 Alright, so this recent buzz around AI and, you know, generative AI, chat GPT, right? All of this seems like a deja vu to me, they told y'all I had a long career, right? And for folks who've been in the field long enough, y'all know why, but for those of y'all who are like fairly new, I'm going to kind of take y'all back in time a little bit, right? Let's go back to 2016, 2017, right? According to Guardian, that was the year AI came of age. There was a lot of AI going on. There were like a lot of, you know, transcription services, AI based. So basically AI went from becoming science fiction to, you know, more concepts that were applicable to your everyday life. And so we had, you know, a lot of like self-driving cars were, you know, kind of on then. That's when it all started. That's also the time IBM had launched what kind of the Watson in IBM was getting popularity. The Watson's, you know, cognitive capabilities. It was all cognitive capabilities and machine learning at that point of time. That's kind of what the AI space was during then. And IBM had launched a lot of initiatives to try and incorporate Watson's AI capabilities into every single, you know, product as best as they could, right? So that was the directive at that point of time. Go figure out how you can make your products better. Go figure out how you can embed AI in your products to kind of leverage Watson, right? Every product wanted the Watson tag and you had to work hard for it, right? And so we did a lot of, you know, idea generation days and hackathons. Hackathons are a thing in IBM where we spend, you know, days trying to come up with like innovative ideas of what we can do to incorporate that. We recently had one for the gen AI capabilities. We call those Watson X, but this one here I'm talking about the Watson hackathons like long ago in 2016, right? I was leading a portfolio of products at that time that helped digital marketers create, plan and manage their marketing campaigns, right? And so as part of our idea generation days and as part of our hackathons, we were tasked with and, you know, go figure out how you can implement, how you can incorporate AI in your capabilities. And that's what we did, you know, like three or four days or one week's worth of ideas. And then here's what we had, you know, we could do predictions, we could do, you know, insights, we could give recommendations, we could help simplify search for y'all. We could leverage national language capabilities and all of that. And, you know, these were like our ideas that the team came up with. And then we decided, okay, let's run this by the users, you know, let's see what excites them. Let's check with our users what they really want to see or what would make them feel empowered, right? And so when we reached out to the users, we really had, you know, at that point of time, two kinds of users, the line of business users who are mostly marketers, very comfortable with their day job. They knew everything about digital marketing, but they were not fairly comfortable or fairly aware of the technology at that point of time, right? So they knew their job, but they sort of knew AI, everybody had seen Terminator, so they all knew about robots, right? AI, at that point of time, mostly translated into robots and the concerns at that point of time were, you know, very real around, are you going to make me interact with robots? Okay, incorporating AI, does it mean you're going to replace your system behind the scenes with some kind of robots, right? How is it going to know to recommend, right? How is it going to, like, I've been in the field for 15 years. I know what I'm doing. How does it know what it's doing? How is it going to generate its recommendations, right? And we had to tell them that, you know, it's going to kind of start, it has models behind it and, you know, it's going to work with the models and it's going to learn as it goes and it's going to get better with time and that made them really skeptical. I was like, okay, so are you going to monitor what I do at my job? Are you going to, you know, watch what I'm doing? Are you going to then generate reports and send to my boss? You know, nobody was, you know, liking all that part. So it was like generally skepticism, not a lot of awareness, you know. People thought they knew but they had some concepts confused and so they didn't know kind of what to do. Mostly intimidation, mostly, you know, unsurety about where is the expertise coming from, right? That was about the regular line of business users. Then we had what we call the technical marketers who were, you know, aware of the technology at the same time, right? And so they were not as confused about what it meant. They knew, they understood, you know, how AI would help make their lives better. Their concerns were a little on the flip side. They were worried about whether we would get it right for them. In the sense, when we started implementing AI capability, so what the feedback, you know, it was like a legacy product. It was there for a long time. Every product has its quirks, especially enterprise products which have been there for a long time, right? And people have spent quite a lot of time trying to get used to it, trying to figure out the quirks in it, trying to work around it. They have a routine. They know I need to do these 10 steps, right, to get my job done. And they were all fairly comfortable with that. So their worry was, you know, is all of this going to slow me down? I'm not going to wait for you. I already, you know, I have so many things to do. I'm a busy person. I need to get things done. Now, if you start throwing recommendations at me when I'm trying to do these things, hey, do this, you know, hey, do that. Hey, hey, you know, are you going to slow me down? I don't have time for all of that, right? That was one part. The other one was, you know, again, to do with it. I've been in this field for so long. So your recommendations, one, they better be good, right? They better be