791: The Messy Intersection of AI, Work, and People, with Joanna Stern

Dave Stachowiak [00:00:00]:
What if you decided to have AI go first for you on virtually everything in your life? Joanna Stern decided to do exactly that and captured it all over the course of a year. In this episode, we explore how those implications are showing up for a lot of us at work. This is Coaching for Leaders, episode 791.

Dave Stachowiak [00:00:24]:
Production Credit: Produced by Innovate Learning, Maximizing Human Potential.

Dave Stachowiak [00:00:32]:
Greetings to you from Orange County, California. This is Coaching for Leaders, and I’m your host, Dave Stachowiak. Leaders aren’t born, they’re made. And this weekly show helps leaders thrive at key inflection points. When I think about inflection points, I’m often thinking about the inflection points in our own careers. Taking on a big new project, taking on a team, moving into a new role. One of the inflection points that’s coming, it’s here for all of us, is AI. AI has become a part of our thinking, our work, our conversations, our personal technology.

Dave Stachowiak [00:01:06]:
And of course, it’s already becoming a part of leadership, and it’s going to be even more so a part of our leadership conversations as time goes forward. And that’s why today I’m so glad to welcome someone who is just such an expert at thinking about the intersection between technology and people and helping us make sense of it. I’m so pleased to welcome Joanna Stern to the show. Joanna is an Emmy Award-winning technology journalist who was previously the personal technology columnist at the Wall Street Journal. For many years, she has been one of the most recognizable and entertaining voices in consumer tech. And she recently launched her own media company and YouTube channel called New Things. Her new book is I Am Not a Robot: My Year Using AI to Do Almost Everything.

Dave Stachowiak [00:01:50]:
Joanna, always a pleasure to get into your work. Welcome to the show.

Joanna Stern [00:01:54]:
Thank you for having me Dave.

Dave Stachowiak [00:01:55]:
I have been reading your column for many, many years. I’ve been following your work. I love how you always think about technology from the intersection of, you know, the technology, of course, but also, what does it mean for people? And you took on this huge project over a year to bring AI into every aspect of your life, like literally everything. What motivated you to do this?

Joanna Stern [00:02:20]:
I think a little bit of insanity, a little bit of maybe it would be good to write a book about something. But the real reason was what was happening with AI. It was really when I started thinking about doing this, it was 2024, and the entire tech industry had exploded with talking about AI, and the hyperbole from these tech executives was just next level. It was, AI is going to change everything, it’s going to change work, it’s going to change the way our healthcare, the way we teach our kids, the way we get around all of these parts of life, disparate parts of life. And I thought maybe there’s something here in a series. And so I thought maybe I would do it as a Wall Street Journal series, I was working there, as you said at the time. And then I thought, people have been saying I should write a book. Maybe I should write a book about this.

Joanna Stern [00:03:04]:
Put this all together in the sense that life is this full year and that all this stuff that you do in a year. Maybe there’s a way to put each AI thing to test in this parts of the year. Of course, the problem with writing a book about AI is that everything is changing in AI and in this industry by the hour. Right? And so books, they’re not the fastest things to publish, but I think we’ve pulled it off. I think the book holds up at least for a couple of years.

Dave Stachowiak [00:03:34]:
Yeah, I think you have. And the reason you have is because, it’s not a book about what model you use and what robot and all of that. I mean, there’s aspects of that, of course, contextually in it, but, it’s where I think your sweet spot is really thinking about this. What does this mean for people in our lives? And that’s the thing I’m hearing a lot from the people in our audience is, okay, thinking about it from, of course, the lens of work, which a lot of us are thinking about in the context of our podcast, is what does this mean for us? And you ran into this in a really big way in your research. And there’s a chapter in the book called the Colleague who Never Sleeps. And I thought it was interesting that you took a day to go talk to a mattress company, Saatva. Is that how they say their names?

Joanna Stern [00:04:19]:
That is, yeah, that is how they say it.

