Artificial Intelligence in the Ticketing Landscape - a Hot Topic Special episode with Michael Fraser from Action / Insight

How will Artificial Intelligence impact the future of ticketing? We explore this question in this Hot Topic Special episode of TicketingPodcast.com, featuring our guest, Michael Fraser.

Michael brings nearly four years of direct marketing and ticketing experience from the Toronto 2015 Pan American Games Organizing Committee. He is currently the co-founder and AI evangelist at Action / Insights in Toronto, specialising in simplifying generative AI for non-technical leaders and executives.

In this episode, Michael begins by explaining the key differences between machine learning and generative AI. He then discusses how AI has evolved from a tool used exclusively by experts to a technology everyone should understand and utilise.

During the 2015 Pan American Games, Michael and his team were responsible for selling 1.3 million tickets - a monumental task, with or without AI. One of the questions explored in this episode is whether they would have approached this task differently had AI been available then. 

Michael warns, “It's not going to be easy and it's not going to feel like a vacation. It's going to feel like really grinding, grueling, hard work. Everyone's just been set up to think that AI is here to solve all their problems, and it can. It can help. It's still going to take so much of us. This is like when you learned to use a computer in the 90s. That's what's about to happen to you.”

As Michael navigates the landscape of AI, it becomes evident that effective communication is our most powerful tool. By breaking down grand visions into manageable tasks, we can create a blueprint for AI that enhances decision-making and operational workflows. Michael's experience shows that defining goals and processes precisely can transform AI from a mere tool into a reliable ally, capable of tackling real-world challenges like boosting ticket sales through predictive modelling.

AI's entry into the ticketing space is imminent, and we should be prepared. This episode will give you a head start.

TicketingPodcast.com is powered and sponsored by TicketCo and hosted by TicketCo’s CEO, Carl-Erik Michalsen Moberg.

Transcript

Speaker 1: 0:02

What do you really know about AI and how do you think it will affect ticketing in the future? This is what we will find out in today's episode of ticketingpodcastcom, where our guest is Michael Frazer from Action Insights. Stay put for some easy wins and get started with AI after listening to this episode today. Hello everyone, welcome to this hot topic special episode of ticketingpodcastcom. Today we will be delving into a topic that no one really can neglect these days, and that is artificial intelligence, or also called AI. According to a recent study, it is said that these tools can replace 300 million jobs. Trends change every six to seven years, and now the new trend is AI. You definitely need to learn AI tools to follow this trend.

Speaker 1: 1:05

My name is Kallarik Moberg and I'm the host of this podcast. With me today is a man who has first hand marketing and ticketing experience from almost four years as Toronto 2015 Pan American Games Organizing Committee. It's a perfect fit, because today he is the co-founder and AI evangelist at Action Insights in Toronto, specialized in simplifying and demystifying generative AI for non-technical leaders and executives. That fits me perfectly well. It should also make him a perfect guest for this hot topic special over podcast, where our topic of the day is AI. Welcome to ticketingpodcastcom Michael Fraser.

Speaker 2: 1:50

Thank you so much, Kallar. Great to have me yeah.

Speaker 1: 1:52

Cool, we'll get to know you better, michael, but before that, will AI steal our jobs in the future? I guess that's something that a lot of people are thinking about these days.

Speaker 2: 2:01

I don't think so. We counter that it's not AI that's going to steal your job. It's going to be a person using AI that's going to steal your job. I really don't think either of those things are imminent. In fact, based on the rate of adoption that I'm seeing, I think that not only is that overblown, but actually the thing that we need to guard against more is being so complacent and slow, passive when it comes to AI that you just let it pass you by and you don't adopt it early. It's not so much that you need to be defensively adopting it to keep your job, but I do think that there's a massive opportunity for those who can adopt it early to not just do better at their job, but just to create more and be more productive and visionary in their own life.

