Everyday AI Podcast – An AI and ChatGPT Podcast - EP 475: AI Without Mistakes: How Good Data Makes It Happen

Episode Date: March 5, 2025

Your data is your moat. Everyone's got AI now. Find out how reliable data can make your competitive edge happen. Barr Moses, Co-Founder and CEO of Monte Carlo, joins us to discuss.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Barr questions on AI and dataUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. the Importance of Data2. Challenges and Opportunities in Leveraging Data3. Adoption of Data Practices4. Data Use Case Examples5.Generative AI, LLMs, and Data IntegrationTimestamps:00:00 Empower AI proficiency with daily insights.06:02 Data observability ensures reliability and issue resolution.07:15 Understanding data's importance is crucial for businesses.13:07 Personalized AI relies on unique enterprise data.15:20 Large enterprises struggle with data consistency, smaller teams advantage.19:42 Generative AI analyzes sports data for insights.22:56 Personalized financial products using reliable data.23:56 Credit Karma Intune boosts external and internal productivity.28:02 Peak data reached; synthetic data becomes crucial.30:36 Recap available on your everydayai.com.Keywords:Generative AI, Data Usage, Data Accuracy, High-Quality Data, AI Implementation, Brand Reputation, Small Business Data Management, Data Systems, Trusting Data Sources, Everyday AI Podcast, Microsoft Partnership, Barr Moses, Monte Carlo, Data Downtime, Data Issues, Data Products, Data Observability, Data Adoption Forecast, Smaller Team Advantages, Microsoft WorkLab Podcast, Data Quality Monitor Recommendations, AI and Data Integration, Personalized Financial Products, Coding Assistants, AI for Compliance Reporting, Large Language Models, Synthetic Data, Real-World Data, Data Governance, Data Quality Management.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. When it comes to generative AI, I think sometimes we just overlook some of the most important things, right?

It's we just want to hit that big red easy button and have it spit out hours of work. And we're just like, yay, good, we're done. But there's that one most important part. It's the data. Do you trust where your data's coming from? Is it reliable? What happens if it's wrong? And well, why should you even care?

We're going to be talking about that and hopefully answering a lot of those questions today on Everyday AI. What's going on, y'all? My name is Jordan Wilson. I'm the host of Everyday AI. This thing is for you. This is your daily live stream podcast and free daily newsletter, helping everyday people not just learn AI and keep up, but how you can leverage it and become the smartest person in.

AI at your company. So if that sounds like you and what you're trying to do, this is your new home. If it's the first time, please make sure if you listen on the podcast, check out your show notes in there. You will see a website, your everyday AI.com. But before we get started, have to first give a quick shout out to our partners at Microsoft. So why should you listen to the WorkLab podcast from Microsoft? Because it's the place to find research-backed insights to guide your org's AI transformation. Tune in now to learn how shifting your mindset can help you grasp the full potential of AI.

That's W-O-R-K-L-A-B, no spaces available wherever you get your podcasts. All right. So thanks to our partners at Microsoft. And as a reminder, if you haven't already, make sure you go sign up for our free daily newsletter on our website. We're going to be recapping today's conversation as well as going over the AI news. Yeah, technically pre-recorded one here that we're debuting live. A lot's happening in the world with AI.

Everything happening at CES. We got some open AI rumors swirling. So we'll have that all in today's newsletter. All right, but enough chit chat. Let's build some more trustworthy AI. You don't have to hear me ramble on any longer. Have a great guest today lined up for you all. So please help me welcome to the show.

There we go. Bar Moses, the co-founder and CEO of Monte Carlo Bar. Thank you so much for joining the Everyday AI show. Thanks for having me, Jordan. It's a pleasure. All right. Let's do this. Well, first, people don't know. What is Moni Carlo? What is Moni Carlo? Great question. So Moni Carlo's mission is to help accelerate the adoption of data and AI by reducing what we call data downtime,

which is basically periods of time when data is wrong or inaccurate. You can't trust it. I don't know if this has ever happened to you, but you wake up on a Monday morning and you see that one of your data products is wrong. like you were staring at the report and the numbers off or something's wrong. You're like, what the fuck? Like, why is it off? And oftentimes it's not only really difficult to catch the issue, it's actually also really hard to understand what's the root cause and to resolve it. So Monica helps solve all of that.

