Pod Insights Possible with Reid Hoffman How fast can you upskill in AI? We did a sprint to find out. About this episode We all feel the urgency: learn to use AI, or risk falling behind at work. And we all know there’s upside: AI can reduce tedious tasks, streamline operations, and boost output. But knowing is half the battle (maybe even less) and implementing AI needs to happen across an entire organization. So what does it take to start?
Well, here at WaitWhat (the company behind this podcast!) we paused all operations for three days to find out. From editorial curation to visual design to event planning, we split into teams for an “AI Sprint.” And this Pioneers of AI episode takes you to the starting blocks on the track with us, as we test new tools, discover their limitations, and find where AI can deliver on its promise.
Learn more about Pioneers of AI: http://pioneersof.ai/
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At the center of AI is people, so we want to hear from you! Share your experiences with AI — or ask us a burning question — by leaving a voicemail at 601-633-2424. Your voice could be featured in a future episode!
Transcript 5,716 words · 15 speakers
0:00 Speaker 00 Hi, it's Aria here. Today, we are bringing you a special episode from our friends at the Pioneers of AI podcast. Their production team did something very cool and increasingly essential in this day and age: a three-day, company-wide AI sprint. The goal: to get the entire staff thinking and making and sharing around how AI can transform their everyday work. And you'll hear a familiar voice helping them do that: Parth Patel, my coworker who you've heard on the Possible Podcast before. Yes, Parth lent a hand and some valuable prompts. He guided the team as they explored AI coding, built agents,
0:43 Speaker 00 and started implementing AI into their everyday work. It's a learn-along kind of story, so let's get to it. Here is Pioneers of AI. Enjoy.
0:58 Speaker 01 Every company has to ask themselves how they'll be disrupted. Every company will eventually be disrupted.
1:07 Speaker 02 That's Taryn Fixell. She's COO and President of Wait What, the production company behind this podcast, as well as Masters of Scale and Rapid Response.
1:17 Speaker 01 So we had to ask ourselves, how is AI set to disrupt our work? And what can we do to get ahead of that so that ultimately, we could be successful in this new environment?
1:28 Speaker 02 Countless companies, large and small, are at a similar crossroads. We could keep doing work the same way we've been, or we can really harness AI to reimagine how we do work. We know that AI can supercharge an enterprise, like automate tedious work, or streamline operations, boost output. But how do you actually do that? How do you start? Today, come along with this one small production company, ours, as we plunge into AI to transform work. And your guide will be Pioneers of AI Senior Producer Rachel Ishikawa.
2:04 Speaker 02 Hi, Rachel.
2:05 Speaker 03 Hey, Rana.
2:06 Speaker 02 So this is a very meta episode.
2:08 Speaker 03 It is. So let me tell you the story. Our company took a very specific approach to leveling up our AI game. For three days, we paused all operations and every single one of us tested how to use AI for our work. And for me, somebody who works on an AI podcast, this was actually a lot of fun.
2:28 Speaker 02 So what did that look like?
2:30 Speaker 03 Well, we experimented with out of the box products, wrote code. Some of us for the first time, like me, developed apps, scrapped apps, and then started all over. We called it an AI sprint.
2:44 Speaker 02 That sounds pretty awesome. Did it work? I mean, is the company now running at full AI speed?
2:49 Speaker 03 Well, that's what we're here to find out and to show our audience what we've learned along the way. I'm going to bring you from start to finish lines and all the hurdles in between. I'm Rachel Ishikawa.
3:01 Speaker 02 And I'm Rana El-Khalyubi, and this is Pioneers of AI.
3:16 Speaker 02 Okay, Rachel, so let's start with a little bit of background on Wait What? What does the company do and how big is the team? Give us a rundown.
3:24 Speaker 03 Definitely. We're a media company. We've been around for about nine years. We make three podcasts, this one and two others.
3:32 Speaker 04 This is Masters of Scale. I'm Jeff Berman, your host. I'm Bob Safian, and this is Rapid Response.
3:39 Speaker 03 And we publish about 200 episodes a year on video and audio, plus our website, social media, newsletters.
3:48 Speaker 02 And there's, of course, the Masters of Scale Summit, which I love.
