 Hello, and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer of DataVercity. We would like to thank you for joining the latest installment of the Monthly DataVercity Webinar Series, Advanced Analytics with William McKnight. Today, William will be discussing organizational change management. Will it hold back artificial intelligence deployments sponsored today by Informatica? Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A section. And if you'd like to chat with us or with each other, we certainly encourage you to do so. To open the Q&A panel or the chat panel, you can find those icons in the bottom middle of your screen for those features. And just to know the chat defaults to send to just the panelists, we absolutely changed that to network with everyone. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now, let me turn it over to Preetam from Informatica for a brief word from our sponsor. Preetam, hello and welcome. Hey, thanks, Shannon, for having me and hello, everyone. My name is Preetam Kumar. I lead the product marketing for data integration portfolio at Informatica. And let me share my screen. Hi, Shannon, can you confirm if you can see my screen? Looks good. Awesome. Alright, so thank you very much for having me here again. So today, I'll be talking about a very interesting topic. I'll be talking about data management for AI, why data management is critical for driving a successful AI or any kind of analytics initiative. And I would be also talking how AI is also critical for data management in terms of how you can scale your overall data engineering, data management, data integration process to drive any analytics and AI initiatives. Okay, so without wasting any more time, I'll just get started. So why is AI important? Because AI gets the most out of the data, whatever data foundation you build, in order to get insights, you need to build a strong AI ML model that can get view insights, which can help drive businesses and gives competitive edge across all industries. And here are some of the key pointers. There are many advantages of how AI helps. But when you when you talk about infusing AI into a data management process, if you're a data engineer, and you're building data pipeline, and you automate some of the processes in data engineering, some of the repetitive tasks, which can help you increase your overall productivity, because you're not doing the mundane task, and you can now focus on developing the business logic, which are more productive. Similarly, the data scientists as well, they can focus more on building AI ML model, rather than preparing or integrating data, which can be automated by infusing AI into it. As intelligence, you can have features like bots, or recommendation engines, which can be embedded in your tool that can give recommendations to you about what should be the next step. You can try this kind of data sets, if this particular data set is not working out here. Also, it enables you to analyze different varieties and velocity and variety of data from coming from various sources. It can be any pattern and any latency, whether it is streaming data, real time batch data from databases, or from application sources, or even the files data as well. And AI enables you to give accuracy as well. By building a strong AI ML model, you can make the right decision at the right time, which can help a retailer to drive successful offers or in a healthcare sector can improve in driving better patient care with AI. Now, to drive successful AI, you need a strong data management foundation. You need high quality data. If you see in the right hand side, the diagram that shows which I was talking about, you need data for AI. You need a strong high quality trusted data with a strong solid data foundation. You can build a strong modern data architectures like data fabric, data mesh, or lake house architectures, which can help in driving a solid data foundation, giving high quality trusted data to your data scientists to build AI ML models. And similarly, by infusing AI into your data engineering and data management process, you can scale your overall data management thing. So if your engineers are building X amount of data integration jobs in a specific time, now they can do more because now they are more productive because they're using AI to drive it. And if there is no AI without trusted data, your models can fail and that can have a very bad and a catastrophic impact across all the industries. As you can see some of the examples out here, chatbots can give you racist tweets because some of the tweets were not cleansed properly. You can have a bad impact in terms of diagnosing patients' diseases with poor models and all. And also in the legal section as well, it can give you with the wrong generative AI models and all. It can have a very bad impact in decision making. And also the cost part as well. You can see that an average cost of poor data quality leads to $12.8 million of loss per year. So how does Informatica helps? Informatica provides an intelligent comprehensive data management solution that can help you ingest data from any source, integrated, applied data quality rules, monitor it. You can help you build the AI ML models with ML Ops capabilities and also operationalize it to drive any kind of AI ML use cases and also generative AI use cases like large language models and all and democratize it for everyone and everywhere. Any user can access it because it's a code-free, no-code, low-code platform with a proper governance and security solutions as well. And this is driven by a Clare engine, which is our AI-powered metadata engine that has certified petabytes of active metadata driving 61 trillion transactions. So this Clare engine helps in driving recommendations to the data engineers, data analysts, data stewards who are using our platform, automate some of their data engineering or data integration tasks and provide insights to them about the jobs which they're running out here. And we have built, we have recently launched a Clare GPT solution, which is our generative AI solutions for data management, and we are infusing it in all our data integration, data management, data quality, data governance capability, which can help drive your proper data management in a very efficient, easy, efficient and cost-effective manner with capabilities like Phenops and all where you can estimate the cost of running the job and how much will it cost, which engine to select for running your job. And that can help you drive strong