Speaker 1: 2:42

Great, I love that part. I was actually starting the day today with reading an article on seven ways you should use AI in your daily life, so I'm trying to learn myself. But what is AI?

Speaker 2: 2:53

AI is interesting because there's two AIs at least. I really think it's really helpful to separate them out into AI before chat GPT and AI after chat GPT. Before chat GPT we're dealing with machine learning. We call that the oracle. It's like predicting, optimizing, automating and that's kind of the domain of experts, where you have math and science PhDs working on these things and they're building models. But with generative AI it's very different.

Speaker 2: 3:24

With generative AI, we call it the interface and it's not about predicting and optimizing, it's about creating and transforming and expressing. And if the skill set with machine learning is kind of like data science and math, the skill set with generative AI is just being able to really clearly articulate something that's important to you in your own words. And so it's not about building models and building tech, it's just about using the model that they built, and that's such a different domain. And so with generative AI, which is what's become so popular, the reason we're all talking about AI is not because something happened with machine learning. That's just been steadily going for years. This new generative AI, this interface to computers, where computers understand us when we speak to them, that is the new phenomenon and it's a user led phenomenon. It's something that's centered on people using the tool, not just building technology projects.

Speaker 1: 4:21

Great, I think that's a good start. For sure, michael, we'll talk more about AI in a few minutes, but before that, we need to know more about you as well, and what can you tell us about yourself?

Speaker 2: 4:32

So myself I am a generalist by nature and I think that that's maybe what kind of helped me see the spark in chat GPT because it's also kind of a generalist and so I've spent my whole career avoiding specialization in particular. When I ended up at the Pan Am Games that was about 10 years ago now I was a generalist. There too, I was doing project management, I was helping in marketing and communications, and ticketing was something that came up closer towards the end of the games. Right, it's a four-year project and you don't start selling tickets on day one. But as tickets went on sale, they kind of tapped me on the shoulder to help lead some of the strategy for that. And what was really interesting in that experience too, is that with these large multi-sport games, these large events, there's a real well-worn strategy and path that you're supposed to take with the tickets, right. So it wasn't so much that we sat around a table and thought, like, how can we do this? There was many, many examples of how to do this in prior events, and so it starts with a lottery and then it kind of goes to like a low period where you kind of try to sell out as many of the tickets as you can that are kind of like lower, attractive seats, and then finally the games are here and it's like you have no choice, you've got to sell all the tickets. It's totally different than the year out. You're like a lottery okay, we've got a year and then suddenly it's a month out. It's a very different story.

Speaker 2: 5:47

So I had that kind of a transformative experience with the Pan Am Games, which was really interesting, and after that I worked at some other companies. In particular, I was at KPMG in Canada for the last five years and that's where I was when ChatGPT was released. And ChatGPT really like unmoored me, like I was there on day one watching it be released on Twitter and watching all of the reactions and responses to it, and it was the kind of thing where my whole world shifted. You know, on that train ride looking through those threads, and then by the time I got to work it was kind of like, hey, everyone, have you heard what's happened? Like did you read the news? And it's crickets. And at first I'm like, okay, that'll give them a bit of time. But actually as time went on, I realized that as fast as we're hearing AI is going to happen. It's going to happen so slowly and that to just root myself in an organization like KPMG, which is great, but kind of a bit more risk averse they're going to travel slower.

Speaker 2: 6:45

That was the kind of point where I made a decision that you know I'm going to have to go out and do this on my own, and I thought to myself, how can I have the largest impact or where can I provide the most value right now? And I thought, really what I should probably be doing is just like going through my Rolodex and calling people up and saying like have you seen this? Have you used this? Yet I really think you need to pay attention to this, and so that's what we do now is we've started up a consultancy my wife and I we both actually left KPMG at the same time to do this and we've started up a consultancy that focuses on educating leaders and, in particular like in a sentence I'd say we turn leaders into users, because a leader right now is someone who is getting hit by all sides on what they need to do on AI, but they don't actually have any personal experience to draw on, and so they defer to technical experts in their midst, and the problem is those technical experts also don't have any experience to draw on, and so, rather than seeing AI as like a continuation of machine learning which is like for the PhDs and for that data science person and something I don't understand that's beyond me we try to hold leaders' hands and really force them through.