Dave Stachowiak [00:04:20]:
Yeah. And I was. It was interesting getting into really looking at how an organization is utilizing some of this technology already. And I think they’re a great example because I don’t think they’re really that unique in what a lot of companies are starting to do with this. They’re bringing AI into the work regularly. And I’m curious, like, how they’re starting to use AI and what you saw in your experience of spending a day with them.

Joanna Stern [00:04:45]:
It’s exactly what you said. So I thought the best thing to do to, sort of, focus on AI for work in this book, was one to look at it in my job, and I’m sure we’re going to talk about that. But to also look at an industry that has been disrupted at that point and even right now, today, in a really big way. And so customer service was the industry. Everyone kept saying to me, every workplace expert, every economic expert I spoke to, even, even the tech experts were saying, oh, you definitely want to look at customer service. That one’s been disrupted, and AI can do that job right now. And so I decided, let me focus on that now again, like maybe a year later I would have focused more on coding or engineers, maybe I would have started to focus on some other areas where we’re seeing AI invade. But customer service was a really great one for me to look at, also in the fact that we all talk to customer service agents or we know that experience as humans.

Joanna Stern [00:05:40]:
And so Saatva was a good one because they started using AI in their customer service. And this is really what I experienced. I went to one of their showrooms. They actually have some customer service agents sitting in their showrooms, which is very unique. And they started using AI both to respond to online inquiries. So the chatbot that you see on the website but also using AI behind the scenes to help human customer service agents speed things up, get to the answers faster. And so I wanted to see this full end-to-end solution they had put in to their business. And it was powered by Zendesk.

Joanna Stern [00:06:18]:
That was one of the ways I got to them because I started looking at who’s providing these AI customer service tools. But not just a huge replacement. Not just the AI voice you hear when you call the bank, or the AI voice you hear when you, you call the airlines, but who’s actually providing the tools to the people that are working on the ground. Because that was really what I was after here, right? What does work look like when we’re working alongside AI? And so Saatva was really this company. It’s a mattress company, which I knew nothing about before. And now I want one of their mattresses. They sell mattresses, but their big priority is making sure you, as a customer, feel like you have a personal connection to the company.

Joanna Stern [00:07:00]:
And so they have made it. So they haven’t replaced all these customer service agents with bots, but they want the customer service agents using AI behind the scenes. And that’s really what I got to experience when I sat in the chair as a customer service agent. I’m doing air quotes right now because clearly I wasn’t really doing the job, but I did. I saw incoming complaints. I saw incoming questions from customers, like where should I- One of them I saw was, I want to test a one of your mattresses before I buy it.

Joanna Stern [00:07:31]:
Where can I go? And I was able to answer that really quickly by using AI to have these sort of preformed templates about how to find a store near you. I believe the person was in Colorado. So there was a- I quickly populated the closest store, and I was able to see how AI was speeding up and making a customer service agent, me, that day more efficient.

Dave Stachowiak [00:07:55]:
I think two things struck me in just your explanation of your experience of talking with them in the book. One is that they’re not necessarily replacing people’s jobs, but they’re not hiring the kind of ways they would be if given their growth and all that because they’ve been able to supplement with AI. So that’s one interesting part of this. The other really interesting part is that people often, it turns out, prefer interacting with AI agents on some of the websites and tools and actually find that, in some ways, to be a better experience than interacting with a human being, at least for some of the transactional, the logistical questions that come up. And I found that interesting in just reflecting on my own experience. I can relate to that a bit.

Joanna Stern [00:08:38]:
I could relate to that as well. But I’m sure you’ve had this experience as well, Dave, where you are just trying to get to a human as fast as possible in the tree of customer service buttons that you have to press on the phone call, and you’re screaming, human, human. I want to speak to a human.

Dave Stachowiak [00:08:54]:
Yep.

Joanna Stern [00:08:55]:
Right? For me, it’s always airlines, I just want to speak to a human at United Airlines. And I could see that they. They know that too. There are certain times when you really do want to speak to a human. But these AI tools, in the background, can be helping those humans get to the answers quicker and be a more efficient. And you’re absolutely right.

