Name: Integrating AI to Unlock Your Team’s Human Potential
Uploaded: 2025-12-11T17:30:55.613Z
Duration: 58 min 16 s
Description: Integrating AI to Unlock Your Team’s Human Potential

Transcript for "Integrating AI to Unlock Your Team’s Human Potential": Hello, and, welcome to those joining us today to Prosci's webinar on integrating AI to unlock your team's human potential. Looking forward to, kicking us off shortly. For those of you depending on where you're joining from, good morning, good afternoon. For some of you, good evening. But very, very excited to have you joining us today. We're gonna kick off soon, but I just wanted to give a couple of, updates this year, for those who are logging in still. We will be using the chat feature, and so if you've got an opportunity in fact, it would be wonderful if folks could maybe just share where they are dialing in front. So we've got, presence from around the world. And so if you wouldn't mind adding into the chat where you're joining us, that'd be great. That's a good way to test out our making sure the chat's working. We will also be doing, a poll at some point today. And so when that comes live, you'll see that, show up actually where the chat section is, and so, just be aware of that. And we are recording today's session. So as we record, we will make the recording available within the next one to two days. We will also post this on the Prosci website. So for those who, want to kind of go back and look a little later on or share it with some of your colleagues, you're welcome to do so. And it's wonderful to see where we've got presence coming in. I see all kinds of, shout outs from across The UK, Vancouver, Minnesota, Lisbon. Amazing. Amazing. So, I'm so excited to welcome you all today. We are expecting a large group, and so we will not likely have a ton of time to get to questions. But if you do have questions, please include them in the q and a, section, and we'll do our best to try to address them. But we will mostly be focusing today's conversation through probably more of a format, that's a bit more familiar in terms of the work that we've done within our research, and so we'll actually kick off here shortly and move directly into some of the materials. For those who I have not had a chance to meet before, my name is Ramona Brown. I serve as our president for North America here within our pro side business, so really supporting clients across the, the the continent. But we are a global firm, and so many of you are likely interacting with Prosci or engaging with Prosci from potentially our my colleagues, if you will, outside of The, US as well. Today, I'm gonna be joined by Tim Creasy. Tim will be our primary speaker today, and, Tim is our chief innovation officer here at Prosci. He is certainly the one who'll be leading a lot of the conversation from many of the conversations he's been having, across the last year with the with the many of our client organizations, and you'll see more about some of the teams that he's been able to share the content with today. For those who are not as familiar with Prosci, I just want to give you a brief introduction. At the, heart of it, we are a research organization, and we deliver enterprise solutions backed by that proprietary research. We are the leading firm that specializes in change management. What we mean by that is how do you actually get the adoption and the level of benefits from those ROI initiatives that you're implementing specifically that involves the people aspect of change. So when we think about adoption, how do we get people to lean in and adopt the solution? How do we get people to actually change the behaviors that we need? That's essentially the framework that we've designed. We've built that that was leading framework, and most organizations are using us specifically on projects really to drive that kind of adoption efforts. If you haven't seen some of the full ways that we support clients, I just want to spend a moment walking through that specifically in the context of AI today since that's the conversation we'll be having. The first piece that we do is actually build change capability. So think of this as ways that we actually touch every member of the organization from an executive level all the way down to frontline managers, helping them understand the work that they play and the role that they play and actually leading those adoption efforts, whether it's through, directly sponsoring that work or actually supporting and activating that work across their teams. We also support organizations with consulting services. This is the way that we actually drive everything from AI strategy development all the way through adoption and, again, enablement. And so think of this as having a partner with you in that solution opportunity, and we do that again across the globe. Licensing and tools. This is just a good way to say we know what works. So if we think about the way that we actually leverage our research, we don't think of it purely as the content that we can provide to those white papers. We actually engineer it into the tools and the frameworks that we use. So for example, our AI diagnostic is a tool that's designed based on organizations who have successfully implemented, AI across the organization. So we've actually had 1,100 individuals take that. We looked at that work and actually used it to build this AI diagnostic, which looks at about 20 different factors and the conditions for success. So how do we know what works or what we need to have in place to actually drive and get real benefits? And it here is from the AI efforts that we're putting in place, and we do that through a number of different tools as well that we make available to our clients. And so happy to talk more about that here as we get along. And as I mentioned, we are at the heart of a research organization. So everything we do, we are looking for what has proven success. What can we show works or doesn't work? And we've done a considerable amount of work specifically around change in AI research. You'll hear more about that from Tim today as we, go through the conversation. But I wanted to call out that this is not only the heart of what we do within Prosci, it's the heart of the confidence, if you will, that we can provide back to our clients because we know, what works and what doesn't work. And then I'll just call out the ways that we work with clients is truly from everything at the front end. So when you're kicking off all the way through reinforcement, if you're familiar with the ADKAR model, you know what that looks like, the importance for reinforcement, and organizations of all sizes. So we've got a couple of examples here of where we've helped drive AI adoption at a smaller firm, United Concordia Dental, more on the, US based digital insurance organization. The ADKAR model that I just mentioned has been a great complement to AI adoption, supporting a greater understanding of the individual change model, all