Speaker 2: 7:56

Okay, you got to make an account and I know it's like it's been a while since you've been doing your own work and you've got all these staff to do these things.

Speaker 2: 8:02

But you need to personally understand this tool.

Speaker 2: 8:05

You need to use this because you're about to be making decisions for a lot of people and you really need to have like a user's mindset when you're making those decisions or, unfortunately, leaders who don't have that use case like experience where they're using it for themselves.

Speaker 2: 8:20

They're making decisions on these like large investments, and there's a lot of bad investments going around. There's a lot of bad salespeople selling you something you can't tell the difference between a lemon and something that's legitimate and you have an unrealistic expectation too about how much AI will be able to do for you and how quickly, and we just do as much as we can to just reset all those expectations for leaders. So it's just like let's just simplify this. This is just something like use chatGPT every day for a few months and then have those conversations like this isn't a big thing and it's also not a rush. We don't have to rush into doing all this in the next six months or you're out of business. No, you have plenty of time. This is actually a great time to slow down and be thoughtful and just take a new approach.

Speaker 1: 9:05

I think it's interesting. So two things just to mention. First of all, congratulations on quitting with consultancy and starting your business, thank you. I mean you've been in ticketing in the Pan American games. Let's say we went back to when you work with the Pan American games and you heard chatGPT coming. What would you do? What would you use it for? You obviously want to sell more tickets. You want to make big decisions on behalf of Pan American Games. How would you use it? How would you utilize it?

Speaker 2: 9:36

So the best way to think about using ChatGPT is first to reel in your expectations. One of the most important lessons I've learned and I've traded more than a million words back and forth with ChatGPT I've been a heavy user since the day it was released and one of the things I've just started to appreciate more even in the last six weeks, I'd say, of use, as I've been looking to apply it to create some repeatable process automation and stuff like that is it's hard to overestimate how much we try to get done in one step. When we ask AI, we want to bring AI the final goal and just be like sell all the tickets, and that's a really big goal to give to ChatGPT. It's like we can't give that to anyone. No one can solve that problem, and so I think, as I was saying before, there's an oracle mindset where you think that ChatGPT is like a crystal ball and as a crystal ball, you're like what is the answer? Tell me the future, what should I do? And we really try to discourage away from that right off the bat and we say it's just an interface, it's just natural language interface, it's just a way to talk to computers, and that's a really, really powerful and important thing, but it's not asking it what to do. It's telling it what needs to be done. And if you take that approach of just telling it what needs to be done, well, ok, great, sell all the tickets. The problem with there is that's too much, and so anytime you want to bring AI and use it in your process, it's like one, you take control, using it as an interface, but two, you break down the steps for it. You have to give it a small enough task that it can possibly succeed.

