It is great looking at our folks that are joining in the attendee list. There's lots of, familiar faces in there, so great to have some of our partners and customers join our conversation. There's a lot of new faces, so nice to meet you. We're gonna be talking about AI readiness. And we're gonna spend some time actually talking about bit some foundational con up thrown at it because it is actually a very big topic. And then we're gonna talk about some of the foundational elements that you're going to need to be successful at it. But for those folks who haven't met me, my name is Robert. And when I took this photo, I did not wear glasses. And now that I'm a little bit older, I do. And I hate looking at myself, wearing glasses. So I'm going to endeavor not to look directly into my camera. On the Managing director for Interworks, we are a full service data and analytics company. We're global. I look after Asia Pacific. So for those folks that were are just meeting for the first time, nice to meet you. Hopefully, we can continue to chat, after this webinar and and do great things together. A little bit about interworks. Again, I said we're full stack, full support. So we do everything from helping you define strategy and vision on where you want to go on you're going to get there. Actually helping build the solutions, whether it's within data or ETL or data science, or analytics or enablement or any of the necessary things, as well as helping you support those applications and tools and processes. Once they've been billed. And we'll meet you wherever you are. If you have a good strategy already developed and you just need some extra hands to do work, We're happy to do that. If you have the ability to support these products on your own, great. We'll meet you wherever you need consider us to disarm and act the puzzle piece that can help, expand and extend your capabilities. A little bit more about Intuitworks in terms of a bit of the scope. We we were founded in nineteen ninety six. So unlike a lot of consultancies, we decided to play the long game. Most consultancies, particularly of our size, their aim is to get enough clients to become, attractive enough to get bought, and then they get brought into one of the the larger teams. We're really happy doing what we're doing. We're really happy working with our customers. So we've decided to continue to remain independent and free to make the decisions that we think are gonna be in your best And we've had a lot of success. Seventy five at the Fortune one hundred are interworks customers. We have spent a lot of time on thought leadership, And you can see that in our blog. So we have a data and analytics blog that generates around three and a half million page views per year. That is crazy numbers. There are a lot of people out there that geek out on data analytics. And in that twenty seven years of work, we've done we we've worked with around eight thousand customers or delivered around two million of consulting hours. They're just a tremendous amount of success and experience that we've been blessed working with really great people. And we have a slew of awards. One of the ones we're most proud of is Forbes recognized twenty five small giants, and we were one of those, that received that recognition. So that was great. And just to give you a little bit more, these are some of the customers both here within Australia and Asia Pacific as well as around the world, and these are some of the technologies that we work with. But, again, we are we are not a full stack. Let me say this. We do not believe in full stack, technologies, we believe best of breed, which is why we've got such an expansive partnership, ecosystem. And you can see with our clients, We are doing public sector. We're doing private. We are doing supply chain, energy, retail. We've got experience across everything. So we're happy to help. However, we can. I'm gonna get into the, the actual nuts and bolts of this webinar. I'm gonna turn off my camera we're gonna record this. And I I can't stand the thought of my face being posted doing a video for the the long term, and in terms of the life of these webinars is recording hopefully, it also reduces the distractions of my face and the funny expressions I make while I'm talking. So let's go ahead and get started. We're gonna start by building some basic definitions. So what is generative AI? AIA, like I said. It's a big umbrella. We're gonna sp talk specifically about the emerging technology within AI, generative AI, or Gen AI, or GAI that is really making waves. And basically what this is, Is it the ability for machines to create, to generate, and produce original content, and there's an asterisk next to original, leveraging human defined behaviors as data points within the power and scale of modern computing. That is a pretty dry definition. So we're gonna down a little bit. So when we talk about original content and I said there's an asterisk there, the types of original content that generative AI can produce, it could be imagery. That is a clearly fake photo. It could be videos, This is, something that you're going to see. You you might already have seen this without realizing it's Celebrity hawking products on Facebook or Instagram that are not actually hawking those products, you're gonna see a lot more of this. It is audio, used to different levels of effect. I'm a massive Beatles fan, so this was quite exciting to get to hear an aged voice, John Lennon, singing along with his, buddies, I guess also, George. And it's also text. And everyone's really familiar with, with chat GPT and all the stuff that you might be doing to write your self review for your annual, annual assessment or, legal documentation or or the all of those types of things where this text generation is really powerful. We're gonna get back to original content, how that's valuable for us in a second. But