the way through organizations of significant size such as the Microsoft. Most of you are familiar with Microsoft, I'm sure. Four to 50% increase in adoption rates, again, through the work that they do with Prosci. So really the goal for today is to give you a sense of how that work that we've worked on with other clients with could be greater insight and research to your organization. And with that, I will turn it over to Tim. Very good. Thank you, Ramona. Thank you everybody for joining us today. We're gonna look at how to integrate AI to unlock your team's human potential. It's a keynote that I've been able to take to a number of different venues, a number of different audiences, and it really feels like it makes AI accessible regardless of the role we're in or how we're bringing it into our work. So this is the kind of the big two chunks that we're gonna explore, how your AI intern works. And my undergrad's degree is in economics and political science. So we're not gonna get into technically how it works, but enough of an understanding so that we can better integrate and interact with it. And then we'll look at the second half, how to work with your AI intern. We'll give you the Prosci AI integration framework to put you, the human, in the center of deciding when and where and how you're going to bring AI into the work that you're doing. This is who you all are on the webinar today. We had over 1,100 people. I think it was up to 1,300 register. About 80% of you are based there in North America. 40% of you are taking decisions around how to build change capability, muscle, and outcomes into your organization at that leadership level. And financial services and insurance, healthcare, pharma, life sciences, technology, SAS, really broad representation from across the industry. So that's who who you all are. I mentioned this is a keynote that I've been able to bring to a number of different audiences around unlocking your human potential, putting you first by bringing that AI collaborator on board with you. And whether those audiences was business communicators or higher ed IT leaders, nonprofit local or local nonprofit leaders, people in the change management space, high school educators and admins, all kinds of different client organizations of industry and shape and size. The feedback that came back was that this is it made AI accessible for me. Whether I was scared of it, whether I dabbled with it a little bit, whether I was already using it, the perspectives of AI as the intern and the framework for putting me and my tasks first really felt like it gave me the steering wheel of bringing AI into my work. And so I was thrilled, to get to bring this to kind of our broader, webinar audience. This so now I'm gonna this is how I jump into my kickoffs if, my keynotes. If you bring me to your organization, one of your associations, I always begin with, you know, Tim Creasy by name, chief innovation officer by title. This is who I am as a human being because I do think it matters. Father of two boys. They now can both drive a little bit older than that, and so they're driving us crazy, but they are everything for me. I'm obsessed with a band called The Avett Brothers. We follow them around because when you attend one of their live shows, you you feel what cocreating a moment is all about, which I think is what presentations and engaging and and teaching is all about. Love the Marvel Universe because of those two boys up there. I got to surprise them picking them up from school one day dressed as Thanos. It was Halloween and they were dressed as Captain America, so it wasn't totally out of the out of the way. If I had a superpower, it'd be pattern spotting, noting the noticing the interconnectedness that I can elevate for other people to use. The thing I'm most personally fascinated by is how we get ideas out of our head into other people's heads. Because I have all these ideas floating around about how to engage with AI as a digital collaborator, and I can't beam them into your heads. And so I have to figure out how to package and pass them and understand your filters that'll reconstitute it in the way that you're going to if I wanna best connect and communicate with you. And I fell deep, deep into an AI hole, back in q four twenty twenty three. And I'm I'm gonna show you some of the stuff I made at the beginning, not to brag or show off what I was making, but because it would help me learn so much about AI. The first was hyper personalized coloring book pages. And what I learned here is that I could assign it expertise in early childhood development. I could assign it expertise in being a coloring book page maker, and then I could make bespoke pages for my niece who loves unicorns and happened to have a pet rat and did judo. And her little brother was a couple years younger, and so I could tell it, now let's make age appropriate pages for a five year old, and I could put the letter J into a lot of the things that he loved doing. Immediate access to expertise and execution, I have IT ed it up right. And this is one of the most interesting fun ones for me, as I still swear that I saw a tear running down my father in law's eye as he watched both of his grandkids coloring pictures of him taking them on boat rides, which happens to be one of his three favorite things in the entire world. After that, I kinda started getting into making emotive animal portraits. I actually burned them on Black Slate Coaster just kind of as a bit of a more expressive hobby. And again, it's not about the slate or about the pictures, although they are pretty fun and entertaining. It was about learning to work with a digital collaborator. Because there I am thinking, I wonder what kind of images burn great on Black Slate Coaster. And now I have a chat window that I can ask and a digital collaborator that'll go do the research and come back and tell me these are the image attributes. I can adjust those a little bit based on what it is I'm trying to achieve, and now we together have agreed upon a design format that we can start using to create all sorts of expressive animals, like a raccoon that looks like a Boston Red Sox fan in 2004 when they finally won the World Series. I call these emotimals. I actually partnered with my digital collaborator to come up with the name. And again, it's not about the coasters or the picture. It's about the process of digital collaboration. This is a blank panty. I'm a 43 year old white male with brown hair and a beard partnering with AI to create magical outcomes. It's neat that I can do it in the styles of post impressionism, Greek frieze impressionism, pop art, neoclassicism, surrealism. I mean, that's one of the powers is having all of the expertise on the other side of the chat pane. But to me, the punchline here is partnering with a digital collaborator, partnering with AI to create magical outcomes. And that's the mindset that is gonna enable us to begin taking advantage of this capability. We cannot