Speaker 2: 11:17

And I think another one of the things that we focus on is just the AIs for leaders, and so it's like who's best at doing that right now? Leaders, Like that's. The job of a leader is to take the big task and break it down into the smaller domains, and then we give it to other leaders and they keep breaking it down and processing it into the smaller and smaller tasks. And so what I would do if I were in that position again is, first, I would write everything down. I would write everything down in a way that was never valuable before or that I would have scoffed at myself back in the day, because now, writing things down and getting really clear in our words about what's important and what we're trying to do and what's already happened and what needs to happen next. Suddenly that's like a blueprint. If you give it all of that information, it really can help you with the next step. So the very first thing I would do is I get clear about just what is even happening and I would start to write a lot more down and be clearer with my thoughts. Then the next thing I would do, once I had all my context arranged in front of me, is I would start mixing and matching it to solve problems. Like I would say, ok, now that I have this block of text that explains to the AI what our broader ticketing mission is, and maybe how many tickets we've already sold and how many need to be sold, how many days are remaining, et cetera, I'd say, ok, so now I want you to calculate like a curve that shows me what are some of the potential distributions of these tickets being sold into the future. That would represent success and show me what it looks like to sell out by the first day of the games and then show me what it looks like to arrive halfway through the games with only half of our tickets sold. Let me see all these different scenarios and if I give it all the data like I explain how many tickets are left, how many days, et cetera I can just start building and issuing commands on top of these that are just in natural language. Like I can just say graph me out all the remaining tickets with this assumption and it'll do it, so you can transform all of these seemingly just like static ingredients into like all kinds of different outputs and so which outputs are really useful to you, right, like it becomes the game. I told the story recently on LinkedIn I think that's how you guys saw me, so I'll just kind of reiterate it here and I'll explain about how AI might have helped me throughout this process.

Speaker 2: 13:36

We found ourselves in a situation at the Pan Am Games where, with about 40 or 50 days to go, we had like a serious deficit in terms of like how many tickets we needed to sell. Like the total amount of tickets that we needed to sell for the Toronto 2015 Pan Am Games was 1.3 million, and that's a high number of tickets, and it's also like an Olympic Games. I think at the time might have had like 5 million tickets, but they're the Olympics. It's really difficult. The Pan Am Games is like a secondary, the Asian Games like underneath the Olympics. It's technically like a second tier, it's just below. But there's a pretty wide brand difference between selling for the Olympics and selling for the Pan Am Games. So when it's the Pan Am Games and you still need to sell 1.3 million tickets, that's challenging. And we were in Toronto, so we were in a market that kind of. You know, a lot of Pan Am Games are hosted in cities that would be several tiers below Toronto, and so there at least it's like a bigger relative impact. But for Torontonians they've got that like they want to believe they're Olympic class and so they don't want to go to the second tier. They're gonna hold out for the real Olympics because they have that view of themselves.

Speaker 2: 14:49

And so there was all these confluence of events and circumstances that makes it difficult to sell that many tickets. And so, 50 days out, we started to run that calculation just manually, because, you know, back then too, our data was so poor. We literally we got an email every day that said how many tickets were sold the prior day, but it wasn't even put in context of like how many are left or anything. So you've got like a heartbeat. And then it's like the middle of March and you've got four months left and you're selling 900 a day and then maybe it's starting to get closer. You're selling like 1200 a day and then you run the numbers and you realize we need to be selling 8000 a day every day for every day that's left, or we're totally ruined. And so to finally like, just like, even like, run those numbers and see our situation in that context, versus the information we'd like slowly been like frog in a pot of water or just like become accustomed to, that was very alarming.

Speaker 2: 15:44

And we went to the management and we were explaining the situation. Everyone was alarmed and my boss at the time did something that was so brilliant. She just sat me down and she did, she drew that curve. She just said well, it's not going to be 8000 every day, there's going to be higher days and lower days. So let's just curve this out. What does success look like? And that was so powerful for like helping us get a new perspective on the problem. And then from there we could start to like apply ourselves. Okay, if this is the curve that we were going to need to hit, what do we need to do to do that, and we were successful actually in hitting that curve. So it was like it was a good turnaround story.

Speaker 2: 16:17

But if I was back in that position now with AI one, it would almost be like well, I don't think I would have gotten into that position because it would have been so easy to capture the data and rework the data on a daily basis. I would have never been so data poor, I don't think. But as soon as I just picked up the situation, just like I did, it's like the first thing I would have done if I was smart about it is I would just sat down and I would have explained the entire situation to it as best I could, and then I would have saved that explanation too. So a lot of people they see AI as like AI is here to solve your problem. They're like sell the tickets. They just want to have that magic output that's going to increase their ROI or solve their problem.

