Episode 234: What Product Leaders Are Saying About AI

In this episode, we delve into the transformative role of AI in product management through conversations with some of the best Product leaders. You’ll hear from Anthony Maggio (VP Product Management at Airtable), Jessica Hall (CPO at Just Eat Takeaway), Karthik Suri (CPO at Cornerston OnDemand), Mario Rodriguez (CPO at Github), Steve Wilson (CPO at Exabeam), Darren Wilson (CPO at Soul Machines), and Tamar Yehoshua (Former Glean President of Product and Technology).

We explore how AI tools are reshaping strategy and efficiency, while personalizing customer experiences. Join us as we discuss the impact of AI, and the future of product strategy. Are you curious about how AI can revolutionize your product management approach? Tune in to discover insights and practical applications that could redefine your strategies and customer interactions.

You’ll hear us talk about:

  • 10:05 - AI's Role in Product Strategy

Explore how product leaders are integrating AI into their strategic planning, focusing on addressing significant business and customer problems. The discussion highlights the experimental applications of AI in enhancing customer journeys and optimizing backend processes.

  • 18:45 - Enhancing Customer Interaction with Digital Avatars

We dive into the world of digital avatars and their role in creating empathetic and personalized customer interactions. This section covers how avatars can be used for language learning and practicing difficult conversations in a non-judgmental setting.

  • 27:30 - Cybersecurity in the Age of AI

Understand the evolving landscape of cybersecurity with AI's influence. Learn about the dual nature of AI in enhancing defenses and creating new threat vectors, emphasizing the importance of rapid AI adoption to stay competitive in the cybersecurity arena.

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Episode Transcript:

[00:00:00] Jessica Hall: I think AI is gonna be very transformational for the industry, for society, for businesses.

[00:00:06] I'm trying to step away from some of the hype and say, okay, which business problems really matter to us? Which customer problems really matter to us?

[00:00:13] And is AI a tool that can help us solve that problem better? So we introduced an AI assistant and we've had a bit of a learning journey with that. We have built something where you can build a basket in the chat. So we are taking the ordering journey from minutes to seconds, which is great. I think that's a real improvement.

[00:00:31] However, we've had to do some learning about what is it that customers want, and what they really want is hyper-personalization.

[00:00:37] Anthony Maggio: there's so much discussion on this. I do think that it is going to be extremely transformative for the role of PM. I think that the biggest thing in my mind is that I think that AI is really actually going to increase the expectations of the PM function.

[00:00:56]

[00:00:56] Steve Wilson: From a product perspective, I know people in the security industry have been thinking really hard about how to leverage even earlier gen AI technologies.

[00:01:04] The biggest worry in cybersecurity is the lowest tech one, which is phishing. Now you look at deep fake technologies and all of these things, people can clone your voice from a three second sample.

[00:01:14] They'll write self-modifying code that will be running around the internet and it's already doing this, looking for vulnerabilities, and your security by obscurity isn't gonna work anymore because the things that used to require human hackers are gonna be automated and 10,000 times as fast.

[00:01:28] And so it's gonna be a pretty rapid escalation in the cybersecurity space where the companies that do adopt more of these technologies quickly are gonna be at a real competitive advantage.

Intro

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[00:01:37] PreRoll: Creating great products isn't just about product managers and their day to day interactions with developers. It's about how an organization supports products as a whole. The systems, the processes, and cultures in place that help companies deliver value to their customers. With the help of some boundary pushing guests and inspiration from your most pressing product questions, we'll dive into this system from every angle and help you find your way.

[00:02:06] Think like a great product leader. This is the product thinking podcast. Here's your host, Melissa Perri.

[00:02:15] Melissa Perri: Hello and welcome to another episode of the Product Thinking Podcast. Today we're doing something a little different. Over the past few years, we've had some incredible conversations on this show, deep dives into product, strategy, leadership, and innovation. And as AI continues to shape the future of how we build and scale products, I thought it was time to revisit some of the most powerful insights shared by our guests on this very topic.