data foundation for building large language models, operationalizing it, and for driving your generative AI application for all consumers. So with that, there are a few additional resources with this I want to end, wherein we have some analyst paper which talks about our autonomous data management capabilities that bridges the gap between the supply and demand of data. We have some interesting blogs about how the power of AI with data management, you can drive it with the proper data management and also generative AI as well. So watch out for we'll be announcing few new, new features every month in this space. And yeah, feel free to reach out to us if you have any questions. Thank you. Thanks for your time. And back to you Shannon. Preetan, thank you so much. And thanks to Informatica for sponsoring this month's webinar. If you have questions for Preetan, if you have questions about Informatica, feel free to submit the questions in the Q&A section of your screen as he'll be joining us in the Q&A at the end of the webinar. Now let me introduce to you our speaker for the series, William McKnight. William has advised many of the world's best known organizations. His strategies form information management plan for leading companies in numerous industries. He is a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database data lake streaming and data integration products. And with that, I'll get the floor to William to get his presentation started. Hello, and welcome. Hello and thank you Shannon. I trust everybody can hear me. And thank you Preetan. Those slides we're looking really sharp and you got us all excited about artificial intelligence and I want to help us along that path and talk about something that can get in the way if we if we allow it to. And that's the changes that we're making to the organization. So welcome to the 45th episode of Data Diversity Advanced Analytics, organizational change management. Will it hold back artificial intelligence deployments. We meet here every month on the second Thursday at this time. And I am currently developing my topics for next year. So if you want to hear something that's in my sphere of data management broadly speaking. Maybe it'll be a topic that I can get into our sessions here next year. Now generative AI, especially generative AI, it's expected to be I expected to be a gold rush in 2024. This is how I've been talking about it. And I think things have things were hit or miss this year. With IT budgets, even budgets for AI, some organizations spent a lot of money on it. And I think it was well spent. I think they got ahead of the game by doing that, but other organizations were really feeling the pinch. But next year, I think we're all going to see a lot more in those budgets and a lot more for AI. So let's talk about making sure that what we do around AI is successful. Rapid change is coming to the enterprise and we in technology, okay, we're the cause of it. There are hundreds of companies that will be built around an API for something like chat GPT and LLM. I look at, I must look at a dozen new AI companies a week these days. And there is so much going on in this area. This technology, I think is a real, a real keeper. And so we have to take it seriously. Startups will not be able to create the AI themselves. That's left to a few, but they can use the API. So essentially they are AI startups. Nearly every industry, nearly every vertical is being transformed today. Some fast and some slow, but every industry is being transformed by AI. Companies are using these techniques and software and statistical models to make predictions to drive this is, yeah, we know what AI can do and pre tam talked a little bit about that. Many enterprises are starting to use tools of AI. And some of them I'll call, they're using the, I'll call it they're using them accidentally. Maybe not with a holistic plan in mind, but others are forging that plan. And so I want to be sure that when you do that, and even when you just run accidental projects with AI that you are including a healthy dose of organizational change management. So I want to get to what that's going to look like. But first let me talk about enterprise change a little bit more because it's coming in a big way. And this, I don't think we're very well prepared for all the change that is coming, even you and I, let alone our user community and those of those that are in the business areas of our business. Look at these changes. Quantum computing, and that's about to hit. And look into this if you haven't, I think the next generation is going to be of tools is going to be using quantum computing with qubits. That could be any proportion of both one and zero states at once. We've seen exponential speed up and all calculations happening at the same time. So it's great for AI. It's great for data lakes and large amounts of data, big data so to speak. Well, let's look at all these other changes data's on the balance sheet. Yeah, that's coming. All the rules for data being on the balance sheet. It's already here. It's an internally created asset with a corresponding cost for acquiring or building. There's a depreciation cycle and it's utilized in a similar manner, broadly speaking, across companies. So that will impose new requirements on us and by the way, all of these that that lend themselves to an industry. These are all billion dollar industries that are around this slide here. Edge computing and Edge AI. Yes, IoT is really driving AI. We're getting we're doing more at the edge, spreading the processing around sensor based data sources are expanding tremendously. The quantum series is a very important data model that we're seeing. We're seeing an explosion in that companies will operate with millions of key variables updated with each new data point. So the need to store data may be reduced not now, not really in the actionable future. So I'm not saying prepared to purge data yet, but that day will come and that will represent a lot of change using more shared data using more shared database of data that we glean off of new pieces of information. And I think the vector databases are a step in that direction. I know they're creating a lot more data, but they are summarizing that data in a series of bits that's very interesting auto generated pipelines based on global experiences joins by data and context. So all the things on the right here automated data discovery. Yeah, the majority of data jobs will be automated. And that brings up a point about organizational change