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Welcome to another episode of unraveling IT. And on this episode, we'll be sitting down with Brian Harrison, the CEO, of course, because technologies, as well as Nate Troy, our solutions architect here at Corsica, and they'll be discussing chat GPT versus the newest innovation from Microsoft, which is co pilot. And which one we feel like you should be paying attention to, and how it's revolutionizing the way that we communicate. Stay tuned. Alright. So I'm I'm here today with Nate Troyer, who's one of our solutions engineers. Mhmm. And has worked for Corsica for, well, feels like a long time. I feel like I've worked for you for a long time. It it does feel like a long time. Probably feels like a long time to you. Not not to me, though. It does. But I I greatly appreciate HR you're being here today and working to talk about AI. Yes. So, especially, you know, AI and how it relates to work. And I have a feeling you probably use AI more in your job than than I do. I think I was one of the earliest adopters because I am very lazy. And I hate writing, statements of work. I hate trying to sound professional, and it usually comes off as somebody play acting that they're professional. When AI can just sound professional, use words like seamless and a couple of the hot, hot words that I think, a lot of, c suite people would understand immediately that I am I just have not had that much depth in. And so I, you know, I just started asking, at at the time it was Chat GPT, just started asking it to, write a state write an executive summary, based on the narrative that I was gonna give it. So, essentially, you know, I would just give it the narrative, like, you know, company a, wants to have a refresh of refresh of their data center. Right? It's been it's hold. It has security vulnerabilities. And, you know, company B, that's right. We don't put actual information in there. Company B is going to, you know, perform the work and all that stuff. And it would just spit out the best thing I had ever read that I that did not come from my hand, but was, I would say, enhanced with AI. That's that's the way we put it in our team. Like, it's enhanced. Yeah. And I I think that's a great way to look at it. You know, I I even tell my kids AI is is great for expanding what you maybe thought was possible or the way you were thinking about something. And when it comes to producing work product, it can, it can give us something that that taps into knowledge that that we don't yet have or vocabulary that we don't get half. Yeah. Thanks for invocap. So, yeah. It's really useful. And, like, the intangibles of using it, now I start Now I when I start writing something by myself, just freehand, I have learned how to write well with a I'm spitting that information out. So when I'm engaging with, people in the c suite and I'm writing emails and things like that, I I don't really use chat GPT anymore to write it for me, because I kind of already understand. I might have it, you know, you know, punched up a little bit, but that's about it. So I think, like, it can be used as a tool, you know, to to teach people how to do that sort of thing. Yeah. And and when when I think of generative AI like Chad GPT, that way, even, even, you know, for the types of things that I write, it, it's almost like a mentor. Yeah. Caps into all this extra, extra sources of experience in a way that's so much faster than than just learning it on our own. And I I find that to be one of the things most valuable when I need to think of something differently. You know, we all get tunnel vision get locked into the way we think of the world, and it chat chat GPT specifically in in any AI can help break you out of that. Yeah. And I think since since the, since the amount of information that it's consuming is so great, you know, you can get, answers out of, out of, specifically, like, chat, GPT's model for things like, What are the what is what is the difference in thought process between the average American and the average German? Which is that something I actually actually Because if we're gonna be going into a a discussion with somebody who's, you know, from Germany, they're gonna have a different way of processing what we're talking about than I will. You know, they're, you know, more collaborative and we're more individualistic. So, you know, it kinda helps you, like, it kinda guide you of where you need to go in a conversation. It was, like, super it's amazing what you can ask it. You know, I was trying to think of ways and things to ask it that it probably doesn't get asked all the time. I just saw I read an article the other day that said it's better at doing, It's better it's better at doing, like, research for, like, like, scientific research than models specifically created for that industry three because it's it's consuming vast amounts of data. Right. Which is it's nuts. It's absolutely and they didn't know that until they asked it a question about that they're like, wait a minute. You know, like, it's it's it's giving us better information than the models that we created, you know, for a specific for our industry. So it's it's really cool. I mean, AI is super cool. Yeah. And I I think for for those of you maybe listening that, that aren't using AI yet, you will be. I I think experience is is what gets people to to come back and and realize that you know, it's not a replacement for people. No. But it