think about AI like an oracle, some sort of all knowing, omniscient, all the answers set there. It's read everything, but it knows nothing until we ask it to do something. AI as the intern is our mindset we need to embrace. We give it direction. We give it context. We evaluate the outputs to come back. It's probably a lot smarter and a lot capable more capable than us, but we are the ones setting the tone for the interaction and what we want out of it. And so that's my you know, there's so much training out there that focuses on how do you get it to summarize an email or draft draft a first draft. I really believe the foundation of AI literacy is the first mountain we have to climb over. How do we help people understand and work with and partner with a digital collab a digital collaborator. It's that cocreation. So that's my first big kinda takeaway that I wanna leave you with. It is the critical mindset that AI is not an all knowing oracle. It is our intern. We provide it direction. We give it context. We evaluate what comes back from it, but it's there to help us get stuff done. It's not some sort of all knowing answer engine we go to. So that's kinda just just my foundation, a little bit about how I got into AI, what I learned from it and working with it, but really to arrive at the mindset of AI as your inter intern, not your work. So, we'll start with that kind of the first half here, how your AI intern works. And I mentioned to you my background has nothing to do with technology. So I'm not gonna give you the technical nuts and bolts, but I'm gonna help you understand how it works or see how it works in terms of how I see how it works because I think it'll help you engage with it better. This first piece is just that I really consider it a digital information probability mashup artist. That's what it's doing, is doing mashups of digital probability. The best explanation I ever saw was in the Financial Times. You can go view it here if you want to. It's a visual depiction of how transformers work. But, basically, the way I kinda think about it is every word if I were to think about it, kinda like a golf ball, every word has divots and dimensions on the edges of it. And those all those edges are what gives that word meaning. So the words sea and ocean or I and we or football and soccer, some of the facets of those words are similar and some of them are different, but that's how words get meaning. When we string words together and put them next to one another, because of how each of those words are shaped and because of now how they're sequenced together, additional meaning comes out of that. That's how we create meaning by stringing words together. It's not only does each word have meaning, but by where it is in order it has meaning. And if I turn that into some big math equations and start to read all those about a bunch of that together, start to do some high end probability, I can guess what the very next word is. And so to kind of illustrate this, I'm gonna have you all play along with me. I want you to jump on to the chat pane. Jump over to the chat pane and get ready. I'm gonna do a quiz. Ready? Everybody's ready to chat. Mary had a little blank. Go ahead and chat. Wow. Look at them. They're lightning fast. Lamb, lamb, lamb, lamb, lamb, lamb, lamb, a hat. If you're raised in Western intelligence, the probability that you come to Mary had a little lamb is about a 100. And look it. They're still going. Alright. So now get ready. Everybody get back to the chat pane. I want you to get ready. I'm gonna ask you another I'm gonna have you chat in something. Ready? So here we go. Imagine you've got this AI bot and it's not read any words strung together in the English language, but it's standing outside the library of all the written words. It's read no words in the English language yet. We ask it Mary had a little. Go guess. Go type in. What would it guess? Mary had a little? Yeah. Headache, money, pretty time, tea, anything, right? The probability is one in as many words there are to guess that it's gonna get lamb. Very low probability. But what if we gave it one book, or 10 books, or a thousand books, or a 100,000 books? We give it the entire library, and now the probability goes right up to Mary Had a Little Lamb. Alright. So now hold off on your hold off on your chat because this is where I think the context is gonna come to life. So we give it the whole library, probability goes to a 100%, it gets Mary had a little lamb. Now what if we only gave it the medical wing? Instead of giving it the whole library, we only give it the medical wing. Mary had a little contusion, tumor, cough. Yeah. Exactly. Tummy ache, liver. Yeah. The probability that it gets lamb isn't gonna be there. It's going to guess something else because of what we happen to give it to read. Now so, yeah. It's gonna guess all kinds of things. Now, what if we only gave it a 100 books out of the sixteenth century English history wing? This is a Mary Queen of Scots joke. Mary had a little son named James. Mary had a little time to get ready for the uniform for the Mary had a little crown. Yeah. Exactly. It is a digital probability mashup artist that's doing math based on what it's read and what we tell it to do. So if you have problem with bark, are you gonna send it down to arborist section or are you gonna send it down to animal behavior section? It matters. That's what shapes the probability of the stringing together of words it does for us. So it's a digital probability mashup artist. Its currency is richness of language. And so I call this, you know, richness of language is the currency of Gen AI. I'll also offer this up as unlearning keyword ease because for as long as up until about thirty years ago, we spoke in full sentences. We put a bunch of words together because they all had meaning. And if you tried to use Google thirty years ago or twenty seven years ago, twenty five years ago, you would type in, where's the best place for me to find handicap parking in public garage in Downtown Boise? And Google wouldn't have had any idea what to do with that because rather than speaking full sentences, it spoke keywordes. And it taught all of us how to speak keywordes. One of the very few things I'll brag about is that I got to be a really good Googler. Raise your hand in the chat pane or give a chat in. Were you a really good Googler? Oh, for sure. You got good at stringing those words together. Right? And figuring out what, in order to get the best likely string of web pages. Now you get LLMs and you go type in Boise downtown public parking garage handicap because that would be a good string in a Google search, and your LLM falls apart because it's taking us back to using full strings of language to communicate. I took four years of Latin in high school from doctor Luis Lopez at Grand Junction High School, never learned to speak a lick of it. It was really hard. But it helped me understand the construct of language. Because the wild thing