3:51 Speaker 03 Yeah, we hold this big three day event called Masters of Scale Summit in October. It's in San Francisco. It's dozens of in-person speakers and performers. There's all this ticketing and logistics that go into it. And we also pop up with other one-off events throughout the year. So I have to say, for a company that's less than 40 people, we definitely punch above our weight.
4:13 Speaker 02 Yeah, that's a lot.
4:15 Speaker 01 So how has the company been using AI up till now?
4:18 Speaker 03 I'll speak for myself. I've been using AI a lot for research, which makes sense since I produce a podcast about AI. But as a company, our AI use has been haphazard. And for a company that talks a lot about AI with leaders in the field on our shows, it feels like we should have better systems in place because we acutely understand the stakes.
4:41 Speaker 01 There is no industry that this will not impact.
4:45 Speaker 03 Again, Taran, COO and President.
4:48 Speaker 01 I want to, number one, make sure that the organization is nimble and has the ability to thrive in the future media landscape that we live in. And number two, I actually really do feel that we have a responsibility to every member of our team to give them access to these tools and help them feel empowered to use them.
5:09 Speaker 03 Taran knew that we needed to retool around AI, but the how was still up in the air. So she phoned a friend, a new friend.
5:18 Speaker 05 My name is Parth Patel and I'm an AI engineer and I work with the Office of Reid Hoffman. And I spend most of my days working with generative AI and advising startups, entrepreneurs. In short form, I'd say I spend 14 hours a day talking to language models.
5:35 Speaker 03 He talks to LLMs all day, not just chat tools, but coding platforms like Claude Code to stay up to date on how to use them, how they're changing and how to teach others about these tools.
5:48 Speaker 05 If you don't become AI native, anyone on your team with high potential is not likely to stick around long term. If they do stick around, it might be because they're like, oh, I automated my job and no one knows. I mean, for a lot of people, they may go build your competitor and like beat you because they're able to go deeper and further and faster because of these tools. And what's at stake is that you're going to be competing with other new entities that are AI native that move in ways that you didn't think was possible.
6:17 Speaker 03 And so Taran made the call.
6:19 Speaker 05 She just wanted to learn from me how I use some of my favorite tools. And she basically looked at me after two hours in and she was just like, do you think that you could teach a whole team how to think like this in maybe like two days?
6:31 Speaker 03 Two days, let's make it three.
6:34 Speaker 01 And I suggested that we pause company operations for three days in order to all align around how we're going to use AI. What can we use it for?
6:45 Speaker 05 And I honestly, I was very skeptical. I mean, I know for me, it's like if I sit next to like a friend for four hours, I can definitely like AI pill them and then like they'll be changed moving forward. But I was like, how do you do this for a team?
6:59 Speaker 01 This is the Wild West. I think that the most important thing that we can do for our staff in terms of upskilling everybody is giving them the courage to start something new. Like you too can be an expert on AI if you just get started today.
7:15 Speaker 02 Okay, so the motivation was there, but how do you actually pull off a three day AI sprint? Were people even on board?
7:22 Speaker 03 Well, Taryn and the rest of our leadership, along with Parth, put together this roadmap for us. They cleared everybody's schedule for three days, signed us all up for Claude accounts, enough seats to cover the entire company. And then they split the company into small groups that were tackling specific questions like, how can we use AI to help us make video faster? Or how can we use AI to surface guest ideas? And how can we make planning our three day summit more efficient? And at the end of the three days, each team would present their results to the whole company. Could AI solve the problem at hand? How? And don't just answer, build an answer.
8:08 Speaker 03 Ready, set, go.
8:12 Speaker 02 Okay, and so the sprint begins. Out of the starting blocks, how is it looking, Rachel?
8:17 Speaker 03 Well, we're a fully remote company, so the sprint started the way all of our meetings do, which is on Zoom.
8:26 Speaker 06 Folks, welcome to our AI sprint. We made it. Today is a day about making things, about building things, about seeing things coming up that we have the opportunity to use and iterate, use and iterate.
8:37 Speaker 03 After the kickoff, each group spent most of the day in their breakout groups. We are working together, getting to know our new AI coworker, Claude, but we were also just catching up with our human coworkers, who we don't get to see too much, you know, talking about the important things in life. Although I hate iterating. You know what? I hate it with a passion. If you water it down, it's better.
9:00 Speaker 03 Starting off, not everyone was feeling so confident about the exercise or about using AI for certain parts of our work, including MG, who's a video editor here.