management. That's a huge issue here, which we will try to address today. And that is all the fear that's out there about artificial intelligence all the misconceptions and so on. There's a lot more change coming a lot of work for a great technologist to be doing for organizations. And I just want to impress that AI is not the only thing here so this organizational change management if you haven't been doing it. Now's the time now's the time. These are use cases for AI in 2024. What we're seeing starting to hit the planning stages for next year's budgets out there with our clients and more in all of these industries in the public sector oil and gas and so on I'm not going to read them all. Maybe you can find your way into this slide in your industry or some related industry but do note that these use cases are both on the defense, you know, preventing for for football analogy here they're, they're on the defense there they're trying to protect the people who are doing fraud detection, they're trying to make sure everybody is safe and doing cybersecurity there's a lot of use cases for that but there's also offensive use cases where we're, we're trying to do like traffic flow management and pipeline etc etc. So these are all things that are part of our business today but they're just going to be exponentially improved. If we successfully uptake AI and to sum it up and to put a put a note out there for you. In terms of change coming, we are at the start now really of general AI, what's general AI that's where the machine has the capacity to understand or learn any intellectual tasks that a human being can yeah that you and I can any tasks. We've opened up that new chapter. The most striking feature of this today is it's a generality, meaning the more general use cases you can give AI and it can be successful with is you don't have to ask it what's two plus three. You can ask it much broader questions all the way to eventually the point of, what should I do for my company today and I being the CEO and taking in all that information so only a few years ago, neural networks were built with functions tuned to a specific task such as translation or question answering and data sets were curated to reflect that AI is starting to have no task specific functions and needing no special data set. So we will adapt sooner or later we will adapt to large language models, just like we have adapted to other technology, but we're the ones that are going to be changed. We're going to be changing the process and that change is going to be more than what we've experienced in our careers today. So unless your process that you're building is 100% automated, you need to you need to team to buy into it, or you're dead on arrival and who you need to influence how you need to influence them. And the reasons why there is the resistance are very numerous. Now a lot of it comes back to work jobs. Alright, I am very much out there documented on the destruction side of jobs with AI I'm not advocating it I am saying that I do believe that there is going to be a lot of destruction hasn't happened yet. It's not happened yet. So, AI can can certainly do a lot of great things it is going to transform success society. I believe that the need for work will be less. And that sounds like a good thing on the face of it. But if we don't take care of the people that are not doing the work that is not going to be there in an appropriate way, there's going to be a lot of pain throughout society and there are, we all know right that there are administrative retail, transportation and manufacturing jobs that are being automated with AI, and maybe we'll get to some form of universal basic income, but I don't want to blow this presentation up today too much. I have another presentation on the future, which I gave earlier this year in this series which you can find when I talk all about that stuff but if your project is not losing jobs. Then your organizational change management is going to be a lot easier. And near term, I have not. Again, near term I have not seen that these projects that are being planned are being planned around job loss there used to be a joke. I've been in consulting now this is my 2050 year in consulting and there used to be a joke that oh yeah we're going to we're going to spec this project and they're going to be able to lay off a bunch of people and that's where they're going to save money. And that was always the voice of saying no that won't happen and it never did I mean I don't think it ever did with my projects and I'm not trying to, you know, bright or anything like that I know it's happened but it hasn't been a lot. It's much better talking to someone one on one about how they can prepare themselves individually for this AI future, how they can revamp their job skills and be much more marketable in this market. So today we're talking about organizational change management. It's going to be harder is especially going to be harder if you're, if your project again is intending to go with this trend of needing to work less in the organization if you're talking to someone that is going to lose their job as a result of AI haven't been there yet so can't say too much about that but I, I would think that a company wants to be transparent about that wants to offer severance transition be respectful and be fair about it. But for those that remain. Let's make sure they're on board with the project and by the way I have no problem with somebody's job changing as a result of AI or any technology that's normal. That will happen. So let's make sure we're skilled up and I think what's going to happen more than job loss is going to be like what we see here from IBM this is was earlier this year right they paused hiring for the jobs that AI can do. Didn't didn't lay off but paused hiring for the jobs that AI can do we'll see more of that, but AI is here, and it is our responsibility to get ahead of it. We are paid for what the customer is willing to pay us for and in the absence of information some people will go to worst case scenarios. And I think a lot of people will are going to worst case scenarios, but it's kind of like the horse and buggy to me, we don't we don't use horse and buggy for for transportation for our business and it's really similar if you're not using AI, you are essentially using the horse and buggy. And, and we know we can't do that we know businesses will not do that. And I'm not speaking in terms of what businesses should do for society, and their people miss speaking