Speaker 2: 16:56

But we can't just skip all the steps, and I think the first step is sitting down and just being like we need to sell quite a few tickets. Here's what we've got. Here's like the ad campaigns. Here's the creative we've got. Here's our messaging here these are the events that are selling well, these are the events that are selling poorly. It's like the first thing you want to do is train up and give the AI all the context it needs and then from there, you could start saying okay, so which ones do we need to prioritize? Okay, now look at the data every day and reprioritize our list of which events need to be hitting. Where are you seeing a pattern versus yesterday's information on which events have started to move and not, etc.

Speaker 2: 17:33

And you can get you out of a slump that you find yourself in because of the systems that you have, where I think it's really pernicious, where you grow accustomed to the systems you use and you stop thinking outside.

Speaker 2: 17:47

You're totally inside those boxes, and I think we've all experienced, in particular over the last 10 years, like not even the Pan Am Games, but the 10 years since, as we all move to cloud and we all move to like software as a service, you have to implement this new program and now we've got to integrate these two services and a lot of those things.

Speaker 2: 18:05

They don't go smoothly and it's new things for you to learn, and there's this like exhaustion of all this change that keeps coming to you and I think AI is a great opportunity, when you're in that space, to use AI to just break out of that. Just break out of everything the ways you're doing it right now, start from scratch and you can just come with a totally different perspective, and AI is going to help you bring different perspectives, bring different lenses to it, help you think differently, help you explore ideas, and that's one of its most powerful early uses and unfortunately, I think people skip past that so much times because they just want that quick thing. They just wanted to know the answer. Just help me out right now. And I think it takes a lot longer than that. It's a much more iterative, like brainstorming, like process to use AI really effectively.

Speaker 1: 18:53

It's super interesting to hear and I think one key takeaway, michael, is breaking things down. Right, you're not going to eat the whole cake in once. You need to slice it up before you eat it, as opposed and I think that's the case as well with with AI and also from a team perspective right, one thing is sitting with your computer and putting in questions and learning etc. We'll also use it more collaboratively with the whole team across different stakeholders. I mean, if you're a ticketing manager, you'll get the same question every day how many tickets have we sold? Right From the CEO. This is what we see a lot. That's the most popular question. But in AI, you can actually put it into context. Right, you can actually have an automatic report going. This is a great result. This much compared to last year. You are going to have to sell this much every day to get there, exactly like you were, etc. So what you're saying, that is maybe wouldn't have made your life easier, but at the same time, you would probably make better decisions.

Speaker 2: 19:53

Right, that's a great way of putting it, and I think that's a key thing is that, in the short term, ai is not going to make your life easier. It's not. It's going to make it more difficult and challenging, and that's not what people want to hear. It's also strange, because we were like, on one hand, we're being set up like it's going to take all your jobs, we're going to need UBI, there won't be any work left. And then, honestly, from my perspective, as being someone who's quite heavily in AI, I'm like I've never been more exhausted. Oh my God, there's so much work to do. It's like nothing has been built yet. Now we've got this new thing and we realize, actually, how poor so much of our existing systems are, and so all I see ahead is just a decade of further integration, of getting more comfortable with this tool and, at the same time, we're going to build incredible experiences and incredible products and services. But it's not going to be easy and it's not going to feel like a vacation. It's going to feel like really grinding, grueling, hard work, and everyone's just been set up to think that AI is here to solve all their problems and it can. It can help. It's still going to take so much of us Like, for instance, a lot of people are like oh, I need to learn AI.

Speaker 2: 21:09

Is this like what we moved to, that cloud service? Or is this like when I needed to use that authenticator app Am I going to? And it's like no, this is like when you learned to use a computer in the 90s. That's what's about to happen to you. You witnessed older people learning computers when you were learning computers in the 90s and it looked really hard for them. It's like that's what's in it for you now.

