. I don't know, Kim. I'm curious. What is your take on the hype cycle and where this sits?
Kim: Yeah, I do think it will survive. From the... I always take it from the non-tech side. So, from the user side, part of the reason that it will survive is the democratization of the technology itself. Right? So, gen AI is basically ubiquitously available to any human being with a smart device or access to the internet. Not true with blockchain, not true with metaverse. Those are, you know, expensive things that cannot be accessed easily. They are also things that you can... And I know I'm sort of contradicting what I said a minute ago, but kind of teach yourself in a low-risk environment, right? So you've got a lot of humans out there who are playing with it as a toy to help them do meal planning, to help them plan vacations, to help make sure they're figuring out the next event for their daughter's Girl Scout trip. So you see that uptake outside the office. What that means, particularly with the younger generations for whom it will be a native technology, is when they get inside the workplace, they're going to demand the cool tools inside the workplace as well. Right? So that's not going away. So that's one where, the democratization is really almost happening outside the workplace, and will in some ways be forced inside the workplace because of the experience of the human in their personal lives. Even if you ask people, who uses it for personal use, who uses it for work use? Wendy, the event we were at last week, almost everybody in the room raised their hand. We all use it for personal use. And we probably had, I don't know, 10% of the room raise their hand they use it for work uses. So it's going to go the way, it's going to go the opposite direction. It's not a work-oriented technology that's going to make its way to the humans. It's not like Excel. Excel started out as a tool for things you did at work, and then migrated to the humans. This one's going the other way.
Wendy: I think... At least, I am predicting, that companies that embrace generative AI tools and really teach their employees how to use them, they will win the race for good talent.
Kim: That's right.
Wendy: I mean, if what we're really observing is a talent shortage, that's not to say that there are people, but not talent. It's that there are not humans out there to do all the jobs that there is work to be done. And if that is the case, and companies are competing against each other for talent, I have to believe that being an organization that embraces advanced technology like generative AI will give you an edge over acquiring the best talent.
Kim: I agree. Couldn't agree more.
Tammy: On that front, like... We're talking about how companies can use it in order to even think about the shortage in the workforce. What about the other side of it, where we're hearing that generative AI will be able to wipe out roles? Like, that some roles just won't even exist anymore. And I heard a client story recently where, we are in a UAT phase of a new tool that's leveraging generative AI to make a process faster, better, and more productive. And the very users who are feeling like they might be replaced are actually refusing to do the testing on it to see if it now can do the job that they used to have. And, like, back to the sniff test, like, they're the ones that are there to say yes, I used to do it this way. This new tool has made it faster, better, cheaper than me doing this process. And so, like, that's a real human issue, right? And I hear this a lot, especially from, I'm in the creative world a lot, is Midjourney going to replace creatives, right? Or... So, I'm curious about what you all have seen around that as well.
Kim: It's interesting. I used to tell a story a lot during the... Anybody remember RPA? And the big hype cycles about RPA? I used to tell a story then of a particular client I was working with, who literally had to hire recent retirees back into the workforce to test the RPA processes, because the current workers were hiding physical documentation, refusing to play along, all that good stuff. Now, what that indicates to me is a leadership team who's done a really, really poor job of thinking, you know, further than the nose in front of their face, right? They haven't thought about the consequences of what they're doing. They haven't thought about the value those individuals are adding to the organization. And ways in which they can add different value to the organization when they have the advantage of these tools. So even seven, eight, nine years ago, talking about RPA cycles, hype cycles, I used to say, what moonshot has the company not taken because they didn't have the manpower, or person power, or computing power, to go try something new? Well, let's take this capacity you just freed up and put it towards building something new. You know, these are people who are going to be pretty interested and pretty invested in learning new skills and moving forward. I also know that there isn't a single technology or advancement in process improvement that we've introduced into... Shoot, even the pre-industrialized world that hasn't impacted jobs. In most cases, it has taken away a small number of jobs and created a larger number of jobs that people never even dreamed of existing. Four years ago, I had never heard of a prompt engineer, and now that is a fast-growing job description out there in the market. So, yes, I do think there will be jobs affected by gen AI and the other AI tools that we're bringing into the marketplace. They will be affected in both directions. We will get rid of some buggy whip manufacturers, and we will create others. Mechanics for actual cars. You know, so I think it's going to go in both directions.
Wendy: Yeah. And I think the onus is on us as practitioners, but even more importantly, I think you hit it, Kim, when you said that it's really important that leadership at companies recognize that this is a make or break moment, right? Are there some jobs that AI will replace? Absolutely. But those humans have the capacity to do really interesting, valuable things that AI will, at least for the foreseeable future, not be able to do well. And so, the key is for leadership to think not just about dollars and cents, but think about value creation and how to leverage humans for the things that humans are great at, and that AI is not great at, like reasoning and problem-solving, and leverage AI for the things that AI is really good at, right? There is leadership required at every step to make sure that the humans are leaning into what they do best, and are taking advantage of the things that humans do best.
