I think we could probably go ahead and get started, Allison. Yeah. Yeah. Great. Well, hi, everyone. Thank you so much for joining us today. My name's Allison Bauer, and I'm the Director of Development for the College of Earth, Ocean, and Atmospheric Sciences here at the OSU Foundation. And I'm also a member of the CASE District 8 Cabinet. So on behalf of the CASE District 8 Cabinet, welcome to our together on generative AI in the workplace. So these togethers are really designed to connect us as CASE District 8 members across institutions to engage in conversations about relevant and timely topics for our advancement community. And so these kind of have a goal of being more interactive than a typical CASE conference session or online training might be. So we will first hear from our presenters, my colleagues Ryan and Scott. And then we are going to move into breakout rooms, where we'll divide you all up into different rooms so you can have a chance to talk with some of your colleagues on some guided questions that Ryan and Scott will provide to us. So if you're comfortable, when you get into the breakout rooms, please do turn on your camera and be ready to talk with your colleagues. That's really the point of these togethers is to gather us together here and learn from each other. So if you have questions during the first part of the presentation, please do go ahead and put those in the chat, and we'll answer those as we can. But otherwise, we'll first hear from Ryan and Scott, go into some breakout rooms, have some discussion, and then we'll come back here together to close. So again, thank you for being here. We really appreciate you taking the time out of your day to join for this training, and we hope you enjoy it. So with that, I'll hand it over to Scott and Ryan. All right, Allison. Thank you so much for that. And I'm going to go ahead and just share my screen here. We've got a few slides for you today. So can I just get a thumbs up just to make sure everybody can see the OK. Thank you, Jacob. Perfect. All right. Well, hello, everyone. We're really pleased to have a great turnout for this learning lab on generative AI in the workplace. Ryan and I have conducted a few of these learning labs at our place of work at the Oregon State University Foundation, and we're thrilled to bring this content to a much broader case audience here today. So some quick introductions. My name is Scott Emery. I'm the Senior Director for Automation and Change Management here at the Oregon State University Foundation. My role here is to lead work in modernizing our fundraising operations using technologies like AI and machine learning, as well as manage change that often accompanies shifts and processes that use technological tools, all in order to help fulfill the goals and advance the mission of the OSUF. So I'll let Ryan introduce himself. Hi, Scott. I'm Ryan Sheldrake. I'm Assistant Director, as you see, of Service and Application Support. So we've got our technology services team that helps with the more mundane stuff of issuing laptops and phones, that kind of basic stuff. But we also help staff learn about the new stuff that's always coming out, the Microsoft world of applications and our other auxiliary applications that just make the show run day in, day out. OK. All right. So here is our agenda for today. So we thought it'd be good to just start off with a little bit about the context around our AI journey here at the Foundation. So we'll talk a little bit about that. We'll get into generative AI and what it is, and then the ways of using generative AI, some practical applications around developing prompts. We'll share some do's and don'ts. We'll provide some examples and a demo on different results that you can get with the same exact prompt using two common generative AI tools. So we want to develop some awareness around that. Then we're going to get into some interactive group work. So we're going to break you up later in this session into groups of five. I think it's around five. And this is where the quote, unquote, learning lab part of today is going to jump in. We want you to come away from this session today having experienced using ChatGPT, Copilot, or Gemini. These are the three more common generative AI tools that are out there today. And this will be a chance to engage with your colleagues and share your experiences while trying these tools with some sample prompts that we're going to provide. We'll provide some instructions before we jump into that. Finally, after the group work, we'll bring everyone back. And we'll have a short group discussion. We'll give you an opportunity to share any comments, experiences, takeaways that you have from interacting with your small groups around working with these generative AI tools. At that point, Ryan and I can also answer any questions that you have. And then we'll have us wrapped up before the top of the hour. So I also wanted to mention before we jump in that throughout this slide deck that all of the images that you'll see were generated by AI. And we did that intentionally. So if you do see something that doesn't look quite right or maybe something that looks unusual or incorrect, we thought it was a good way to reinforce that while generative AI can certainly be useful, and we're here to talk about that today, that it is not