 Hello, everyone, and welcome to ProductCon. My name is Alexandra Caldwell-Wendman. I'm a product director at Salesforce and I'll be your moderator for this discussion. Today we'll be talking about the future of product management and automation. As we know, automation makes the product management world go round and there's hardly a product manager out there who doesn't rely on some kind of automation in their workflows. But what does the future of automation look like? Industry experts are here to give us a sneak peek at what we can expect and see in the future. So today I'm joined by Vasil, Kristin, and Jared. Hi, Vasil, I will turn it over to you and you can introduce yourself. Thank you, Alexandra. So yes, my name is Vasil Mwadruv. I am with Moengage. I run a strategy over there. And for those that don't know who Moengage is, we are a user engagement platform. We help companies that have basically B2C customers to engage in, do a lot of personalization at scale. And on average, we do something like, three to five billion, for example, personalized messages per day for over a billion users a month. So. And Kristin, can you tell us a little about yourself? Yeah, of course. First of all, we're so happy to be here and we're happy to be sponsoring ProductCon. Hopefully everyone is having a great start to their day. My name is Kristin Ditch. I'm the head of product marketing for Embedded Solutions at Workado. And Workado is a leader in enterprise automation space. And we're probably best known for our integration or iPads technology. We have a low-code interface called the recipe which kind of looks and acts like a drag and drop logic tree that allows users to connect systems but then also create automations between those systems. And I'm specifically focused on our Embedded platform which takes that foundation of our core product but then adds an extra 20% of functionality which includes API management, governance and system administration functions that are really built for product management teams to help them solve customer integration and automation challenges at scale. So I think this is gonna be a great conversation, happy to be here. Excellent, and Jared. Hi everyone, I'm Jared Lees with Gainsight. I, with Gainsight, most people know Gainsight with respect to the customer success angle. We built that category and had a lot of great success with that product line. We also support product experience which comes into analytics and adoption and helping to automate some of the onboarding and adoption capabilities that you may have as you launch features to product. My focus, or launch features to market, my focus is on a little bit more on that go-to-market and adoption side right now where we're looking at how do we support the customer experience and the user experience within products where we can use that as a growth engine for your product success. All right, so as we know, automation is one of those things that when it's done well, people don't notice it. So how would you guys describe automation and its value to someone who maybe isn't an expert in the area? I can raise my hand. Go ahead, Jared. If that's how we do this. Well, in my opinion, well, first off, again, thank you everyone for starting your day with us and we appreciate the opportunity to be part of ProductCon and the support that we have here. Within automation, I look at it as like a way to kind of provide the ability to repeat a task or a process with very little oversight. I think the important thing is you wanna check on it every once in a while just to make sure it's still doing what you expect it to do but for the most part, you don't have to think about it. Yeah, I concur with that. The one thing that what we look at with automation in our use cases where we help our customers is that certain things can be automated just like what they've been doing, whatever the task is happening. But many of those tasks may need to be personalized. They need to be fine tuned for specific user or group of users in order to really help with that growth chart, right? So you can't just think about repetition but you have to kind of use data to figure out how to make that adjustment for the right users. So I think this is where automation really is gonna be more interesting in the future is how do you do this in a way of scale and automated process that is actually a little bit fine tuned for that specific end user in the end? Yeah, and Vasil, I'm so happy you said data because I think that's such an important part of how automation is growing. The systems that need to be connected or the data that might be critical to how an automation starts, there's so much that is accessible to teams now and being able to incorporate that as well as the actual process or the team that's involved and then design it in a way that feels super intentional and kind of an extension of how humans would have done it on their own before maybe between our desks or between departments within a company and pairing all of that together, I think to your point really helps improve the efficiency of it, improve the resiliency of that automation and that process and then be able to do it at scale especially in such a decentralized global world that we have now post COVID. Yeah. Wow. And then can you each tell us a little bit about how automation works for your business and how you're using it to serve your customers? I'll start again. So what we do in our case will give you an easy use case. Let's say that you have a mobile app and a mobile app needs to