Tammy: Yeah. And I think it's, you know, too, it's like, bringing in that human-centered design up front, I think, as well, like, bringing the humans who you're going to be enhancing or improving or maybe not replacing, but making them faster, better, smarter, with this information that they can get at their fingertips, and making them a part of that. And I think, to your points as well, like, the leadership coming in and understanding that this is going to be a problem and hitting it head on, right? So if you're going to have... In this example I used earlier, if they're going to be using it, you should have already had a conversation with them ahead of time around what this does mean to their role. And if it means they're no longer going to have a role, you've talked about that. Like, transparently. And you have a plan forward. Are you going to invest in reskilling and upskilling them to change into a different department or function? Or are you going to give them a severance package and help them with upskilling and training elsewhere to go to another company? But I think it does require that upfront, you know, understanding of what you're going to be doing, how you're going to be doing it, and then that transparency in communication, so that you don't end up in that situation.
Kim: Yeah, I think that's right. The thing I would add, too, is, given AI adoption in organizations is still relatively new. And a lot of the operational and efficiency gains that companies are experiencing are what I think of as fingers and toes. They're tasks. They're not whole jobs, right? So, even as we're talking about bringing in these tools to enable or expand the capabilities of the human, it's not like there is widespread elimination of entire jobs. There might be elimination of certain tasks, but again, that just changes how you need your human, your humans to be redeployed across your organization in support of an operating model that has slightly changed with new processes that work faster. You know, in more cases today than not, it's fingers and toes. It's tasks.
Tammy: Yeah. I think about, just even software development, like in Launch's backyard, I think about the impact of generative AI and being able to develop software. And, you know, what we're seeing is that our senior leaders have more time to solve some of the bigger problems, versus supporting more junior software developers. Like, they actually can get more done on their own. It increases efficiency pretty significantly, and it actually reduces some of the mundane tasks that they don't love to do anyway, and it enables them to do more of the strategic thinking, more of the actual coding and development, versus solving for some of the challenges. So, can you think of any other examples where you've seen the impact that it's, you know, on the outset it looks like, well, maybe we won't need as many software engineers, but really what it does is it uplevels their capability of what they can spend their time on, and reducing that sort of mundane-ness of their role?
Wendy: Well, I think, you know, the introduction of robotics to the factory floor is a great historical example, right? Did that replace some humans? Yes. But it also made workforce and workplaces safer for humans. And so, there can be real benefits, if what you focus on automating or leveraging AI for are the things that are less safe, or less, I guess, to use your word, I think Tammy, I loved it. You know, mundane or more tactical than strategic. The opportunities to upskill and to lean into the more challenging, strategic, thought-provoking and frankly, joyful activities of work become a real possibility.
Kim: The one place I'm really hopeful this really comes to bear is in the middle management layers, right? Middle management is the layer that gets pinched. They get pinched by everything. They get pinched from above and they get pinched from below. So, if we can use AI, when we use AI, to take more of the mundane, more of the administrative, more of the rote off of their plates, now you've got somebody who can really help uplevel the layers below them, help skill and care for employees, and create that engagement that we know creates stickier workforces. They can be better leaders to the leaders above them, and better develop their own leadership capabilities to move up the ladder, if that is something that they care about. But I really hope that we'll see more and more applications to help that squish, that pinch in the middle manager layer.
Tammy: What are your thoughts, sort of, on crowdsourcing? Especially when it comes to work. It's different for your personal life and at home. But when it comes to work, and we're talking about these tools that we now have access to, how do you think about that crowdsourcing? Versus, like, an organization coming in, and typically with a tool, it's like this is how you're going to be using this moving forward, versus a, almost like that experimentation, crowdsourcing approach to how employees can use those tools?
Wendy: I would say that crowdsourcing is so important when it comes to learning the nuances of tools. So, you know, let's take a tool like Microsoft 365 Copilot. There are best practices, absolutely, for how to leverage it. But every implementation and every enterprise and every group within each enterprise, has their own set of nuances that no corporate-level training is ever going to satisfy. And I don't know about you, but I feel a sense of excitement when someone shares a new tip. Hey, guess what I did? Let me show you how I learned how to do x, y, and z. I almost feel like I'm spelunking and have discovered something that nobody else has, or that there's a small group of us that have figured something really exciting out. It adds this level of, I'm going to use that word joy again, but it brings a level of joy to the use of technology that I think, if you just stick with... And I hate to call it rote, because there's a lot of fore-thoughtfulness that goes into creating training material, but that is standardized training material, that can be absent of joy sometimes. And that spark, that ingenuity, is oftentimes what propels adoption.
Kim: Yeah, I'd say, to me, you absolutely have to have the crowdsourcing. And it's almost the difference between, air quotes, "the AI," so kind of like you hear people talk about "the Google" as though it is a singular one thing. So, AI is not one thing. It's many, many things. So I think a combination works really well in the workplace, deploying something that is fit for purpose, built for purpose, has tremendous value. Here is a tool that I'm going to give you, that's going to help you with this thing that we do all the time and it's awesome, and it's an AI tool. Not the AI tool. Right? But if you have AI more generally, that crowdsourcing is incredible. It's where creativity comes back in and it's where humans experiment and play and push boundaries. It's where joy is found. When I figured out that I could get it to help me put conditional formatting in an Excel spreadsheet, which I never remember how to do, that was a joyful moment for me, that saved me a lot of time. But we need multiples of those things going on.