always accurate. In fact, it is commonly inaccurate. So that's something that we're going to stress more during our time together here today. Okay, so before we jump straight away into the world of generative AI, I wanted to share a little bit about the AI journey and some examples of how we're using this technology here at the Oregon State University Foundation. So the first thing I wanted to mention is this exponential growth of data that we've seen over the past 15 to 20 years. I'm certain that this is not unique to the foundation. I'm sure that you're all feeling it and experiencing this at your respective organization. So this includes things like biographic data, names, demographics, contact info. It also includes things like engagement data. So this involves details around giving, volunteerism, data that's related to participation in events and other interests. This, by the way, provides invaluable information for predicting future constituent behavior. And then the final kind of data I wanted to share is transactional data. And this is often called behavioral data, and it refers to information that helps us understand what content resonates with our audiences. So this includes things like click-through numbers, the number of views on a page or a video, those kinds of things. And it's the kind of data that allows us to personalize constituent experience around like, for example, future marketing campaigns. So all of this, all this data is fuel for AI. And through this, we're better able to understand behavioral patterns of our donors and recommend funds based on those donation patterns. We'll also talk, another way that we use it here at the foundation is to track and personalize donor and alumni interests. So we've used AI and machine learning to do just that. And then practical work that we have around the foundation using generative AI tools, some of which we'll talk about later today. And that's the focus of our work. All right, so talking about generative AI and what it is. So looking at this, using these concentric circles, the outer circle being artificial intelligence, which is obviously a technology that works to emulate human intelligence. And then within AI, you have machine learning. And this is a form of AI that uses data to make predictions or insights. And then within machine learning, you have generative AI, which is a form of machine learning that uses large quantities of data to produce content. So we wanted to just show using this visual, the relationships between each of these technologies. All right, so there are a growing number of generative AI tools and models that are out there right now. For today, we're gonna talk about these three. So working from left to right here in the slide, we've got OpenAI's ChatGPT. Microsoft has their product, which is called Copilot. And then Google's product is called Gemini. And these are probably the most three common generative AI tools that are out there today. They all essentially work the same way. They use AI models to answer questions that we pose to them. So I wanted to share that there are some subtle differences in using these tools that I personally noticed, and I'm sure that many of you have already started using these tools. Maybe you've noticed this as well. I think that for now, I found that ChatGPT is probably the fastest to generate content. They all generate content pretty quickly, but I think that ChatGPT seems to be the fastest among the bunch. I also feel like it's generally the less accurate of the bunch. They all can generate text and images. Again, I wanna stress that none of them are perfect, though so far I found Copilot to probably be the most accurate in general of the three. They each produce results in different ways. So for example, if you were wanting to create like a vacation planner, for example, maybe you're going to the Czech Republic and you wanna ask it, create a vacation planner for going to the Czech Republic in October. I think Gemini probably does, I found that it does a nice job of incorporating images frequently into a step-by-step planner. It can be fun to take the exact same question that you have and try it out on all three and just see what kind of results that you get from each of them because they all operate under different models. So Ryan's gonna demo some of that as an example later today. All right, so using a generative AI tools, there are a few practical applications that we think that would be useful for you to use and you're probably doing them. Many of you are doing them already. So you can use generative AI tools to summarize meeting notes. So if you've got raw meeting notes, you can take those meeting notes and you can drop them directly in, copy and paste into any one of these three tools and ask it to develop a bullet point summary for you. You can take an article that may be a lengthy article or a report or something that you have that could otherwise take you a really long time to read. You can ask it to summarize that article for you. You can build a presentation outline. You can have it write some initial draft copy. Again, I do recommend that if you do that, generative AI is a great way to make an initial first step at writing copy, but always expect that you're going to have to go back and edit that copy for accuracy, fact check it. But it's a