about Latin is like the any noun, the noun boy, is actually spelled five different ways depending on how that noun is being used in the sentence. Is it the subject? Is it the indirect object? Is it the direct object? And so we would build these Reed Kellogg diagrams. Does anybody remember these Reed Kellogg diagrams? But essentially, these are ways for us to you diagram out how words are creating meaning by how they're getting strung together. And that's, the foundation. It helped me understand how we create meaning out of words, which has helped me engage with generative AI in a new way. I've I've always been a fan well, I always enjoyed playing around how you can change entire meanings by moving prepositional sentences around. I also got frustrated by people who didn't pay attention to the tightness of messaging that is getting created by the strings of words that they are putting together. The successful CEO sings with the executives in the elevator on the way to the Top Floor, Move the phrases around a a little bit, and we have a completely different meaning. So Wittgenstein said the limits of my language mean the limits of my world. And I think that actually holds true in this generative AI space in a couple of different ways. The limits of the language it has read to build its understanding limit its world of understanding, and the limits of the words we use to engage with it limit how we can can can work with it. So I started to really understand, as a digital probability mashup artist and what it works in is richness of language, there are a couple ways I can start to play with it. And this is one of my really early experiments. Hopefully, you start to see kind of the applicability here. I call this text flexing. Text flexing. So I'm gonna paste my long form bio that marketing approved. Here it is. As a business editor, I'd like you to transform it based on particular guidelines I give to you. Now we always get worried about AI hallucinating. What I'm asking it to do here is take a chunk of my words and bring them to life in different ways. Because if I'm applying to speak at a at a conference, they might need it in less than a 100 words. Do you know how long it takes to get that big bio into less than a 100 words? It's not easy work, but now it's like that. Or what about a 140 characters? Or what about 50 words appealing to executives? Because it's a MIT a digital probability mashup artist. So once I tell it appealing to executives, it goes and reads to understand what strings of words executives like to see together, and it can infuse that into my bio. What about 80 words appealing to a project manager? A 150 words appealing to scientists? What about 75 words in a bit more of a casual tone? You know, maybe I need it for a different kind of engagement. Maybe I'm going to support Prosci, up in Montreal, and I need it in Canadian French. Maybe I am going down to Mexico City to support Victor, and I need it in Spanish. Maybe I need it in 60 words like a proud mother-in-law would say. Its ability to text my flex that, my flex my text that I gave it is one of the powers that it has. Now if you look at the bottom of the mother-in-law one you see, he's not just a brilliant at his job, he's a true inspiration to us all, which is proof positive that AI hallucinates. Because I have never heard anything like that from my mother-in-law before. But, you get the point. And once we got imagery, I could actually turn it into iconography that would represent my bio. It's not making stuff up. It's asking my intern to take a chunk of text that's already been approved and bring it to life in a lot of different ways. And it can do that because where's my computer geeks out in the room? My IT geeks in the room? Yeah. We got some binary code on the screen. That's my bio that's my bio in binary code. And once I can boil it down to binary code, that digital mashup artist can do whatever I ask it to do to it. And that's ultimately how AI is playing, mashing up zeros and ones. This is the framework I use when I think about how I engage, you know, because, like, there's a lot of different things I wanna ask them to do. And I think this is it will bring that AI intern mindset into a prompt engagement framework. So there you are. You're the commissioner of the work. You have something you wanna get done, something you wanna learn, something a report you wanna generate, something you need to edit. You have a task, an aspiration, a goal, a challenge. You have something you need to get done. And then you've got your AI intern. And they have biases and a base that they learn from. They have capabilities and system prompts. And I actually think about each of my AI tools as a different one of my interns. I have about four or five that I'm using regularly right now. I use m three sixty five Copilot for my work stuff. I use Kaya for anything change management related. ChatGPT is my go to for brainstorming and a good bit of writing because I built a slash in my voice prompt into GPT now. I've really leaned into nano banana for, image generation. So I've got a bunch of these different interns. They all have skill sets. I need to define the canvas and put paint on the palette if I really wanna get good output from this, digital collaborator. So think about these five examples. Write a letter. That's a pretty poorly defined canvas. Write a one page letter. It's a little better definition of the canvas. Write a one page letter to my HOA, my homeowners association who usually rule with an overly bureaucratic hand. Write a snarky one page letter to my HOA. I've put even more paint on the pallet. Go do some research on height restrictions, on holiday decorations in my neighborhood, and then write a strongly worded one page letter to my HOA to accompany a voided check. You can imagine what each of those five prompts would have generated in terms of the output. And if at the beginning, what you wanted was that snarky, well researched one page, and all you asked for was write a letter, you might say, look at all the work I have to do now, and I didn't have any idea what I wanted. But you didn't tell it what you wanted. You didn't define the canvas, and you didn't put paint on the pound. So what is there to do in San Diego? We might go type that in, and we might be like, oh, wow. That's neat. Look at the response that came back. But if you really want something rich and personalized, add some levels of definition. I'm going to San Diego for the weekend. We're staying at Mission Beach. We have 16 to 14 year old boys. We're going to a concert. It was the Avett Brothers, at Jago's Park on my partner's birthday. We fly in Friday out Monday. This is what we like to do. When you add paint to the palette, you get richer outputs in return. So instead of from what do I ask it and how do I prompt it, how can I better define a canvas? How can I put better paint on the palette? And the outputs are gonna blow your mind. Write me an email. Write this type of