Joanna Stern [00:09:17]:
They highlighted, as I spoke to many executives at this company, we are growing in a really big way. We’re opening stores across the country even faster than we were a year ago. We would have to be scaling up on that customer service. We would have to be hiring more and more people. They’re not saying they’re letting people go. They’re just saying we’re not hiring as much in that customer service area because our human agents can do far more now with AI agents.

Dave Stachowiak [00:09:41]:
Yeah. And one of the really interesting experiences was the person you talked to there who’s leading this team of, and really leading a lot of the AI bots in addition to the work she’s doing with people. And I’m curious, like, when you talk with her, what is this experience like of having gone from managing more of a traditional customer service team to now navigating a lot of work with AI?

Joanna Stern [00:10:07]:
Yes, I spent quite a bit of time with Stephanie Young, who oversees much of this customer service, but also really oversees the tech and the Zendesk integration that they’ve done, where they’re bringing AI to the desk of the customer service agents. And so, one thing that I really was asking her about was, one, yeah, are we displacing human workers here? But two, how are you building these systems? How are you building the systems to know what the human agents really need? And she said it was a lot by looking at the patterns of the data they had. Right? And of course, this makes sense, and this is why customer service is so ripe for AI, is that there’s so much data and there’s repetitive data. People asking similar questions, people wanting to return things, people wanting to look for stores, having questions about inventory, questions about the product. And these are all things that can be filed into pretty neat categories. And so that’s one of the big ways they built this system in the back end. And when I was talking to them, they were looking to build more, right?

Joanna Stern [00:11:09]:
They were looking to templatize more and to put more in the hands of these agents as they pick up a call or they see what the email’s about, and having more at their fingertips to use the AI to respond. And have the information just there before they need to go looking for it.

Dave Stachowiak [00:11:25]:
And could I just say this is like one of the weird things about this time and this situation is I think we for sure have episodes coming in the next few years on not just how to lead and manage people, but how to lead and manage an AI team, bots, agents, right? And this is reality already in a lot of customer service organizations. And I’m curious, like, in your interactions with them and your interactions with Stephanie, what’s that transition like? Like, at a human level, from having done everything through people and now bringing technology into things that people used to do?

Joanna Stern [00:12:03]:
Well, Stephanie seemed pretty excited about doing this. She was pretty interested in the systems that they were building and the ways they could take the data and all of these tickets. It was really shocking to me how much comes through just in a few hours. So that by that, by that point, it was like thousands of tickets and questions from customers had come in just in the morning or the few hours I had been there in the morning. So the level of work that people had there was overwhelming. And so I thought that was one key thing that she was really interested in, and making this system easier for humans. And I think we do think when we’re talking about this topic so much right now that, oh, we don’t even want to think about, oh, we could take this part of our job, we could take that part of the job.

Joanna Stern [00:12:50]:
But there are so many parts of our jobs that get one, very repetitive, two are overwhelming because, you know, you have to do all of this and you don’t have a lot of time. And then three, it’s just not all that interesting. Right? It’s not challenging. And so I think those were some of the tenets she was using as she was building this technology for the employees, for these customer service agents that are responding to people day in and day out. And I think those lessons can be applied to really any kind of industry right now.

Dave Stachowiak [00:13:21]:
Yeah, I think it’s really interesting that she’s emerged in that space, and you noticing that in that oftentimes, when we enter into this conversation, we are thinking about it from the either-or, like, okay, a person’s going to do this, or it’s going to be an AI agent or bot or whatever. And I don’t think we often start that conversation by thinking about how can I actually, and we, as an organization- whatever the organization is, use AI to make people’s jobs better, like more enjoyable, more fulfilling. And yeah, in the short term, that might mean there’s not as much hiring, that some positions go away, but that there’s also potentially a big opportunity to actually make the experience of work for the people who are still doing the work better and more engaging and more delightful.

Joanna Stern [00:14:09]:
Yeah. And I think that I did have the opportunity to talk to one of the customer service agents on the ground there, and I saw her take a call and use some of the AI tools. And this was a very run-of-the-mill call. It was somebody who wanted to do a return. Right? And the process of doing a return is very formulaic. And so I was able to see sort of the prompts and the scripts she was getting on her computer to do this. And so I was able to see this woman respond and use the AI tools, but it didn’t, it wasn’t like, oh, some big part of her job had been taken over in this moment.