Speaker 2: 21:30

Now you're the older people and I mean this like everyone in the workforce, even if you're like 23,. One of the things that's so surprising about a 23 year old if you really think about it, it's like they've never experienced change in their life. They grew up. They pretty much had a phone as soon as they would have had a phone Like they had a phone when I had a phone and they've never witnessed that transition from like no computers to computers. And that was a long time. That took people forever, and that's what we all have to go through now with AI A whole years long learning curve for everybody, and I think the world will look just as different pre computers and post computers as it does pre AI and post AI, but that's the proper orientation that you should think about. It's just another 90s learning experience for everyone.

Speaker 1: 22:19

How long do you think it'll take before a Premier League uses AI in their daily life to make decisions?

Speaker 2: 22:26

So this is what's interesting is like okay. So one of the ways I think about this is to go all the way back to like electricity. And if you think about electricity, we discovered electricity and then, over hundreds of years, we built a grid to actually deliver electricity to people. But for a long time we had it, but that didn't mean you had it right. Like had to come to your community, we had to lay all the infrastructure, build all the power plants, etc. And so you don't have a choice Like if it's like 1834, it's not obvious that you can just have electricity right here. We got the grid first. The grid is established.

Speaker 2: 23:07

Anyone in the world can turn AI on for themselves anytime that they want. Everyone got the new resource at exactly the same time, and so there is a question of like when will 75% of people use it every day? When will 90% of people use it every day? I think 75% of people use AI every day personally, not just integrated behind some service, but like are directly interacting with AI, probably like 2028 or 2030 or 2032. But I think, rather than focusing on that kind of like societal thing, it's just a question of any individual can become part of that cohort whenever they want, and right now it's reflected to you as if everyone's doing it.

Speaker 2: 23:52

But I assure you almost no one is doing it, and even the people who are doing it like one of the things we always clap for people is like, if they have an actual chat GPT subscription, like do you pay for chat GPT? And if they pay for chat GPT, we're like, yay, and that's less than 5%. Like no one uses chat GPT. As much as you hear about everyone using it, no one uses it and so the biggest opportunity right now is to just start using it yourself. You'll be years ahead, no matter what you see on the news about how it looks like everyone else is already doing it and you're already too late. It's totally not true. You can be a top 1% AI sophisticated user in like two months, right, and you'll be at the absolute top of the world.

Speaker 1: 24:35

That's motivating for us to hear Michael for sure. I hope so, we'll get on it as soon as we're finished with this episode. There you go. I think it's super exciting and I mean, the event industry was so creative. Who has to make so many decisions every day? And you know it right Because you've been in it. One question I was curious on as well. I mean, you worked with a massive event even though you're mentioning tier two right, the 1.2 million tickets and I hope you succeeded in the end.

Speaker 2: 25:02

I don't know if you did and I will say, in terms of the complexity of hosting event, it is one of the absolute most difficult events to host in the entire world. It's obscene for a person who's not steeped in that culture of like multi sport games. There's a circuit. They all go from city to city around the world. There's like a small group of people who do this work and they hop across all the events that are currently happening and it's totally insane. And it all for like two weeks and yeah, so well, it might be like second tier from a marketing or a brand perspective. In terms of the difficulty of hosting it, it's outrageous. No one would agree to do it. You know what I mean. Like if they knew what they were really in for.

Speaker 1: 25:44

The true rock stars. Yeah, yeah, yeah. But before we sum up on some important takeaways today, michael, what have you learned from the event industry now that you're running your own business?

Speaker 2: 25:59

What I learned from the event industry actually is that one. I hate events. I truly despise events because everything is like hinges in the moment, right, like you don't get a second chance and it's all in real time and it's stressful, and I really respect the people who not only like operate but like thrive in that environment. It's so not me. And so one like definitely learn that.