let's go through the other sort of green things that I had highlighted in that definition. So human defined, what do we mean? It means that AI, models have to be trained. So we wanna ensure that when somebody interacts with generative AI model, whether that's an internal employee solving an internal use case or an external customer, prospect, or a person, solving an external problem, might be a customer regulator, whatever, that we have predictable outcomes that are gonna maximize accuracy and give a great, a great outcome for that interaction, for that transaction, that event, whatever it might be. And to do that, you need to train these models. And there's several things that you need to do to train them. Here is a very simplified, sort of lifecycle of training, you need to gather as much great data as you can and throw it into the AI so that it could start to read it. You then train it. This is a good outcome. This is a bad outcome, and it will learn this little machine learning on what you're looking for. And then as you sort of work through that, of course, you have to make sure that you're getting good results. You then deploy it widely. You monitor it, and then you retrain and optimize as you go. So, yes, generative AI is gonna be very powerful, but it cannot work in the absence of human beings, making sure that it's doing what it needs to do and really, very carefully outlining goals that we want AI to go and achieve. And we'll talk a lot more about this in just a second. Now the last bit, modern computing. With the ability to replicate, closely or approximate human creativity or decision making. At the computational scale and the speed of modern computing, Generative AI is a very powerful tool, and there's a couple of estimates that I pulled from this particular study from Oliver Wyman. Just in a few years, it's anticipated it's gonna boost the global economy by twenty trillion. And I bet you anything that when we actually get to where we can assess its impact by twenty thirty, that will be a number smaller than what it actually contributes. Cause everything that I am seeing is accelerated year on year in terms of what impact generative AI is going to have. Another factoid that's quite interesting save three hundred billion work hours annually. That is a massive cost saving It's a massive efficiency boost, but it's also something that you're going to have to then consider. How is this going to impact people? And there'll be a worry from your from your work staff. Am I going to be redundant for this? And we'll talk about how you do need to consider the people element in this. And it doesn't replace your workforce. It will replace roles and functions for sure. But massive efficiency and massive opportunity and massive value creation as a result of gender to value. There is no one is saying otherwise. There where there is disagreement or debate, I'll illustrate that for you. But overwhelmingly, the thought is that this is going to be highly transformative to the way human beings work and literally our business environment, as a whole, probably as much as the invention of the the personal computer, the cell phone, the steam engine, you name any of the great inventions of mankind. This is probably going to equal it or rival it. We're talking that scale. Okay. So what does this all mean? Original content, human influence modern computing. Well, the easy definition is is that generative AI, and we're talking about this in a data and analytics context, Generative AI can create and modify and improve data once it's properly trained and deployed. That is very, very useful. Just in that particular context for us as data and analytics professionals. That AI generated data as we have seen can be in a lot of forms. It could be an actual data, you know, fields and records, or it could be an unstructured data. It could generate auto files and all the things that we saw. So very, very powerful. And with good training, generative AI can replicate human side and ability at scale. And when I mean at scale, we're talking if you've got the infrastructure deployed and the compute nearly limitless scale. So the ability to have meaningful engagements with customers individually, globally, or the, the ramifications of this, the use cases are are are nearly infinite, but I'll give you sort of an idea of what the very common GI solutions might be. So, as I mentioned, they can improve, they can, correct, They can generate data. So automatic data curation and semantic layer generation. You are already seeing tools doing this now. It will go and find sectors of bad data. And, again, you're training it to help it do this. You can then tell it to fix that data generate business logic based off of other data sources, link and connect these data sources together. A lot of the ETL tools are building these features or have already built these features. So it is a massive accelerator. The one of the biggest challenges we have, from analytics, if you think about the second wave, which is self-service analytics or analytics over over porting is you're only as good as your data. Well, now AI is getting into that data layer into that semantic layer, and that is a massive step forward. For the very, lowest rung of technical business user to be able to go and interrogate information. Very powerful. As I already mentioned, customer service support, and that could be through multiple channels. It could be via web or chat or app or phone. I get AI generated telemarketing calls all of the time. And it's kind of fun to to interact with them and see how they sort of take my responses and come up with instit processing and coming back with sort of their responses to it that will only get more sophisticated and more effective, less so for the telemarketers. Don't call me. I'll call you. Interpretation of disparate, disparate data types at scale. That's a very