Everyday AI Podcast – An AI and ChatGPT Podcast - Using AI to turn Conversations into Revenue: A leader’s guide

Episode Date: November 14, 2025

Everyone knows AI needs your data to truly work. But, what about your company's reasoning? 🤔Buried beneath the modes and models, features and agents is something so fundamental that we almost always overlook it: the friggin gold that is your company's conversations. It's your expertise. Your secret sauce. Your decision making. Your competitive advantage. This is what you do about it. Using AI to turn conversations into revenue: A leader’s guide -- An Everyday AI chat with Dialpad's Jim Palmer and Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:AI-Powered Communications Platforms ExplainedEnterprise Data Security in AI SandboxesTurning Business Conversations into AI InsightsLarge Language Models for Conversation AnalysisLeveraging Unstructured Meeting Data with AIReal-Time Sentiment Analysis for Revenue GrowthAI Automation in Contact Centers and SalesEvaluating and Fine-Tuning Language Models SafelyResponsible AI Automation and Red Teaming PracticesTimestamps:00:00 "Turning Conversations into Revenue"03:31 "AI-Powered Business Communication Platform"06:16 AI's Expanding Role Everywhere09:51 Secure Communication and Data Insights15:46 "AI Innovation and Collaboration"16:56 AI Hackathon Accelerates Innovation21:56 Optimizing AI for Accuracy23:17 AI for Insights and Connections27:34 "Evaluating and Building AI Trust"31:46 Preparing for Automation in Business34:31 "Aria: Model-Agnostic AI Solution"Keywords:AI-powered communications platform, Dialpad, large language models, business automation, customer conversations, conversation analytics, structured data, unstructured data, conversational AI, voice data, meeting insights, sales optimization,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 working with large language models and AI,

we all know that data is everything. We need clean data. We need structured data because that's what's ultimately going to help us leverage AI to hopefully grow our companies. But I've been talking about for a very long time. There's a source of legit gold that your company is sitting on that you're probably doing nothing with. Because it's not a spread.

sheet. It's not something structured that you would feed into a large language model. Normally, it's conversations. It's meetings, phone calls, jumping on with other people, maybe webinars, anything, right? But when we are talking and when we are engaging with others, there's so much valuable insights that come out of that, that we should be capturing, collecting, cleaning, and using in whatever AI model that you are leveraging at your company. So that's what we we're going to be talking about today and just how we can use AI to turn those conversations into revenue. And we're going to be getting a guide from a leader doing it at a global scale. So I'm excited for today's show. I hope you are too. Welcome to Everyday AI. My name's Jordan

Moulson. If you're new here, we do this thing every dang day, at least Monday through Friday, bringing you an unedited, unscripted look at the realist in AI with a daily live stream podcast and free daily newsletter. So if it's your first time, welcome. It starts here. But if you want to take you to the next level, make sure you go to our website at your EverydayAI.com. There, we're going to recap the highlights from today's show, as well as keeping you up to date with all of the other AI news. So I'm excited for today's conversation.

Enough of me chit-chatting. Let's bring on someone that's been doing this for a long time and really knows how to take and extract the value of our conversations and better use that with AI. So live stream audience, if you could. help me welcome to the show. Jim Palmer. He is the chief AI officer at Dialpad. Jim,

thank you so much for joining the Everyday AI show. Thank you, Jordan. Thank you for the invite. One quick comment, you're going to have to start working weekends because the way that AI is accelerating, it's hard to keep up with. But it's so excited.

It's true. Yeah, every weekday AI just didn't really roll off the tongue quite the same as Everyday AI. but, you know, I'm obviously very familiar with Dialpad, but maybe for those of our audience that aren't, tell everyone a little bit about what it is that you all do. Dialpad is an AI-powered communications platform. We have all of the different types of channels and all tools and essentially platform for our customers to communicate with their customers.