Speaker 2: 26:23

The other thing I would say just like in terms of like an event is it's so healthy to have a day in the future where everyone gets fired, the event is over, we're going home. It's a limited time, massive scale project. It's such a set of healthy incentives when you're working in that structure that most companies they don't take advantage of any of that set of incentives. They have perpetual. It goes on forever. There's no clear start and end, there's no milestones and the days just blur together, whereas, like when we did that calculation on how many tickets we needed to sell, it was like this is real, the event is really coming and we really have to do this, and that's just a totally different pressure and motivation and I thought it was tremendously healthy for the organization.

Speaker 1: 27:10

Great to hear and, yeah, we've been talking to a lot of heroes out there who's doing this every day, their whole life, which is amazing. So super interesting conversation, michael, I've learned a lot, and I'm sure our listeners has as well, but if there's two to three key takeaways that you want our listeners to bring with them out to this episode, what would those be?

Speaker 2: 27:32

So I would definitely say just be an AI user. Don't worry about what it's going to do for you. Don't worry about getting value out of it right away. It's going to take a while. Just be a user. Log on to chatGPT, create an account. Create, pay for your account. Try to use it a few times a week. Don't put too much pressure on yourself, but just try to make it something that you're invested in and that you're learning.

Speaker 2: 27:56

The second thing I'd say is break down steps when you're using it. If something isn't working, you're probably trying to do too much at once. So if you can break it into two steps, you have a higher chance of success, and the more steps you can break it into, the better a chance, especially if you can figure out and this is a tough one which order do the steps happen in. It's like which step needs to come first and which step needs to come second. These, I think, right now, these are places where you should actually look to the human to be responsible for the steps and the order, and then GPT will be really good at looking at each individual step and accomplishing it. And the third and final thing I would say is don't look at it like a crystal ball. It's not a magic eight ball. You don't care what the model thinks, but instead of the crystal ball, you should really think of it like a cauldron.

Speaker 2: 28:45

And a cauldron is different than a crystal ball One. It's just a big empty pot. It doesn't do anything. And that's actually really exciting, because the way that a cauldron works is what you put into it. It's about ingredients, it's about recipes, it's about vision, like what are you trying to do and how do you assemble the ingredients in your life, which can be data, they can be experiences, anecdotes, they can be articulations of what you think are important. This is your job. Your job is to arrange these things. And then what's cool is, depending on your recipe, depending on your arrangement, you throw it into the cauldron, it will come to life. So right now, if everyone just spent more time idly stirring their pot and experimenting and, yes, having things blow up in their face, that's the highest hope for you right now. That's where you should focus your energy and just have those realistic expectations and just ground yourself in use.

Speaker 1: 29:42

Great advice, Michael. It's been a true privilege to have you as our guest today. We've learned a lot, but if any of our listeners wants to get in touch, Michael, what do they do?

Speaker 2: 29:53

You can find us at actioninsightai or find me on LinkedIn. Michael Fraser, Action Insight. You can find me on there pretty easily in Toronto and, yeah, definitely reach out. Connect on LinkedIn. That's a great place to get me and you can kind of follow along with what we're doing. We try to post with just various insights about AI and where it's headed, and we're always focused on that non-technical audience. And then, if anyone's listening and you feel like you do need maybe some help or some training, Action Insight, my consulting agency. We specialize in educating leaders and their teams on how to approach AI with the right perspective and hopefully not only save them some time, get them started in the right way, but maybe avoid some really bad investments along the way as well.

Speaker 1: 30:37

Avoiding bad investments is a good motivation in itself, Michael. Thank you so much for participating today. You've been listening to theticketingpodcastcom, where today's topic has been AI. I hope you haven't been scared. I definitely recommend you to check out chat, gpt and all the other amazing tools out there. Get to know AI and you'll be top 1% of the rest of the world. So good luck with everything You've been listening to theticketingpodcastcom. Thank you so much to our sponsor, ticketgo, for powering this podcast. My name is Kallarik Moberg. Until next time, have a great day.