Joanna Stern [00:14:48]:
Right? She was able to just do that call more efficiently and move on to the next one. And that next one might be something that was a little bit more challenging than this pretty routine return.

Dave Stachowiak [00:15:00]:
Yeah, indeed. I think it’s interesting, just in the space you are in, your own career of having been an employee for the Wall Street Journal and now transitioning to running your own business, you’ve been on both sides of this in a really big way. Just in the recent past and, you talk about in the book that you hired an assistant to do research for a part of the book, and then you didn’t hire her for the remaining part. And it wasn’t because she didn’t do a good job; it’s because AI was doing, had so many tools and resources. And I think it was interesting how you went back and had a conversation with her about that. What was that conversation like about? Like, hey, we just went through this, this really big transition ourselves, and how we were working together.

Joanna Stern [00:15:42]:
So yeah, when I started the book and we started on the reporting process from the book, I realized I’m going to need some help with this. And so I went out and looked for a reporting assistant, and somebody recommended a woman named Maya Tribute to me, who was a great researcher, and she had some extra time to help. And I needed some basic tasks done. I needed research for the book. I needed companies that were working in different areas, self-driving, robotics, healthcare, all the things that ended up in the book. Needed to do research, find the companies that were working on this stuff. I also needed to find contact information for those companies, go to the websites, find the names, the email addresses, the phone numbers. And then I needed to reach out to some of those companies to start setting up meetings, seeing if they were the right people. And that was the stuff I really tasked Maya to do before I started writing.

Joanna Stern [00:16:29]:
And so I paid her the chunk of change for that project beginning of 2025, maybe end of 2024, and then maybe mid-2025 when I needed another chunk of research done. I didn’t really need to go back to Maya because these AI tools with deep research, whether it was ChatGPT, Claude, Gemini, were all so good at those tasks, right? Pulling lots of companies that work in different areas, finding contact information on the web, and then even a step further by that point with some of the agentic stuff, sending emails on my behalf. And so yeah, I didn’t hire Maya again. And so yeah, I went back, I talked to her about this, and she, I think her response was really good, was that, she was more worried about some of the younger folks coming out of college. She had already had a couple of years under her belt working as a reporting assistant, as a reporter, but she was worried how are they going to get that experience. She was like, I don’t really love doing some of that work for you, right? Now I can work on some more higher-level tasks. But I think her reaction, and I actually saw her a few weeks ago at a book event, she’s still working in this space. She’s embraced using AI in her job to do some of those tasks, the things that I tasked her with, so she can spend her time on some higher-level type of work you talk about.

Dave Stachowiak [00:17:48]:
And in fact, there’s a beautiful chart you did in the book on what you call an AI applicability score. And folks who have done some research on this. And you looked at just your work today, and what are the kinds of things that are in what you do replaceable by AI? Maybe even AI does better, and things that not so much like AI may attempt this, but it’s really not good. And you say we should all do this for our work. What did that look like, that process? And then what was the value for you in going through that?

Joanna Stern [00:18:22]:
Yeah, it’s funny actually, as you say that, I think I should redo that. It is a really important type of exercise for people to do, and I think they should continue to do it every six months to a year because of AI progress. And look, I won’t take all credit for this. Eric Brynjolfsson, who’s a leading Stanford professor and probably the leading expert on AI and jobs and the economy, talks about how we have to look at AI, and the impact on jobs on a task at a task based approach, where we look at the tasks of the jobs and what I can and can’t do. And so I decided, let me apply that to my own job. What are the list of the things that I do? You know, both day in and day out, but also more broadly. Right? Deep thinking about what stories I should do, deep thinking about how I would approach reporting… Those sorts of things.

Joanna Stern [00:19:08]:
And so, yeah, in the book I wrote out all of those, and I then also had a little chart with little robot heads giving a rating at that point to how good AI was or wasn’t at that job. And what’s interesting is that almost everything has an AI score. AI could do some part of the task, but there were other parts of the task that it just couldn’t do, right? Or in some cases it couldn’t do. So, for instance, one research. Yeah, it can do research. And I believe the score is pretty good on research. But then there’s interviewing, right? Going out and actually interviewing the people. And that’s a huge part of my job.