more as a scientist in terms of what is what will be. They already have some AI. I mean you probably do with virtual systems automated data processing tools and even call transcription software and perhaps customer relationship management, underwriting and fraud detection as appropriate managing security intrusions automated processing and development on an eye it's slowly but surely and sometimes not so slowly creeping in and it does represent big change, big change organizations implementing AI have recognized the need to make significant changes, and people instinctively don't like change to begin with there is a conundrum right there, and that's what we're going to try to minimize within our organizations. When you add AI it's going to make the issue even worse if you don't get ahead of it. So demonstrate how it can help the company, instead of letting the fear grow. In order to enable cross functional collaboration that's required, provide a more robust and up to date it infrastructure and manage new risks that can jeopardize the trust in AI. So why is there resistance, where is it coming from. It's coming from these things, and everybody's going to be a little bit different change period. Change drives some people crazy it drives all of us crazy at some level right but some people, you don't want to move their flower pot, and that kind of change. Yeah, that's part big part of the resistance ROI concerns. Is this really going to drive ROI for the business or is it a sinkhole for a bunch of money. Is it just a trend now some of these. Some of these things I'm mentioning here are are overreactions and some of them are under reactions. Yes, I will drive ROI if done right. And you know the change part that's maybe an overreaction credibility is a technology credible the terminology I don't understand it. I can't go along with it nobody's teaching me organization and governance are we doing this correctly to business goals. Overall, or is that department over there kind of running rogue with AI and who knows what's going to happen. Or is it aligned with the values of this company. Surely we have values. How is AI aligned with that that's a really good topic to get ahead of focus on process, not on the outcome and competencies. I'm sorry, a focus on process and not the outcome is when you focused on the outcome the process to get there is more flexible so focus on the outcome a lot of people will focus on the process and that's what I'm trying to say. So competencies do I have the competency in this new world. You see, AI driven projects, and I'm going to start talking that way AI driven projects because AI is not a project in and of itself right it's a it's an enabler for the things that we do as a company that it enables automation. It enables the ability to bring in all data to the analysis all that data we've been leaving behind because it just wasn't that important. Well, now it becomes more important because we can actually process all the data of the company. Our data leaks are blowing up as a result. We're going real time all the time. It's not not a batch environment anymore. I can't remember the last time I was spec to I spec to project and the requirement was batch. I mean, even when you don't have a immediate real time requirement. You don't need me to understand that it may get to that. And so we're all specking things in real time now. It's going to be job changes. There's going to be new roles, more in different technology. Oh yeah, we're going to usher in a new wave of technology. It's going to be some new players. It's going to be some big players that get it and move with it and some big players that don't and fall by the wayside this may be the death knell for some some big tech companies out there. It's artificial intelligence and that brings some baggage right artificial sounds not real and I've actually had this conversation with a business person in an organization that we were we were specking an AI project. It's artificial. It's not real. How can I trust it and that sort of thing. So yeah, you're going to have all if you're a champion of AI, you're going to have all kinds of conversations if you do O.C.M. Be patient. Be patient with these people understand where they're coming from. They know things you don't maybe they can, you know, they definitely know things I don't. And I love to learn from them and and them for me. That's how I look at it. AI driven projects require organizational transformation more than just the right data and a good database and good technology. A whole lot more to it now the architectures are getting more complicated not less is probably going to stay that way for a few more years until we see some some great frameworks take over for AI and there's a lot of lobbying to be that great framework happening now in the vendor community. Keep an eye on that. It presents great opportunities but also poses significant implementation risk. The cost of these projects is actually going up. It's there. They tend to be higher than non AI related projects. I think that will change over time it should but currently that's the way it is and counter many risks that are people related. Yeah. That's why we're here. We're talking about those people related risks. So what is organizational change management. It's the people side of change is how to facilitate people from the current state to the future state with high technology adoption and usage. It's something that I've been doing for my projects for a long time, but I just see the need today for it to be done for all projects and really out there and adopt it. So understanding how your stakeholders are looking at the problem and focusing on the people aspect of that activity. Honestly, it's not my favorite thing to do it may not be your favorite thing to do but I gravitate to those things that nobody else wants to do that are required for success and somebody needs to within the organization. And so I'm happy to be part of success that's that drives having stakeholders part of the current state analysis and the solution that's organizational change management. Artificial intelligence requires strong organizational change management. I noticed some people are putting this under DevOps and that's okay. Whatever you classify that works. What you don't want is the project to look like the picture in the background where there was a little hill there and we tried to build a train track through there and we just sort of bull through. And it doesn't work. People experience a range