great way to at least get a start on writing any kind of draft copy. And then as I mentioned earlier too, you can also generate images and videos using generative AI. In some cases, you may have to use a paid version or an enterprise version to get access to some of these kinds of features, but these are some things that gen AI tools can do for you. So we wanted to just ask you all, so what are some ways that you use it? So go ahead and just enter in the chat. What are some ways for those of you who are using generative AI? What are some ways that you're using it today? And that could be whether personal or maybe something at work, but just to get a sense of some ways that you're already using generative AI. Right, drafting donor thank you letters. Absolutely, job descriptions. Yep, absolutely, Linda. Yeah, helping with speeches, making them more engaging. Yeah, you can absolutely ask any one of these generative AI tools to take something that you have and then give it a tone. It's kind of funny because Ryan, and hopefully I'm not stealing your thunder here, Ryan, but Ryan was working recently with our CISO on a security program that is themed after pirates. And so I know one of the things that Ryan was working on one of the things that Ryan did was ask one of these generative AI tools to write some copy in a kind of a pirate tone. So you can ask it things like that too. Yep, Alex helping with project proposals. Yep, these are all really good ways of using generative AI tools. So excellent, so we've got some folks that are already using it, fantastic. Okay, so in using generative AI tools, there are some data privacy and information security recommendations that we wanna make sure that you're aware of. So we recommend that you don't enter sensitive or confidential information such as financial or medical information, login credentials. This is not an exhaustive list, but any kind of sensitive or confidential information not to enter it in there. And why? It's because these tools are still under development and generative AI models, they generate new information that's based on their models training data. So even though the data may only be stored temporarily within the model for improving that AI model, we still think that it's generally an accepted sentiment that you don't want to enter people's personal, private or sensitive information because there's a possibility that it could be used as training data and it could appear in someone else's results. So protecting privacy, we feel that that is essential. I'm sure that most, if not all of you feel the same way and there are new laws that are being written and new laws yet to be written about data privacy. So again, we generally recommend not entering personal, private or sensitive information when using these models. And Ryan's gonna talk a little bit about ways that you can avoid doing that. Okay, I am gonna hand things over to Ryan now to talk about what is a prompt and then we'll eventually get into our exercise. So over to you, Ryan. Thanks Scott, it was great to see all the chatter there about how people are using it. We of course don't know who's going to arrive to one of these training sessions. So we don't really know your experience level but this is meant to be kind of a, make it approachable, make it something that you could take away today and say, I'm gonna give this a try or if it's already kind of a daily driver for you maybe there's gonna be a little tip or trick here that you're gonna walk away with and help those kind of conversations that you're having with Gen AI be a little bit more robust and more productive. So for those in the room that don't have a lot of experience with this, I mean, you're gonna be using a tool today that is gonna have an open blank space. I saw somebody saying, I use it to kind of help me with writer's block and yeah, it's a great way to do that because you've got a general idea in your head. You're probably all familiar with Googling something and you might wanna Google when's your local pizza shop gonna be open on next Friday or something like this. This is a little bit more in depth. You have some goals, you've got kind of a target in mind of what you're going for. So you must prompt this tool and you're gonna pose a question or maybe a statement and asking for help. You're asking the AI, Gen AI model to give you some more information, some insights or like many have said, some kind of creative thing. I need to create X, Y, or Z thing. That could be a picture, that could be text, that could be a whole number of things. You may have come to it with some amount of content or it may, like I said, be just kind of a blank page. So we hope that you're gonna be more comfortable at the end of this with using prompts. Scott, next slide. So it's a conversation starter as it says in this first sentence here. You're using your clear, plain language to prompt it and you've got a goal and you've got some kind of direction that you're heading. And you've got also thinking in the second bullet point, your desired audience. Is it for you? Is it for your fellow colleagues? Are you trying to build something at a certain level for someone? You know, you could put it in a context of, you know, someone with, you know, CEO experience, or you could say someone just starting out in this industry. So you