Joanna Stern [00:19:45]:
And I actually did, for the experiment, I made an AI avatar interviewer of myself. I worked with a company called Otter. They used transcriptions of my interviews. They used a voice of mine to make a voice clone. And I actually sent this voice clone out to interview Eric Brynjolfsson, funny enough. And it did a pretty okay job, like, I don’t want to say it was terrible, because my AI avatar came back after interviewing Eric Brynjolfsson, and it was a decent transcript. There was stuff I could use from it, but it was not as good as me.

Joanna Stern [00:20:16]:
And I’m not, I don’t think, I’m just saying that, I think, you know, well, we could talk about that. People think that AI can’t be as good as them at certain parts of their jobs, but I did try that. And so the score on that is far lower than some other tasks in the job. And I think, all in all, this is just an important exercise to see where AI is starting to do parts of your job and where you bring real value.

Dave Stachowiak [00:20:37]:
Yeah. And it kind of gets back to what we were talking about earlier of the, how do you actually make the work more interesting, joyful, and lean in on the things that are uniquely human to you? And that I think all of us have- We look at our roles, and we divide it into the tasks that we do each day or each week, whatever. That there’s things that AI is doing really well, and there’s things that are not. And thinking about, like, all right, where do I bring AI in to really help support that sort of like, administrative stuff right now? And then where do I partner with some of this technology in order to make it more interesting, joyful, effective? And that’s, boy, that’s the challenging part about this is, like, really being able to look at that objectively and to think, okay, where do I bring this in?

Joanna Stern [00:21:25]:
Absolutely. And I think we can all… I guess the concern that some of us may have, though, is that as we start to look at those tasks and we start to see, oh, AI can do that thing, but I struggle with that. Right? It might be the hard part of the job, and I struggle with that part, but AI can do it now, so why do I need to struggle with it? And for me, I think that actually comes to, it’s not writing, it’s more about outlining and structuring. I’ll just give this more of a concrete example in my work, and maybe you have an example in your work. But for instance, for me, one of the hardest parts of my job is doing a lot of reporting, going out, going after a topic I’m really interested in,

Joanna Stern [00:22:06]:
Getting lots of stuff and then narrowing down to what is the important parts of the story? And that’s a real struggle. That takes a lot of brain and mind power for me. I just finished a video about Siri, the new Siri, and we shot hours and hours of footage. I had a lot of thoughts that I wanted to talk to you. I interviewed five or six Apple executives. And so the hard part for me in that process is taking all of that information and narrowing it down to a story that I think is interesting. AI can do that.

Joanna Stern [00:22:37]:
I don’t know if it can actually do it that well, but AI can do that. And I could use AI as a crutch there, or maybe as a first pass. But there are some people who will say, okay, AI can do it. I’m just going to have it do it, right? Or there are other people who say, actually, the struggle to get through that process is what’s really important, is what makes me really good at my job, and I still need to do it. And so I think that’s where we’re sort of coming up now is that AI is going to actually be able to do so many tasks. But where are we as leaders, as creatives, insert whatever you do in your job, going to want to make your humanity your own impact versus just leaning on the simpler tool?

Dave Stachowiak [00:23:21]:
Yeah, indeed. And I’ve discovered something similar in my work, which is where can AI do the administrative, research, assistant kind of things and help me to take the things that are less interesting and less personable to me and my skill set and accelerate that. And one example, I use now, I think what you said, first pass, was like a perfect representation of how I’m using it now. Like when I’m preparing to talk with guests, interviews is, I’ll often use AI in some way to do a first pass on a person’s interviews, book, whatever, and just get a sense of, like, all right, where, especially if I don’t know their work fairly well, like, where are the interesting areas? But then, then it’s my job to look at that and say, okay, what do I know our audience needs from all the conversations I’ve been having? What do people? What do I need to zero in on? What are the things I need to surface? And I find AI to be pretty mediocre at that. Too poor in most of my work.