of emotions during the process of transformational change that range from anxiety, fear and depression to acceptance and commitment of the changes to come. So one one quote I harken back to is from Warren Dennis. He said, managers are people who do things right while leaders are people who do the right thing. Yeah, we need leaders need to do the right thing do organizational change management people risk require attention on AI driven projects. If the leaders are not aligned with the transformation departments may feel they have little no input in the change process all the things that you see there. If company leaders believe in the program they will get involved in champion its implementation when there is resistance at that level. That's that exacerbates the challenge within the organization for AI to succeed. Remember, it's about driving culture to culture each strategy for breakfast so you may feel like it's always your project that has to do the extraordinary to get noticed well this gets back to my comment from the power slide. Yes, yes, change is happening so rapidly that there is no cookie cutter to projects, I will, I will come to organizations and they cannot tell me their path to production for what we're doing because it has changed so much. Every project gets to forge a new path to production, in addition to bringing an artificial intelligence and doing the predictive maintenance on the airplane or whatever it is that you're trying to accomplish yeah. There's all that there's all these things that you may not think that you need to do but if nobody else is doing them, maybe you're the one somebody needs to, and sometimes organizational change management falls into that category. Change readiness and organizational impact assessments can provide insights into the people risk associated with the implementation so it's not a bad idea to comb through your organization, look at the org chart, etc. And kind of figure out who's going to be on board, who's not going to be on board and why I'm going to come back to this point in a few minutes for artificial intelligence, what do we want out of people. Here's what we want, want them to be happy. Okay, but we want them to use the distinct AI advantages, not just keep creeping back to the older ways, we want them to accept that AI is part of the company future, not resistant, but accept that we let them to think, in terms of AI, not just be I not always be I not always we're going to use the we're going to use the this we're going to use that to solve a problem but think in terms of what is it what is it you're trying to do. And AI just very well maybe able to do that better and contribute, contribute how AI algorithms can grow their effectiveness within the organization. AI has always stood on the on the shoulders of human intelligence and it will continue to so people and change. People are all over the board in terms of their readiness for this, and their, their their adoption cycle for this very change and it's this is true for change period. There's a lot of changes in your life. You know, you're going to find yourself somewhere on this range and on the spectrum. You're going to be an early enthusiast of it you're going to be a visionary but where a lot of people fall is right around so called chasm. It's a lot of work that needs to be done around the chasm in an organization for AI. Once you get studies have shown that once you get people beyond that you get make them becoming a pragmatist somebody that's somebody that is being pragmatic about it is accepting AI is trying to do some of the things I had on my prior site thinking about it. Trying to think about it in terms of replacing some of the be I were doing etc, and then you can kind of get them on the path but there are stages of change that people go through as they work their way to being a complete supporter. And these are professional stages, it's kind of like the personal stages, which you may have seen the start with grief and denial anger depression bargaining you know, you know that one that we all go through for a lot of things right. People will go through these things for business change pre contemplation failing to recognize the need for change everything's fine right contemplation seriously considering it preparation making small changes that's a great sign to see. You see some little acceptances going on and finally direct action towards the goal some real thinking, some real contribution. Education is the key to all of this is the key to organizational change management. We should educate in many ways, early and often and dispel the misconceptions and this concludes everyone focus on explaining why the changes being made instead of emphasizing the technology, make it a shared process get people involved. These are the misconceptions that people have about AI that the change is going to happen over again, some of these are fears. Some of these are irresponsible. Some of these are under underestimating what AI can do like the last few here right AI is just another gimmick notes not AI is too expensive. So it's not things are expensive right AI is too complex will figure it out AI is not reliable. It's reliable enough to do many things right, and it's not ready to take over the world, or any of that stuff, by the way, right now. A little maturity cycle for you here you got tactical tools to automate and save time and money that's where a lot of people are today, and all the way to creating autonomous agents. Yeah, that's running the running of the business with just the data. Wow, organizational change management for AI project let's get into some of the tactics now I definitely always want to leave you with things to do action items, the things that you can do today towards the end. AI is definitely the next tech hype cycle. I'll give it that. And everyone's kind of glomming on to this because tech really needs a savior right now. Cloud is there cloud is a savior yes, is it enough well look at mobile cloud social big data they these raw hits but we've had some misses to in terms of hype cycles web three whatever that was crypto and VR work in their way but didn't quite land the way some expectations were about that I still give it some time though, but how much of this organizational change management that I'm about to give you, do you need to do. We'll give you make yourself a little spider graph like I have here. And these are the big questions. And I think you'll find with a lot of AI driven projects that the, the spider is almost