[00:02:37] In this special compilation episode, we're gonna go back to the moments that spark new thinking. Understand how AI is reshaping user experiences, enabling smarter decision making, and redefining what it means to be a product leader today. You'll hear from a mix of voices each bringing a unique perspective on how AI is not just a tool, but a catalyst for transformation. Let's jump in.

AI raising the bar for PMs

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[00:02:57] Melissa Perri: There's been a lot of talk lately too, about how AI is gonna put product managers out of a role or completely disrupt like what we do. What's your take on how things are gonna change for product managers specifically with AI?

[00:03:11] Anthony Maggio: Yeah, there's so much discussion on this. I do think that it is going to be extremely transformative for the role of PM. I think that the biggest thing in my mind is that I think that AI is really actually going to increase the expectations of the PM function. When you think about the impact on the role, there's a lot of things that PMs do that AI is actually already quite good at: taking data and analysis from many different sources and using that to craft strategy and set goals or write PRDs like, and I think we're not all the way there yet, but you see the, with the pace of model development, LLMs are actually becoming quite good at some of those types of activities.

[00:03:56] On the strategy side, I do really think it's going to raise the expectations. I think as PMs we're expected to know the market, know the customer, know the business, all to inform the product strategy. And historically there have been so many inputs that it is difficult for PMs to stay on top of all of those things proactively. What has actually happened in practice is that you probably loosely follow your market and competitive news, meet with customers, review feedback on some type of some type of basis. Now, like it's actually very quickly becoming possible to monitor and proactively engage in these sources in real time not for customer feedback, for example, what are the themes that your customers are talking about right now, and how do you have confidence as a, as a PM that the products that you're building or the features that you're developing are actually addressing the top volume of customer needs or the biggest, revenue opportunities that you see out of your customer feedback. Or on the market side, what has changed in your market landscape this week? What are the kind of latest movements of your competitors or even, actually monitoring and analyzing things like 10 K forms from your customers. What are the commonalities across changes that are impacting your customer's business? I think that, on the strategy side, like PMs are now going to be expected to be able to speak to these types of inputs in near real time. And so product and, product ops teams, I think are going to play a really important role in ensuring that they have systems and toolings to be able, tooling to be able to capture and measure this data and use it to inform strategy.

[00:05:41] And that's one of the areas that I think we will. We'll just see the biggest shift in in that it will, it will really just become an expectation of the role that you're using this technology across all those different verticals to keep a pulse of your business.

[00:05:55] That's the raising expectation side. I also think that on, maybe on execution, it will make a lot of things easier. And there's so many parts of the PM role that end up being focused on non-value added work. I actually, one of our customers recently used the term bad admin for this. They said our PMs are just stuck in in bad admin.

[00:06:18] They're spending so much time like writing, weekly updates or preparing decks for executive strategy alignment conversations. And on that side, I think that we will see AI significantly reduce that type of bad admin work that PMs end up, facing the brunt of, in many organizations. And we're already seeing a lot of interesting use cases that our customers are coming up with to use AI to reduce that type of admin work.

[00:06:50] So you can see, already some ways where some of those more administrative tasks will become automated very quickly.

Using AI to improve personalization

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[00:06:57] Melissa Perri: So one of the big strategies for your company is around AI and integrating AI into your roadmap. Can you tell me a little bit about how that came about and what you're thinking when it comes to that?

[00:07:08] Jessica Hall: Yeah, so I think AI is gonna be very transformational for the industry, for society, for businesses. There's loads of excitement about it now and rightly 'cause I think it's really interesting.

[00:07:21] However. Yes, we are looking into it, but I think the real key is what is the problem that you're trying to solve, which is the fundamental product question always. I'm trying to step away from some of the hype and say, okay, which business problems really matter to us? Which customer problems really matter to us?

[00:07:38] And is AI a tool that can help us solve that problem better? So the result is at the heart of what we're doing rather than the technology itself. And I think that's really important. Having said that, I think, whenever new technology starts to gain traction, it's our job to also understand it, to research it, and to know how to apply it.