can kind of change the tone and the context how this is gonna be returned to you. And let's see, as Scott has already mentioned in the third bullet point, these should be considered a first draft. This, you'll also see in today's examples, this, the content returned to you can be modified. It can be refined. You're gonna check for accuracy. You're gonna see whether it fits the bill, so to speak. As Scott mentioned, you have some of these images. If you are creating imagery, it can be sometimes humorous because it also sometimes have little quirks. And in the AI world, those are sometimes called hallucinations, which is just a funny term to begin with. But you just go with it and review for content. And that's why you also wouldn't put in sensitive information because now this language model that it's dependent upon may then use that information for further reproduction down the line. So your, what goes in, you know, also may come out at a later time too. I love to, you know, we'll see how these prompts could be used lots of different ways. You're either creating, creating's fun. If you're not creative, you may learn that you can be very creative. If you feel like, I don't create content, it's like, well, give this a whirl. You wanna create a document, an email, even presentations, but it's fun to see what comes out and how you can put your own spin on it, so to speak. And I guess there's that soccer ball that made me think of the word spin. We did create this kind of goal imagery here of a soccer ball going into a goal. And, you know, I created this using Gen AI and ultimately, you know, I got it to make me an orange net and I wanted it to look a little bit more like it was, you know, punching through the net or things like that. So it's kind of that dialogue that was going back and forth. You can use it for editing in the second bullet here. So perhaps you already have content and you need it rewritten. You want to change it somehow. You had a great start at it, but you think, well, let's throw it into Gen AI and see what it comes back with. Maybe it could trim things down if you're a little verbose or perhaps, you know, put it into some bulleted format for you, kind of condense it, or perhaps you're a little lean and you need more and you don't need, you know, the full book on it, but you need more. So that's, and I see a comment here, you can also edit in AP style. So if you have some kind of content and you need it in another style, great use of it. Third bullet says, summarize or understand. So you've got information again, but you want it, you know, just boiled down for you. That's super helpful. Asking or learning about a particular concept. You don't know anything, but you'd like to learn. And that's kind of akin to like the Google question of things, but oftentimes you'll find these Gen AI tools come back with a nice summary for you versus trolling through the links that Google might provide you, you know, just a standard Google question. I know some of my colleagues have also used it for, you know, given let's say Oregon State University, the website, and you want to use the search function in there, you could search a concept or a college or, you know, some specific research going on, but you know, that within the context of that website, then you got to look through all the links that it provides versus you throw a question into Gen AI, say, tell me about Oregon State University and this program or this research, and let it do the work for you. Let it troll through all the results and find the right one for you. So there's lots of effective uses of time for you using these systems. Also, just looking at the notes here, another person saying rewriting emails for different audiences in the world. In the organization. So essentially wordsmithing, great use, absolutely. I've used that again and again. We have a kind of like a Friday tech tip, and oftentimes, you know, we're, we have something, but it's like, well, let's run through Gen AI and see how it can be improved. So we have kind of, you know, just back to these topics what you just saw, but just reiterating it. You've got something, you know, initially you'd like to say or ask, and here is content, summarize this document. And you could either put in a link or you could copy and paste something in. There are some limitations on how much text you can put in. Most have about a 4,000 character limit, but we'll see in Copilot, they do have a notepad feature, which allows something like 18,000 characters. So if you are more in like the computer coding world, that might be helpful. And there could be other tools that get you beyond some of those limitations. But let's see, second option is modify the output. So use the summary above and write a 500 piece that explains the topic to beginners. So it's a great kind of condensing tool for you, or as we've already said, changing the tone, making it sound professional or adding humor, or as Scott mentioned in, you know, kind of a recent example, I said, we've got this content, but let's make it sound more piratey. And so just for the fun of it, it came out great. And then the last one, as the example, convert the answer above into text for a presentation with one slide for each key point. So this is just an idea for, I want this condensed, I want it modified somehow, and