complicated way of saying that if analytics or AI right now is like a calculator for your business, you throw in some functions, it gives you back a number or gives you back a report or dashboard. Genative AI can really fully evolve into a central nervous system or the brain or the cerebral cortex. A very easy example. I just did a keynote speech at, a health care conference for data and analytics in Melbourne. And one of the things that we were seeing all over the place as well as what I was talking about is health care and this is true for probably everybody have a lot of different types of data. They've got, imagery from X rays and CT scans and all the sorts things. They've got scans, like doctors, notes, handwritten notes, patient reports, files, all that kind of stuff. They've got audio dictation, all that kind of stuff, videos, maybe it's from, telehealth meetings and things like that. Ai will be able to start to digest all of that Again, at scale, so individual results, as well as aggregating all of this together to see patterns, and then generate, not just here's what we saw, but predicting what's going to happen. What's really interesting about that when you start to think about all the devices that we wear, you know, your watch, your smartwatches with the heart rate monitor, your sleep cycles, and things like that. Let's just say, in ten years, probably a lot sooner, you have the ability to opt in. Maybe your insurance incentivizes you to share your smart device information about your health. Your heart rate, your exercise, all that kind of stuff. And based off of this combination of these different data sources, from your GP all the way down to your devices. You don't get a call saying, Hey, you're thirty five. You need to come in for these checks. It might say, Hey, We're seeing some things that mean you might be two years away from this particular condition. Let's go and tech tackle it now. These are the things that are very much realistically on the horizon. AI empowered self-service and analytics for the enterprise. We are already seeing this in Tableau and Power BI and thought spot and all the other analytics tools out there. I type in a request, and some sort of co pilot generates me a dashboard, or generates me a formula, to go in and and query my data And, again, in software development, they're doing similar things. Tell me what I wanna do. The code is is very easy for the AI to go and look and generate and get you your answer. Managing events and transactions, so any retail business AI is going to be transformative. And, of course, enriched user experiences with increasing accuracy. And we're already seeing this on Instagram, and Facebook and all the social media where they can start to see the things that you are interested in for better or for worse and and making sure that you're getting an experience that is going to be more enjoyable, more effective, whatever. And we as businesses can then think about how we're actually going to enrich internal user experiences so they can find the data or find the analytics or find their answers that much faster. This is just literally a a sampling of what generative AI is going to do, and it's going to be very commonplace in the next twelve to twenty four months. So why now? Why is all of this bubbled up? I mean, I don't think very many people do what ChatGPT was eighteen months ago. But just in a very short time frame, all of this stuff has now become commonplace for Our conversations, our parents' conversations, your grandparents' conversations, it's everywhere. It is reached the sort of poll popular zeitgeist of our common discussion. Well, there's a lot of reasons for that. This is a rough timeline. The very first AI model, came about in nineteen fifty two, and it was a little checkers program. You can see there I've got a whole bunch of different lines. So, like, everything in history, it is a culmination of different factors that all sort of merge together to then create a revolution, like exploring the world, require technological, sociological, and political things. I was a history major before I got into tech, as you can see, that all happened around the same time to create this massive step forward for, our particular revolution. And this is an a an AI revolution that we're talking about in sort of the most strictest definition. So the simplicity of new AI user interfaces, it is much easier for common people to interact with AI and get value. The technological advancement of AI, and we are talking about massive steps forward, in terms of the creativity of the people that are building these technologies, the effectiveness, tons and tons of advancement there. But even if those two things were true, we definitely had to have the next few before this actually could become a thing. The explosion of data generation. Every two years, the global amount of data doubles. That is an astounding statistic. Which means if you took all of the world's information and put it into a single book every two years, you would have a new book, and it's probably getting faster. So there's so much more data. We need machines to help us keep up. The proliferation of on demand processing power, this is the idea of Moore's law. Every two years, or I think they're actually taking that back. Every six months, compute power is going to double and the cost is going to have. And that trend has been pretty consistent. And as compute has gone into the cloud and quantum computing has become more and more developed and available, we now have Almost infinite compute power available to us when we need it. And rather than having to buy all these server farms ourselves, we can literally turn it off, which means the ability to go into AI and process massive amounts is now cost effective as well as possible. And then, of course, we have smarter data platforms for unstructured data. We can now