basically help a user guide them through some kind of user journey, right? And this could be a B2C app, this could be a B2B app, it doesn't matter. And so we, for example, use a lot of data to first understand better what are the people that are coming into the app? Are they certain age, certain, they have certain, I don't know, income they're looking for one thing versus another. And so based on this, you can kind of go and optimize the journey and the flow of the users to go one way or another. And by optimizing this with a AI engine, machine learning, whatever you're gonna be using or whatever you wanna call it, you can automate a process but also optimize the process. And so in our case, we try to optimize the user journey for optimal, for maximizing revenue, for example, just like you, Jared, you guys are doing that. It's about growth. It's about really increasing revenue in the end of the day, whether you're selling goods or services or whatever. So we are able to do this by using a lot of data first, coming from CDP data, from first party, second party, whatever data they have, this many times happens either in real time or we need to do prediction about what people are gonna be using. And then that data, basically, we started doing A.B. testing and based on A.B. testing, users are being offered one or another path in their user journey. So this is what we do in our case. And I'm assuming it's something similar with Jared is doing as well in their cases too. Yeah, Vasily, you're right. And I like your comment as well as Chris and how you added on to the initial comment with the automation component where it's not just about having the data but being able to do something with it, putting it into a location that you can do something because that gives you the contextual interaction. And if we think about what we're trying to accomplish with our users and give them that personalized or that human felt experience, that data point allows us to contextualize that particular interaction and then having the right tools and systems in place that we can, whether it's an automated trigger that surfaces a type of welcome message, if you will, for new users to log in or whether it kicks you off into a predicted workflow that the system knows is most applicable to the scenario or tasks that you're trying to accomplish. That in my mind is one of the keys is it's part of the foundation or a maturity path where you do that with the intent of having data available to trigger off or provide that contextual interaction for the individual so they feel that end experience. And then how are product managers leveraging AI insights and analytics to make more informed product decisions and better serve their customers? Maybe I'll continue just on that thought and then throw it back to the group is with that data and analytics, we did talk about the usage for the customer side where you're able to kind of trigger, use that as contextual examples to provide experiences for the users. There's also this component where you just get better insight into how people are using the features and functionality that you intend them to use. And in some cases, you can understand path flows, like maybe you have a particular workflow in mind and the user tends to follow a different pattern, right? That's good information that you can take back and help to prioritize roadmap decisions. So you can look at two things and well, more than two things, but I look at it as like ROI aspect or that business case like, hey, we invested time and resources in this particular launch, how did it go? Is there anything that we learned that we can then take back for the next iteration? Or then the second thing is like, are there any surprises or aha moments that people are using the technology in ways that we didn't intend, that we can then take that back to help prioritize and feed our roadmap discussions and decisions? Yeah, and I would add onto that. We had a really interesting product launch last year that it wasn't actually really any new technology. It was just to your point, looking at how our customers were actually using the product. And we kept noticing these patterns of people creating the same kinds of workflows over and over again. And so our product management, product marketing teams worked together with our enterprise architecture teams. We ended up creating these kind of jumpstart packages for people to just start to see value faster. And so we called them accelerators. They launched, they were one of the most popular things we released last year. And it was just this idea of creating automation bundles for people that would just accelerate their time to value and using our product. And if we weren't looking at data, if our teams weren't doing that analysis of what people are actually using the product for, not just how we intended for them to use it, we would have totally missed that. And then being in product marketing, I'm always thinking about how this changes our story that we're taking to market. And so in a similar train of thought, we were looking at the product usage data and ended up releasing our first state of business technology report last year. Just kind of pouring over all of that information and all of those analytics that we had to understand how our customers were using it and then inspiring an art of the possible for a broader audience with a piece of thought leadership that was really well received. And I think a nice encapsulation of some of those use cases that we would have totally missed if we