this would be a great tool. One other dimension is, we're getting close to demo time too, that there are things that I know, for example, that Microsoft is experimenting with, and there's a paid version that you can get with Copilot that will do some additional things. So as Ryan mentions here, you can use any one of these tools to have it generate an outline of a presentation, if you'd like, you know, one bullet point per slide, you know, one bullet point per slide kind of thing. If you, I'm kind of curious to know if anybody here might have the paid version of Copilot at your place of work, but we don't yet have that here at the foundation, but what Microsoft promotes is that, you can have Copilot create the slides for you, not just create the outline, but it'll actually build the slides for you within PowerPoint. So, yeah, I mean, things are gonna keep evolving in the space, and if you have the, you know, the ability to work with a paid version of, in this case, Microsoft 365's version of Copilot, then you're gonna get some additional features like that, that could prove useful, so. All right, Ryan, should I go ahead and stop share? I think we were gonna work on a demo here now. Oh, did we lose Ryan? I believe we did, Scott, so. We can either have the demo operate under you, or perhaps we can break our rooms. Oh, wow, Ryan just said that his laptop just died on him. Okay, well, let's see here. So, I think that I can, here, I'm gonna go ahead and stop share on this. Okay, and here, I'm gonna pull up, here, I can demo this for you. So, I'm gonna pull up Copilot, and I'm gonna ask it the same question, just to give you an idea of the kind of different results that you can get with this, so. Let's see, anybody got a good question that you'd like to ask? You can go ahead and post it in the chat. I can come up with one here, too. Do you mind if I just ask without writing it out? Yeah, go for it. Oh, okay, good. So, I mean, just because we're filling time, I had one question I wrote down for later, but, you know, I keep, I've got kids in high school, and I work at the UO, and I work with students, and I read scholarship app essays and everything. You know, we're really promoting using generative AI in the workplace, you know, this is a perfect example of it, but it seems to be, like, a lot of the syllabi that are coming out are really being restrictive about how current students can use generative AI. I just was curious what your thoughts on that were. You know, that's interesting, because, yeah, I've got, there's a couple of coworkers that are working on their grad degrees here at the foundation, and, you know, what I've seen so far, because there are no laws right now. This is a case where, yeah, Tyrell mentions it before, where, yeah, Tyrell mentions it being a copyright issue. So this is a case where the technology is moving faster than our ability to create laws and regulations around it. So I have seen where instructors have tried to instill more of a culture of attribution, even though there aren't strictly any laws around it, and it's kind of questionable. Well, how do you attribute works to something that's generative, you know, right? Yes, it's pulling information from training data, but to answer your question, I've seen cases where faculty are asking their students to still provide some kind of attribution to the work that they're doing, whether it's, you know, it's their thesis, and cite whether or not they've used any particular generative AI tool to help, you know, help them in writing that thesis or help them in writing that report. So that's what I've seen so far. And I think, like I said, it's just been a way of trying to develop a culture around, you know, attributing work that people are referencing in things that they're doing within school. So yeah, is that a practice that, what have you seen here at the U of O? I haven't seen much at the U of O, and I'm just been looking at what my kids' stuff has shown. And I just feel like, you know, the what the applications that we're saying, like create a presentation or do this. Like last year, I helped my eighth grader. He came to me and said, hey, I really think that we should ask generative AI to help me with my speech because the teacher wants us to do a speech. And I wrote a rough draft. I think AI could help. And I said, well, let's see what happens. So we took his rough draft of his eighth grade graduation speech, plugged it into AI, said, bring a quote in from the book that they read, make it funny and relevant to eighth graders who are graduating that want to give advice to sixth graders so add a little to it. And it did that. And it showed us, you know, the source for the quotes that they got, you know, that were kind of appropriate. And he turned it in and then he was selected as one of the speakers, right? I said, did you tell them that you used AI to create, you know, to take your rough draft? His draft was great. You know, it was his personal stuff. I said, did you tell him? He said, no way. I can't tell them I used AI. Okay. Yeah, yeah. It's, I think that it's an evolving issue. And I think some faculty are doing their best to try to stay out ahead of it, but there's no, because it's so new that, you know, it's something that people are still working to get their heads around. But I appreciate you sharing