[00:07:58] So we've been experimenting with a few things. In a few different areas. One of the things that's quite interesting is personalization and improving the customer journey. So I'm sure many of you that are watching or listening are always thinking about like, how do I improve conversion? How do I reduce friction in my journey?

[00:08:18] We all know that's important. So we introduced an AI assistant and we've had a bit of a learning journey with that. We, we have built something where you can build a basket in the chat. So we are taking the ordering journey from minutes to seconds, which is great. I think that's a real improvement.

[00:08:36] However, we've had to do some learning about what is it that customers want, and what they really want is hyper-personalization. They wanna say. Hi, I'm feeling tired. I'd like some comfort food or and after something healthy, what do you recommend? And they want to get some instant recommendations, build the basket and go.

[00:08:56] And that's where we've had to kind of experiment. And there's been some learnings there on the speed, the accuracy, and the types of personalization. And we've also been analyzing what people write about so that we can start to build it out. So we also see. Not only do they want to order, they wanna know where their order is and they want to ask for help through this interface.

[00:09:15] So we started to build in customer service flows, and we started to look at how do you automate, when things go wrong? Because unfortunately, sometimes they go wrong and you need a refund or you didn't, there's an item missing from your order. So what can we do to make that experience also really frictionless?

[00:09:33] How do we automate it? So we just, we say, yep, your refunds on the way, really sorry. And they can go on with their day. They don't need to send an email or phone somebody or any of that. So these are areas we've been experimenting with. With 750,000 partners on our platform, you can imagine that the amount of data we have around menus and things like that is huge.

[00:09:56] And setting up a new partner requires us to set up a new set of data every time. So other things we've been looking at, which are really impactful are, we've. We've trialed an AI menu upload tool. So you can take the printed menu from a restaurant, scan it, create all the data entities that you need, upload, and get that partner online.

[00:10:16] That was something that could sometimes take up to four hours, and we've reduced that. Massively, we've reduced it by over 50%. And every day as it's learning, as we are improving it, it's getting faster and faster so we can onboard our partners more quickly. We can give our colleagues more meaningful work so they're not typing in menus that they're working on customer problems and other things.

[00:10:36] So there's lots that we're experimenting with, I think to make the customer journey more efficient, to make our to remove some of the repetitive work in our business so that people can work on more interesting problems. And I think there's loads more to come with that as well. Those are really cool examples of how you're leveraging it.

Reimagining products with emerging AI

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[00:10:52] Melissa Perri: When you're thinking about the new technologies that are coming out, like AI, and all of, there's so many different things that are rapidly changing. How are you as the CPO staying on top of these emerging trends and trying to figure out where do they fit into our product strategy?

[00:11:09] Karthik Suri: It's a great question. ~Now,~ the most important is ~then ~your strategy, right? How do you bring along the entire team towards, think about the AI strategy? And the most difficult thing is how do you bust the tyranny of now? Your massive backlog in an enterprise software, you have customer commitments, you have fewer releases because you can't push code because they have their own validation systems and our customers is, in, in their endpoints, etc.

[00:11:31] So how do you balance the tyranny of now? So our approach is to actually trafficate this. One is, Make sure that you have embedded experiences, companion type experiences in all of your products, whether it's your learning product, your talent mobility products, your performance, writing your goals, connecting it, getting baseball cards for each person about them, their structure, their check ins, summarizing all of that. Make sure that it is sprinkled in every element of Galaxy. So we enable all of them with robust, usable, engaging, captivating tools to create that magical experience.

[00:12:02] The second is then where do we then promote native experiences? Where do we ensure that we are reimagining the product relative to the assets that we have right now? In a way that others would come and do it if we don't. So in other words, disrupt your business before someone else does. Bust the innovators dilemma, do the double flip, et cetera. So that is the second piece. And then the third piece is for your existing experiences, you How do you create that overarching overarching usability factor that whether it is multimodal, whether it is personalized, whether it's adaptive, etc.