Tammy: (Laughs) That's the nerdiest thing I've ever heard come out of your mouth. I'm sorry. That was... That was incredible.
Kim: (Laughs) Yeah. And my team, we've really embraced that crowdsourcing. So, we've got a Teams channel where we're sharing ideas. Hey, I used it for this. I used it for this. Oh my gosh, look what it did on this thing that was so great. So I went immediately to the Teams channel. It did conditional formatting in Excel for me! It made my day. You know, I mean it's... (Laughs) That's part of the joy.
Tammy: We are talking about these things that give us joy. Like, it really does. Like, the excitement and the energy in the room when we start talking about these things, like, we're clearly in the right roles. Like, we're doing the right job in our life and we're very fortunate. I do feel like, though, there's a whole population maybe, who's not in that mindset of like, this is the future. I'm excited about it, I love to go spelunking and I want to figure that out. And I think that, like, there's a whole group of people that I think we need to make sure, or organizations need to make sure, they're bringing along. How do you think about maybe organizations that aren't necessarily digital, like, natives, or aren't necessarily digital-leaning, and they have, maybe, people that, this is going to be new to them? How do they think about that?
Wendy: You know, a phrase that clients hear me use a lot is, lean into literacy. And, you know, a lot of times I'm talking to the CIO or the CTO or the head of HR or, you know, leaders in large enterprises. And you will always, in a large enterprise have, anybody all along the adoption curve, right? You're going to have those early adopters that are in because they believe in it, right? And then you have those that are in because everybody else is, and so there must be something good. And if I want to do x, y, z I'm going to have to. And then there are going to be the last folks that say, I'm not going to do it until you make it. And I think the one universal truth there is, there is nothing bad that can come from education and literacy as it relates to AI. My phrase lean into literacy is the biggest advice column, if I could create one that I would have. (Laughs) In order to bring everybody on the adoption curve along for the ride.
Kim: Yeah, I... The one thing I would add is, it's the leaders, too, by the way. Right? So, when we talk about thrivers, survivors and deniers, it's sort of the way I think about, kind of the thirds of people anytime we introduce new technology. It's the leaders, too. So a lot of the leaders, you know, particularly... Well, all leaders. All leaders who are leading functions or businesses, and sometimes even in the technology space, who may have their own fears, or their own hesitancies. So, one of the things that's kind of unique here is, the leaders who we are asking to lead with growth mindsets are also people who are struggling with their own growth mindset, right? So we've got to make sure that we help the leaders be comfortable and lead through, in some cases, uncertainty and vulnerability, and expose their own learning path to literacy. Wendy, to your point. That's going to help a lot. That's going to encourage more and more folks to move towards that mindset when they see that it's not, you know, it's not a me problem, it's an everybody challenge. I think that's part of great core leadership, and it shows up at times like this, when we're making pretty significant leaps and advancements in tools.
Tammy: I love that. I mean, I talk a lot about psychological safety, just in the work that we do. When you're going to do things that have never been done before, or you're going to push the boundaries of what might be possible, if people don't feel safe to do that, to put their thoughts out there, to put their ideas out there, out of fear of failure or out of fear of risk, or that makes them risk-averse. And I think, you know, I talk about that a lot right now, because a lot of organizations still have that very, sort of, old-school mindset where leaders are up on this pedestal and they're infallible. And when we do that, then you're right. Like, you don't get to show that vulnerability. You don't show that, like, I'm trying something new. This is new for me, too. And I think it's really easy to tell people to have a growth mindset without creating, sort of, a path and, like, a space for that growth mindset to grow and develop. And it can be something that's learned, but you have to have the right space for it, and create that environment that allows for that, allows that to thrive.
Wendy: Yeah. I think the fact that you used that word, vulnerability. If I am struggling with adopting and I'm the leader, how am I going to model that to my workforce? That's tough, right? I mean, that's asking a lot of our leaders. Now, you know, I think that's a different conversation, whether it's asking too much of a leader or not. But I do think leading with that humility and vulnerability is critical, especially when you're trying to drive forward, not just a growth mindset and teach your employees to have a growth mindset, but when you're trying to teach embracing change, right? Change is hard. I do really well with change when I know to what I'm changing, but unfortunately, sometimes, not only do we not know to what we're changing, but the path to get there is murky and messy. And that's... It's just hard. It just is. And that notion of feeling vulnerable and sitting in that vulnerability and recognizing that it is okay, and it is part of the human condition, is the challenge that's in front of all of us, I think.
Tammy: Oh my gosh, that coming from our Chief Generative AI Officer, everybody.
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Tammy: Just to put that in perspective. Well, ladies, this did not disappoint. For the record, this was absolutely amazing. Thank you both for being on here. I just love the way that we talked about humans at the center of everything, whether it's leadership or developing new tools, or how to learn and lean in and be vulnerable. It was all goodness. And thanks for joining me today on Catalyst, the Launch by NTT Data podcast.
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