that example, because I think, you know, between yours and mine, that it's, you know, it's clearly something that we're still trying to get ahead of. So. Yeah, I think it's very useful for editing. I don't think it should be used to plagiarize as plagiarizing is not, you know, something that we should be encouraging, but I think it could be really useful for students. It'll be interesting to see. Absolutely. Yeah, yeah. It is just a tough thing around like ownership of generative AI works, because it feels like, well, we're getting this information from someplace, right? But again, that legal landscape around generative AI, it's still evolving. I think that it's still really important to stay informed about the latest developments as laws. I mean, you know, being at the U of O, you know, we in Oregon have a new Oregon Consumer Privacy Act that is really focused more on the consumer. That's more around data privacy than it is around AI. But yeah, just to stay informed around the latest developments, particularly if you plan to use this content in a commercial or a public setting. So. Ryan, you're back. You know what, Jene, I can't help you with is catastrophic laptop failure. It just started really working hard and then it decided that was it, that was done. So, but the benefit of being working in the tech team is I went and sourced another laptop. So here we are again. Woo-hoo. Okay, Ryan. So we ended up just taking a couple of questions while you were getting that new laptop set up. So I think that we're ready to go to still just do a demo of a few different tools. Okay. All right. Let's do that. I'm gonna share a screen here. All right. Well, who's excited about the new iPhone 16? You know, one more increment notch up here compared to the 15. Here's an article and it's straight from Apple and it's, you know, it's got lots of visuals, very pretty, you know, talking about a whole bunch of stuff, Apple intelligence and you're reading and you're reading and your brain's going a little swimmy and then you look over and you see the scroll bar. It's like, wow, we got a long ways to go here. This is a big article. Hey, how about Gen AI helped me out here and maybe summarize this thing. That would be a perfect use for Gen AI. So I'm gonna jump over to Copilot. We're gonna summarize this article. So I've got down here in this prompt, please summarize this with that link. Okay. We're gonna do it both in Copilot and Chat GP, just kind of compare and contrast, see what we get. It's thinking, it's thinking. Well, it's thinking, well, let's see. And that kind of speaks to Scott's comments. Sometimes, you know, Chat GPT might be a little bit faster, just depends what it's working on, what's chewing on. So Apple's introduced this, you know, 16, here's the highlights and it's got a whole bunch of summary for you, bulleted, bolded, easy to read, quick snapshot, right? And if you wanted, if there was an opportunity for additional commentary back and forth between you and the Gen AI tool, you could ask again and it would still have this in context, knowing that you're still talking about this. So you could ask another follow-up question or more functionality from it, or we could initiate a new conversation with this item right here and start fresh. So I'm gonna jump over to Chat GPT here and I'm gonna ask it to summarize, see how it does. And notice this one, it did not provide the bulleted summary for you. It's just a paragraph, and so take it how you like it. Maybe that's more your style, or you do like the bullets over in Copilot. Maybe we could just say, add bullet points to this summary. So now we get that bulleted approach if that's more your style. And I'm not going to go into it here, but you could really, having the two tools, having asked the same question of them, you could do a little compare and contrast, see if they're both coming up with the same content for you and perhaps being used with your colleagues or something like that. Or conversely, we could have, if you had a big article over here in a web browser or something, you could have just grabbed the text of the page, and you could have dropped it into one of these tools and say, please summarize this. So that works well for summarizing colleagues' content. If you've got some totally text-based thing out there that you just want to not have to read the whole thing, it's the Clip Notes, right? I'm going to do one more example here and just get some thoughts brewing here for us. Let me get logged out. Hey, Ryan. Yes. I think just a quick time check with what's happened today. I want to make sure that we also have time to get into our activity. Yep. So why don't we go ahead and jump to that part? OK, OK, we can do that. Right. Yes, we want to make sure that people have an opportunity to try this out and also work with your colleagues, not just listening to Scott and me, but you have an opportunity to talk with your colleagues about how this tool could aid you, how it's used daily for you, just kind of spark some ideas. So that's where we're going to move next. I'm going to stop sharing. We really have a great opportunity here with you and your colleagues. We are going to use breakout rooms. And we're going to put you into small groups. Don't be scared. They're all your peers. You're all part of the case. It's good stuff. We're