It's the business-to-business use cases, business-to-consumer use cases. And we try to make this as seamless and as easy to use and navigate and grow and scale with your business to better communicate with your customers, better understand your customers. And we've been at it for quite some time. I think we just hit our 15 year anniversary. And we've also got a lovely slogan here, first in AI, best in agentic. And first in AI in a sense that we acquired, actually the company that I co-founded and started. a great many years ago, almost 11 years ago, but we had been acquired by Dialpad seven years ago to directly integrate AI, a state-of-the-art AI at the time.

And I will admit that it's grown and changed and evolved a considerable amount since then, but it's been a huge investment for Dialpad for a great many years to have that be basically seamless AI and being able to bring, you know, seamless insights to help our customers better communicate. You know, obviously the generative AI kind of boom of large language models over the past couple of years has kind of changed everyone's viewpoint on who should be using AI or how we should be using AI. Obviously, at dial pad, you're very tuned into conversations, at least on the front end. You've mentioned the agentic side. But talk a little bit. How has the landscape changed? in your view over the last couple of years.

Well, it's changing hour by hour, minute by minute. It just keeps accelerating. I don't remember even 10 years ago lamenting at what was coming through, even just with academia and the papers, you know, the PhDs, postdoctoral, all the advancements were moving faster than anyone. Even a small team could really keep up with. It was amazing. And then now I can't even tell you the multitude of how much more it's advanced,

but a couple of really big shifts happen in a very short period of time. And I'd liken it to, yeah, with the Abin of large language models, there was a huge push to three years ago that basically showed the world what's actually possible. If you invest the right amount of capital, the right amount of data, the right amount of compute to be able to generate an LLM that is capable of things that, yeah, there might have been a few folks in academia that might have said, this might be possible, but we haven't seen it before. So there was just a huge shift ultimately.

And what had been building up all the way up until that time about three years ago is that we're starting to get integration for all of the data to go into large language models. And now there's a lot of advancements on top of that, not just language as in like digital communications, emails, text messages, chats, that kind of thing. But now we've got audio. Now we've got video and meetings and webinars. But what essentially happened was, I think, the biggest shift because we can start, even as a casual user, someone at home or me having built this AI team in a large company like Dialpad and building AI, we found ways to not only leverage that and added its functionality for our end customers, but also we use it internally too. And I think there's like, Jordan, there's so much to talk about here. But I think what's really important is calling out the, there's the AI that you can. build for your customers as part of your product to sell to your customers or or AI that you can

use in conjunction or the complements other vendor software that you're using to build just your business, but there's also AI that you can use for your team, AI that you can use internally, AI that you can use in the home, right? And I think there's a lot of really great stories even with what the success that I've had and with what DialPads had of the AI that we built for our customers and the AI that we use to better improve. the way that we're building things, the quality of what we're building, the speed and execution, how we basically scale it internally and to help us scale a business. So I want to maybe zoom out a little bit here and go thought leadershipy on this one.

You know, I kind of started off the show today talking about how much value that there is in just conversations, right? And it seems like everyone's, you know, rushing to the next data source or data set to improve you know, AI implementation at their company. How much, just how much value is there in the conversations that we're having every single day? And maybe how much of it would you say is just being wasted, thrown away and not leveraged? Discarred, but there's a lot of things. We've got to do this, we have to do this safely. But we are getting so much better about leveraging that data, and especially with voice. And let me just go off a little bit of a tangent,

because that was part of my original pitch, that pitch of, hey, here's the idea, is where we want to take the last offline data set and bring it online. And there's a lot now that we kind of bucket this to bringing it online. We want to add structure to it. We don't want to extract the meaningful information and insights. And that was kind of the pitch of starting Talk IQ. And that carries through even still to today because we want to leverage the important parts of those kinds of conversations, whether it's you're talking to another business, you're talking to another customer,

or are you talking, there's every use case that kind of involves a way that humans communicate. And I think what's what there's, there's still a massive opportunity in that because, yeah, look at the lead up for the last two, three years where there's a lot of training for massive machine learning models being built on things that we're able to get for free on the internet, but it only has so many modalities or, you know, it's really only digital form. and now we're starting to find that there's more data sources. There's the speech. This are things that are happening even in private scenarios, right?