look at an audio file and understand what's happening in there versus it being a black box to old databases. Practical application of AI versus potential as well. Ai is no longer a buzzword. We're actually mapping it towards game changing application, which is vital. If businesses are going to invest, you can't invest in, maybe this will help us to this is going to save me money. This going to mitigate risk and this is gonna create opportunity. I also wanna draw a little, line. I attended, a tech conference last year, and, IDC presented their twenty twenty three future resiliency survey, which was updated every year. And they had a really, profound finding. We're talking about generative AI, but there's another wave of AI that's coming called artificial general intelligence. Or AGI, which just means not just the ability to create human like output, but to actually mimic human thought. Very science fiction y. The IDC resiliency survey had that predicted to arrive in twenty thirty five. In twenty twenty two. That's what their survey said. I sat in on this presentation, and they updated that. They accelerated that to twenty twenty eight. So just in a year, they moved it seven years forward. I would be very surprised if it stayed at twenty twenty eight. It is going to get closer as we get further and further along, and these technologies continue to get even better. We'll talk about AGI in just a second. It's different than general or generative, AI. So now that we've sort of defined what AI is, generative AI, let's make sure we understand what it is not. It is not a destination. It is not, hey, we got to general generative AI. We're done. Yay. High five. Good job, everybody. It is not the solution, and it is not a buzzword. You cannot invest in buzzwords and be successful. So what do I mean by that? Genative AI is a tool that can solve problems. It can solve new problems, and it can solve them differently than current tools, which is where the value is. You must think in terms of value when you're thinking about generative AI. Every new technology has had buzz around it, and there have been early adopters that have done it just for the buzz word, data science, whatever you wanna think of. But only when you connect it to actual value, are you going to actually get something from it? Like reducing cost? Mitigating risk, and creating opportunity. For those folks that are not c level executives on this call, This is the order in which you should attempt to sell this idea or this technology as a solution to your leadership, reducing cost, mitigating risk and creating opportunity. Those are gonna resonate in that order with your leadership. And AI generative AI is great at doing all of those. Again, it's a tool. It's not a destination. It's not the solution, solution to role solutions. And you can't treat it like a buzzword. It's gotta be tied to solving problems or reducing pain. Also, getting back to artificial general intelligence. This is where there is disagreement. When we get to artificial general intelligence or AGI. I saw a stat I was preparing this, something like fifty to fifty one percent of AI experts think that art of official general intelligence will be the end of the human race. That is a huge number. And I wanna caution you hopefully, elating if you're panicked. Genative AI is not that. Genative AI is a very different tool than artificial general intelligence. So if you are inclined to get worried about the Terminator or Skynet coming, This is not that. This is a tool that we can train to look at all the things that humans have produced And then generative AI can produce things very similar to that. It is not human reasoning. So if you are thinking, that we are in the end days. It's not junior to AI that's gonna bring that about. It's probably TikTok. That's a joke. So The four key requirements for AI readiness, if you want to do generative AI, and I would strongly strongly suggest that you do because if you do not, it will be a massive, competitive advantage for the rest of the market. There are four things that you have to start working on now to be ready so that you can take full advantage of it. And I'll I'll tell you what they are, and then we'll go into each one of them. So it is strategy. It is your data governance. It is a unified data platform, and it is how you think how all of this intersects with people. You master those four things, And mastery is a strong word. If you get to where you've got competency, let's say, you don't have to master them. I I think that's a journey versus a destination. Then you're gonna be well set to start leveraging generative AI across multiple solutions. So let's jump into what I mean when I say strategy. You need to understand what we are going to do with generative AI. What problems are we gonna solve? How do we sell that value, solving those problems to our executives? We need commitment. Just like the investment in any new technology, whether it was, hey, we're gonna start doing self-service analytics, or, hey, we're gonna build a cloud based, like, a cloud data warehouse, or we're gonna start doing data science. You need investment from a resolve and a financial investment from your leadership. You also need to make sure that you're doing this ethically in terms of how you're using information, and potentially you might be using your customer information, personal information, as well as compliance emerging regulatory standards. Most governments, whether they're state or federal or international, trade and and regulatory agencies don't have a full handle on what this is. So as you use generative AI, and the types of data that is powering it, there is going to be some evolution there. As you are building this strategy, also need to honestly look what you're capable of today, and then figure out where you need to go. Maybe you've got a clear strategy, maybe you've got a