We're fortunate to work with some of the world's best data teams ranging from companies like Fox, Roche, Kred, Karma, and men, many others. It's probably the part of my job that I love the most, getting to work with amazing customers on some of their hardest problems. So in a nutshell, right, a company comes to you before, what happens after they come to you, right? Like if everything goes right, they just better understand their data and how it works with AI. Like, what's the end result? Great question. So I would say, you know, there's lots of people today like data analysts, data scientists, data engineers, machine learning engineers, building what we call data products.

The data products can be a journey of AI application or it can be a report that your CMO is looking at every day or a thing. or it could be a pricing recommendation, algorithm. It could really be a variety of data products. And those data products are often wrong. The biggest issue are sort of based on wrong data. And the biggest issue is that oftentimes data teams are the last to know about that. And so, you know, the very sort of the very first kind of table stakes fundamental thing that we do or that we help organizations is be the first to know about data issues. So no longer the days where data teams are sort of caught by surprise by data issues and sort of hearing about it from someone else.

Like that's the worst thing that can happen that you didn't sort of catch that. Never happened to me. You know, I'm sort of asking for a friend, if you will. I'm kidding. But, you know, that's sort of like fundamentally the very first thing. And that's really the thing that sort of the Monique Krollo kind of set out to really solve, you know, when we founded the company five years ago. And that's been sort of the, you know, I want to say like the first. first frontier. I think since then, what's become even more apparent is that that's only like

first half and in a sense, maybe even the easier half of the of the work. Actually, the really big challenge sort of where I think that the sort of AI reliability in industry is heading is not only knowing that the issue existed, but also answering why. And should I care, what should I do with this information? Because oftentimes data team are just inundated with alerts like this is broken, and this is off, you know, this data is late. This data has never arrived. This field is, you know, looks a little bit off. This number is missing.

And but in those instances, like the hard thing is actually the answer. I have all these systems working together, but what is actually the root cause? Is it something that went wrong in the data? Is it that the job wasn't completed? Is that there was a change in the code? Those answers are really, really hard to answer. And so a lot of things that Monacoa does, not only Monacrollo, just observability more broadly, sort of the field of data observability, if you will, is about answering or helping data

teams answer the question of something went wrong, should I care? And if so, why, and how do I resolve that? And that, honestly, is a lot of what observability actually sort of store it out with. So observability, we didn't make it up in data. We actually sort of, you know, borrow the concept from, borrowed the concept from engineering teams from software. So observability and, software engineering is very well understood with organizations like Datadog, obviously, you know, who doesn't have Data Dog today or something like Data Dog? Every single engineering team has something like Datad and relies on a solution like Datadog to make sure that the software that their building is reliable and can be trusted and sort of up and running, if you will. And so data teams,

in my opinion, should be doing the same. It's a little bit of a new area observability, if you will. I think Gardner sort of forecast that's over 60% of organizations will have data observability in some sort of fashion form in the next five years. But it's a new area. So it's been only recently been defined. I mean, just in the first like three minutes there, I think you answered like my first five questions. I want to hit rewind just a little bit on the like, why should we care?

right? And I think, at least in my viewpoint, and maybe this is for smaller and medium-sized businesses, but they often don't even really take the time to fully understand, you know, how their data even works. So they're like, okay, well, we know we need rag, right? We know we need to bring in our own data, you know, to work alongside, you know, a backend API, right? But why does it ultimately matter whether they get their data right if they are using, you know, a different, you know, Claude, Anthropic, Gemini, et cetera. It's a good question. And let me just take us back like 10, 15 years ago when honestly, maybe it didn't really matter.