9:11 Speaker 07 I think the first day I was like, oh, no. Does this mean that I'm supposed to use AI to video edit entirely?
9:21 Speaker 02 That's understandable. It's so important to draw boundaries around AI and be intentional about what you want to delegate to AI versus what you want to do yourself.
9:30 Speaker 03 I agree. Like, there's so many parts of my job that feel really tedious, but there's parts that I don't want to outsource. For example, making editorial decisions.
9:40 Speaker 02 OK, so the groups got started. What platforms are they using?
9:44 Speaker 03 Well, most of us were using Claude, but Claude, as you know, is a single-player tool right now. So there's no collaboration layer. So one team member had to screen share over Zoom as they navigated Claude on their own computer.
9:59 Speaker 02 Let's dig into that for a second, because that's a very important point. That is one of the biggest limitations of tools like Claude today. It's changing very fast, but it's basically not like Google Docs, where multiple people can be in the document collaborating. It's kind of more like Microsoft Word, really.
10:16 Speaker 03 Yeah, that's what Parth was saying, too, is that he expected this to change in the coming months. And I know this is something that you've talked about, Rana, how quickly this is all iterating.
10:27 Speaker 02 Totally. The AI that we have today is the worst it will ever be, and things are moving so fast.
10:33 Speaker 03 But, you know, there were some other technical hiccups, too. At one point, Claude Desktop stopped working altogether.
10:41 Speaker 08 Claude Desktop failing to open for some users.
10:43 Speaker 03 Oh, interesting. Every time we hit a snag, Parth was available to answer our questions. And remember, most of us were AI novices, so there were a lot of snags.
10:54 Speaker 09 I was wondering, since we're starting with Claude code, is there a way that we should like sort of prep in Claude first before we start in Replit and get it like a good base of code to begin with?
11:04 Speaker 05 So you can you can import projects into Replit to work on something that you already have. GitHub is probably the best way to import, but you can also import a project as like a folder of files.
11:15 Speaker 03 And with those snags, there were some important lessons that we learned along the way. There's so much to discuss, but I narrowed it down to three lessons because, you know, everybody loves threes.
11:26 Speaker 02 Let's dig into it.
11:27 Speaker 03 First lesson. Engage in conversation with AI. Treat it like a colleague. The more back and forth you have with your AI, the more specific you get, the better the results are going to be. So Team Gatorade, aka Jodeen, Lietal, and Stephanie, they were working on a guest speaker engine, a way to help us find cool guests for our podcasts and for live events.
11:53 Speaker 02 Rachel, that's such a great example. As you know, I've been thinking a lot about like, what kind of work do we want to delegate to AI versus do ourselves? In this particular case, we have our weekly meetings where we discuss various guest pitches and whatnot. That's not going to go away, right?
12:11 Speaker 03 Yeah, I think that's right. We'd still have our weekly meetings. We'd still be the ones figuring out who's going to land on our shows. But we'd have this other tool that could come up with new ideas that maybe we wouldn't think about. We first started out by asking Claude how it would find guests for podcasts and live events, and then how it would organize the database. That's Stephanie Stern, Senior Talent Executive. She leads booking guests on all of our shows. But from there, we actually backtracked, asking Claude to ask us clarifying questions before creating this comprehensive database. They asked using a voice-to-text tool, so it's easier to have a natural conversation with Claude.
12:53 Speaker 08 Before we start building, can you ask us some questions so you can get a better idea of the mission of our company and how we typically select guests and speakers for our podcasts and events?
13:09 Speaker 03 So, ask lots of questions, ask the AI to interview you, and when you reach a roadblock, you can ask the AI itself for help. This is something Parth recommended again and again throughout the sprint.
13:23 Speaker 05 This is like the first time we have a computer that can use language and that can speak. The idea that you can wield a computer through natural language means that you kind of have a steam engine for knowledge work. It reminds me of like Harry Potter and like spellcasting in Harry Potter. It's like if you know the right combination of words, like things start happening.
13:43 Speaker 10 Wingardium Leviosa.
13:46 Speaker 02 When I was first learning computer science, I programmed in C++. But what Parth is saying is you basically don't need to learn C++ or any other programming language for that matter to build your own software. You can basically use just plain English or Arabic or Chinese to prompt the AI to build software on your behalf.