[00:12:36] How do you create that layer on top of it such that it's highly unifying into the next gen form factor? So we call all of this next gen human experiences. The one other thing that we've been lucky enough to do is we've been acquiring a couple of companies very AI native companies, while we have a pedigree of about, you know, better part of a decade of doing like AI and generative AI. Getting the founders in and having them in our staff and doing some reverse integration.

[00:13:04] Is help us reimagine some of these things. We're already on the forefront of generative AI for developing human skills, and we have the generative AI product leadership now blend that as well.

[00:13:16] So helping us disrupt ourselves has been hugely helpful. We also do design partnership programs. So our head of design she organizes with a few hundred customers watching how they work. This is more than just user groups. This is more than basic design research. This is co opting our customers to co author the future.

[00:13:34] and also bring their friction points, reduce that as much as possible through existing and leapfrog technologies that we have here. So it is a journey, but all of this is on the foundation of something that we extremely solidly believe in, which is responsible AI, responsible technology in general, accessible technology.

[00:13:55] So first, so there are five, six ethos that we are very passionate about. One is privacy and security, goes without saying. But accountability and explainability, which means that if you are making recommendations, how are we accountable to the fact that this recommendation is the right one?

[00:14:11] How can we make it explainable in terms of the fact that it mitigates the bias? Linguistic mitigations like, salesman versus salesperson kind of stuff. And then human in the loop. If something needs to get kicked out, it can get kicked out, so you can set and dial how much ever you want.

[00:14:30] We also have a multi LLM strategy, a composite LLM strategy to minimize hallucinations and adjust temperature settings. And lastly, transparency. We are very open in terms of explaining how we built architecturally and how our AI Works. So that is our broader story. The platform portion, the innovation founder mode, so to speak, and deep understanding and intimacy of our customers and their needs.

AI behind GitHub Copilot

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[00:14:55] Melissa Perri: So let's jump into it. Let's talk about co-pilot. 'cause this is the, thing that everybody is talking about these days. GitHub super successful with your launch of co-pilot. Tell us how it got started.

[00:15:05] How did you start to imagine this like AI native product and what AI could do to help, developers to fit seamlessly in.

[00:15:14] Mario Rodriguez: Yes. And yeah, one of the things that I actually also love about copilot is the co-part. We have this thing where the human is at the center and then we're augmenting you to do more and to really spend time in the things that us humans are really good at, which is creativity. Or we have an advantage on, which is creativity.

[00:15:32] But yeah. And gitHub this thing called the GitHub Next Team. And the GitHub Next team is in charge of looking for not only Horizon one initiatives that can make GitHub better, but really more about Horizon two. Think two years from now or Horizon three. Things that may never actually happen as well. they had been in talks with OpenAI, and OpenAI had this thing called GT three internally, I think it was called something like Codex for us at the Codex Model. And they have been collaborating with them on that technology essentially saying, look, we have these LMS are very special at understanding natural language and solving problems specifically in the coding space.

[00:16:14] And we have seen that played out. These models have gotten significantly better in natural language understanding and significantly better in understanding code and being able to code as well. And the GitHub next team created a paper that said, is this science or fiction?

[00:16:30] More or less, if you wanna think about it that way. And that was the start within GitHub on, oh my God, is this something special or not? When I first saw it. What really impressed me and why I said, wow, like this is probably gonna change everything. In our space we have been able to add a complete code for a long time that we have this technology called IntelliSense, and there have been a lot of ML models that helped you do those type of things. But what I had never experienced in my life is you being able to go through a comment and describe something in natural language, and then have the, the system or the AI or the AI model, understand that and then translate that into code. And that natural language conversion to code was some, again, something that I never experienced, but I'm like, oh my God, we gotta have, we have to go all in on this.

[00:17:23] Melissa Perri: Hey, product people. I have some very exciting news. Our new mastering product strategy course is now live on Product Institute. I've been working on this course for years to help product leaders tackle one of the biggest challenges I see every day, creating product strategies that drive real business results.