out to the edge, all the way around will business process change. Almost certainly high number of stakeholders with the potential to be on supportive, maybe maybe not but likely how widespread organizational implications, some of the things that we're doing with AI pretty widespread our jobs changing. Yeah, jobs are changing is your organization not used to change well maybe maybe not. How's your organization been recently with changes that it's done outside of AI. Bring that to bear on this. So, I'm going to give you three big things to do. And again, education is the key these all kind of fall under that umbrella of education the first is stakeholder management. This is at an individual level. Who's on board who's not. Sometimes people ask, well how many stakeholders can we manage this way. And individually, well, I'd say about 25. And then we get into some of the other ways that that that we manage people but some of the desired results here are business leaders and staff support the changes. One question I like to ask is, what do you not like about your job. And why do I ask that question because that question is typical of what AI is addressing today. And a lot of the things that people don't like about their job, the things that just sort of kill time and replace that fear with positivity gain respect by listening stakeholder management process activities, focus on identifying the stakeholders, assessing the stakeholders and then influencing the stakeholders in the way that is appropriate for them. Now, everybody's going to get the broad communications that I'm going to get to, but these are individuals that are really key to the success of the project. And I've been on projects. Maybe you have as well where one out of the 25 people cannot be on board with it and the project sinks. And that's a shame. So let's make sure that these key stakeholders, whether they're 25 or five or 35 that they are, we get them on board very individually. And these are some of the dimensions that we want to analyze them by today as we step into our project. How do they prefer communications. And we've just added a new wrinkle in the past year or two, haven't we, when we're mostly working from home or a lot of us are working from home, right. So that removes some preferences, and it adds some others so keep that in mind what are the key issues where are they currently with this project if you were to have to wager a guess and we're doing this in our war room right this isn't something we're out in the, you know, the main lobby, putting on the whiteboard right we're doing this in our war room we're evaluating these 25 or whatever number of people, and are they read yellow green on this project. Green stop yellow in the middle and G for green, which is go. Right. What is their, what is the desired status, obviously it's at least yellow for these top stakeholders green is really the desired status though, but how do we move them up. We'll talk a little bit about that what is that what do we desire them to do for the project what is their role. What are the actions, what are the messages that lead to those actions what's our action plan for that person. And sometimes, sometimes I will assign team members with the appropriate skills to buddy up with these stakeholders and make sure that they touch them. You know what I mean, at least once a week with messaging about the project that we want them to have and by inundating them with with our message, they can they they have to relate to it right well maybe maybe not but at least it helps. And let's not forget our early adopters we focus so much on those people that are on board date that are not on board excuse me. But the early adopters we got to take care of them to, we have to continuously solicit their feedback they'll take them take them for granted, without communication and progress enthusiasm can lead to frustration. Spread the decision making around to them, make them valuable for being early adopters make them stay in the green zone. Now we're also going to give the organization some broad communications. To build organizational awareness and commitment to process the technology changes this addresses the big changes. Our desired results are total company commitment and support to implement the change vision this addresses the big changes, the key objectives desired results and alignment. These are outcome based communications. We want people to think this way. I am connected to people on the AI journey with me. I'm not alone. There are others at my place with AI that we have forums we have ways to get together. We're on the same page and we're growing together with this thing so it is important to connect people, which is harder now that a lot of us are working from home but still necessary. My leaders have a shared vision for AI. We want them to know what that vision is. I think there's enough there with AI that it should get the executive boards attention. And the executive board should be starting to create visions for the company in regards to this set of technology called AI. And it's that important. There is a roadmap of implementation. We see the projects coming up that are going to use AI. We know why we're comfortable with that. And I can leverage AI. I can help the company with AI. Wow. That is that is the goal right there. I love it when my users, people in the business otherwise are coming up with ideas to leverage AI. Now, this is a big topic that I do not have time to explore in depth. AI ethics, but I can tell you that it's a good thing for organizational change management for people to know that we're approaching AI ethically. And it's the right thing to do too, by the way, of course, right? Responsible data collection, responsible development, ensuring trustworthiness, having explainability there, eliminating discrimination. We know that's a creeping problem with the data that we're using for training AI models. And there are things we can do about that. We want to be doing them. We want to be making sure people know we're doing them and that we're doing all these things right here, privacy as well. All these things. So maybe this is a topic for next year. I don't know, but AI ethics helps your organizational change management. The other thing that helps is organizational training. Remember, I said education is the key. It's the key across the board. One, two, and three are all about education. Three is about it, right? Organizational training. Training the effective company team to use the new business