certainly is an enhancer for your own experience. Right. It's like being able to ask expert anything that that you want to you. And and really, Jeff But you have to understand the answer that it's giving. You know? So there's a component. You have to be you have to be a part of the process though, you know. Yeah. So, generative AI is super useful for business. Mhmm. But we definitely see some risks, and and you started us off with kinda want at the beginning where you mentioned an example of where, where we'll use AI to help enhance maybe something that that we're writing or going present to a customer. And specifically with with chat GPT, there's some risks around, you know, where is that data going? Right? And we live in a in a world where now, you know, I I I see people really willing to give up personal information, maybe accidentally. And, I I observe or or talk with people who I feel like are probably doing that in chat GPT now without without thinking about where's my company data or my personal data going? If you type it into chat GPT or any of these generative AIs, it's their data. Right? So, I mean, you kinda have to be careful. They've written there. What's interesting is they've written their terms of service in such a way that's basically, like, all all risk is on you, which I can understand that. So, I mean, you just gotta know what you're dealing with when you when you get involved with that sort of thing. Yeah. And and I think, you know, take take Bard, for example, where you can drop images in and ask it to analyze those, the the risk goes up pretty substantially Mhmm. If if you start trying to use it to do your job. Right. And especially in an industry like ours, where, part of part of what we do is have data about other companies So it's really critical to to know where that goes, which which kinda, which brings me to to another topic which is co pilot. And Microsoft's generative AI designed around the M three sixty five ecosystem. Now you've done more research on that than I I think you got, like, a test flight of that, didn't you? Yeah. Well, I I've seen a lot of the demos used little bits of it. But it's it's not out yet for small business, which is, you know, in Microsoft's world, anybody who's not a giant enterprise. Well, what do you think so how do you think this is gonna change the landscape for people who are already using the Microsoft ecosystem. Like, what if they're if they're you, you know, guys, if somebody like you is sitting at his desk and he gets an email from somebody asking for financials and stuff like that. Do you think all I do is email? Yep. Pretty much. Okay. Some some days it does feel way. Yeah. So I'll be fired at the end of this, but guys. Don't worry about it. I will never be back. The big differences are that, you know, one, I I don't have to put my information into, you know, chat GPT or or BAR for example, Microsoft is is using those large language models on, my data that already exists in m three sixty five. So it's already designed around my ecosystem. And and the way Microsoft is looking at this is is really application based, but then also broader than that. Through some of some of the chat interfaces. So, you know, I think of that example you brought up, we need some financial analysis on some, you know, maybe in some revenue. Co pilot will will look through my email, and I attempt to gather the data that I need in order to respond to that. And it'll it'll even, you know, potentially draft a response me. That's crazy. Which is just like what we would expect ChatGBT to do. Yeah. It's not integrated. Yeah. Information. And what's great about it it's it's protected. It's it's kept within that space. And it it is a force multiplier four businesses in the right way. Right. Right. So how do you so, like, especially with with our company specifically, like, how do you see it augmenting our our abilities here. Yeah. So it's great if it helps me save time. Right. I think that the bigger impacts longer term are around customer service and how do we interact with our customers? Because that ultimately is our business. There's a lot of other things that that we do or talk about, but we are a a client focused business. We wouldn't have a if it wasn't for those clients that work with us, that we provide security and support for. And so chat GPT does it really solve any of that for us? It it might give us, you know, some some lipstick to put around, you know, a doc that we send out. But what copilot has the potential to do for a company like us is to give everyone the content that they need around that vast array of customers in a really timely and meaningful fashion. And so imagine feeding in who that customer is, who they normally work with, what projects we've done recently for them, what service we've done for them, and then being able to get a summary or a response as someone on the front lines who's interacting with that customer and is able to have that full view. Again, all that data is available now. Yeah. Just we as humans can't go out and in fifteen seconds, go find it all, assemble it, and create some It makes sense out of it. Right. And and give a cohesive response to it. So when I think of what co pilot can do for businesses, search can increase our productivity, but it can really drive us towards a better customer experience, which is what most of us are are after because our customers really drive that revenue and the success of the