So there's like that kind of that last data frontier has just been like pulling, trying to get access to that information, but do so securely. So I think that's something that is really part of you. I'd love to say, hey, our differentiation with Dialpad is we're helping not only build the communications to, well, better communicate, but we're also going to getting safe and secure access for our customers to tap into all of the ways that they communicate with their customers and be able to build off of that. You know, bring the structure in, train off of. And of course, we go through all the security. We go through with so many different steps and

protections and compliance to be able to do this. But it does help because what happens is you start to see a lot of patterns emerge. And not to claim I'm a linguist by any means, but I think this really gets the linguists excited in the room in the sense that they're, and especially in a business use case, they're, they're actually, we're saying a lot of the same things. And can I just go off on a tangent just with like a really good use case? That was all, you know, a dial pad. So we like to sell our contact center part of our solution. And they said, we sell the teams that support or use dialpad to support their product. So they've got a pool of agents that are able to respond to their customers as they're calling in.

And there is such a high level of common questions, basically asking the same question with a few words that are different. And then that right there is so right for not only being able to extract that, you know, what was this question about? What was the answer? But also automation. So there's so many of these use cases, these common use cases that are like, that's where I see this. massive acceleration, and especially in the business use case. And then you start to think of even outside of that. How am I using that internally for our business? How am I using that potentially at home? There's a lot of the smart home movement, et cetera. But just a lot of those, the common

questions, the common answers, the common automations that you could potentially build. So that's why it's just so so exciting because we've kind of proven that there is a lot of that commonality and we've been able to build up a lot on the accuracy, the speed, the real time, the real value, like the real ROI ultimately when it comes down to for a customer. So, yeah, you gave some great examples there, right? And I think that there's a lot of direct lines that can be drawn, right, between, you know, analyzing conversations and, you know, customer service, sales, right, and seeing certain phrases, certain words, maybe push things in a different direction, right?

very direct line between conversations and revenue. But I'll ask you this, maybe outside of using a certain product or service from dial pad, how or maybe why should the average business leader out there pay attention to conversations, pay attention to meetings, pay attention to phone calls, or at least start thinking about capturing that and turning it into knowledge. Why is it worthwhile or is it worthwhile? I definitely think it's worthwhile. I mean, even just me personally, what did I miss in that meeting?

I take notes all the time, but I've started using other tools to help me take notes. And I could list off a great number of startups and even open source and totally free software to help you do that. And it is helped. I've seen the immediate effect. I can go back to that information. I've built a gym, you know, graph of knowledge. I've got chatbots for myself. of it it's painfully obvious how much it's helped. And if you can think about that, even if you're able

to kind of have that breakthrough on a personal level, trying these tools and doing so safely, right? And not just absolutely trusting whatever you might get from the biggest large reasoning model out there, but also just being, you know, finding where it works and where it doesn't work is so much easier to do right now for AI. So I think that's, that's the big accelerator, right? Is that People are finding whether they're working for a company and they're forced to use AI. No, no, no, they're finding ways that it's actually helping them in their, whether it be their personal life, or they're hearing about a friend that might have tried this. Or they're listening to your podcast, Jordan, and they go, ah, this person mentioned something that sounded really, I might try that out. And what we're basically building on right now is this massive kind of show and tell.