very great data culture. And you've got a centralized data warehouse that's sitting on the cloud, like Snowflake. But maybe you have zero data governance. That is a big step you've gotta go take, and you've got to take it. It's also, I think, valuable to look at. What are other people in my vertical, in my industry doing? There is value to being first. There's also value to waiting in C. And if you're more of that type of industry or that type of company, go get a good look at what your competitors are doing and where they're finding value. That is a lower risk way to go do it. You need a center of excellence, and we've been saying this for ten years. Particularly when it comes to analytics or data or whatever, you need a cross functional team of SME, or experts, in each of these essential domains that's going to make data and analytics work so that you've got a, I a full three sixty view. And then you also need to to build yourself a queue of use cases. And what I mean, a queue, I mean, you're really looking at two different make axis. Here. One is, what value is this use case going to bring us if we solve it? Blow, medium, high, critical, whatever you wanna say. You are then measuring it with what's the difficulty to go execute. Easy, moderate, hard. And then, ideally, you can start with the use cases have high value, but are low effort. And this is a big win and building momentum within your organization, getting the community across the value of this, as well as keep as well as keeping your executive leadership engaged. All of these things are critical. Once you have your strategy in place, it's time to start thinking about how you're going to govern this new tool, this new technology. So in terms of data governance, you have a lot of data, and there's a fair chance most of that data is not very good, or it's not the data that you would want to train a generative AI model on. So controlling what type of data gets put into your models is important. Because if you put in bad data, it is going to skew potentially for a long term, efficiency or optimal optimization standpoint, the results you're getting out of your model. So, again, you also have to think about what requirements do we have in terms of accuracy? How fresh the data needs to be? Does it align specifically with what we're trying to solve? Are we bringing in ancillary stuff that might just be confusing or or change the trajectory of the answers. The long term cost of bad data is not just true for generative AI. It is true for everything. Overwhelming, I've got other presentations where I have these stats, but something like seventy percent of business and or other data experts data leaders have said their business users, seventy percent, are making decisions with bad data or inaccurate data. So The long term cost of bad data is prolific across everything that you're going to do. The other things you need to understand is risk. This is an AI model, which means it is going to give answers as well as it is trained to the full extent of the data that it's got, which means there is a risk that it does not give a great answer every time. You need to understand what your risk is if it does not. Is this an emergency? Like, if you're in health care? There needs to be some auditing or oversight to this. Is it somebody looking about return policies for their socks? Lower risk. You need to understand what yours is across each solution, each use case Data privacy and security policies. Again, I already mentioned this is gonna be an emerging set of regulations, from external or or third party sources, whether it's government or or or compliance across different industries, but you also probably need to start developing these rules and procedures and guidelines for your organization. Once you have an idea of data governance, of how you're going to use this technology and then govern the data and the user's interaction with that data, you will take a massive step forward. You've now got a framework to then build the infrastructure to then make these solutions work, but you've got to do this homework before you do anything else. Unified data. And again, because we are now talking about the cloud, this is a far easier thing to do than it was, say, ten years ago. Hell, even five years ago. And the same thing that I just mentioned is is absolutely critical here. Good data is essential for everything you do, reporting analytics, self serve analytics, data science, modeling AI. It's all critical. Hopefully, through your experience and your time, in your roles, you have discovered that. You put bad data in. You're gonna get bad results. It's essential for training your models. Performance is super important too, which is why cloud has such a revelation for AI. Because we can't wait for queries that take twenty minutes to run. Like I said, I'm getting telemarketers and I'm talking to an AI bot. About my preference in, I don't know, Chiraz. If I answer and I get modem tones while AI is processing seeing my answer, that's obviously a useless solution. So the be the ability to dial up and dial down your virtual warehouses computer is super important. The good thing is, is that the tools that AI is already producing is actually helping this. So it's almost like, the the tail wagging the dog in a way. I'll show you some once we get a little bit further into this presentation, I'll show you some specific technologies partners that we work with, and they've got AI that is doing all sort of automation in terms of data quality, observability, data management, ETL transformation integration. So it's making it that easier. The other thing that I'll say, and we're gonna touch on this, in the next webinar in April in a lot more detail, which is this own your own data. I guess it was like a big trend in the nineties to sort of buy a fully supported third party, and you just chuck your data in