going to drop a PDF into the Zoom chat that has a list of prompts. You're welcome to use any and all of those as much as the time allows for. Or if you've decided amongst yourselves you'd really like to discuss this one area, then do that. And be prepared kind of just to maybe come back at the conclusion if you've had some aha moments to share those. Please start with introducing yourself to your case colleagues. Just then real quick, bring yourself up. And then discuss. And that's the goal is please have the option to use Copilot, Gemini, any of the AI tools. And do a little compare and contrast. And come back, and we'll talk about all the amazing stuff you learned. And Ryan, just so everybody knows here too that we've got the case example topics, that PDF is in the chat. So if you'd like to go ahead and download that now, that probably would be best. I'm not actually sure if you can get to it once we get you in the breakout room. So it essentially goes over much of the same information that Ryan just shared with you. But if you want to download that, it provides some of those example prompts. So all right. Jacob, are you assisting with the breakout rooms? They have all been opened, and people should be able to join them. Awesome. Well, I think in the interest of time, we were going like 10, 15 minutes. We'll probably err more on the side of about 10 minutes. But I think that that's still going to give you plenty of time to make introductions with your colleagues, name, title, organization, and then pull up, if you've got a browser, pull up a browser and try using some of these prompts. And then we'll come back, and we'll have some time to answer questions and just to share out any experiences, takeaways that you might have had in your experience with your group. So those returns to the big room are always a bit abrupt. I was just sharing with Sheila and her group that she had a question about, where do you go if you have a university license or a license, otherwise an enterprise license, to access Copilot? And I was just mentioning that because the Oregon State University Foundation, we don't, at least not yet, have a license. I'm not sure where you might go other than I think that if you're logged into Microsoft 365, there is the waffle in the top left corner that you can get access to all the 365 applications. My guess is that you could go to that waffle and then select Copilot and then be able to use the enterprise version of Copilot from there. But I'm just, that's just a guess on my part. That's how I get access to all the other 365 tools. So OK, all right, let me share my screen here. OK, so we wanted to hear from you now. So we have a number of questions we wanted to pose to you. Hopefully, you had a chance. I know there wasn't a ton of time, but you had about maybe 10 minutes or so, 10 to 12 minutes to introduce yourselves and just to hopefully talk about maybe get into one of these tools and ask it a few questions or share some experiences with your colleagues around it. So these are just some questions that we would have for you. What worked? What didn't work? Was there anything that surprised you? Would you actually end up using the results that you had? Did you trust the generated information? So just want to ask if anybody would like to share, you're certainly welcome to enter any comments or experiences within the chat. But feel free to just raise your hand here within Zoom. And we'd love to hear any experiences that you might like to share, conversations that you had around this topic within your group. So looking for any hands raised. Stephen, yeah, go ahead. Hello, so Stephen, Washington State University. We did a little bit more of a fun prompt. We did a planet trip. And we did discuss a little bit of the hesitancy because we picked Singapore. And so how much do we know about Singapore and how much of this is true? But we talked about how at least it gives us a good starting point. I only really gave it two prompts and we got quite a bit of information. I need a travel itinerary for a trip to Singapore from Seattle, Washington. The trip will be 10 days. And I'd like a mix of sightseeing and a focus on trying local foods. And it kicked me out an entire 10 day itinerary, including travel. And then I said, can you take the itinerary that you've created and give me an estimate of how much money I'll need to take? And it gave me a breakdown of money for flights, accommodations, food, transportation, sightseeing and activities, miscellaneous shopping, and then gave me a breakdown of it as a easy viewable screen. But like I said, the biggest thing that we hesitated on was like, OK, but how much of this is actually true? But again, it gives us a good starting point. Absolutely. Yeah, thanks for sharing that, Stephen. And I'll share, I've done some similar stuff. I've also tested to see how recent some of the AI models have been working, like what kind of training data they've pulled in. I've asked it very similar things to what you just mentioned, Stephen, and gotten down to, hey, I'm going to be in this location on this specific weekend. So tell me some things that are going on that weekend that might interest me. And it's been interesting to see like it'll sometimes pull up events that are happening that weekend that might be interesting to you. So yeah, great