14:06 Speaker 03 Exactly, which is why it's so important to treat your AI like you would a coworker.
14:11 Speaker 02 So that's a good first lesson. What's the second lesson, Rachel?
14:15 Speaker 03 Lesson two, look for areas of your work where there's too much clicking around. All those tedious tasks like manually entering data into an endless field of spreadsheets. Instead, see if you can use AI. To help bring that lesson to life, let me introduce you to my coworker, DeAngela.
14:34 Speaker 11 Hi, I am DeAngela Napier, and I am the Special Events Project Manager for the Masters of Scale Summit.
14:41 Speaker 03 Again, Summit is the big three-day live event we produce.
14:45 Speaker 11 Before we started the sprint, I really just thought about, of all the things that I do, what's something that I thought could be better?
14:53 Speaker 03 Her work involves lots of details.
14:55 Speaker 11 There's a couple of little pain points where you're like, but I wish this was better. So I was thinking about the hotel management because that's a big part of what I do as it gets closer to Summit.
15:09 Speaker 03 This is important and tedious, keeping track of everyone's travel information, their hotel and preferences, like what floor they want. And this information isn't static. Travel plans change a lot.
15:22 Speaker 11 Yeah, I'm totally guilty of that.
15:25 Speaker 03 Look, a lot of people are, and D'Angelo is the one tracking it all on a bunch of different spreadsheets, which means a lot of clicking around, taking information from email or Slack, even text messages, and then inputting it again and again.
15:41 Speaker 11 Yeah, this is the kind of work that AI is really good at, pulling and organizing data that is often scattered all over the place.
15:48 Speaker 03 Yeah, so she started building a real-time dashboard. And D'Angelo isn't the only one of my coworkers hoping AI can reduce the clicks and the cut and paste of it all. There's so much backend detail work around registration, ticket codes, tracking responses. Taryn had a really good description here.
16:07 Speaker 01 There is so much invisible work in everyday roles. It is very easy from the outside to look at somebody's role and go, like, what does this person actually do all day? I think this is where AI introduces a really meaningful shift in creating workflows that are observable, explainable, and shareable.
16:27 Speaker 11 The people in our department know the things that I do, but I always get the feeling that most people don't. I come from a military family, and my dad was like, it doesn't matter what accolade you get, just do the job well. And so I'm gonna do it well, anyway. But it's nice to have that recognition.
16:45 Speaker 03 I asked D'Angelo what advice she had for others getting started with AI.
16:50 Speaker 11 I would just say, don't be scared. Just try, just try. Then start thinking about how you can apply that in your career, because a lot of people think that AI is here and I'm going to be replaced. And that doesn't have to be the case.
17:03 Speaker 03 The thing is, I'm not sure if that's the case.
17:08 Speaker 03 In a minute, the elephant in the room. Are we learning how to use these models? Or are we training them to replace us? That's after a short break.
17:22 Speaker 02 All right, we are back. Let's call it the second lap of three in this AI sprint. How's it going?
17:29 Speaker 03 Well, the 12 teams are making headway in their projects. Some have even built prototypes.
17:35 Speaker 09 We are working on a web-based app that can help us review applications for Summit much quicker. It has a queue for our applications. It allows us to like score them.
17:45 Speaker 03 Some are testing off-the-shelf products.
17:48 Speaker 12 Some of the Descript tools that we hooked around and discovered are actually pretty useful.
17:53 Speaker 03 And others are still in the exploration phase.
17:56 Speaker 13 I feel like we might be a little behind, but this particular process is really like wrapped up in a lot of other things that are going on, both in the sprint and at the company.
18:08 Speaker 02 Got it. What is this all costing, though?
18:12 Speaker 03 Leadership said that the highest cost at this point is paying for seats to use tools like Claude and Replit. There's also the cost of tokens, though most of the team members haven't maxed out on that yet. You also have to consider the cost of people spending time learning, which means they aren't doing the other parts of their jobs.
18:31 Speaker 02 Absolutely, Rachel. There's a lot of hidden costs here that we often don't talk about. So so far, you've shared two of the three lessons you want to highlight. Lesson one, engage with your AI and treat it like a colleague. And lesson two is make AI do the repetitive tedious work. What's lesson three?