Like, it just didn't, you know, we weren't really using data so much. Definitely didn't have any journey of AI. And so we could kind of get away with like data being wrong most of the time. Worst case, someone just told you had to go ahead and fix it. Like, no big deal. You moved on with your life. right? But I think a lot of things that have changed in there's been these sort of various sort of eras, if you will. And I think the first era was where a lot more people started using

data. And so you can no longer get away with like looking at the data only once a quarter. Now you have like millions of users, you know, pressing ordering an Uber. And so you can't get the time of when your car is coming to be wrong or you can't get the price to be wrong. Like for example, if I see that the Uber car is going to be arriving in 30 minutes, I'm not going to be waiting for 30 minutes. I'm going to be signing off and going to a different platform, right? And so, yeah, it matters that the time for which, for when the Uber is going to arrive, that data needs to be accurate because otherwise you're going to lose your users. So that was sort of the first wave where people, just a lot more people were using data and data products became a lot more

important. Then there was a second wave of generative AI, which is now sort of happening. And actually, interestingly, you know, we recently did a survey among about 200 or so data leaders. And we basically asked, you know, how many of you are sort of deploying generative AI or building derivative AI in production? Can you guess the answer? How many are doing that today? I'm guessing a small amount. Actually, interesting, 100% of them said that. Oh, okay.

Literally. Yeah, I was surprised you. Every single person on the survey, and these are all like, you know, data leaders from credible companies, 100% of data leaders are currently building something that's a journey of AI. Now, the second question was, how many of them actually trust the data that they're going to be using? Adobe just introduced an entirely new way to create, bringing the power and precision of its

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You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Yeah. And that's interesting. It is a good point. And I was kind of shocked by that, right? Because a lot of the studies I read say that even in enterprise companies, you know, I think the latest study is only 5% of companies have generative AI solution top to bottom, right?

Fully implemented, right? But I guess it's got to start with the data first in trickle everywhere else. Is this maybe also, right, speaking of shifts in generative AI, one thing that I'm personally always dorking out about is kind of this shift from the attention economy. to the intention economy, right? Like to be able to better understand, you know, what users on the internet are going to do before they even know they're going to make a decision. And that starts with data, right? I think we've been hearing for five, 10, 15 years.

Oh, data is the new goal. But is it even more and like increasingly more and more important because of generative AI? Yeah, 100%. And so going back to that survey, only one out of three leaders feels confident. in their data that's feeding their genitive AI model. So like most of us, two-thirds of us, don't have confidence in the data that we're using. And so, you know, to your question, why does it matter more in this new world or in generative AI? I'll explain that given one example and one sort of more sort of theoretical example. But the first sort of, you know,

sort of real life example, if you will, this was a couple of months ago. Someone sort of, this went viral somewhere. Someone sort of Googled, you know, what should I do if the cheese is slipping off my pizza? And Google was like, oh, no problem. Just use organic super glue to like, I don't know if you saw that to like put it back on pizza. Like that went viral. And you're like, okay, well, if you're Google, maybe you can get away with that kind of answer, right? Like, sure, I'm going to continue to use Google tomorrow, right? But most of us can't afford that. We don't have the luxury of spitting out such falsely or, you know, clearly misinformed answers. And so for most enterprises, the reliability of the data that you provide is actually intertwined

with your brand and your reputation and the impact on the top line and the revenue that you're generating. And so that's sort of, you know, one example, you kind of bring that to life. More broadly, though, you know, if you think about what companies are sort of tasks to do now, we're seeing, you know, every single data leader needs to do something with a. I have AI now. How are they doing that? Because today, every single one of us has access to the latest and greatest LLM model, right? Like, we can all switch between them. We can all use them. And so in a sense, we all have access to models built by, you know, thousands of amazing PhDs, right?

Like, we can all do that. So what's my competitive advantage, right? How am I going to build a better data product than my competition or what's my long-term moat. And what I believe in, what I'm hearing from my customers, is that the moat is actually the data that you have because it's no longer simply, you know, connecting to an API and actually building a journey-to-a-I product, the power of building a highly personalized genera-I product is based on the ability to use first-party sort of enterprise data. So I can actually build a way better product if I know something about you, Jordan. And if I know about your background and I know your habits, I can actually build something that's personalized for you. And the data that I have is something that arguably no one else has.

And so I think in, you know, for leaders thinking about what are they going to build or how are they going to use genitive AI, the data that you have is the moat. That's actually how you gain competitive advantage and build data products. And so if you believe that's true, then the quality and the reliability of the data that you're using is of utmost important. Because if the data that you have is inaccurate, then what's the point of the moat that you have? And I think this makes a lot more sense and is resonating for those that work at larger enterprises, right?