stuff. And I love the fact that you did some follow up questions that built off of your first question, too. So that's certainly one of the great things about using these tools right now. So thanks, Stephen. Yeah, absolutely. Anyone else? We're checking the chat here, too. Let's see. Some questions here. Would love to learn how to see how to prompt to get a PowerPoint presentation created. That's something, yeah. Yeah, Caitlin. And I know that that question came a little bit earlier. It came before we went into our rooms. But yeah, I think I kind of got into that question already. Ryan mentioned some rooms discussed whether their organization has produced policies on generative AI use. So we have done that at the OSU Foundation. A lot of it is built around the recommendations that we are sharing with you today. So it's a start and certainly not evergreen. It's a living document. But how many of you all just maybe just a quick show of hands, how many of you have generative AI policies of use in your organizations? OK. And it could just be also a policy on do not do these things. It's more around the surrounding data privacy and more so than do it for these things. But it's more the don't do it for these things. OK. Yeah, I saw some indications from folks that, yeah, that your organizations haven't yet done that. Yeah, I think a lot of organizations haven't yet gotten in front of that. So this technology is continuing to move really quickly. Looking for any hands raised too, if there's anything that you'd like to share, anything that might be interesting to share with the rest of the group from your group chats. Let's see, it was mentioned that you can use AI to organize information in Excel from reports. Hmm, that's something I haven't done. That might be something, yeah, this would be a sound lifesaver. It might be something that you can use within that paid version of Copilot. But if anybody has done something like that and you'd like to share what that is like, I would love to hear what that experience might be like. OK, only guidance I've heard is not to use donor names. Yes, that is correct. Group spoke, let's see, Francisco mentions our group spoke about maximizing our AI tool by getting more specific help, using it more as an assistant. Absolutely, that's some way that we've talked about using it here at the OSU Foundation is that think of it as kind of your personal assistant. If you want to get some draft copy started, as many of you have mentioned today, the vacation planner, if you need something to be summarized. And it could be a real time saver if you don't have to read that 17 page report. If you just want to get, it's kind of like the cliff notes of the report, but a bullet point summary, it can really be a time saver that way for sure. Andrea asked, curious about, I'm just time check here, curious about the AI companion on Zoom sending hosts a request. Yeah, I've started testing it. Ryan and I have actually started testing out the AI companion. And it actually is pretty convenient. I mean, the only thing I would say is that make sure that if you try the AI companion, by the way, this is Zoom's new, what they're promoting is their new AI feature within Zoom. If you have an enterprise license of it, I believe that you have AI companion. So it gives you an ability to get a summarized AI generated transcript of what has come out of your Zoom meeting. So what I've seen so far, it's actually, it's pretty accurate, though you still have to go back. If it's any kind of meeting summary, go back and fact check it and make sure that it got it right. Sometimes AI will make stuff up. So make sure that you do that. And the other thing that I would stress around that too is that make sure that people are aware that you have that on before you go into your meeting and before launching into anything. Because I have seen cases where it starts doing AI transcription and people might say things that they might otherwise not have liked if they didn't know that they were being transcribed. So it's something to mention and preface before going into a meeting. Okay, I know we're just about at time here, Allison and Jacob. Yeah, in fact, we are at time here at 12 o'clock. So I really wanna thank everyone for spending some time with us today. Oops, sorry, I went off share a little too quick. I wanted to share our contact information. Here, let me put that up here again real quick. So this is how you can reach Ryan and I if you have any follow-up questions, scott.emory at osufoundation.org and ryan.sheldrake at osufoundation.org. And you can also find us on LinkedIn as well. And I recommend, if you haven't done so, there's tons of newsletters, daily summaries, digests and whatnot about AI out there. Sign up, the pace of change is just staggering. And so the more you learn and the more kind of helpful tips and like, hey, here's a new prompt you could try and might just spark an idea. So just really recommend, stay on top of this. The pace is super fast and we're all learning together. So thank you so much for your time today and apologies for the interruption there mid-presentation. Thank you, Scott and Ryan. And thank you on behalf of Case District 8 for joining us today. We really appreciate it. Thanks everyone. Thanks for coming.

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