[00:17:39] If you're ready to level up your strategy skills, head over to product institute.com and use code launch for $200 off at checkout.

[00:17:46] Mario Rodriguez: Now, the interesting part of this dilemma is the following. What we were playing with, at least in the research lab, was just a technology, like it could do things like solve python problems. Sometimes it'll take a hundred shots to do it, which is not something that you could ship in a product. Like just imagine if to solve anything, you have to tell it to do something a hundred times.

[00:18:08] That's not something that anyone will buy. So we have this amazing technology. We have these preexisting tools that developers use. How do you marry those two things? And that's easier said than done. Sometimes you're gonna be too early with a technology. Sometimes you're actually gonna go and put it in a product in the wrong way, and then you're gonna, not necessarily waste the technology, but you're too early with the wrong assumptions as well.

[00:18:31] And yeah, and that's the graph of product in my opinion. It's that complexity of the problems. It's no matter how good my PRD is, like at the end, the product has to actually make it and be good. So we played a lot with that tech into making it in, into GitHub copilot. And the simplicity of what we shipped at the beginning was incredible.

[00:18:55] There were only three things that make copilot successful in my opinion. Number one is we actually made it. So as you're coding, you're not interrupted. We have this thing called ghost text, and as you were doing your normal coding, then it will come in and then tell you, do you wanna take the suggestion or not? At the very beginning it was a pop-up. We had many ways of doing that in the ux and we found one that worked. The second thing was, it had to be fast because we chose that modality on showing you the suggestion it had to be really fast, so we got it down to a hundred milliseconds, which really meant that we kept you in the flow developing as well. third thing is we needed to bring more context in. So the suggestion was something that was more personalized to the code base and to you and not to what the model actually knew about in the world. A lot of people get confused, like they think of the suggestions come from all of this corpus of code, and yes, a little bit of that is true, the context and the LLM together with that, it's what really generates a new thing altogether and just, we just needed to tune that. And those three things is what make a product successful, is the fact that it was a ghost text, the fact that it was super fast, the fact that it was contextual and it gave you pretty high quality suggestions because of that. And then we just watched that take off. We were the first co-pilot in, in, in the name and in the product as well. And we're very proud of that.

AI’s risks and impact on cybersecurity

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[00:20:21] Melissa Perri: When you're thinking about AI and as it relates to cybersecurity in the future, what kinds of trends do you expect to pop up?

[00:20:28] Steve Wilson: It's something I gave a lot of thought to. I think there are serious, both offensive and defensive considerations in what this is gonna do. And it's interesting because from a product perspective, I know people in the security industry have been thinking really hard about how to leverage even earlier gen AI technologies.

[00:20:48] And I worked on a product like that at Citrix, which we called Citrix Analytics for Security, where we could measure the keystrokes and mouse clicks that were going on in Citrix environment and find people, spoofing other users and change their risk scores based on risky behavior. And we were actually building machine learning models of how all the users act.

[00:21:07] And it was really cool. And so you've been seeing more and more of that in recent years where people are building pattern matching engines based on machine learning into their security products, looking for intrusion and things like that. What's interesting with the chatbots coming along and the, that capability growing so dramatically is the threat vectors are now crazy.

[00:21:28] The biggest worry in cybersecurity is the lowest tech one. Which is phishing and it's, email phishing and it's also spear phishing. These things have suddenly dramatically in the last few months gotten so much better. Yeah. We used to joke about like the Nigerian print scam, where you'd get like this little chain letter that was in incredibly poor English asking you to send money and.

[00:21:51] All the phishing training you takes as the first thing is is it really written in good English? Is it grammatically correct? 'cause if it's coming from somebody in North Korea or Russia, it's probably gonna show those signs. They don't anymore. They're all perfect. They're all flawless. And now you look at deep fake technologies and all of these things, people can clone your voice from a three second sample.