18:49 Speaker 03 We're going to get to that in a minute, but before we do, I want to address something for a moment. This big question, is the sprint about upscaling us or is it about replacing us? I mean, we've all seen the headlines at this point. Companies have been cutting thousands of jobs and some are saying it's because of AI.
19:09 Speaker 02 Yeah, but some of that might be AI washing, right? AI will disrupt some jobs, but it is my fundamental belief that AI will also add a lot of new jobs to the economy.
19:19 Speaker 03 Yeah, I do agree with that, but at the same time, it is kind of weird to see Claude do aspects of my job and do it, I would say, almost as well or just as well. It's a little bit of an ego hit. And just to put a finer point on this, we used to have an associate producer for this podcast and currently we don't. AI is not the reason why we don't have an AP right now. But I will say that I have been using AI a lot to do the job that an AP would do. Wait, what? And its leadership is very transparent about this tension.
19:55 Speaker 01 It is uncomfortable as a leader to ask your team to train on technology that they fear will replace them.
20:02 Speaker 03 Here's Taryn again.
20:04 Speaker 01 And also, I feel a sense of responsibility to make sure that everybody on our team understands how to use these tools so that they're well positioned for a long career.
20:15 Speaker 03 Taryn's upfront that she doesn't know what any of our jobs, even hers, will look like in the future, but she made it a point to address that uncertainty head on.
20:25 Speaker 01 I felt that by creating a space where we were doing this collectively, it countered some of those fears and would make it more productive.
20:34 Speaker 02 Right, so having the sprint be a co-created project, rather than something that is imposed on the team top down, is giving people agency to think about how AI is impacting their work.
20:44 Speaker 03 Yeah, and that's how it felt for us, too, as we were doing the sprint. It felt like that even to MG, who is a video editor here and one of the team members who started out pretty against AI.
20:56 Speaker 07 I did not use AI before the sprint. I think like, there's a few different reasons. Number one, as an artist, I'm also a writer and a performer, and I feel bad for artists whose work has been scraped from the internet, copied, pushed through this like sausage maker AI thing. And then I think also, you know, environmental concerns are a big one for me, just sort of worrying about all of the processing power and how that is affecting our planet.
21:29 Speaker 02 Yeah, these are valid concerns we've talked a lot about on this show. So how do you bring somebody like an MG on board?
21:36 Speaker 03 Well, MG, you know, is the kind of person who approaches skepticism with deep curiosity. They were part of a team that was working on ways to find how they can use AI to get to rough video cuts faster, which is no small order.
21:53 Speaker 07 There's like importing all of the files, organizing everything in Premiere, and then creating the multicam sequence, syncing the premixed audio, and then you get to like, okay, great, now let me pick the shot, you know, and do sort of like the rough pass.
22:07 Speaker 03 A video editor's role is creative and technical. It's kind of similar to our audio engineers and design team, too. These are all areas where AI is advancing fast. They can make cuts to video, do graphic layouts, manipulate audio files to improve the sound.
22:26 Speaker 07 But like, it became clear that that was not the purpose of our AI sprint.
22:32 Speaker 03 Because these tools can't perform at the level of experienced, talented humans, the end product quality is just not the same. So we're not looking to fully automate these areas, at least not yet.
22:45 Speaker 07 It was not to like, take away the parts of our jobs that we love the most, or that are like, creative, that are human, that are fulfilling, that are artistic, but rather to like, get us to those aspects more quickly.
23:01 Speaker 03 Which brings me to the third and final lesson I want to share with you.
23:07 Speaker 03 How to make decisions. As you bring AI into your work, you'll need to think critically about what to delegate to it, what to keep in your own human hands, and then you'll have to decide whether to build or buy.
23:20 Speaker 02 Yeah, you want to build a framework where human judgment is irreplaceable. So for example, for this podcast, we still get to decide who gets to be on the show and what questions to ask. And then, of course, there's the age-old question when it comes to new technology, build or buy? Again, for instance, say you want to use AI to generate social clips for the podcast. Do you build this yourself using Agentic AI? Or do you buy an off-the-shelf product? And what's the cost comparison in terms of time, money, and resources?
23:52 Speaker 03 Yeah, and the team's instinct in this scenario was to test off-the-shelf products for video.
23:59 Speaker 07 So I was looking at different products, so many of which were like, brand new and having new versions like every single day.