And they already have a data warehouse or data late. They're using Amazon S3. I don't know, right? But for maybe those medium-sized companies that don't have their data game strong, right? But they have it in a lot of different places, right? Maybe they have some, you know, floating around in different places in Google or, you know, and there's CRM, et cetera. How do these, you know, smaller and medium-sized organizations, how can they take advantage of that?

Because what you said there is true, data is the moat. But how do these smaller and medium-sized organizations start to actually pool all of this data together so they can, you know, leverage generative AI with it? Yeah, I mean, I'd start by saying no data is better than bad data. So if you have shit data, I'm actually not convinced that you should be using it. I actually think it might be better to make sure that you have data that is reliable and trusted. You know, I think just to give you an example on Monte Carlo, Monte Carlo as a company, we're about 200 or so employees.

We build a genetic AI products, and it's of utmost important that the data that we have is accurate. And so I think even if you are a small organization, you know, the bar is not lower. In fact, I think it's higher. In fact, I find that enterprises, you know, large enterprises really struggle with getting their data together, really struggle with having a source of truth. Like if I have multiple copies of the data, for example, I mean, even answering questions like, you know, how many customers do we have or large organizations needing to figure out sales compensation.

It's really freaking complicated to do that because the answer that I get from my finance team is different from what my sales team is saying, different from what my marketing team is saying. And so every different team is looking at different sets of data. And so getting an answer is really, really difficult. So I actually think medium size and smaller organizations have an advantage. You have, you know, you actually, in fact, you know, I think it's a, I actually think smaller teams move faster. Like, there's probably some proof of that. And so as a smaller team, you know, you're probably small but mighty. And so make use of the data that you have. And you have the advantage of being able to move faster and actually innovate faster because larger organizations now are

way, way slower and obviously way more risk-averse. So I think smaller organizations have the benefit of being able to try a lot of things, experiment, move quickly, and double down on some of the experiments that are working. And by the way, that's sort of the large majority of what we see companies do, both small and large, basically have like this mandate to go experiment. in the organization and like have lots of teams, you know, try out different, different things and sort of build different applications. And, you know, companies understanding that they will come up with a centralized strategy only later. All right. So I do want to talk about some of these use cases, but we're going to take a quick 20 second break and have to shout out one more time our partners

at Microsoft. So why should you listen to the WorkLab podcast for Microsoft? Because it tackles your burning questions about AI at work. Like, how can I guide my org's AI transformation? How can AI help maximize value and create new products and business models? What mindset shift do we have to make if we want to tap into its full potential? Find the answers on WorkLab. That's W-O-R-K-L-A-B, no spaces available wherever you get your podcast. All right.

So, Bart, I do want to jump into it a little bit here because we've been talking about like some of the issues with getting good data, having reliable data that you can trust. So what happens when it does get together? Maybe could you walk us through a use case or two, just for those that maybe are just getting their data feet wet, so to speak, so they can see, hey, how do these, when good data and good Gen AI comes together, here's good use cases. Yeah, absolutely. And it's been a really fun sort of hearing kind of, you know, a variety of use cases and sort of innovation. I'm really excited. And honestly, like the hype around this is so big, but I think even if it materializes only 10% of the way, that's enough to be such a disruption

for us and for future generations. So I'm really excited. I'll give a specific use case, actually one that we use at Monte Carlo. So, you know, one of the challenges that we have is oftentimes when this is sort of very meta, but oftentimes when we work with data teams, they actually don't know the state of their data, and they certainly don't know why their data might go wrong and what might go wrong there. And so if you need to set up sort of coverage with data quality monitors, you don't always know how to get started, especially if you are a less technical user, that might be a lot harder. And so what we do is actually sort of built data quality monitor recommendations, where we actually sort of profile the data that specific customer,