business. So outside of our our our industry, just outside of IT, we're, like, somebody who's in a factoring job? Like, how do you think that that's going to, I mean, affect sales, affect, the quality of their product? Like, what how do you how do you see it implement it. Yeah. So so in let's use manufacturing as an example. It Because we're in Fort Wayne, guys. Yeah. You know, manufacturing is our is our largest vertical. Yeah. It is. It is. So, we work with a lot of manufacturing companies. So so there's a couple things that that need to happen first one is starting to adopt the data frameworks available within M three sixty five, because that's what's going to bring the information that that we can start applying, co pilot to you. And and so that means that building some connectivity between ERP systems and the Microsoft ecosystem. Starting to bring that data integration into, the the Microsoft three sixty five domain, so that we can start interacting through Excel, through Word, with data from these other systems. And you need people to do that. Right? Yeah. Absolutely need people to do that. You need, you, you need that connectivity but but in a in a world of SaaS applications, everybody has an API. Everybody has, you know, the ability to get that data into something like a Power BI. And that's why, using those external tools to start building those dashboards in in viewing your data, becomes really valuable when you have co pilot, because now you can interact with that ERP data, with that scheduling data, with whatever it is out of those other systems, through a safe AI ecosystem that ties into your productivity apps that you're using all the time. So if I if I was a production manager and I wanted to write a report on scheduling and throughput, if I have those dashboards already built in power RBI, then I can have co pilot help me construct those reports and pull that data those various reports. So, like, one of the things I saw was that you can essentially, since, since chat GPT is, you know, open AI. Right? Right. Which is also behind co pilot. Right. Does is Microsoft do you think they're gonna develop something for people who maybe wanna more hybrid and stay on prem and have, you know, access to their, you know, on prem data? I I think at some point, the in the the connectivity is is there now. It's a little cumbersome to to get it where it needs to be because it it'd have to be accessible through through, you know, what used to be power automate. Yep. And I I don't know. Microsoft is pretty fully invested in getting everyone in, Yeah. No one was surprised that they would have won a pay to play model, but two, their own AI. Right. No. And I I don't think anybody was surprised with that. Yeah. But I I like it as as a business leader. I look at it and say, I can have confidence that I can turn my people loose on AI and do so in a way where I don't have to worry that my my company data is gonna and, you know, on a surface. Well, yeah. I mean, we have we have clients now, and prospects asking us about, you know, what type of policy, Have we what type of policies have we seen out there surrounding AI usage? Like security policies? You know, that that was I I had not heard that just even two years ago. No one was asking that question. Before chat GPT, it just wasn't available Well, yeah. In in your browser to to go and input company data out on the web? Yeah. I mean, the just the the quickness with which this has grown is in is incredible. It's not a new thing. I think it was started, like, nineteen fifty seven. Like, like, into AI is starting to figure out how to process AI and all that. But it really took off when, you know, the graphics processing unit started in play, all the all the hardware that's now involved and how fast it is. And just that everything all that data is now out there for it to consume This is not going away anytime soon. In fact, I think we we have to be wary about malicious use of it too. Yeah. I mean, Right now, chat GPT has some it has some guardrails up in in in case if you wanted to do something, Hey, can you write me, write me some code that will, you know, delete everybody's information off of their c drive? It'll say no. But you can get around it by asking it in a certain way. You know? And so, I mean, I can just see people creating tool sets out of it that I mean, at some point in time, I think it was you who said something on to the effect of one day, we'll just have our AI go talk to their AI, but I think the day is also coming in when, you know, we'll just have our AI defend against their AI. Yeah. I I we're we're likely closer to that than I'm sure it's happening. Realized now. And I think as much of a force multiplier's AI can be for us in in a productive business, it can be in a in a harmful or malicious business as well. And we we certainly see that and and I think if if history has told us anything about threat actors, it's that they're willing to invest time and effort that a lot of times, a a regular commercial business is not willing to invest. And that that keeps them, ahead. Of many of the the small to midsize businesses that we work with that that don't have a partner that's helping protect them. So it it's it's gonna keep growing. And in chat, GPT has guardrails, but there's plenty other ways to engage with AI and produce malicious content. If if that's what your desire is. Right. And, man. Yeah. That's kinda it's kinda scary. That part is kinda