there, and then they give you all these reports, and they give you some API access, that they own your data or or more accurately. They own your metadata and your semantic layer. From my role as a consultant, looking after other people's data and helping them solve data problems, the biggest problems we have is migrating people off of those systems into their own warehouse because it is so hard to get that information out. By design, they want to make it hard. Once you're in, they don't want you to get out. That is their revenue. So everything you can do to build your own platform and own your own metadata and own your own semantic layer is a massive step forward. We'll talk a lot about that in our April webinar. Like I said, which is we're talking about BI platform flexibility. So you have the ability to move tools, whether it's an analytics tool, or your data, your UTL, or add tools to solve specific use cases without an owner's amount of effort to unentangle yourself. Very important. That's one of the biggest pitfalls that I see organizations do. I won't call out names in terms of specific providers. Message me, and I'm happy to tell you. Privately. And finally, we get to number four, which is people. You need a self sustaining data culture, which is going to bring about a lot of value. And we'll talk about that here in a second. But part of the criteria for a self sustaining data culture is everybody needs to understand how to use, read, interpret, and interrogate data. Now they don't need to be a data scientist or a data engineer. You need to understand the different cohorts in your organization. This is my executive leadership team. I wanna define them as a special cohort. These are my end users. They are report consumers. These are my report creators. These are my data stewards. These are my data champions. However, you wanna build your cohort, and then you didn't determine what are my goals for data literacy, for data competency, by cohort, and then you go and execute your training regimen. So you can empower and enable them. You also need a community element here. Self sustaining data culture doesn't mean you've got your thousand people now ready to go do data because as there's churn, or you grow, you're gonna bring other people in that have not been trained in that one single effort. It needs to be self sustaining, and by making it into a community, particularly on the analytics front, which is the broadest case, and generative AI will be a part of that too. You can then bring people into the value that it's going to have for their role and for them personally in embracing data and analytics. If I'm good at these tools or I can use data to solve these problems, that's good for my career. That makes my job easier. Incentivization is important. And by building community and embracing these tools, you are going to help people see why this is important. They also need governance training, again, how you use this data, how you use this tool so that you don't get yourself in trouble, and you don't create risk for the company. Super important. A lot of the risks can be mitigated by permissions and how you create groups and assets and things like that. But people still need to understand. If if one of those things slips through and you see sensitive data, what's the protocol? Trump engineering is sort of a, a, a, a new term, and there will be more of these. But basically, if I'm interacting and now language with an AI. There are ways I can type those questions that are going to be more effective in getting me a good answer. People need to know when I'm talking to, a chatbot or whatever, how can I best and most efficiently get back what I need? So things like prompt engineering and and assist evolves into other sorts of disciplines, those things will come up. The good news is is that they're actually quite easy to learn. You just need to be able to get that message out efficiently and reinforce. Once you have a self sustaining data culture, or you have invested this in your people. The best thing that you're going to get back from this is this grassroots factory or farm of use cases. Once everybody in the business understands the power of AI and analytics and data, They're gonna be putting their hand up and say, I've got a problem we can solve. I've got a problem I can solve. I've got a problem over over there or whatever. And as these things start to rise up, not only are you going to have better decision making. You're going to have more automation and more efficiency, you're going to also create opportunities because you're gonna get business users who are on the front line of problems. Intersecting with technology to make these things easier, and you have to have a data culture for them to be thinking in terms of data solutions. The other thing that you're gonna get when you make this pervasive across your organization is you're going to understand the things that employees are concerned about with these technologies. Am I going to lose my job with generative AI? And if my job is at risk, how can I cross train so that I can remain valuable and important to this organization? Make sure you address the people element in this. Everybody wants to be a great place to work. Everyone wants to feel happy and satisfied and secure in their role. So let's address that on the front end. So now that we've covered the four competencies, I'm gonna tell you that generative AI is not this thing that is coming. It is here. There are there are obviously solutions and use cases that you are going to intersect with or encounter or start to build in the future. But the tools that you are probably going to be working with already have generative AI embedded and and a lot more are coming. I originally titled the slide AI will find you for you happen to go find I have. I was like, that's kinda creepy. I might scare some people, particularly as