 Thank you for the introductions, Anugha. Hi, everyone. I'm super delighted to be here, and thank you so much for joining me today. I hope you've all been having a wonderful time at UX India so far, and I'm sure that there may have been a lot of conversations where you've been getting to hear about AI, and I'm going to say that this is going to be yet another one that focuses specifically on the role that AI can play in user research. When we talk about experiences, what would you say that you look for? Simplicity, something that's more useful, something that can also be equitable in a sense that it caters to individuals from all walks of life, and of course, you may want it to be something that helps you reach your objectives. Without further ado, let's get started. Have you ever wondered how artificial intelligence is, not just transforming our world, but also redefining the very essence of user experience? Today, I'll talk about the dynamic and continually evolving intersection of AI and UX research, where innovation meets empathy, and technology meets humanity to shape the digital landscapes of tomorrow to enable us to create some intuitive, meaningful, and impactful experiences. There are so many facets to user research, and most of you here would have your own processes, but I want to start with sharing a glimpse of a day in my life. Whenever we start a research project, we want to understand more about the context, so we start with cross-functional meetings, and then we try to identify the need for research, what really needs to be investigated as part of these, and then we jump into research planning, where we define a research problem, we try and identify what the assumptions and hypotheses could be, understand more about the context, and how can we be running the research as needed, and then we move on to, say, research logistics where we either work with an ops team to help with recruiting participants for us, or in the absence of an ops team, we just look for other ways to recruit. We then schedule sessions, set up an arranged logistics for resources for having these participants come up, and then comes the meeting part where we conduct the research, communicate with users, interact with them, learn about your day-to-day, and what nots, right? So this also may involve debriefing with the team, sharing early signals, anything that's top of mind, and then we go into another meeting section where we dig more into the data deeply, identify what's existing, anything that's new, that's surfaced, that's resurfaced, and try and connect those insights to products, trying to identify themes, patterns, and opportunities. And finally, we might end up preparing a report or share findings in a different way, an assessment part, and it doesn't end here, right? So it goes back and we need to close the loop somewhere, we may investigate things that require follow-ups or multiple follow-ups as well. So imagine sometimes we're on multiple projects at the same time where we may be in different phases in the development cycle. All of this can get really time-consuming, sorry. So one of the struggles that product teams have been facing in situations like this are in terms of obtaining timely, rich, and actionable insights. So sometimes teams are also even forced to move forward amidst uncertainty. They choose to either do some sort of episodic research or ad hoc research to wait through situations like these or even sometimes they're forced to go on with no research at all. That's where AI enters, and with the ability to handle complex tasks, we've been in the recent times hearing a lot of buzz about AI, right? And AI is pervasive in our day-to-day activities, and it has also the potential to improve our lives in countless ways and unimaginable ways. We are in the experience era, and AI is already changing the way in which we interact and experience the world around us. For example, we're moving towards, say, more intent-based experiences where we're able to gather users' needs and goals of coming onto a particular interface, and through that, we're trying to showcase what was relevant for them, and which may be effective. We are also transforming towards a more personalization, efficient, and engaging, engaged-first experiences as well. And all of this depends on a variety of factors. Maybe cater towards some of the use cases that you're working under, the volume of data that you have, what are your outcomes, what is that you're expecting out of this, and the feasibility to try and achieve what you want with what you have. Let's look at this from the aspect of people, product, and processes, right? So, starting with people, looking at AI and UX as a practice, its adoption and acceptance has been maturing over the last few years. It's been rising up from being flavors in a recipe to being a catalyst that drives critical aspects to becoming an essential ingredient in the recipe. And when it comes to decision-making, say you want to try out clothes, but you don't want to go into a physical store. There's always ways in which you can try on clothes virtually and understand the fit and how you look with them. So people learn for things that are more simplistic, that are relatable, and are also easily consumable. One way in which AI has been helping in this manner is to understand their intent and make each experience their own in the way that they want to perceive things. So AI also has been helping with bringing more clarity and in getting deeper to figuring out what's meaningful for us to know. And through analytics, for example, we're also able to see trends on, say, similar content, things that we may like, which are also being influenced by what we're doing. And this can help save a lot of time, right? And then moving on to how AI can influence product, it has been much more easier to introspect into the data, the speed of integration, and the quality of output has also improved to be multiple. And in some cases, AI has also helped with, say, jump-starting problem solving, that it makes time to market significantly faster than ever before. And it has also been quicker to, say, deploy and develop new products and services, thereby also accelerating innovation and growth. And talking about the last bit, so how AI influences processes, the use of AI has already improved efficiency, reduced costs, and increased accuracy in various fields. And now, with the help of AI, we're also able to sift through, say, large unstructured datasets quickly and help make informed predictions and judgments through that. And to now get us started with how AI can be helpful in UX research. So UX research or user experience research, as you may know, at its core, is all about understanding user behavior needs and motivations to improve the usability and functionality of products, right? So although AI