scary. It is kinda scary. But I think what I think the upside of this is you're seeing a lot of AI now, being adopted by cybersecurity companies, you know, that have specific products, you know, that it essentially in the background consuming all this information going, that doesn't look right. That doesn't look right. That doesn't look right. I'm pointing that out. And, correct me if I'm wrong, but AI is also in involved in Microsoft's cyber security. It is. It is. Mhmm. Yeah. So I I think we're gonna continue to to see a a bit of battle between Good AI and malicious AI for for a long time. Okay. Yeah. I mean, pretty sure we will too. Yeah. For sure. So I think so co pilot is available for most businesses in in roughly three months. It's available in the enterprise right now. And I know I'm really excited to to get it deployed to some of our customers to see how it can can have an impact in a pause way. But in the meantime, you mentioned these policies around AI usage. I I don't feel like policies or are what's gonna be the answer? Because I I think we all recognize, you know, policies about as good as the paper it's written on. Oh, yeah. Without some enforcement mechanisms. What we're not seeing a lot of now is is companies saying, Hey, will you block these domains so that I can hacked my business because I I think deep down, there's this desire to say, well, I know I'm getting better work product. Because some of my team is using AI. So I expect to see a CS shift as co pilot comes available and and there's a subscription model to get generative AI to to be productive in business. I expect that that we're gonna start blocking some of the generative AI tools from from corporate That's interesting. I did not think about that because they're already paying. Most of these people are already paying for m three sixty five business premiums to like that, you know, that's gonna start moving people away from the the quick and dirty eye. We got a chat GPT account. I it can write my email for me. We'll soak and auto pilot. It's right there. Oh, by the way, it's consumed most of your, your emails and your, in your, in your word documents. In a secure way. And it can give you best response because it knows the most about this specific portion of your company or this specific person who's emailing you. Right. That's really cool. That is really cool. Yeah. It's a great product. Yep. Alright. Well, Nate, thanks for joining me today. Thanks for joining me. You're well, Bill. You're well. I'm the one who runs the company based on, on the vest. Yeah. You're right. You came well dressed today, and I, I appreciate that. So, thanks again for Yeah. No problem. I'm sure we'll have other topics we talk about in the future. Okay. Alright. So are you ready to get started? I'm ready to get started. Okay. Well, I'm here. Who do you think runs the company? Let's try it. Okay. Leave it in.
We want to see really strong data governance, management, and documentation prior to to rolling out Copilot. Educate people on on how to use Copilot. Build relevant content to show the the people that we turn on this tool for how to use it. We'll jump right into this. You know, I think I I say this every time I talk about AI. It's on everyone's mind. It's in the news. We see it. We hear about it. And I get asked on a weekly, if not daily basis, you know, how are other businesses using AI? How do they plan to use AI? AI? What should our strategy look like? We also see that that a lot of companies have turned on Copilot. It's interesting. About thirty percent of the folks on this call are using Copilot in some fashion. So many have turned it on. It's easy to buy a license. It's super easy to to get something started. What I'll tell you I see is it's the new shiny object. It it gets used pretty heavily for a couple of weeks and then starts to slow down in its use. I'd say that there's a lot of people that that don't know how to use it effectively, don't know what they don't know about what Copilot can do to to help them. And, you know, most, if if not all, I would venture to say haven't really prepared for the risk. So, there's a lot of confidence that that Copilot is protecting our data as we're searching, but there's some other there's some other opportunities for us to better prepare and better protect our sensitive data as we use Copilot. So thinking in in terms of Copilot's main uses, I I wanna talk about it in in two different ways, and and I'm gonna focus more on one than the other today. And that is there there's one one aspect of Copilot, and I think this is the one most people are are typically thinking of and that's the productivity side. So this is engaging Copilot through Word, through Teams, on office dot com, through the Copilot app. And this is really all about producing more output more quickly. It's streamlining our our day to day activities. Examples that that I like to use of this is, you know, an HR team that needs to write a new policy can quickly get a skeleton of that policy out of a tool like Copilot and then edit it and and update it from there. I always include the code snippet part as a reformed programmer. I think how different my life would have been if I didn't just had have to Google how to do something, but I can actually ask a tool to write the code for me. And then on the automation and analysis side. So this is much more about creating flows, creating those repeatable, intelligent processes that can