24:07 Speaker 02 And did any of them rate as, "Oh my god, we have to have this"?
24:12 Speaker 03 Not really. A lot showed promise, but there was always like, one issue. So maybe the program didn't offer, you know, an audio transcription, which we really need when you're working with podcasting. Or maybe the plugin used the wrong kind of file.
24:28 Speaker 07 So much of it is brand new and I would be super curious to see where these companies and like their products are now. I feel like they're probably radically developed, but it was cool to sort of try out and see different things that worked. It's a tall order with or without AI.
24:45 Speaker 02 Yeah, so maybe Wait What can consider building their own AI agents to solve this issue in the future.
24:51 Speaker 03 Yeah, I mean, I would think so. I mean, like MG said, it's a very tall order. It may make sense to wait and see what the off the shelf products look like in the future, so we'll find out.
25:03 Speaker 02 So what happens as we wrap up day two of the sprint? Was there any clear winner?
25:08 Speaker 03 Well, Brana, we'll get to that in a minute after a short break.
25:29 Speaker 03 At the end of day three of the sprint, each of the 12 groups presented their findings. Team Gatorade, aka Team Guest Speaker Engine, made some real headway on their database.
25:41 Speaker 14 So what do we build? We build a standalone webpage. Ooh, this is sexy, right? It is. It has some really good information on here.
25:52 Speaker 03 Picture a dashboard with different tags and guest suggestions. There's a little context about each guest, too.
25:58 Speaker 02 OK, but how good were these suggestions?
26:01 Speaker 03 Well, there are some suggestions that were almost too on the nose, like who doesn't love Oprah?
26:07 Speaker 02 Yeah, Oprah, you're welcome on the show anytime.
26:10 Speaker 03 Yes, Oprah, please come on the show. But, you know, in reality, we're also looking at other people who might be lesser known names and the app was pretty good at coming up with some of those people.
26:21 Speaker 02 What about the other projects?
26:23 Speaker 03 MG's group presented and no surprise, they didn't find any perfect AI tool that they were confident in.
26:31 Speaker 02 OK, so maybe Team MG didn't win the race, but who did?
26:35 Speaker 03 Look, it wasn't a competition and there were so many cool projects that came out of the sprint. One of my coworkers, Taylor, built this really cool tool to monitor all of the incoming application and ticket sales for Masters of Scale Summit. But if I had to pick one top contender, there's one person who really stood out.
26:57 Speaker 11 Okay, so this is the Summit Hotel Operations Reimagined.
27:03 Speaker 03 I bet that if D'Angela had demoed her hotel management dashboard in person, the whole team would have given her a standing ovation. But on Zoom, it sounded a little different.
27:15 Speaker 10 Um, folks, please.
27:20 Speaker 03 We were all clapping on mute.
27:23 Speaker 11 I have to say that I was actually surprised that people thought it was so amazing because I just thought, well, I'm just helping what my role. But then, you know, as, as people were talking to me about it, I just thought, yeah, I, you know, I could really find other ways to apply this to other people's roles that could help them because, you know, once you have one system, you can repeat it.
27:50 Speaker 02 Okay, so I'm picturing every team crossing that finish line. Maybe some are stumbling. The crowd is cheering. So now what? Everyone's all in on AI?
28:00 Speaker 03 I wouldn't say that, but people are crossing that line with a different mindset than they started. After everyone presented, we reflected as a group. And MG, former AI hater, saw things differently.
28:13 Speaker 07 I'm very impressed and I'm very pro people using AI in ways that like, yeah, take away some of, you know, the hateful tasks. My mom and I would say if we have to do something like taxes, it's then we have to go to the Ministry of Hateful Tasks. So, you know, using AI to minimize your time spent at the Ministry of Hateful Tasks? Great, I love it.
28:36 Speaker 02 Wow, I really love that. So it sounds like we saw a real change in people's mindset. But what's next?
28:44 Speaker 03 Well, truthfully, a lot of that is still being worked out. Something that was really clear the moment that the sprint ended is how incredible this experiment was in generating ideas. Like, when you have a brainstorming session, writing ideas on the big notepad is easy, but actually implementing those ideas is a whole other thing. Taryn put it like this.
29:06 Speaker 01 It's not enough to simply do a three day pause if we're not then taking the learnings and applying it to our day to day workflow. The point of doing a three day pause is to apply it to your day to day workflow.