we talked about, artificial general intelligence. I was like, alright, it's already here. How about that? So I've I've listed some of our partners here. There's this is not an exhaustive list of tools that have been integrated AI, and it's certainly not an exhaustive list of all the AI features that they've already included. But what I'm most excited Me personally is is where our data platforms are starting to integrate AI. Because what does is it creates this downstream effect for the analytics tools to do a lot more. So Snowflake for instance, we won't cover all of these, but Snowflake for instance, they're they're they're developing they're they're going to release, I think, next month, cortex, which is their engine for generative AI. So LLMs or large language models, you can grab other ones and and and bring the compute into Snowflake. You can do Snowpark for machine learning. There's just ton of stuff. Ai powered, data apps, document AI, which is a pre trained model to go and read, unstructured data and get data out of just a ton of stuff they're developing, which makes it very, very exciting. And then as you start to think about how that flows downstream, Now tools like Tableau and PowerBI and thought spot, these end user tools for them to go and explore and interrogate data become ever more powerful because they've got more data available to them. And the number one goal here for these tools is is that a non technical business user can get the data answers that they need. And that might be, I wanna see a dashboard of our sales in Thailand over the last year, by quarter for these particular SKUs, presto change o, you get a dashboard, or it might say, can you help me write tableau formulas to help me get a level of detail calculation for this. Presso change o here's your formula, or it might be building a an app that you can put out. Like say, for instance, we've got a lot of companies that, have franchises a perfect example of this are people that are sort of in this fast food industry. Right? Or or or maybe they've got a centralized hub managing all these healthcare facilities. But the people on the other end in the franchises or in the individual locations are trained in other things. Might be nursing. It might be managing the the actual fast food restaurant. And then building a way for them to find answers that they need without having to go through a very technical interface. So thought spot, for instance, it's got thought spot sage. They are the leaders right now in transforming plain English in to query language. So these types of things are all really powerful. And with all of these tools, they are constantly spending money in R and D to get better and better and better So we're gonna see a significant increase in the level of productivity features and the speed that those features are going to get to you. I've been mentioning that AI embedded into, into these technologies can help you identify bad data. It can help you fix that data. It can help you integrate that data. It can help you, transform that data And, again, with good training and all that sort of stuff, these could be a couple of clicks of a button to kinda get through this workflow. I'm specifically giving you examples from Informatica. So Informatica now has a cloud based version of it called IDMC, and Claire is their AI. It's very cleverly AI in the middle of Claire. And all of those features are there now. So whether and again, the thing that's really great about informatica is it is very much a platform that looks after governance, quality, MDM, Master data management, ETL, all this other stuff. So a a a awesome back end. But again, If if you're working with tools that aren't on this list and I'm not meant to show you the only tools that are using AI, let us know. We have a a huge partnership ecosystem, and and we can help you wherever you might be. So speaking of helping you wherever you might be, after this email or rather after this workshop will be emailing you. And one of the things we wanna talk to you about is how we can start to help you. And we've created a couple of products or workshops that we think will be helpful. One is an AI exploratory workshop for executives. Meaning, Let's have this kind of conversation with your leadership. And we might, you know, be up in person, take take the folks to lunch and just have a very candid conversation about what AI could mean for your organization, what other people are doing for it, the sorts of things to worry about, and having a conversation about the four essential, foundational things that you guys need to be working on. Coupled with that would be an AI use case discovery workshop, which is where you'll be working with one of our technical experts, Ozusena, one of our team members is on the call. She's got a amazing AI background data science, data architect. She might be, for example, the person you're sitting down and having a chat with, And then the goal here is just to grab groups of your business people, stakeholders across different business units, and understand your pain points, and then help you figure out how generative AI can solve some of those problems. We've already built exhaustive list of different verticals and industries that are using AI, whether it's within their finance team or marketing or supply chain or HR, customer service, whatever it is. So we'll be able to sit down and help you ideate on the potential solutions so that AI isn't a buzzword. We're actually mapping it to real value. We'd love to talk to you after this webinar about how we could give these to you. Again, there's no charge. We we really are excited about working with everyone that we can, and our goal is to build customers for life. So what to do from here? There are two, QR codes here. One is the top one. If you wanna talk to us about how we can help, or maybe you just want a conversation about