help us run our business better. I'm not gonna spend as much time on that today because that that's really a phase two as we look at what do we need to do to adopt Copilot as a business. Alright. So just thinking in terms of how does Copilot work and and what does it do, it's important to understand that that there's indexing that that has to happen. And the way that Microsoft has implemented this this tool, it's using a a semantic search that is going through and and indexing. And and really what semantic search is all about from a a really high level is about creating relevance and understanding between similar concepts so that when you ask Copilot for something, it doesn't just do a a word for word search. It it matches the the intent of what you are looking for and builds those relationships much like a person would. The example I like to to use is think of this as asking for food and somebody gives you an apple. They know that an apple is food, so they would provide that to you. In in traditional searching, you would say, you know, where is food in my documents? And it would return to you exact matches for that. It wouldn't find the related food items that are part of that. So semantic search is is really all around building that that conceptual understanding of your data. So so why does this matter? It it's important to know that, you know, right now Copilot is is indexing, as as Microsoft rules out, the semantic indexing, a number of different types of data. Your own user mailbox, of course, documents, PowerPoints, PDFs, and more and more types of data all of the time. This means that, you know, Copilot does not from a a risk and data exposure perspective. It doesn't give anyone access something that they to something that they don't already have access to. But what it does is it allows someone to find maybe what they weren't supposed to access, but accidentally have the ability to to see much more quickly. And and so, the example I I'd use there is, you know, you have an HR SharePoint. Someone inadvertently is given more permissions to that than they need. The odds of them finding that, and going out and trying to access it are pretty low. Asking Copilot a question that finds an an inference into one of those files that's been indexed could return to them a result that that you as an organization would not want them to have. That brings us to this topic of now that we understand, you know, what is Copilot doing behind the scenes with our our private data, What do we need to do to to get ready for that? So data readiness is is something that I think most organizations that are using Copilot today have not gone through a data readiness exercise. And and it's it's easy to skip this step. Copilot is interesting. It's easy to to implement and turn on. And we may be inadvertently exposing our our company data or providing people access to data that that we didn't mean to. And this goes across multiple different types of data sources. So the the way we recommend starting with with the Copilot exercise is to to go through this data readiness process. And and that's really to go through and identify what are my sources of information that I want Copilot to have access to. Review those for for quality. And and the reason this matters is, you know, AI is is great. The the semantic search is is really powerful, but it can't it can't do it can't do miracles. So if we have bad data, bad structure, inconsistencies, that is going to affect the output, the quality of that output. And then we wanna we wanna enrich and enhance that data. So we wanna add additional context to the most important piece of the data. Where this really matters is is not so much in in a word document, but as we start to to see Copilot dig more into the analytical pieces of data as Copilot's capabilities expand into Excel documents, into, other types of systems, it's going to be very important that we create the right kinds of metadata that allow Copilot to make those inferences in in data relationships. And then lastly, we have to to protect and secure our data. And and this, by the way, doesn't just apply to Copilot. It's really important that that we start to put controls in place early on that protect our data from from being sent out through an AI tool into an an area where we may not have control over it. So what does this data collection process look like? It's really important that that we start with really reliable cloud services. So so we want to choose where those are stored. And, as much as we can consolidate that as an organization, it's it's really critical. So we certainly work with organizations that have data in multiple cloud services. As much as possible, try to standardize on SharePoint and OneDrive. Or if you're a a Google shop, stick with with the Google Docs structure. Don't introduce SharePoint, OneDrive, Google Docs, and Dropbox. We see that. And and what that is is that's a recipe for a loss of control of data. And create this this inventory. It's important that that we start to reduce the duplication of data, that we put it in a structure, and create a map of of where our data lives so that that we have a strong handle on that. And understanding where where our data lives, it it moves us into the the next phase of of what we would recommend to be Copilot ready. We have no shortage of of data sources. And, you know, as business decision makers and and those responsible for our company's data and information