[00:22:10] The next phishing attack you're gonna get is gonna be your spouse calling you on the phone, asking you for the bank account number and her voice and her cadence that's where this is all going. The next one is with these advances in the bots that can now write code. They'll write self-modifying code that will be running around the internet and it's already doing this, looking for vulnerabilities, and your security by obscurity isn't gonna work anymore because the things that used to require human hackers are gonna be automated and 10,000 times as fast.

[00:22:40] And so it's gonna be a pretty rapid escalation in the cybersecurity space where the companies that do adopt more of these technologies quickly are gonna be at a real competitive advantage. Wow. That sounds ridiculously scary for what it's gonna be. I already seeing some of this happening and it's blowing my mind just how crazy people are getting into hacking stuff yeah.

[00:23:00] That's, I'm glad you're working on it. Glad you're trying to fix it. But that's really interesting. I think it's gonna pose some challenges too for, leaders as they're trying to think about, how do I prioritize security? That is one of the big things we talk about in with chief product officers and CTOs.

[00:23:16] How do we make sure that we're prioritizing the things that you need to get done right, versus just the net new features and building building the stuff that may be strategic on those areas. So what's your advice for, like CPOs and CTOs when they sit down and try to negotiate this? What should they be looking at for security?

[00:23:34] How should they be staying on top of it so that they know, what's coming up and what could be helping with that? I think in a lot of ways there's a parallel to how you think about security with the way you think about quality. And, every PM has had to make this trade off what do you call it, the iron triangle: I got quality, I got features, I got time to market. You could put security in as another box there or. Treat it as a sort of similar super box with quality and just talk about technical debt and non feature oriented capabilities. And you could put performance, security, quality all up there with all those illities that you know, make a big difference in your product.

[00:24:13] So you need to find ways to firewall, off time, resource and effort to deal with those things because otherwise they fester.

AI avatars and emotional interaction

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[00:24:22] Melissa Perri: So you are chief product officer of a really interesting company called Soul Machines, which makes digital humanoids, which is a long way away from I think what we think about as caricatures online like Wallace and Gromit, which was years ago. But how, you know, how does soul machines get started and how are you harnessing AI to bring in these very lifelike images of people?

[00:24:47] Darren Wilson: That's a very good question. And yeah, it's been a while since someone's mentioned. Listen, Romme to me, so maybe we'll get back to that later so I can try to remember. Soul machines were started by Mark Segar. He was a special effects guru working on blockbuster movies and his area of special specialty or was, motion capture, particularly around the face on large, on major movies. At the time of working on those films, he developed some technologies to support that work. That it span out into a research project that ultimately became Soul machines. And his big singer, his big story was really about the face being access to an emotional connection for users and for people using technology beyond the way that we used to.

[00:25:28] So he built these digital avatars that could connect to customers potentially in, in more empathic ways. I joined the company five years ago. To introduce a level of kind of design thinking as well as product thinking or attempt to bring some product thinking into what was at that stage.

[00:25:45] A very research focused, engineering led team. And, so the challenges that we had at that stage, were building a design and product team to try to help not just a very engineering led company evolve into more product led, which is where we are today.

[00:26:01] But also transition from a very vision based company that trying to an aspirational, selling a major vision to big enterprises to actually democratize the process of building digital people so that more smaller companies can work on and build and create digital avatars for their own unique use cases.

[00:26:21] And what are those use cases like? Who needs digital avatars? So that's a very good question, and I'll be honest, it's been a, we're still in the process at the moment of trying to identify those like unique use cases that actually bring real value. A lot of the work that we've done up to this stage.

[00:26:39] You asked about the connection with ai, and I'll come back to that shortly. A lot of the work that we've done at this stage has been around democratizing the ability to create avatars and also reducing the time for, to build these very realistic, customized faces and unique faces from three to six months to build to 30 minutes really for an individual to try and create them.

[00:27:03] The original pitch, the original opportunity that was being explored at solar machines as I came on board, was digital assistance to help with a sales drive or to replace a call center or to connect your customer to your brand in a very personalized way. What we're starting to find really the value that we're starting to explore as we've opened up the platform to get more users on board through our free tier.