your data or your AI potential or maybe any any of the number of things connected to this analytics, data science, community enablement data culture, scan that, and and let's get the conversation started. Alternatively or maybe in conjunction with, if you wanna join our next webinar where we talk about some of the big pitfalls that people have in making technology selections, and the advantages of maintaining the flexibility to move across platforms as new features or pricing models and efficiencies arrive. That's what we talked about in April. The advantages of BI platform flexibility. I'll be delivering that one too. So if if you didn't like my presentation this time, I apologize. I'll do better next time. We have time for some questions. No one has put anything into the q and a, probably because I forgot to mention it. So if you do, chuck them into the, the q and a. I'm also keeping an eye on our chat here. If you don't, it was a pleasure speaking with you today, and hopefully we can continue the conversation. Ready. It looks like there are oh, here we go. Question. What are the key steps in identifying AI and data science use cases? This is this is a a nuanced answer, so I'll try to give it to you in in a couple of succinct points. Obviously, the best use cases are gonna come from your user base because what you wanna do is solve business problems. And, ideally, it's the people that are closest to that particular aspect of the business that are gonna identify those problems for you. There are things that you can do for an IT We want it more efficient. We wanna do our costs, whatever. So the best way to to identify use cases in that regard is, understanding the pain, problems or inefficiencies that your business has. And to do that, you have to have a great conversation going with your users, which is why data culture and community is so important. The other part to that, again, I really believe in building a queue of use cases based off of prioritization, which is a function of value and effort. But the easiest wing the easiest way to sort of qualify that is how much is this costing us, and and you can quantify that cost in terms of time resource, as well as financial resource. That's the easiest way to get that sort of funded, I think. Got some other questions here, Sasha. I might just push on. Are there any of your customers augmenting their generative AI activity using LLMs with their own internal data. If so, how are you seeing organizations do this? Absolutely. Again, I'm a big believer in owning your own IP, so if you can generate an LLM or large language model, which is the ability to train based off of different datasets, an AI model to do the different types of outputs with this, images, data, audio, whatever, I think you should. And there are a lot of technologies like Snowflake cortex, which will allow you to do that in the same data environment as where your data warehouse, your data lake, might be. And then, obviously, because it's in the same environment, connecting that to downstream analysis or data science is all right there. It is probably still a bit in the infancy of people sort of learning to crawl before they can walk, which means a lot of work needs to be done to make sure you're training your model really well, and you've got great data. So there are use case specific solutions I would start with versus trying to boil the ocean. Mark, hopefully, that answers your question. And then the last question that we've got so far is in your experience, what are the common pitfalls when it comes to AI readiness? Thank you, Charlotte, for that question. I would say, there's probably a couple AI is pervasive in the news cycle, as well as in any industry publication that you might see. So your CEO might go to a business web, you know, seminar. Goes there for two days learning about the different vertical stuff, know, this is how we do supply chain. And then there's an AI session. And he comes back very, very excited and says we're doing AI. Go do it. And then the troops scramble and organize, and you've got AI, but you don't have any problems to solve with it. Which means it never really gets escape velocity and then gets put away shelfware. That is true for almost every emerging technology. In that you're not tying it to value. I keep saying that, but from my experience in working with hundreds of companies and thousands of people in my in my journey at Innovworks. This is a lot of the problem that I'm seeing. The other thing that I I think people do is is that they try to paint a scope too large for it to be achievable. This is the whole idea of boiling the ocean. For us to do AI, we've gotta fix all of our data, and that's gonna take five years. And so we'll get back to it then. No. No. No. No. Pick a problem, let's solve that problem. And when we do, then we'll pick another problem. And guess what? After solving four or five problems, you guys as an organization are gonna be really, really good at doing that process, but also will now be x percent data deeper into your AI journey while mapping it to very critical successful outcomes. That is the best way to start, I think. Hopefully, Charlotte, that answered your question. And again, if you wanna continue this conversation or you have a question that you didn't get to ask during the webinar or you wanna follow-up with one of my answers, just you'll get an email, immediately following this one, but I'll just respond back and say, yeah, I'd love to chat with Rob or I'd like to talk to One of your AI or data experts or whatever it is, we're always happy to take a call and help, however, we can. That looks like all of the questions. We've got five minutes remaining in our hours, so we'll stop there. Again, thank you so much for joining. Very happy to see returning friends and partners and very excited to see, new faces in the chat. Let us know how we can help.