technology, it's it's really important that that we start to think about this differently than we've thought about it in the past. Our data no longer lives behind the firewall. It lives in different SaaS applications. It lives in these different sources of cloud storage. It is a a very broad set of of data, and and that means that the opportunity for us to lose or compromise that data is is greatly increased. And and that's why we would recommend implementing a a DLP or or data loss prevention system as part of a copilot rollout. So we've taken our data. We understand where it is. We've documented that. We have a good handle on. We don't have a lot of duplication of data. We wanna start this process that that says we wanna put some governance around our data. And, you know, on on one of the slides earlier, there was a a a little note that said, you know, Copilot will honor types of labels, sensitivity labels on on data. And I I think this is a really important concept for us to to think about when we deploy a a solution like Copilot that that enables rapid search and collection of of data and presentation to users very quickly. We need to to get a governance and and data loss prevention plan in place. And so what what DLP is is it is the process of safeguarding the sensitive data against unauthorized access, but more so breaches. And and when we think of breaches, it's it's where does this where is this data going? I think if if we were to to have a poll that that said, you know, how confident are you today that no data left your organization that shouldn't have? My guess is that there wouldn't be super high confidence. So it it also protect protects against, you know, unintended deletion. Now now notice it doesn't say unintentional, but it is protecting us from the loss of data that may contain confidential information. And then it also helps us as we need to comply with privacy and security regulations, and and there are certainly more of those on the way. So this is a great chance to talk about a solution that ties right into how Copilot works, and that's that's Purview. This is Microsoft three sixty five's DLP solution. It's actively being updated and and released, including the AI hub for, Microsoft Purview. And and what the AI hub does is is it not only tracks the usage of AI from a Copilot perspective, but it has plugins that allow you to control and monitor AI usage in other third party tools, and and we'll talk more about that. So this is this is around a governance service, And the natural next step in in us moving our data to the cloud and to these multiple sources is that we can use a tool like this to discover catalog map and then manage and identify the risks over time. So a couple of these examples are are the use of of encryption and sensitivity labeling. These sensitivity labels are really valuable to classify information and have a sensitivity level. This requires some work as an organization to identify what are those sensitivity levels, what applies to those. But what's great about the AI Hub as part of Purview is that it takes some of the manual work that went into DLP solutions in the past out of the hands of of the administrator. And so it's able to to start to identify on its own the PII, the the types of information that we wanna make sure that we protect as an organization. So moving on to the the next piece of this, which is is really around how do we use a tool like DLP to to ensure compliance. We can put a compliance framework in place through Purview that that helps enforce the use of that throughout the the journey along kind of this data discovery mapping and protection path. So to summarize what we're at today, we want to see really strong data governance, management, and documentation prior to rolling out Copilot. So what what do we need to do to to monitor and and then ultimately train our teams around, you know, awareness of the data implications here as as well as how to use these AI tools. So first, you you know, usage monitoring in in system logs. Right? This sounds like we're talking about the the same platform of, you know, five, ten years ago for for those of you in the security space. But but this is a a much different process today. We don't have a central location where we can monitor the exfiltration of of our data from. I it could happen from anywhere from on a mobile device to, a third party that that we might not even be aware that our team is using. And so leveraging those tools, I mentioned AI Hub is is a great example that that has browser plugins and other capabilities to to really start to watch the movement of our data from our various systems and and start to collect profiling and and understanding around where is that data going. Because, you you know, the approach that that we don't wanna take as a business is is Corsica, and I I think most of our our clients and and most of you are probably the same way. We don't wanna just turn off these productivity tools. We want to equip people to to be able to use them, but to still be able to to protect our organizations. And so starting to to roll out this plan of data understanding, data monitoring, and then, you know, adoption of Copilot and other AI tools is is a really important part of that. And then some proper training. So so here's here's one of the biggest gaps that I see in in Copilot usage is a lack of understanding of what is Copilot capable of, and where should I spend my time