[00:27:29] As opposed to the previous model where we charged a lot of money for kind of a long-term project. The use cases we're starting to see are really getting to be super interesting where it's actually where an empathic kind of, really personal connection is useful. So rather than being an area of transactional kind of interface, which is what effectively the internet is about today, a digital media is today where very transactional, very getting get out.

[00:27:58] What we're finding, the digital avatar doesn't work very well in that space. There, there's limitations with the technology. The the response delays too is too great. So inserting something like a, a fake face or a fake human between an experience that people are used to is actually not very successful.

[00:28:15] But what, where we are finding that there are successes is helping people make difficult choices or helping people have difficult conversations or practice difficult conversations. For example, this interview today, I've done a couple of times with an avatar where had the avatar playing your role.

[00:28:35] And helping me stumble through words and that practice the loop without judgment and talking to an emotional face that actually connects to you is super effective and super useful. One of the big use cases is really language practice. We had a Korean influencer who used one of our, one of our avatars a couple of months back to practice speaking English and just practice responses.

[00:28:59] So it's that engagement without judgment is an area that we're explor. That's a really cool use case. I could see, I like the idea of the practicing without judgment. Like I personally speak really awful Italian, but it's because I can't practice, so I can understand it extremely well, but I'm so afraid to go practice it because everybody just kinda looks at me weird.

[00:29:17] And I don't get a chance to speak with native speakers, living in South Carolina, where actually I did find two people who speak Italian here, which is very random, but That's right. Wouldn't expect it. But I don't get to talk to them as often, so I've always, I've been like, man, I wish I could could practice more.

[00:29:30] And I've looked into like other courses, but I love the AI component of that too. Like not having a real human there judging you, judging your your feedback and knowing that it's okay to say whatever you want. I think what we found is that we went into this business of trying to create a level of utility that, that existed in that, that worked in existing channels or an existing markets.

[00:29:51] But actually as the technology matured and as chat GPT arrived and Gen AI came on board, we found that we can, people were starting to use our digital people as re prompting them in different directions, like taking the one that we had on Soul Machine's website. They were reworking that to be a sleep coach.

[00:30:10] Giving it a brief to say, I would like you to be this. So that kind of looking for that personal use case that, making a digital person that is adaptable. Because the technology is really geared towards being able to connect with the viewer emotionally, or respond, in terms of expression that in appropriately, it works really well in those areas where you don't want judgment or you want a response or you wanna see if things work. And once you layer in a chat GPT you're starting to have this kind of fusion of an experience that is a little bit unique.

How PMs can stay relevant in the AI age

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[00:30:42] Melissa Perri: There's a lot of talk out there too when it comes to AI and everybody keeps saying this about a lot of these things, people might be replaced. When it comes to some of these tasks and what we're doing, and obviously we're talking about some efficiency plays here, what would you say to people out there who are listening, product managers, other people who are worried about that? What would you say to concentrate on, to make sure, to ensure that they have a place in this new way of working in these new workplaces?

[00:31:07] Tamar Yehoshua: Don't be scared of AI. Use it. Use it to make yourself better. If you don't, somebody else will. And then you, your job may become obsolete, just like with programming languages.

[00:31:19] Is there new languages out there? If you're an engineer and you're not learning the new languages and you're still like. Programming in Cobalt or Fortran, you're gonna have fewer opportunities. So understand the tools, use the tools, use them in a way that makes you more productive, because I don't believe I believe some jobs are going to go away, but new ones will be created.

[00:31:39] So be one of the people who's learning and can have the opportunity to do some of those new jobs.

[00:31:45] Melissa Perri: I think that's definitely wise words for people out there, especially if we're working in tech. 'cause we're moving very quickly.

[00:31:50] Melissa: Thank you so much for listening to this episode of the product thinking podcast. We'll be back next week, and in the meantime, send me all your questions to dearmelissa.com and I'll answer them on an upcoming episode. We'll see you next time.

Melissa Perri