email. Write this type of email to this audience. Right, this type of tone of email to this type of audience, right, this type of tone of email to this audience in order to achieve this. Can you add in type, audience, tone, goal? And whatever your the thing is you're working on, can you add on layers of paint to the palette to help that thing come to life? My third piece kind of just in terms of how AI works is around these evolving AI capabilities. And Ethan Moloch, if you don't follow him already and you want to learn about AI, Ethan Moloch has gotta be one of your follows. He always every so often here releases these which AI to use now and updated opinionated guide. So you can tell he's clever. This one here released at the beginning of the year, and he gives you a list of all the different tools that were out there. It was all the way back at four o and the reasoners. But this line right there is the thing that really got me. Execute code. Generate images. And it got me to start thinking about AI differently, that AI really is a bundle of capabilities. It's a bundle of capabilities for interacting with zeros and ones. Back in 11/30/2022, we got the very first set of capabilities, which was I'm gonna call it Converse. And, basically, what it could do is I could type in a string of words, and it could extract the meaning out of that string of words to understand what I wanted, and then it could string words back together that felt like they matched what I'd asked for. That's what it's doing, extracting meaning out of a string of words, stringing words back together that feel like they have meaning to me. That was that very first capability. Then we got the ability to see a document. I can upload a document. It can read and make sense of what's in that PDF. Got the ability to chat even better. The ability to access the web. So now instead of just the library it read when it learned the first time, I can tell it go do some research and come back to me with a brand new set of zeros and ones to make sense of. It could then learn to analyze data, to execute code, got to the point where it could see an image. Because when we looked at my binary, my bio and binary code, every single picture I've ever taken on this phone is also binary code. It's just zeros and ones. So any image, it could start to make sense of what's in that image based on the zeros and ones ones underneath. It could turn noises that we are making into the words that we are saying and then understand what those were. Then I got the ability to create an image. This is the very first one I made. My son was very hard to get out of bed back in a couple years ago. And so I dropped him off at school one day, pulled over in the parking lot, paid to buy five tokens to Dolly three, and generated this art cartoon alarm clock that looked like it had just gotten beaten up by the kid that was waking up in the morning. Got the ability to converse with a voice, and then latency got to a point where it actually felt okay to talk back and forth with it. You got the ability to apply, logic and reason, the ability to see what a camera sees, the ability to create a video clip. And and and, again, my timeline starts, stops at the beginning of 2025 here, but then we got into the agentic frontier, the ability to call tools, work across different platforms. This is what AI is. It's this bundle of capabilities for taking in digital information, doing stuff to digital information, and kicking out digital information. And then those capabilities get bundled up into the tools that we use. So whether and it doesn't matter which tool it is. You have access to all different kinds of audio tools, video tools, your general productivity tools, your chat bots, the agents your company is making available to you. All of these are uniquely bundled capabilities of interacting with zeros and ones. This is a shift I've been thinking about a lot lately here. And so this is kind of the first time I brought this forward to a big group. But in previous digital transformations, we taught our humans how to align their work to the screens and fields of the tool? How do you take your work and your tasks and bring it into the screens and fields of the CRM, the ERP, the PLM, the ABC? Generative AI is totally different. We're equipping people with a set of capabilities that they have to figure out how to infuse into the work they do, into their most important and most mundane tasks. And this fundamental shift, I don't know that I quite understand exactly what all this means, but I do think this flip of the script, rather than humans bring your work into fields and screens, it's here's a bundle of capabilities, humans bring it into the work you do. That is the flip that we're trying to figure out with AI adoption right now. Here's kind of a fun cartoon. This was a one shot prompt for non nano banana three when it came out just a couple weeks ago, and I was really starting to play with it. So a single prompt to create a multi panel cartoon describing my observation about how we have fundamentally shifted the orientation of digital transformation. And boom. There it is. So that's the key takeaway from this first piece about how your AI intern works. That, richness of language is the currency it works in. It's ultimately a digital probability mashup artist, and it's a bundle of capabilities for engaging with zeros and ones that we get to infuse in the way we do work. So that's kind of the first half. I wanna bring Ramona on real quick just to, say hello to the group. I know that we are actively stepping into supporting clients down this path, and we did have an opportunity for people to reach out if they wanted more more support. Absolutely. So for those who may want some additional information potentially on the, AI doc diagnostic I referenced before, again, an an opportunity to understand how you actually leverage proven solutions, if you will, with the organization. Wanna make sure to give you a couple of options today. So if you would like to have a member of the pro side team reach out, you'll see an opportunity in the poll here to answer with yes, please reach out to me. Or b, you may not be the right person. So potentially there is interest, but, how do I direct you to someone in my organization who's leading that implementation, if you will and or not today, but please keep me updated. We, as I mentioned, have a number of upcoming webinars and continued conversations we'll be having around the topic, and want to make sure that we have a waste of waiting, make sure we can come back to you. So you'll see the poll here. Awesome. So there is a little tab that says poll next to chat. You can click that and answer the poll. I also see a number of people chatting in, and I know Rocio and team will curate those when we get on the other side as well. Alrighty. So that was kind of our first half, was how your AI intern works. The back half is gonna be how to work with your AI intern. So if we start