processes and align the roles. So we train, we don't expect people to know just because we train them on the key objectives, the desired results, and how they align with the company. Now the team becomes equipped with the knowledge, skills, and competencies necessary. Now, one thing I used to do for my projects as I used to hold brown bags. And now I'm kind of dating myself because I don't know if this is still a concept anymore in companies now that everybody is remote. But I know that some companies are still doing forums like this and I applaud that. That's great. And AI certainly lends itself to doing something like that, where bring your lunch. We're just going to talk about AI outside the context of this or that application. But let me help share some information that you need to know about AI and that alleviates some of the frustration and concerns. So where does all this OCM come from? I've given you three key things to do now. Is it embedded in a project or is it centralized? So there's no one big right answer here, just needs to get done. I'll give you a recommendation here in a minute. Let me make sure you understand. Embedded in a project to support that project, it's focused on the project. There is a tendency with those situations to neglect OCM because there seem to be more pressing issues in regards to the project, right? Getting it all done, getting it to production in time, making sure that it works. All this and that, right? Of course. But I say OCM, we don't go to production without and OCM is just as important as those things. Or you can do a centralized SWAT team like we do for the PMOs in many organizations, like we do for security in many organizations. So they support multiple projects. They will be loaned to a project or two or three at a time, but they will be making sure that organizational change management happens. As a consultant, usually working on a project. That's not something that is great for me from that perspective, but I do believe it's great for the company. So I do like that for some OCM. So I think the real right answer is to mainly orient it to projects, but also have executive or centralized or maybe part of data governance SWAT teams that make sure that the organization knows how to do OCM. You cannot, you cannot just tomorrow say, hey, everybody, now let's start doing OCM as part of our projects and walk away. It won't happen. You have to carry that water to the projects, make sure they understand what it really means and maybe even adopt it for the project, right? And you need some help for that. It doesn't all come from the project team. The project team does the last mile for sure, but I think the centralized team can be very effective there. So to sum it up, these are some of the suggested work products and some of these are going to be program specific. Some of them are going to be release and project specific and some will be both. Okay, that's up to you. But stakeholder management, analyzing the stakeholders coming up with a number I threw out 25 whatever it may be stakeholder management plan. How are we going to manage them? What are the impacted job roles? And what are the job changes that are coming the sooner you know this the better the sooner you know it and and you act on it. People don't feel like they're getting hit with an anvil in their face with the project when it quote unquote goes to production right. Give them a job transition plan from from A to B from where they are now to where they need to be. And then second one was broader communications. We're going to broadly communicate this and there are many cultural ways which you can do this poster in the lobby poster on the internet or you know posting on the internet site. The executives say it in the all hands, you know as part of this or that logo what what have you make it important make it that important. And then we have organizational training. Who needs training, develop the curriculum materials, do the delivery and evaluate your effectiveness of that training. So in conclusion, AI is here, and it represents big change. OCM is essential to organizational transformation to AI. Choose the applicable work products. Education is the key. Don't push it off to the end, insert the work projects into the plan put them in your backlog. So they get pulled off an action in your sprints focus on stakeholder management, broad communications and organizational changing, making that soft or seem soft, making it hard making it a real tangible part of an action oriented framework. AI powered products can and will continue to provide a significant competitive business advantage for enterprises. Investing in AI can put a company steps ahead of the competition but in order to fully realize the productivity and efficiency games that AI promises. All users and stakeholders need to be on board with the changes. If the leaders implement effective organizational change management. They will see the benefits of AI throughout the entire enterprise with little resistance and great reward. But remember, even if you do all this OCM, it looks like this. Your plan and reality as long as it's trending upwards, I'm okay. I'm happy with that. I know projects have bad days, bad weeks maybe. But we just want the trajectory moving upwards towards the end game. And that if that is if that is you, you have to be happy with that know that that's reality, especially in these new areas that you're breaking into AI, but you can do it. Just bring organizational change management along for that ride now that has been the formal part of my presentation I turn it back over to Shannon for your questions. William, thank you so much. If you have questions for William or pretend feel free to submit them in the Q&A, and just answer the most commonly asked questions, I will be sending a follow up email by end of day Monday with links to slides and recording today. So diving in here, a couple questions pretend for you that came in. This Informatica data management integration and quality center use AI to prepare data for AI. Yeah, so that's a great question. So we have a clear engine which I showed in my presentation. This is our AI powered Merida engine, which drives lot of our automations in data integration. Not in data quality, but in data integration definitely in building your pipelines gives recommendations, also in our catalog as well. And very soon, we will be launching the clear GPT which is in private preview that will that declare co pilot stuffs, which I was talking about