We use Anthropics Cloud 3.5 Sonnet. And one of sort of the kind of advantages of working with LLMs is that they have a really strong semantic understanding. And so we can actually use with a combination of profiling, the data and the metadata, a bunch of other kind of contextual things that we bring together, we can use that to actually help define what monitors you should be setting up. So I'll give sort of a really, hopefully an easily understandable example. you know, we work with sports organizations, for example. And so if you take like a, you know, like a baseball organization, for example, and you think about like pitch types, you know, and actually like baseball and in sports in general, collect a ton of data about, you know, different athletes, different players and a lot of sort of data about the games itself on a ton of statistics and analysis for anyone who's seen Moneyball and others. One of, you know, one of the things, one of the types of data you might collect is,

you know, the type of pitch and also the speed, the pitch. And so, for example, using analysis, you can actually learn that, you can actually determine that if you have a fastball, that should always be over 80 miles per hour. And if it's under 80 miles per hour, then maybe there's a problem. It's not really a fastball, right? And so that's sort of a recommendation that we can make using generative AI or using LLMs to say, hey, you should set up this data quality monitor.

And we can do a lot more, you know, that kind of help users actually make sense of their data in order to drive what sort of data quality monitors they need. And that's a good example. And I think it really illustrates a point. So, you know, because we can all relate to, you know, oh, classifying a pitch, right? And, you know, you never really know what it is until, you know, you see it or, you know, you're watching on TV. But maybe could you walk us through one, maybe one more example of, you know, how good data and, you know, knowing that you can rely on it and how that can really make a difference? Yeah, for sure. So another example of, you know, I think a journey of AI use case that's really cool is something that credit karma from Intuit does.

So, you know, for folks who don't know, you know, credit karma is sort of a financial assistant that's based on AI. And so can make recommendations for you on how to best manage your finances. And so like I mentioned before, you know, any organization has access the latest and greatest, you know, OpenAI, API or others. What Credit Karma has that no other organization has is information specifically about their users. And, you know, they serve hundreds of millions of users. And they can tell, you know, you have this specific credit score and you've had this Honda car for the last 10 years. and you're going to be selling it at this time and you have this kind of history, all that information can be used to help make specific recommendations for you

about your specific financial situation. Now, the downside is, you know, we want to make sure that we're not surfacing to you the wrong credit score. So you, Jordan, should be able to access only your credit score and not my credit score, for example. And also the financial recommendations that are being made to you, should be based on your data and your data alone. And so I think the power, you know, Credit Karma actually builds rag pipelines.

And so they use LLM and actually like enrich them with data that they have about their users in order to build these highly personalized assistance, if you will. And so, you know, I think being able to actually build such a personalized product that's also based on reliable, accurate data, results in a really, really strong outcome for customers. That's one kind of example from the financial world. There's a lot more examples where companies make good use of LLM and Gen AI actually for efficiency internally, which I've seen.

So, you know, the credit karma is more an example of an external data product that gives you a really strong ability to make an impact on your customers externally. If you think about seeing value internally as well, lots of organizations, the most basic example is where organizations see, you know, an increase in engineering productivity. So where you have a sort of coding assistance, that's like the most basic. I think that most organizations are seeing today. And, you know, I think that often helps for, you know, more sort of junior and experience engineering, but also senior engineers as well. So if you have a largely sort of junior new organization, you'll realize even more benefits in that. But I think, you know, some numbers like,

You can increase significantly the number of the ratio of sort of number of coders and code that's being reviewed with with LLMs. Another example is, you know, in the pharmaceutical or sort of medical space, but also in insurance, there's a lot of compliance reports that are being shared. And those oftentimes can take six to 12 to 18 months to generate. And those include, you know, a lot of internal data, but also sort of status and protocols. And, you know, a lot of sort of like wrote in sort of manual report writing, journey of AI can significantly reduce the time based on that. So if you sort of use kind of the data that you have off the shelf and actually train it on past reports, it can actually generate really good example or at least draft to start out with.