hadn't been looking at all of the data. I just wanna go back to what you guys were saying because it's really important. And I hope people kind of think about this in the future. For me, a lot of products nowadays, you can build the base and by using data and essentially automation on top of it, whether this is AI, whether it's machine learning, algorithm or whatever, you can then fine tune the product for different users, for different use cases, without the need to go and rebuild and have a build for a new app, every month or every two months. Try to do this at a big bank or security company. It's like every six months, maybe there is a big build or maybe there's some security things. But by building something that is very flexible, that is data driven and have the right tools, whether they're from integrations from a third party AI vendor or somebody else to allow you to do this type of testing and fine tuning the user paths, whatever you're trying to do in your application, you can do wonders in terms of the time to market to understand better those patterns. You can test and get analytics for something between days and then fine tune just between a tool like ours or somebody else's, change the bath, change the outcome, change pricing, change the colors of one thing versus another. And unfortunately, because we live in an intern, kind of a global market, you can't just assume that only people in California are gonna be using you app. There are gonna be people around the world in different countries and different regions that have very different usage for every one of the channels of engagement in our case. Some people like to use SMS here a lot. You know, the country, this is very expensive or video or something else. So it's something that is very interesting for the future of product managers to think about how to build flexible products that can be influenced by data and artificial intelligence or other engines and build on top of it. And Vesteel, that's a really excellent segue into my next question, which is basically how does this relate back to the end user? Ultimately, how does an organization investment and automation benefit their customers? I'll let somebody else talk for a little bit, but. So we're a provider that allows product management teams to solve customer automation challenges too. And so that's definitely the lens of where I'm thinking about this question. And our product sits at that intersection of integration and automation. So that's really key for how we think about things. Every product manager who is watching today probably has a feature backlog that could keep their engineering teams occupied for years. And we know product management teams and I've worked on product management teams where they're just always struggling to juggle those requests relative to the amount of resources that they have on their engineering teams. And so from an integrations perspective, about two thirds of the product managers that we work with say that they're just drowning in integration requests. They've received these from customers, they're getting feedback from analysts, and they can't find a path to think about solving for these at scale. And depending on the size of their integration or their engineering teams, an individual integration may take anywhere from two to six months for their team to build. And so by connecting their system to our application, they're able to unlock hundreds of integrations immediately and then they can get their core engineering team back to the product roadmap at hand and things that they're uniquely equipped to solve. But because our user interface is also low code, our main modal is called a recipe. That one recipe modal allows non-technical resources. So maybe a product manager, maybe a product owner, maybe even a professional services team to connect systems and write automations in the same interface. And what's really cool about this is it kind of takes all of that domain knowledge, the customer knowledge that you have as a product manager, and then think about the jobs to be done that your customer is trying to achieve. The processes that your product touches, the complimentary systems that your product touches and the day-to-day of your user, and then you can add immense value to your product in no time. So PMs can kind of anticipate the needs of their end user, centralize those processes of that user in your product and have it be initiated from your product. That's gonna improve their experience with your product. It's gonna maybe even improve their end customer experience. Certainly reduce human error, improve your employee satisfaction because people aren't doing those mundane tasks between systems. And it just pays dividends for our customers. They have the ability to win new business. They can retain existing business better and ward off competitive pressures. And a lot of them are generating tons of new revenue from it. So I loved that one product manager was considering it kind of like a moat around his castle. Automations allowed, each automation that they added kind of allowed him to grow the size of his moat or the depth of his moat and just kind of create a ton of value for their team with product really being at the center of driving them. And then when it comes to automation, what are some modern product development best use cases and what are some of the mistakes that you guys are seeing when it comes to setting up automation? One of the things, I'll raise my hand again for this one. With respect to, I mean, the