Dave Stachowiak [00:24:20]:
And in fact, I don’t normally do this, Joanna, but I, given the topic of our conversation today, I went to ChatGPT in preparation for our conversation and said, ” Hey, I’m interviewing Joanna Stern. Here’s the topics we’re talking about in this conversation. What should I ask?” And 90% of the questions that it came up with were not things that I would spend time asking you. Not that they weren’t good questions, but they weren’t relevant to our audience and this conversation in the context. And that’s where, like me needing to be the human in the loop as, as folks say. Right?

Dave Stachowiak [00:24:54]:
Of then taking the time to do that, but being able to get their way faster than I would have before using it. And I think that’s the part that, like, has been really helpful for me.

Joanna Stern [00:25:01]:
Yeah. And I think there’s someone, probably some leadership coach or somebody, who has come up with better terms for this, but I think the administrative and drudgery type of stuff is one bucket. And then there’s the higher-level task or higher-level thinking. Right? Maybe these are the terms. I’m such a business leader and thought leader now, where I think there’s just going to be this shift. And it was doing a talk a few weeks ago to a bunch of financial advisors, where I was trying to make the point, as I do in the book, that we need to be control of how we use these tools, that it can become very easy to lean on these tools for the thinking, for the things that we might find hard when we’re sort of just giving up on something at the end of the day, like, oh, AI can do it.

Joanna Stern [00:25:49]:
And we need to better understand, in our jobs, in our roles, okay, yes, we know that part is hard. We know that part is the part that makes this a little bit of a struggle for us. But those are the things that make us better and probably deliver higher, higher value to the companies or to ourselves or whoever you may work for. And I think that is happening across industries. I mean, it was happening certainly, you know, I was talking about my experience to this group of financial advisors, but then at the end, many raised their hands and said, yeah, I absolutely do that. At the end of the day, I’m tired, and I just throw it into Claude because I think it can do it. Right?

Joanna Stern [00:26:26]:
And it wasn’t like a shame on them for doing that at the end of the day, or anything like that. It’s just that helps us identify these moments when we’re tired, when we’re working hard, or we’re struggling with something, we want the easy way out. And that we should, you know, as push ourselves is maybe we’re not going to just go straight to the AI as the crutch.

Dave Stachowiak [00:26:44]:
I mentioned that I went and did some conversation with ChatGPT and like questions to ask you, and most of them weren’t relevant. One interesting question that did come up, and this is the AI question, which I think is ironic, is what did this project teach you about being human?

Joanna Stern [00:27:00]:
I think actually what I was just saying is that being a human is hard, right? We struggle a lot. We have to deal with both the struggle in our relationships, which I talk about in the book at length, whether it was having relationships, romantic relationship, or more therapist, or emotional relationship with AI, that there’s struggle in those human relationships that are important, that make us human. But I think, you know, as it pertains to work, work is that part of our lives where it should challenge us, right? Because if we didn’t want to be challenged, we would do something else. And for most of people, and I think especially most successful people, we keep taking on bigger and bigger challenges because if we keep doing the same thing, we get restless and we’re like, oh, this is just… Humans aren’t supposed to do the same thing all the time, or we try to be better at that thing that we’re doing all the time. And so I think it taught me that humans do have it hard, but that the hard is part of it.

Joanna Stern [00:27:56]:
I actually do quote Tom Hanks’s character Jimmy Dugan from A League of Their Own at the end of the book, this quote, which says it’s supposed to be hard. If it wasn’t hard, everyone would do it. And I think a lot of us can relate to that in our jobs, right? We try to work hard at something so we can be good at it, be better than others, whatever gives you the motivation to keep doing something. And so, yeah, being a human is hard. AI can make it easier, but we should really still think about what’s the humanity in that part of the task or the question.

Dave Stachowiak [00:28:28]:
One of the things folks will discover when they get in the book is, you really did bring AI into every aspect of your life. Transportation, your medical appointments, I mean, virtually everything you embedded yourself in with the tools. And you, probably more so than anyone I’ve talked to, have really lived this and gotten into this. And I’m curious, having gone through that experience for a year, of doing that, and now talking with people about it and having and teaching people about this, what, if anything, have you changed your mind on about AI in the last year or so?