is a relatively new tool in UX research, it does have the potential to revolutionize the way that UX research is conducted. There are ways where it can be effectively employed and others where some caution is necessary. And AI can help UX researchers save time, improve the quality of their UX research and also create better experiences for everyone. It is seen that according to the 2023 State of the User Research Report by user interviews, a fifth of researchers are already currently using AI in their processes, their research planning and other ways. And an additional about 38% is also planning to incorporate that in the future. And I'm sure that with AI's increased use and popularity, it's important to be mindful of the extent to which we rely on these tools as well. But the bigger question here is, do we really need to leverage AI? Is AI a good fit or a bad fit for us? So to think about that, it starts with wanting to understand the user experience that we want to create. What is it that we want to share with our customers and the audience that will make a stand-up? And then set expectations on what our desired outcome would be. There can be so many different things, but to make a distinguishment between what are the primary and the secondary goals that we have is very key here. And it's also important in the process that we ensure that a user or human is always available to control whatever happens within the process, right? And they have the choice on the decisions that they're making and they're able to make them in an informed manner. And overall, it's also important that we ensure that there is transparency of what's happening so that we're able to share a much more detailed approach with them. So in the next few slides, I want to share about areas within UX research where we may be able to employ AI very effectively, versus areas where we'll need to actually have more caution when we're applying AI. And to start with areas where we can effectively use AI to this state, research planning has proved to be a very key area here that AI has been exploded. Say you want an initial draft of your research plan or you want to collect assumptions and biases of the context that you're going to be working under and or also say you want to scope down questions. You have a lot of, you have a list of questions, but you want to scope those down into things that are achievable for you in the time being and then things that may be put for later. So this can be helpful there, as well as like say building templates for how you want to plan research and conduct research as well. Another area where AI has been explored is recruiting participants for research where you may be able to create initial screening surveys where you're able to screen in and screen out participants according to your criteria and also may help with say short listing respondents through a set of criteria that you have already been set. And another meaty part where this has been explored is analyzing insights from research. Say you want to do sentiment analysis in a virtual world and you want to pull out themes, somewhat relating to qualitative coding that you do. This has been explored where there has been enormous potential and you may also be able to say you don't have a note taker that's available for your sessions and you're able to actually transcribe notes using AI and even create important highlights that you can share with your team after. And these are areas where we think that AI can be effectively used for today. And on the flip side, I've called this slide not to AI yet because things are ever evolving and of course there's bound to be changes here as well. But here are some areas where we may need to continue to use AI with caution and in conjunction with traditional methods. We've seen that human in the loop is necessary to ensure research rigor at this stage where things are currently. So AI cannot ask on-the-fly questions or move the participant from going on a tangent to come back to the topic. So steering them towards the direction that you want the research to happen, that's not possible with what we have right now. And AI tools right now, if you see, they do help with distilling insights from this study that the data was obtained but to not capture context or background on what happened previously or what's the big picture that the teams are looking to learn more about. The very essence of AI being say, non-emotional, almost unbiased vessel means that it'll never truly be able to answer the big questions. And I've added yet to that as well. And like any other emerging technology, AI also needs to train for accurate data. And in the nascent stages that it is in right now, AI depends on quality data and the capability of AI is also limited in a sense that it cannot honestly assess qualitative aspects such as user emotions or preferences. And in its current state, AI can't know what's relevant to the team or the researcher. So helpful insights may be buried in a notion of irrelevant information. So identifying and prioritizing those helpful insights can become really cumbersome and time-consuming for anyone that's working with us. So I would say, please don't restrict using AI in these approaches, but please do use it with caution and in conjunction with any traditional methods that you've been already following. One thing to think about as I close out here is that whether you wanna force fit AI into your workflow, since there may be a perception that the world or your target audience is looking for AI related specs, or say you wanna work with AI naturally rather than force fitting, right? AI definitely can help save a lot of time. However, knowing the limits and drawbacks of using AI in your developmental process is essential and beneficial. We shouldn't entirely rely on it, but use it in conjunction with traditional UX research methods of working and ensure accuracy and reliability of results in that way. And over time, I'm sure that AI will surpass expectations and ease up ways of working and revolutionize how we work in itself. But to summarize, AI can be super beneficial and encourage data-driven dashes in making and transform our methods, but at the same time, it's essential to balance human power with the AI capabilities for continued success. And like all technology that we have today, AI isn't without challenges and it's still in its development phases. And so there's a lot of skepticism about its ability to understand and predict human behavior. So all of that is understandable. So over times, I'm confident that the models will improve and this will also build user trust in making AI-powered methods more efficient than its current form. So one thought that I want to leave us all with today is think about what can we be doing today to make it much more meaningful for us in the future?