mistakes and like what to look for kind of go hand in hand where if you think about AI and ML or machine learning and I think in the future it's gonna become more table stakes where everyone's gonna have it. And right now it's like, oh, I need this as AI. It's kind of like a flashy thing in some cases. Some cases it's not, in some cases it's like, oh, that's cool, we got this. It helps you get more investment from the board or whomever. But the idea is to have like a purpose in mind like similar to when we develop SPMs. What is the problem statement or what are we trying to solve? And how is this piece of technology gonna help me get there? We've heard a couple of great examples from Cecil and Kristen already with respect to time to value for the customers. On-boarding at Gainsight's really focused on product analytics and being able to surface those insights so we can then take action on that particular piece of information whether it's through knowledge bots that are automated that help customers or users find that value easier. But the one thing that I also wanna hit on is the value back to the organization. Anytime we as an organization have to develop, put PM and engineering and other types of resources against development of something it's taken it away from a roadmap item or from something that's user facing or supporting the user. And so being able to find those components and that right there is one of the purposes or ideas. What are those tasks that we have that we want to look to automate will also help free up these additional resources. And so I look at it as like the purpose making sure you have the purpose for what you're trying to accomplish for that problem statement. And then I also think an important thing is to track it, right? Like maybe it's time gained or efficiency there's some head counter hours that have been freed up that can then focus on other activities because from a business perspective that's gonna enable you to build the case to get additional investment and to use the moat analogy to continue to strengthen the moat that you have around your business operation unit while continuing to deliver customer value. There's many examples out there where we've had great products and great features that have been released to market but they don't really land with the users because they may be complicated or don't quite understand the workflow. And so being able to kind of keep all that in mind while developing for that end user. I want to take a different approach to that. I do agree with you and hands down I agree with you but I want to kind of play the devil's advocate on this. I think that we have a very big opportunity as product leaders in this world where things are changing in real time. We live in a real time. We need to do I think a couple of things. One is to really think bigger about the vision of where the market is going to be more of a fault leaders in a way of pushing new technologies and testing them. Investing in something that you believe in and by understanding again what is happening on the market in terms of trends. We are seeing by the way Salesforce I just read that Mark is investing in NFT cloud right now. So you're investing in something that you think is going to be big and you can test it. Maybe you're not going to put this full blown product but you can kind of be prepared for where the market is going. And I think this is very important and I see many companies that are not especially bigger companies are not really are kind of sitting behind a while we're going to wait and then maybe just going to do an acquisition. I do think that yes there is deficit in development always there will be set up a team just to do tests and just to go and experiment because there is a lot of cool things out there that can help you differentiate. It can help you again solve a problem in a different way. And unless you start thinking outside the box if you want to call it whatever others are going to come and eat your bread and your breakfast and your lunch. So the mult is not going to really matter that much. Yeah, but so I think you and I are saying so I agree with you. It's in a different way. You have your core that you're focused on, right? And then you've got this, I'll call it the R&D or the innovative section that you're continuing to yes, this to advancing you can use the automation advance even further. I mean, just look at what's around the corner. Look at what happened with Airbnb. Look at what happened with Uber with all these companies that came and changed entirely the business models. And it caught all these other companies by surprise because they always thought that something needs to fit in the box. And if it's a little different, it's not going to work. And product managers need to start thinking that way too. It's not just for startups. Every company needs to think as a startup in order to be successful in this market. It's just otherwise, what are we doing here? And that's a really interesting concept too because I guess, do you have any tips for how you scale automation and when you think about the future of the space? I don't. I may have my... Again, I think the automation is using best practices. If you look at how automation of containers and others have changed the way of development, how GitHub and sharing code has really changed how we develop products, how we collaborate around development. I just hope that more and more people kind of are sitting on a cutting edge and testing serverless and no code and low code and more APIs