So those are kind of examples of how folks. are really gaining internal efficiencies. There's also some clever ways, specifically around structured and unstructured data. So oftentimes folks find that it's, I would say in general, kind of the whole unstructured data stack is very new and is just emerging. Like I think this is very early days for unstructured data. One of the things that are hard is how do you monitor and observe unstructured data to make sure that unstructured data is reliable? Like that's really, I want to say very, very early days of that, something that Monicaa, obviously, we think a lot about, but also our customers obviously think a lot about one good example of how you might use LLMs to better

observe unstructured data is we work with an insurance company that has customer support chats. And if you think about customer support, customer support conversation, that's largely unstructured. data. And you can use LLMs to actually structure that specific support chat and give it a score based on, you know, what is the reading of like the tone and the conversation and the resolution and understanding whether, you know, that support conversation went well or not. And basically assign it a score between zero to 10 on how it went. And one of the sort of use cases is that is then you can observe that structured data. Like let's say some, let's say the LLMs, LLM gave it a score of a 12. Like, what does a score of 12 mean on, you know, a score between 0 to 10, right?

And so in those instances, you can actually make sure that that data is reliable. So there's a lot of kind of clever ways in which folks are using LLMs to structure the unstructured, if you will. Yeah. Yeah, I love that. And that's something that, you know, I'm a huge propode up, especially for smaller medium-sized businesses. It's like, yeah, use LLMs to turn that unstructured data into structured data that you can actually use. So I would be a huge. be remiss if I didn't ask you this, though, because, you know, it's been a growing, growing trend, at least of 2024 is, you know, using synthetic data. What's your thoughts on that, right? Like, like, I love what you said, like, having no data is better than having bad data. Is using

synthetic data better than using bad data? Like, where do you stand on that? And is this going to be a big play in the future? Yeah. I mean, I think there's, I think, I don't remember a little, I think there was a former scientist of OpenAI who said, like, we are at peak data right now. Like, there's so, like, we, you know, sort of maxed out on, on the data that there is. And, you know, we now need to turn to synthetic data in order to kind of make advancements. And so I think it's definitely an interesting time for synthetic data. And I think there's going to be a rise for that in terms of, you know, how we're going to train LLMs and, you know, how we're going to sort of reach even better performance, if you will. but I do think that there's no, obviously, no replacement to, you know, real world data that enterprises need to use.

And I see most of the attention and the time spent there. So it's actually interesting. There's this return to some of the maybe more unsexy things like data governance is sort of like rearing its head now. I haven't heard that, you know, that word in a couple of years. And now there's like a, you know, a return of data governance. And so I actually think a lot of like, you know, what what was old is new now, if you will. And perhaps synthetic data is in that camp as well. All right.

So we've covered a lot in today's conversation. Barr, we started with, you know, the reliability of data and how observability worked. We gave some examples, you know, and talked about the future of data as well. But as we wrap up, what's maybe the one most important thing that you think our listeners need to know, especially those making medium and long-term decisions on how their companies play in the AI space. What's the one most important thing they need to know about reliability of their data? I would say, you know, your genitive AI product is only as good as your data. And so, excuse my language, but if your data is shit, then your generative AI is going to be shit.

And so getting that in order is the first thing. And actually, that's a really tall order. It's actually really freaking hard to do that. But I think there's no other way. I do think that we are seeing more and more organizations actually seeing ROI on their Gen. AI products. And so it's high time that any organization starts to, if you haven't already invested,

then you're already too late. Love to hear it. Yeah. That's a great, a great warning call to all of you people that are somehow still sitting on the fence in 2025. I don't get it, but there's a lot of you out there. So, Barr, thank you so much for joining the. Everyday AI show and taking time out of your day to help us all better understand data.

We really appreciate it. It's been fun. Thanks for it and good luck to us all. All right. And hey, y'all, that was a lot to take in. Yeah, just so much a data avalanche of great information. If you miss something, maybe you're on the elliptical and looked away. Don't worry.

We're going to be recapping it all on our website. Your EverydayAI.com. Sign up for the Free Daily Newsletter where you will find a lot more insights and compliments. supplementary, supplementary info to go along with today's conversation as well as everything you need to know to be the smartest person in AI at your company. Thank you for joining us. Please join us tomorrow and every day for more everyday AI. Thanks y'all. Meet Firefly AI assistant now live in Adobe Firefly, the Allman One Creative AI studio. Just describe what you want to create in your own words and the assistant handles the rest.

Orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express and more in one, conversational interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.