using it. It's fun to go have it write some poetry. Those are the the things that that I think people like showing off. What can AI do? But, ultimately, we we wanna use this as a as a multiplier in our productivity. And and so what are the right places to use that? And so having proper training that that helps people use Copilot responsibly, I you know, I feel like that's kinda like the the IT, you know, safe answer. But but, ultimately, what we wanna see is we wanna see our companies make more money because we're using AI really well. And when I hear a business decision maker or or an executive ask me, how should we be using AI? That's the easy answer. It should be a a productivity multiplier. And if it isn't, then we haven't properly trained our teams to be able to use it. And so what what we don't see a lot of is the interactive training of let me show you how to use Copilot effectively in your role as a finance leader or in your role as a customer service adviser or on the front lines. And so we really encourage organizations take the time to build the training, to have the training, and show people what this is really capable of. And then also show them what are those those pitfalls that that they could run into. Where are the places not to go, and where should you not use AI in terms of areas when it comes to to matters of legal advice of human resources? There are areas that that we know we don't want departments to use AI. So this this gets into to developing these best practices. All organizations today should have an internal policy around the use of generative AI tools. If you don't have one, we can certainly give you a AI generated template to start with. Just kidding. We have real documents we can can help you leverage to to get these in place. And and these really hit two different avenues. One is what's what's our acceptable use of AI tools within our organization? How does it fit into our values and our culture? The second piece is where are we using AI that touches our customers, and where should we disclose that? So those are the the two kind of policy related items that that we typically recommend. Educate people on on how to use Copilot. I had a whole slide on it, and then I brought it up again because it's just so important that we build relevant content to show the people that we turn on this tool for how to use it. And then we need a process. So Copilot is one piece. Today's mostly about Copilot, but there there has to be a process for approving, implementing, and then monitoring these tools, not just AI tools, but all of these SaaS applications. If we've gone to the trouble to inventory to understand, now we need to put some controls in place so that we can keep up that documentation. Documentation is only as good as our ability to maintain it, and so we need to we need to have that as part of the governance around our organization. And then we need we need our executive teams to be talking about these things at the executive and board level. This is not an IT issue. Protecting our data, proper use of AI, and how do we responsibly use these tools going forward is a discussion that that should be happening across the organization. It it needs to be built into part of the culture of who we are. And, when when someone asks, you know, what are the ways that we should use AI? You you've gotta get into the the culture of the business. Where where are the values of the business that require human interaction? Where can we automate things or use AI? What are the expectations? You know, I mentioned that this transparency around, you know, those those two parts. I'll I'll just say that one again too because I think it's really important. Most people expect in in while there's no legal requirement today, we expect there will be legal requirements to to provide transparency around AI usage. So best to start with that. If you're using it in your organization today, especially where it touches folks outside the company, but even inside, we need to make sure that we're disclosing that. So let's review. Document and secure our data first. Gain an understanding of of what we want to do with conversational AI. The productive side, start there. We haven't even turned on a Copilot license yet. If you wanna do that in a limited fashion so for folks to use, I I think that's okay. But organization wide, we have to be thinking about how are we going to deploy this more broadly, and that requires us to take these couple of steps first. This gets to then, I'm ready. What do I start to do? The licensing and Copilot's first, integrating Copilot into your workflow and how people work. This is something that that we have consistently seen needs help. People tend to to get a tool like Copilot and they kinda poke at it for a while, but they don't necessarily integrate it into the way they operate. If we wanna fully leverage AI, we have to build it into the workflow for each department. And as technology leaders or or stakeholders, we need to ensure that these tools that that we're deploying are being fully leveraged and utilized by our teams. And then you have to provide feedback. The this piece, I I think we're all used to only providing mostly negative feedback in in general when we interact with a a tool or a third party. It's really important that that we provide the feedback through the mechanisms built into AI. It learns from those interactions.
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