on the lines of what I would have done anyways, given the situation, right? Because if it's counterintuitive, then I'm going to be confused and I'm going to trust my intuition. I'm not going to go with your recommendations, right? Because I kind of have been in the field, I know what I'm doing, and I'm just going to trust my gut feel, right? The second is, unless you tell me how you're generating those recommendations, back it up with, you know, some kind of a reasoning. Tell me how you've arrived at that reasoning. And then it'll help me to trust it better, right? That said, I'm not just going to blindly accept your recommendations, okay? I want to kind of test it out with a small sample first and see if it really works. See if it gives me the results you're saying it will give, right? And if it doesn't, I just want the ability to turn the whole damn thing off, you know? Just give me a way to do my job like I do it. I don't even want the AI. So give a switch, turn it on, turn it off. Clearly, Mark, this is AI. This is regular stuff. So I know that, you know, okay, this thing, I need to test it out. I like it. I keep it. If not, I just turn it off. So that was kind of the whole, you know, feedback that we had listened to at that point of time, okay? Back to today. All of this Gen X, you know, this Gen AI, this AI capabilities, and then we are in that space where, you know, all product companies are, again, in this hyper mode of enabling AI for everything, right? What has changed with the users, okay? So has it changed a lot, right? Sure. I mean, the awareness has increased, right? There's a lot of information about AI available on the social media now, on basically every single source, right? People have asked chat GPT itself, you know, tell me what you are, right? And it has done that, right? So, but yes, there's like not as much fear factor now. People are not intimidated. People have used AI one form or another. People are fairly comfortable talking to chatbots and all of that, right? But there is still this real gap of, you know, who are these people who are really aware, right? People like you and me, people who are in technology, people who are early professionals, right? People who are designers, who are in the product space, who own products, who want to make their products better. So all of this excitement now is in that category of people. There is still this other category that still exists. You know, the older users, the longtime users of your products who are still kind of in between that I'm aware and I'm not aware kind of a space. They know a lot more terms these days because, you know, there's enough WhatsApp, enough, you know, Facebook, enough chat GPT everywhere, and they've used it. But there is a recent survey that Forbes ran in July of 2023, right? And the numbers were fairly astonishing there. So 77% of the folks are still concerned about misinformation being generated by AI. So that trust factor or the trust issue is still there, right? People are really, really concerned now about their jobs being taken away by these AI capabilities, right? So while they are fairly comfortable using AI as consumers, they are really skeptical about allowing AI in their work life, right? Because they are worried that the moment AI is, you know, they are teaching it to take away their jobs, right, basically, and they don't want to do that, right? And so job displacement is a real fear, right? Privacy concerns, like I said, people are fairly comfortable with AI tracking things like their social media usage, things like, you know, what do they buy, where do they drive, and then, you know, any kind of recommendations that you might generate or AI might generate around that. All that is good, but are they really open to AI stepping into their work life, trying to monitor their work usage? Not so much, right? Not really not so much. And so all of these concerns haven't gone away, right? And so when we start off or to look at designing for incorporating AI capabilities, or when we start out, you know, reaching out to our users, trying to figure out what would make their life better, right? All our user research mostly is centered around, you know, what are your goals? What are you trying to achieve? What are your pain points? How are you using our products? And, you know, all of that, right? What I'm trying to say is, maybe, maybe, if you're trying to pitch in AI capabilities, we should also try and do a little hopes and fears exercise with them to try and understand, you know, are they comfortable? If not, why not, right? What are they afraid of? You know, what are they worried about, right? What are they hopeful about? Actually, there are use cases where people will tell you, yes, you know, something like this would be nice. So it's nice to kind of get that insights from your users. Let's look at, you know, deeper motivations rather than immediate needs because, you know, they might need something today, but AI capabilities are here to stay. They learn over time. They get better with time. So focus on those kind of, you know, sustainable long-term needs when you're trying to think about how you can make your, you know, how you can leverage AI capabilities to make your products better. That said, yes, we want to make our users feel empowered, but again, I think the first step to do that is, you know, understand a little bit about what is bothering them. Take some time to educate your users about how we are going to take, how we are going to make their life better, you know, how maybe, you know, a little bit of level-setting expectations around this is what this means, this is what this means, this is how we are leveraging these capabilities, and this is how it's going to make your life better, right? Help alleviate some of the fears, and then I think we'll have a little less resistance in terms of user adoption. So that's it, I guess. Oh, and I'm an IBMmer, by the way, as she mentioned, and we are hiring, so please feel free to join. Thank you.