29:20 Speaker 03 We're still figuring that out.
29:22 Speaker 02 So how is this going to roll out? Because, you know, I see a lot of organizations doing some version of this AI sprint, and then nothing happens. In reality, it's actually pretty hard to go from experimentation to integration of these AI workflows. So this really isn't the end of the race, right?
29:42 Speaker 03 Yeah, the sprint is over, but here comes the marathon, right? So we have this task force that's dedicated to figuring out which projects to pursue and how to implement them. Some of the projects from the sprint are actually combining to these super projects. For example, the guest speaker engine from Team Gatorade is joining forces with this larger speaker discovery app. And there are even more AI ideas in the pipeline since the sprint ended. There's around 30 of them.
30:15 Speaker 02 But I imagine there were some hurdles.
30:17 Speaker 03 Yeah, there are. Security is a big one.
30:22 Speaker 02 This is something we talk about all the time on Pioneers of AI. Any system needs to have guardrails against things like prompt injection. And when you're dealing with data, especially personal information, it is so important and critical to have systems in place to protect that information.
30:39 Speaker 03 Yeah, and since our company does ticket sales, we have sensitive personal information like people's email addresses, some financial information. One thing the task force developed is this security AI agent. They call it Warden, as in the warden of a prison, and it helps keep everything secure.
30:59 Speaker 02 What are the other hurdles?
31:00 Speaker 03 Another big one, measuring ROI, which isn't a clear-cut calculation because we don't know yet how much rolling these projects is actually going to cost.
31:12 Speaker 01 New technology is hard to budget for, especially when nobody's used it before. This isn't a category that we've spent on previously or that anybody has spent on previously. I actually know a CFO who just told me that her team went from spending $1,000 a day to $1,500 a day per person in a span of a week.
31:35 Speaker 03 Right now, Wait What is paying for every team member to have access to Claude and Reblit, but it may not make sense to do that long term. Plus,
31:44 Speaker 01 we don't know how many tokens something's going to take. Right? Like, our security platform takes up quite a few tokens, but we have to run it for a period of time to figure out exactly how many tokens it's going to use over time.
31:58 Speaker 03 Once the true cost becomes clearer,
32:01 Speaker 01 we're going to have to determine what the ROI is. Are some things better to do manually because the cost of having an agent do it is too expensive?
32:10 Speaker 03 In the coming weeks, these AI projects will roll out for the rest of the company. And leadership plans to use our weekly company-wide meetings to train us on how to use them. This kind of slow, strategic work isn't headline catching, but it's the kind of work where we see really meaningful impact.
32:31 Speaker 02 I 100% agree. A lot of these use cases aren't sexy, but they actually really change the way people do work. And they can be powerful. And we need to hear more examples of how teams are systemizing AI into their workflows.
32:45 Speaker 03 Well, I'm happy to bring part of that story to our audiences. But you know, it looks like, Rana, we're coming to an end of our episode. And I wanted to leave on a note that we often end our Pioneers of AI episodes.
32:59 Speaker 02 Signature question?
33:00 Speaker 03 Yep.
33:04 Speaker 03 What does it mean to thrive in the age of AI?
33:08 Speaker 11 What does it mean to thrive in the age of AI? I think that if you are in a state of just experimenting and trying things, you're going to feel motivated and inspired and more confident and understand that our mind is limitless and you can actually, as my parents taught me growing up, you could do anything you want to do.
33:32 Speaker 02 I love that. AI opens up doors to what's possible, and I think that's a really good note to end on, Rachel.
33:38 Speaker 03 DeAngelo's great. The whole team is great. And you know, maybe one day, Rana will make a part two when we have all of these ideas from the sprint figured out. And until then, you'll catch me on the track, still chugging along.
33:52 Speaker 02 Yeah, Rachel, I know. This is messy work, but also so fun. You got this.
33:57 Speaker 03 Oh, thanks, Rana. Yeah, I hope so.
34:12 Speaker 02 Pioneers of AI is a Wait What original production.
34:17 Speaker 02 Our executive producer is Yves Tro. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pugh. Video editing by Eric Purcell. Original music by Ryan Holiday. Our head of podcasts is Lital Malad.
34:38 Speaker 02 You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening.
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