and faster integration rather than having to build everything from scratch or reinvent the hot water. There are tools for that, go and use it. I think this is the way to scale automation. AI's and stuff are not really set up for that type of product development yet. I do believe they will be in the future. There's specific very niche use cases for AI and machine learning that can scale a specific piece of the puzzle but not in a bigger scale. So I don't know, but that's just my answer. And what you just started on there is actually I think a really nice tie back to something Jared was just saying around product teams having a lot of intentionality with what they're trying to accomplish when they start with this. I know I've worked with product and engineering teams who have a ton of immense pride in learning a new language or building something in-house and there's nothing wrong with that but it comes with an opportunity cost of something else and I think what we're all aligned on is that there's a ton of innovation to be done. There's a lot of testing from our perspective like how can you customize the end-users experience with how they interact with automations by market segment or geo or whatever else that you can't ever get to that degree of sophistication with what you're actually trying to build if you're worried about the utilities that are behind the scenes of all of this and there are so many fantastic products that are alleviating so much burden off of teams by being experts in that particular component without kind of passing any of that technical depth burden onto the team so that the team can focus on the unique market problems that they're trying to solve. Yes, best of breed, best of breed integration integration integration. You can sit and talk about this from beginning to the end of that conversation you're gonna have to talk about it. Well, and I like the aspect too both of you brought this up where it's in addition to the technology it's having an openness yourself, right? Like try to stay on the course so you don't get, you don't wanna deviate too far but you still wanna make sure you're flexible and open enough to kind of look around the corner and say, hey, let's give this a shot and try this out to see if it will help us expand the business or add additional value to the user. Yeah, and I think that's such an interesting thing of trying to hold yourself accountable to looking beyond your immediate product goals and thinking about the business goals or the strategic objectives that you have and the easiest path to achieving those things. And then what else do you guys expect to see as you think about the future of the space? One thing I think is Kristen was talking about this earlier that you guys started building some pre-built blocks, right? I do think some of the things that we are working on for example ourselves is templates, just like pre-built blocks that are for user engagement that they are essentially an automation tool for how you are gonna create a user journey for engagement, right? So we're gonna see a lot more of this which is a preset action for setup automation. Now you take this and then once you put the data in you're gonna see some interesting things. Everybody kind of in our space talks about this full automation and full automated cars or whatever else is gonna be. They are the perfect kind of automation when you think about it. Our space is not ready for that yet. So what we are doing in our case we have an AI assistant that we're gonna be launching very soon which takes again data, a lot of data and throughout your manager's journey of how you built the journeys, the product for the end user it gives you ideas of what to do. Instead of making it for you this is where the human touch is. Does that make sense for that use case? Yes or no? The data shows it does, let's test it. So having a little assistant that can assist you make better decisions or says you know what don't do that because it's a different time zone and that doesn't make sense to do whatever you're gonna be doing. I think this is one step that we're seeing before this full blown automation that everybody's kind of so excited about which I don't know when it's gonna happen but it will happen so eventually. I think to that point it's not a replacement but it's a combination or a hybrid of that human and computer assist if you will. I also feel just looking at other trends of technology it's gonna become more accessible to the end user. You won't need advanced degrees or domain knowledge in a particular space. It'll be more accessible to more people because we have a variety of backgrounds. I've seen PMs come from all sorts of educational backgrounds and levels of experience but making these types of AI capabilities available for all end users not just specialties or PhDs somewhere. Yeah, I couldn't agree with you more. I think GlowCode is the future of all of this. I'm even seeing friends in engineering teams that are preferring it just because of the amount that they can tackle in a short period of time and seeing it as a beneficial shortcut. So I think there's a lot there. The last thing I would say though another piece of that is we talk a lot about how we analyze data to make decisions. I do think that in the future, a lot of that with the assistance of machine learning or whatever algorithm is gonna be happening in real time more and more. We're pushing for this, we have tons of use cases where we have to do things in real time. I get a notification that something is