that will be the next level where we are infusing some of the API is of the clear GPT, the LLM stuffs into our data management tool, which can really, really simplify the overall data management across. So but it's still work in progress. So keep it so keep an eye on it. We'll keep posting more stuff supported actually in the future. Perfect. And one more quick question for you here. Does Informatica offer a data observability tool? We have data observability in IDMC for each of our services for data quality for our data integration for our data ingestion when you can monitor your data data pipelines. If there any failures that is happening, you can take remedial action out here. So yes, we do have but it is across different services out here right now. Perfect. Thank you. And any examples. So William, any examples out there of organizations that have implemented great OCM for their AI projects? Oh yeah, there's plenty I don't I don't know that any have given me permission to use their names. I think of some of my clients, like a big oil and gas company that basically did what I just talked about under a little bit of my guidance. So I know that they did and what they did and they are exploring fields for pipeline development and this sort of thing. Some of the science is a little beyond me, but I know enough to help them in ways of building out their data lighter for the AI that they are doing and it's going to affect a lot of it is already affecting a lot of people's jobs. Again, nobody's losing their job, but people's jobs are changing and it was looking back now that we're two thirds of the way through the project. Now as we look back, I can see where the OCM that we did early and often really paid off because we're not getting the resistance that we very well could have got from the business community and and from all the workers, really as well. So they were touched as well in this process. Everybody was really, and it definitely is paying off and I have other examples of that, where at different levels of the OCM process that I talked about was implemented always to to success, meaning it paid off. Do you have anything you want to add to that? No, I mean, I mean, the William has covered it pretty well actually. Okay. I think I can, I can say from, from a data management perspective, my narrative was that we always talk about like you need to have solid data foundation, which I showed okay, high quality trusted data for driving AI. AI is also equally important to in in driving your data management or data engineering initiatives. So so that is something which which organizations should keep in mind. Because if you're not infusing AI, which means you're doing the same manual jobs, manual hand coding, which takes a lot of time and you'll not be able to scale your, your data pipelines, which can impact your overall analytics and initiatives. Great. We've got just about four minutes left in this slip in as many questions as I can hear you make distinction between AI and specialized program systems. I think some of the functions you've mentioned were more programmed, quote unquote. Hmm. Hmm. I haven't heard that phrase specialized program decisions if I even got that right. I will say that a lot of the O.C.M. that I talked about here applies to all kinds of projects. And it's, it's something that I've been doing again for a couple dozen years in one way shape or form and it's evolved. And it's as simple as it can be and still, while still being effective I've had it more complicated. If you look back me teaching this few even a few years ago. I didn't give you three work, three work product categories I gave I think having like a, it was too much. And we had to simplify to see more uptake. And so that's what we're done. That's where we are. And yes, this applies to all sorts of things. So if you're not doing AI, but you're doing something that impacts the company and its people. I'm preaching and feel free to jump in on any of these questions as well. If you have anything additional to add to them. Yeah, sure. No, I think William covered it pretty well again. And so I'm going to slip in at least one more question here earlier you stated to the AI. Explainability would not be needed. How do you reconcile this for AI explainability as part of ethical AI. Yeah, well, here's the thing about explainability at least in my view. I see, I see a pedal to the metal kind of approach to AI I see that the US is competing with China. Very hard in this area. And I see that as a result of that level of competition or at least I'll put it this way at least that is what is cited a lot by companies as a as a as a reason for pedal to the metal AI. And I think that that is that is pushing any kind of boundaries to the side. Now we did see just yesterday, there was some activity I believe in Congress on this issue of guardrails around AI we'll see where that goes. But I personally don't think it'll, there'll be very much come out of that and that very much will end up being needed to be explained now explain as a squishy word right explainability to the level of what we usually talk about in terms of AI explainability that's pretty deep if you're able to explain it in terms of bits and bytes and how the algorithm works and all this sort of thing, but but explaining as I put it in context of OCM and as we must put it in context for AI ethics has more to me to do with explaining up to the point of and then the algorithm takes over and it has, you know, it looks at these variables and it makes decisions and so on, but without going way too deep with it. I hope that that explains sort of the reason why I said I think that the need for the explainability that we're talking about today will will will not be there. At some point in the future it's just talk now anyway. And, and I think the competition will will push that push that aside but internally in terms of our programs, we need to be explaining things kind of in general, up to the point of the algorithm. Perfect. Well, thank you both so much for these great presentations and commentary but I'm afraid that is all the time we have scheduled for the webinar. And thanks to all of our attendees for being so engaged in everything we do and joining us today. Just again a reminder I will send a follow up email my end of day Monday for this webinar with links to the slides and links to the recording and thanks to Rebecca for sponsoring today's webinar and help making it happen. Thanks y'all. Thanks for chance. Thanks, William. Thank you.