Joanna Stern [00:29:02]:
It’s a really good question. I don’t know if it’s that I’ve changed my mind. I don’t think I shared enough of this at the end of the book. But I think I was a little bit worried about AI adoption when I was writing, because people were adopting this stuff so quickly. They’re using it to replace search; they’re using it to help communicate. They’re using it to answer questions about their medical information. They’re using it to answer questions about their, about their kids. And I probably didn’t say clear enough that I thought people would just accept this. Because we kind of have seen that with past technological waves. We just kind of accept it.

Joanna Stern [00:29:35]:
So, where I think I’ve changed my mind is that humans are not just accepting this right now. And we are seeing this backlash to AI in a number of ways. We’ve heard the boos at the commencement speeches, we’re seeing the rallies in different towns, people protesting the data center builds. And I think that’s really promising. Now, I don’t think we should all go out and start hating AI and reject this technology altogether. But just the fact that there are fractions that are really thinking about the consequences of this, and they’re not just accepting a tool that can do everything for them with all the other shortcomings that come with it, whether they be environmental, whether they be economic, whether they be societal, is a really good thing.

Joanna Stern [00:30:23]:
And so I guess I’ve changed my mind on a little bit around the hope and the embrace of this technology from people.

Dave Stachowiak [00:30:33]:
Joanna Stern is the author of I Am Not a Robot: My Year Using AI to Do Almost Everything. Joanna, thank you so much for your work and for sharing it with us.

Joanna Stern [00:30:41]:
Thank you for having me. Thank you for not using ChatGPT to ask all of your questions.

Dave Stachowiak [00:30:47]:
Of course. If this conversation was helpful to you, three other episodes, all related to AI, that I think you’ll also want to check out. One of them is episode 674: Principles for using AI at work. Ethan Mollick, one of the top voices and researchers on AI, joined me for that conversation. We talked about some of the key principles for AI today. Those key principles still hold up even a year or two later. The technology has changed massively. What hasn’t changed is some of the principles that he’s been leading the conversation on in his work, episode 674 for that. Also recommended, episode 710: Becoming an AI-savvy leader.

Dave Stachowiak [00:31:33]:
David De Cremer was my guest on that episode, and we talked about the both-and of AI. Not people or AI; It is going to be both whether we like it or not. And how do we work together to figure out, how do we utilize AI and the right applications? How do we make sure that we lean on people in the right situations and applications? And how do we bring both together? David’s got a great message for us in episode 710 on how to do that and how to think about it, and then finally, I’d recommend episode 766 with Ruth Gotian: Using AI to make networking easier. One of the great applications of AI is to utilize it to do the administrative work. A lot of the kinds of things that we could automate or have a research assistant potentially do in the past, or an executive assistant being able to utilize AI for a lot of those tools and resources so that we can spend more time of putting into the relationships that we should never automate, that we should never have technology take the lead on. And networking is one place where there’s a ton of opportunity to do that. Ruth talks with us about some of the resources she’s used and some of the ways she’s put together in-person events utilizing the strengths of AI in order to support that.

Dave Stachowiak [00:32:49]:
Episode 766 for that. All of those episodes you can find on the coachingforleaders.com website. And I’m inviting you today to set up your free membership at coachingforleaders.com you’re going to get access to the entire library of episodes searchable by topic. One of those topics, of course, AI is all those episodes are filed under, along with many others. Of course, we’re going to have lots of future conversations coming too, but there’s a lot of other aspects of leadership as well. Dozens and dozens of topics that you’ll find there to be able to track down exactly what you read need right now, all of it at coachingforleaders.com. Set up your free membership there for access to everything. Coaching for Leaders is edited today, as always, by Andrew Kroeger. Next Monday, I’m glad to welcome Lyudmila Praslova to the show. We are going to be talking about creating leadership paths that work for everyone.

Dave Stachowiak [00:33:41]:
Join me for that conversation with her. Have a great week and see you back next Monday.