And there's obviously the socials are picking this up. We have our own internal show and tell for, hey, we have engineers, we have customers support. We've got all these different business units that are excited to be continuing to try AI, right? Not just try it and it doesn't work and then they give up on it, but more they try it, they take notes. And then they'll find time to try it again for maybe the same use case or another use case and see up. Now it works. And so the show intel is showing what works, what doesn't. And we're getting much better, even internally at Dialpad, at being able to know,

hey, where we would, what's a higher level of accuracy, a high level of trust for doing these certain things versus others that we can, you know, keep trying on. And so now it's this feedback loop internally even where we're all kind of challenging each other. How can we use this better? How can we use this more intelligently to build a better product, to have a better customer experience? And then what also is kind of a neat part about this is it's almost like a hackathon for the engineers in the room or the folks that have worked at companies that have done this where some of the best features, some of the best tools, and sometimes even the best

spin-off companies have come at a hackathon projects where you get to sit back and say, I just really want to have time to solve this problem. And now AI can help with that. And we've had, I could talk about our hackathon and our, we have a hackathon we do internally for purely product facing. Now we have a hackathon just for AI. And it's been a huge accelerator for us, not only just in productivity, but also potential features that we've been adding to the product. So my whole point with this is like, I see this.

And yeah, I'm fully entrenched in the middle of the AI, AI world. But I'm also seeing and I'm looking and I'm hungry to see where people are, are they pushing back? Are they skeptical? What are they skeptical about? Where have they seen it not work? Right. And I think we all, are growing to this, this, how we can use this, right? Is it a calculator or is it something more? And so we're just, it's becoming far more real. That's what's so exciting. So I do, you tease something out there.

You said some of the tools that you're using some startup, some open sourcing. So I want to follow up on that, but just have to take a quick, quick little break here for a word from our partners first. It's a problem I hear all the time. The gap between the AI champions and everyone else in your organization is sizable. You might have half a team that wants to fine-tuned models by hand, and the other half doesn't know what an API is. How do you get them working together on AI that moves the needle without creating a security nightmare?

That's where area really shines. They built one platform with three ways to work. Your developers can go full pro code and build custom agents with Python. Your business analysts can use the low code tools, or your domain experts who've never coded, they can use the drag and drop no code builder. Everyone's building in the same secure governed environment. No shadow IT, no security gaps. And because they're model agnostic, you're not locked into one vendor's ecosystem with area.

You can even A.B. Test different agents against each other. Try different models. And when you're ready, deploy to production on the same platform. Your AI strategy should unite your team, not divide them. Check out area in today's show notes or on our website for a free trial. Go to AIRIA.com. Get rid of the AI gap and move forward with a more resilient AI ecosystem. All right, Jim, so walk us through. You tease us there a little bit. And I love especially people who are chief AI officers who have been in AI, you know, a long time way before the chat GPT moment. What are some of those tools or techniques that you're even using personally, you know, to maybe leverage more value from your conversations? Oh, boy, how much time do we have?

It's my favorite. And this is just more on a personal level. Like, what do I do at home to grow and how am I leveraging this? And how do I get more access? I really want to see what's kind of under the hood, right? It's almost like I want to get a certain type of car that I can work on as opposed to something that's just going to, you know, I don't need a flying car. But when it comes down to it, I want to bring models in-house.

I want to get models in on hardware. in my house. I want to work with those. I want to try and tune those, fine tune. There's so much that you can do. But having it run essentially locally, but you're always having enough compute. I mean, there's a huge investment that it takes. It's not going to just run on your cell phone and be even remotely as accurate as anything you might get as the top foundational model providers out there right now because those models don't fit on this type of compute. I'm pointing down here because my phone is right there. So it's like, what's that balance? And this all kind of comes back to, my team has done so much work over the years. We've had basically peer-reviewed,

published papers at academic conferences. And academic conferences have started to kind of open up to industry, you know, companies that are making money and ROI and everything, actually starting to contribute back in this academic work. world. But what we're, what I love the fact that my my team has been able to show, hey, you can take something and optimize it, like a machine learning model and optimize it so, so much so so you can get the scale. You can get the real ROI. Your cogs don't go. Your costs don't go just absolutely out of control. And you, you have so much more control over all of that as well as accuracy. And this all comes full circle to what I was saying a little bit earlier with. There's

a lot of the same things that are being said in a lot of conversations. And it's the same thing, even for the AI that I don't want to have running in my house kind of thing, where you can, is it fine tuning as a kind of a very popular, but also very difficult process to basically start to optimize these models because what you see in a lot of business context. And I think this is a really important walkaway is that the amount of things that are said are the goalposts, So like the differences between what's said and what's not is very small compared to what a lot of the AI for general use, right? Like general use foundational models out there. They're trying to be as accurate as they can for every possible use case, every single language, every single voice, you know, modality as some like to call it.