to understand it as a digital probability mashup artist, how do we step into working with it? And people are talking, I I mean, what's what's AI gonna do for you? What's AI gonna do for you? What's AI gonna do for you? And I I kinda got bent out of shape. We're asking the question wrong. The question should be, what are you going to do with this new bundle of capabilities for engaging in any of the digital information work that you do? It's putting you, the human, in the driver's seat. And so this is what I've really been building out and where I really wanna lead, this keynote and then also where we're taking our clients. There's this whole bundle of capabilities that make up AI. It can suck in digital information, kick out digital information, do a bunch of stuff to digital information. I'm actually doing some writing right now about AI as a bundle of capabilities, AI a a b c. But that's what AI is. And then you have all of these tasks that make up the work that's happening in your in your work based on the industry you're in, the geography, in your job role, your aspirations, your goals, the challenges, what's happening right now. This is the opportunity to find the unlock. It's where is the intersection between the tasks we're doing or want to be doing and the capabilities AI provides us. So Jason Snowin, our growth COA, very early in the AI journey, introduced me to this website. There's an AI for that. And I'm not pitching the site. It's basically just a library of AI tools that are available. But it had this button on it called job impact. And and so I went ahead and clicked it, and that's what fascinated me is there is a whole list of job titles, and you could go click on any of those job titles. And what popped up was a list of the tasks that that job engages in and a list of the AI tools that were emerging to help with that task. And so I clicked through all sorts of the different jobs that were in there, and this was kind of a solidifying moment for me that task is the unit of impact. Task is the unit of opportunity. Task is the unit of disruption as we step into this AI space. How do we find the tasks that we can elevate and integrate AI into? And so we are probably all familiar with the notion that you can teach a man to fish or you can give a man a fish, feed him for a day, teach a man to fish, and feed him for a lifetime. When we give people prompt libraries, kinda copy and paste use cases, that's essentially like just giving people fish. The AI integration framework is teaching them to fish. It's giving equipping them with a framework that they can use to make sense of their own personal integration of AI. And so this is the way we lay out the integration framework. We've got three buckets of tasks. The first bucket are what we call my work. That's the human exclusive aspects and tasks and parts of your job. At the other end of the spectrum, we have for me work. That's the stuff that I could fully automate, the tasks that are completely automatable and hand offable. This latest anthropic study that just dropped last week had about 20% is what their coders were estimating. They were able to move into the form we work right now. And then I have what I call the magic in the middle. And that's where if we can bring that AI digital collaborator to the table with us, we can get our work done at higher quality, in less time, with less mental strain, and with more enjoyment. And so those are the three buckets if we were to kind of visualize how this works. And I'm a huge proponent of task before tool, task before tool, task before tool, especially with this AI space. Many of our previous digital transformations, we threw the tool in and aligned tasks to it. In AI, we've got to lead task first. So we weigh all the tasks out on the table and we start to sort them out. Which ones take presence and passion and improvisation are really part of the me that I bring to my work. It's the stuff that I love doing and it it's where my values show up in the work I do. That's the my work stuff. What is really rule based, routine, repetitive? It's low risk. It already makes me feel like a robot, so I can probably teach the robot to do it. That's the for me work. And then we've got the stuff that goes into the middle. Where could I where where am I doing research in zeros and ones? Where can I use iteration or sample size? Where am I stuck with a blinking cursor and I just need to get a first crack? That is all with me work, and it's the stuff that I set in the middle, and it's the stuff that I can do better, faster with less mental strain and more enjoyment if I can really tap into it. I think the neat thing about the model is it's quite dynamic. Once we understand the buckets, we can move tasks across the buckets. As your own exposure hours go up and you get comfortable and better and explore different boundaries or frontiers, you might move tasks around. As the tool as as you get access to different AI interns. Maybe your company rolls out a brand new one for image creation. Now you've got some tasks you can start to move around. Maybe the tool you have access to just got a big upgrade, and now its capabilities went through the roof on a particular challenge that you're waiting for. So now you can move some of those tasks into the with me work. And you can just decide that you're gonna move stuff around. The other thing I read in that anthropic study this morning was that they identified 27% of the tasks they completed were things they couldn't have even done or imagined doing had they not had AI as that digital collaborator. So there's actually a whole frontier of opportunity and possibility of tasks that come into play as well. So on the left hand side, just to give you a bit more of the details, emotionally complex, ethically sensitive, improvisational, and it's the stuff you love doing. That's the stuff we start to put into the my work. On the right hand side, the for me work, it's routine, it's draining, it's time consuming, it's rule based, it's completely repetitive and repeatable, and there's nothing really uniquely human about the kind of work that's being done there. And agents, especially over the last six months, have really opened up what can go into the for me bucket. And then the with me bucket is where I could use smart support. I could use iteration and sample size. And I keep myself a little what I call my AI intern task list. So these are the eight tasks that I will lean on my AI intern for. Text flexing, drafting, summarizing, expanding. So taking chunks of text and doing stuff to it or researching, brainstorming, analyzing, illustrating. Anytime I have a task that has one of those aspects in it, I'm gonna try to tap on my AI intern and see how they can help me first. And this is an equation I brought to folks for a while. First drafts in a flash plus Smee polish is where we get the unlock. Right? It's that first draft in a flash is what this AI intern can provide to you. You still bring