happening. I'm getting a trigger that something is happening with my bank account. And the system needs to make a decision what's to do and it needs to happen in real time whether somebody is trying to get into my account or I don't have enough funds. And so prediction is gonna be a lot more in the future. A, this is what happened last time and this time of the month with your accounts you should do this. So this is where I think there was gonna be a lot more prediction and a lot more real time of everything of how products are built. So you have to think about how products are built and how you apply automation. It's not just automation but how you predict something or stop something from happening. And what advice do you have for anyone working at a product company or working in product who wants to learn more about AI automation and the space? I would say start small. Don't try to learn everything you can all at once because there's, and look at specific use cases or problems that may be directly applicable to what you're currently working on. Cause then that will give you, I mean, it's great to kind of go out and explore and get that knowledge but having a little bit more contextual application to your day to day of what you're trying to accomplish then it will, I think it'll help accelerate the learning and then you expand from there. Yeah, I mean, I've been blown away just as I've been bringing on new technology into our team stack of how many people seem to have spent their time during COVID building YouTube tutorials or building their own videos without leadership. I mean, you can find commentary on just about anything it seems these days. And then kind of in that same train of thought there's also just so many free courses that are getting built up. I feel like in the last couple of months almost every week it seems like there's a technology company that's buying an educational arm and really investing in that kind of training for customers. And those can be hugely beneficial. I'd be remiss if I didn't point out that workado has an automation institute. It's a free on demand self-guided learning portal that gets into a lot of automation and how actionable it can be. And you can stop by our booth in the expo hall and learn more but you don't have to just go to workado's automation institute. I mean, there's just so many free courses through online educational systems that I think have a great starting point for someone who's looking to learn more. To add to this, I think learning is gonna be the most important thing. But there's two, I have two thoughts. One is follow where the VCS are putting their money. Go and look at, there's plenty of interesting reports of figuring out what AI startups are getting a lot of money. Go and see what they're doing and pay attention to them. Definitely what Jared said, start small, try to solve a very small problem. Not everything is AI. It could be just machine learning algorithm that, you know, automate something. It doesn't have to be an AI. But the other thing I was gonna say is actually try to push you and your kids earlier into this. I was telling my daughter when she was in high school, you know, get a little bit more analytics classes, some data classes that's gonna be helping you. And she's like, but I'm in biology. Why do I need this? My daughter graduated from Santa Barbara, used Santa Barbara. She now flies a drone actually for the Benioff Institute and she teaches an AI to find what the Great White Sharks in the Bay. So it is a human touch, but try to get kids early to learn this because it's gonna come in handy no matter where you are and what do you do in a few years later? So that's just my advice. I have to say flying a drone to look for sharks is very cool. Yeah, it's a dark-eyed project. Check it out on the internet. This is really, really cool stuff. And it's using, I think, Salesforce AI behind it, so. Well, that concludes our question and answer portion of our panel. So I'd like to thank everyone for joining us. And I'd like to thank Vaseel and Kristen and Jared for your very thoughtful answers. So I would like to also give you each a moment to share some of your takeaways for our audience. I think that we are all kind of looking at this. We were kind of talking about this before we start recording. We're looking at this from a little bit different angle about the same type of kind of solution. Use data, try to test things, automate things as much as you can and keep going at it. Don't give up, start small. And hopefully, it works for everybody. There may be more than one way to solve a problem. Yes. And that's okay, right? And that's the importance of being able to test it. And I like that you've been in the development process, right? Like, feel fast and feel often. And you'll learn from that and you'll continue to expand and still trend in the right direction. Okay, great. Yeah, and I love that we all had a through line of the importance of the human meets data side of things and the intentionality, the benefits of proactivity that can come with all of this and how it can improve the experience for customers or employees alike. So definitely a lot of commonality. Thank you, Alexandra. Thank you, everybody. And by the way, stop at our booth at Mollie Beach and check out what we're doing or somebody is gonna be yelling at me why they don't say that. There's enough time to visit all of our booths. So we expect all of you. We all do so many things. Yes, thank you everyone so much. Talk to you soon. Have a great time. Bye.