So with that, that being able to shorten the things, you know, increasing the accurate. for the amount of things that are said or the things that you want to try and extract from conversations gives you so much so much advantage from an accuracy standpoint and everything else. So that's the stuff I love doing is how do we optimizing this? And then what's the next phase of that?

Where are the other advancements? How do all these things tie together? So yeah, home automation is a little bit of, okay, it's terrifyingly fun, I will admit, but it's like I like to do it on my own. But I know there's so many other tools, services, tie-ins, integrations. But it's like finding those opportunities for where I wouldn't immediately think like an

LLM might help me. I'm starting to find that there are opportunities where that could help. You know, one thing that, and I love the use case of, you know, talking about, you know, even like personal models, right? And I'm big on the small language models of the future and how it will be for voice and just actually powerful edge dictation, right? But one thing I'm always still doing is using AI to know a little bit more of the unknown, right?

Kind of the Jihari's window type exploration, right? Obviously, I have way too much content that I've captured conversations, voice, right? And being able to find connect dots that I maybe didn't know I should be connecting, right? in understanding and maybe tying together some sentiment that could improve my podcast or could improve, you know, trainings that I do or something like that. I'm wondering, is there maybe a use case on the sentiment side that you found that maybe you're like, hey, once we looked at all of these calls, all of these conversations, maybe we're able to connect some dots in raise revenue or save time after the fact that we didn't know.

Oh, it's a good lead up. This is great. Okay, it's one of my favorite features because it's not so much of the, oh, we've created a whole new field of science, but more, this is an immediately obvious use case. We have an AI C-SET. We're generating a customer satisfaction score on every single call. And it's at face value is just a, it's a percentage. Was this something good or was this something bad?

All of us on this call have, hey, would you like to stay on two minutes and enter the survey? And you have to press a one through four, one through five to, and it's horribly biased in the sense that the people are going to stay on her. I'm really mad. That's a very negative score. So here we are building this, this, we can gauge the sentiment in so many different ways. We can add more context to that, whether it's prior conversations, other channels that we're communicating with that customer, other inputs, right? And it's a kind of a classifier type of problem. And large language models, small language models, massive reasoning, MOE models can all do, classification very, very well. But it's, it's that kind of input that you don't just, oh,

hey, I run against a model. I got, oh, it has a low sentiment and then I shuffled that off. No, it's just, it's yet another input into something like if you're saying, hey, I want to use the biggest and the best that I can to give me other insights that I might not have known about to. But if you have things like a sentiment that you can add to the, maybe the transcript from a podcast, to link to say, you find another person in the middle, I want to talk to them, right? And getting all of that context, yeah,

that's still extra information that you can bring in, extra context that you can add to another model. And I use everything is basically my point, not just, oh, hey, bring it in-house like I was talking about

and use your models that you're trying to run on your cell phone or in-house, but try everything. The idea that I think has been kind of marketed as deep research is, I'm going to admit, that's amazingly, amazingly powerful. I use it. I'd say most of the business functions and go-to-market that I've seen successfully. That almost feels like table stakes for a lot of the go-to-market teams. Anybody who's

trying to sell anything uses a deep research-like feature. And I'm going to admit that it's absolutely amazingly powerful. But you also have to think about the limitations of it. Where is it getting all of its information from? It doesn't know everything. So the more that you can add to it based of prior conversations, other inputs, that's, that's kind of the, I think, the breakthrough as users, right, as you trying to find, you know, new content for your podcast or A says, hey, what's a new potential revenue stream we haven't even thought of or a way to kind of, you know, sell the specific feature to an end customer because they've talked about it in another context. That's massive.