experience, expertise, previous times you've been in situations like this. All that subject matter expertise is important to layer on top, but it's starting with that initiation. This is how we start to unlock progress. So when you start to think about what goes in the magic in the middle for you, where could you use support in getting started? You know, just getting off the off the off the line. Inspiration or maybe iteration. Instead of giving me 10 in a one headline, give me 10 headlines, and let me pick and choose and meld those different outputs. So we call this an AI integration map. We start to build them out for different job roles or different people, but it's a thoughtful, human centered approach to deciding when and where we can bring AI into the work that we're doing. I'm gonna show you some examples. These are gonna be, eye charts, because there's a lot of words on them, but it's to give you an an idea of the breadth of what these could look like. So I always start when I do these presentations with my job. Like, here's my job, chief innovation officer to change management research and professional services firm. And because people are like, Tim, aren't you scared AI is gonna take your job? I said, no. This is how I'm going to work with AI in my job. There's some things like inspiring vision and organizational direction or navigating ethical innovation that'll always be Tim work or it'll always be chief innovation officer work. There's some stuff like managing innovation portfolio dashboards or administrative workflow for idea intake that I could ultimately put into the box and let them do it completely. But then we got the magic in the middle. And I'll focus you in on number three, enhanced research capabilities. The research team has fully integrated AI capabilities into the five step research process to where insights are turning around in seconds and minutes instead of months and quarters. And so just to give an understanding of if I start to look at my job and think about what's the work I do, what can I offload completely, and where is the augmentation opportunity? I mentioned I took this presentation to EDUCAUSE, which is an association of higher ed IT professionals. So we went down to Nashville, and got to present to them. They're not chief innovation officers at a change management research and professional services firm, but somebody in the room was a director of IT infrastructure and operations. And so we have an engine where we can put in a job role and bring to life what are the human exclusive, the my work parts of that job, what about the for me work, but then also what about the with me work. So again, unless you happen to be a director of IT infrastructure and operations, this is interesting but not necessarily relevant. What about global communicators? Because we got to take this presentation to the IABC Global Conference this past year. And so instead of a room of IT higher ed professionals, it's a room of international business communicators. But I know that someone there is a global director of enterprise communications. We can take their job, their context, the tasks that make up that job, and start to sort them out into the my work, the with me work, and the for me work buckets. I live here in Boise, Idaho. Was able to do a presentation for a group of nonprofit leaders. They're having a hard time right now. And I was thinking, if I can help these nonprofit leaders by understanding how to work with and through an AI intern, they might be able to have greater impact on the community. So I can take, What about a nonprofit leader in Idaho navigating turbulent environment? How do we help them start to understand the surface area of AI automation and AI augmentation so that they can achieve what they're setting out to achieve bigger and better and faster than they had been before? This is the Better Foundation. It's a philanthropic foundation in the state of Colorado. They give scholarships to high school students who are going to college in Colorado. It's actually the way I got to go to college. And I've stayed connected with them over the years. I got to present at their annual trustee meeting, which is actually at a high school. And so we presented this. What about the integration map for trustees at a private state based philanthropic foundation? And then also what about for a twelfth grade social sciences teacher, and service learning coordinator? Because I was at Grand Junction High School, and there were teachers and administrators in the audience that day, and I wanted them to start to understand how if they had a digital collaborator running alongside them, they could make more magic happen in the classroom. Marrying the capabilities with what it is we do is the essence of unlocking AI. This is AI literacy. It's not telling people how to use it in this document application or in this spreadsheet application. It's helping people understand when and where AI can augment and bring their work to life. And here's one for the change management practitioner. Because there's a Prosci audience. We often have a number of change management practitioners in the room. What aspects of being a change practitioner is that human exclusive stuff? And I'll tell you what, that's the stuff that probably drew you to change management. It's what brought you to the people side of change was that cultural alignment and adaptation, facilitating high stakes meetings, building trust and empathy. You now have a digital collaborator that you can tap into to help you get done all that time that's you in a screen, planning, prepping, drafting. All of that can be amplified to give you more human time. So that's the Prosci integration framework. My work is around the stuff that requires people and presence and passion. If it's routine, repetitive, and rule based, figure out how to give it to the robots. And if you're looking for initiation, inspiration, iteration, that's where you tap into that AI intern for that augmented with me work. So that's kind of the framework, the three buckets that lays the foundation for us. We start to flex it a lot of different ways as we engage and support clients, Because once we understand the framework from the Me perspective, we can actually start to extend it to The Us perspective, which is it gets really interesting. We're now going from individual productivity to infusing the organization with AI. Because teamwork, you know, teamwork flows are bundles of activities. They're bundles of tasks where we can start to figure out, are there opportunities to bring AI capabilities in? Project plans are sequences of tasks and activities, where in this project plan might we find opportunities to infuse AI capabilities. And, essentially, departments and functions are amalgamations of tasks and activities to try to drive the organization forward. So I'll give you just a couple of examples that I've brought on on some of these different stages. If I brought it to your organization, the example would be bespoke for whatever the changes in the content in the context are for you all. This happened to be, kind of a private equity shop. So we're looking at the different activities that it takes to complete an acquisition of an organization, sourcing deals and reviewing teasers, executing NDAs and receiving SIMs, screening opportunities with high level financial analysis. And, again, unless you're in the space, these are just a string of activities. But you can imagine how those would be the workflow of executing a deal and acquisition. Once I got the activities laid out, we can now sit down and say which of them are our work. It's gonna be the humans that are doing that part of this work. Which of this is for us work that we could fully automate? And then where do we see the opportunity at intersection for collaboration to drive greater outcomes? Again, just to give you an example of what it looks like to apply and layer the framework onto a set of activities. This is another way we we kinda did it for a group where we take maybe a team or a project activity, like preparing and teaching a new unit. This is for those, teachers and administrators or maybe reviewing grant proposals if you're that state based philanthropy. Take that big bundle of work that we're trying to get done and start to say, where might I be able to bring in my AI digital collaborator? This is why task before tool is so important because we are originating with the tasks and the humans that are putting those tasks together and then deciding how to allocate that workout. So this is I'd encourage you to kind of think about this one. Think about the common set of activities that your team's completing, you know, in a daily, weekly, monthly motion. You know, what of that is our work that requires passion and presence and empathy? It's where we elevate the people part of that work. Where could we start to free ourselves up by offloading the really routine based, and then where can we together? There's five of us around the table right now. We pull up a hypothetical sixth chair. We have a new AI teammate. How can we, as a group of six, drive greater effect? So this is how when we dive deeper with a client, we'll really start to break and bring the, framework to life across the layers of AI adoption that we know are happening. When we did our AI adoption research, we researched these three levels of AI adoption and integration, how individuals are bringing it into their day to day work, how teams are using it to solve bigger problems in new ways that they could never solve before, and then how leaders of organizations are trying to bring it infuse it into the value streams and the way they actually operate as an organization. My work with me work for me work lives at all three of those levels. So this slide just gives you a bit of clarification in terms of the type of work that belongs in each of these quadrants. And then this is kind of one of my more favorite, presentations of it, which is those plain language questions. Questions we could ask and answer and reask and reanswer to understand our AI strategy. And this is what again, copy and paste use cases, copy and paste AI strategies, they're going they're not going to be worth the paper they're printed on. Answering and asking the questions pertinent to your industry, your organization, your current situation, your aspirations, your goals, how work gets done there, that's how we're going to bring this forward. So that's kind of the key takeaway on this back piece, that the ProsIA integration framework gives us a common language, it gives us an understanding, and it gives us agency to let the human design the loop. I often call this my favorite over beer or over coffee conversation because on a napkin, you can sit down with anyone to start to understand their work and how AI capabilities overlay with it. So that's what we covered today. That is the integrating AI to unlock a Human Potential keynote that we, that I've had the opportunity, the honor to put on a number of times over the back half of this year. Hopefully, a lot of opportunity to deliver it again as we go forward next year. I am gonna give you, before we leave, my 10 most powerful words in generative AI. When I deliver this in person, I usually ask for a drum roll. So if you do a little drum roll on your desk in front of you, what questions do you have for me before we begin? And you're gonna write that at the end of your prompt. Next prompt you do, you're gonna write the whole prompt you thought you're gonna have it do. And at the bottom, you're gonna write, what questions do you have for me before we begin? Those 10 words. And it's gonna come back with five to seven questions. And what's what's powerful here is by asking those 10 questions, you've pulled out the chair for your AI intern to come sit next to you and help help you and work with you. So you've opened up that AI intern mindset simply by asking the 10 questions, and there's gonna be about five to seven questions that it's gonna ask you. And you're gonna add the detail, and at the same time, you're gonna be thinking to yourself, woah. It was just gonna make up the answers to those before. And the output you get because you added the right paint to the palette with those questions and the answers you're providing back, that's the difference between just kind of general generative AI outputs and mind blowing AI outputs. And I leave every one of my keynotes with this phrase, Ancora Amparo. Allegedly, some of Michelangelo's last words. He's 87 working at Saint Basilica. And they translate loosely into yet I am still learning. And so here is Michelangelo, one of the most brilliant beings on the planet, on the way out says, yet I am still learning. So I came across that, saying in 2020. It helped me get through 2020. And I also think it's just a great way to embrace what we have going forward, that every moment is an opportunity for us to yet still be learning. Thank you so much for your engagement and sticking with us today. I know Ramonda's gonna jump back on, to kinda wrap us up. Thank you, Tim. That was an incredible session. I know a number of folks are looking to maybe potentially get some additional information. Again, we will be posting the recording, on the website and sending the recording in the next day or two. But that said, if you wanna scan the QR code here, this is a great opportunity to see how ProSight can be helping you get the most out of your AI investments. Again, we're really committed to help organizations drive adoption, whether that's through strategy development. It could be begin thinking about how to integrate the framework we saw today, any number of ways. So please don't hesitate to, to reach out to us through this QR code or just take a look and see what additional information there is available. Thank you all for joining us. Truly hope that was a valuable session for you and have a great rest of the day, afternoon or evening.