You're starting to connect all those dots. So I use, use everything you can, try it out. But, okay, here's what I want to come back to. Because we're going off on this, you know, Jim, what do you recommend? Here's one thing that has persisted through as long as I've been in this. Going back to kind of your earlier question of, you know, Jim, you've been doing this for a long time. The one thing that persists ultimately is how are we evaluating? How are we just not just sending a prompt to an LLM and getting a completion and saying, that looks great?

What are we doing to build up the trust, right? And it's obviously it's been improving the accuracy so much. about it screams we should trust this. But if you're a builder and a practitioner of AI like we are, like I am, or if you're a company who's using a vendor who's promising all of this amazing agentic functionality, I would still implore everybody to find a way to evaluate it, to test it. And not just one quick, we refer to it as smoke tests, like is it smoking or is it not kind of thing, but more start to build up those test cases, right? Like you're, Jordan, I assume you're building up this massive treasure trove of all this structured data from all of your prior

podcasts and all of your research, right? And how are you going to, if you're asking an LLM or a reasoning model to do something, you do it, you pay for that completion back, you save that. And then there are ways that you can build a test on top of that. And you can use other LLMs to test other LLMs and start to build up this whole network. So just coming back to this, if you're you're just starting in using AI, if you're already deeply entrenched and have 12 different vendors that all kind of do it really, really, really well, invest in a way to just evaluate it because there's a lot of things. It's getting better. And in some cases, you know, it's changing and might get better or it might get worse. I can't give any promises. But the fact is

that it changes. And my, I'd say just looking back even over the last three years, it's definitely changed for the better. It's definitely improving and a lot of accuracy. And all, all the players are, it's amazing, but just keep updating those tests, those evaluations. And I could spend another hour talking about the ones that we've used and I like, some that are really hard to constantly have to maintain, but just find a way to, to store and to allow you to test that over and over again so that it doesn't become, you know, too much work to do that. So that's, yeah, all right.

Sorry, I could keep going on this whole spin, but no, no. I mean, you bring up some great points, and I love even, you know, the concept and I think it's a good practice of, you know, understanding evaluations and how you evaluate and why. And, you know, what role the human plays in augmenting, you know, with a certain LLM in that evaluation process, obviously extremely important as well. So, you know, Jim, we've covered a lot in today's conversation. But, you know, as we wrap up, I'm not going to ask you to predict the future, right? But I will ask you this, right? Because we're already starting to get into this, you know, year-end mode and, you know, 2026 is right around the corner. Aside from using dial-pad, right? But aside from that, how should business leaders be looking at the relationship between meetings, conversations, and using AI to tie it to revenue? What is your most important takeaway? Or, you know, if you were to advise someone, hey, in 2026, you need to be doing this.

What is that thing? Oh, everybody's just going to say, oh, he's going to say agentic. Yeah, I'm going to say that. But I'm going to say there's a couple of things. And it's not, it would be also kind of exercising, not caution and not trying to put fear, but responsible automation, right? And it's what are those use cases that you are confident you can automate? Because right now, and even going back to doing this AI thing for a long time.

and I've been trying to sell AI dream and trying to make it a reality. And it's definitely, it's a lot more real now. But before it was all about time savings and how can we more efficiently close the gaps, the knowledge gaps, those kinds of things. But right now there's this huge leap into that full-blown automation because that's how you can kind of prove this is how you save money. But there still is no, there's no guidance on how does it actually work and work 100, So I think it kind of comes back to the, you know, the evaluations and everything like that.

But coming in as a business and they say, hey, I'm going to get ready for 2026. I'm going to spend some time and some money, not just diving into a solution just yet, but figuring out what can I automate and what should we not? What protections do you need to put in place? And I'm not here to, you know, fearmonger or anything, but those are the kinds of things that if you, if you jump in and, oh, this works amazing. is an edge case, just a random, if this one thing happened, is that going to cause harm in some way, shape, or form? And there's so many things that I can, you know, I advise on this. And this, this is part of my talk track for years, but I use the concept of red teaming and not going back to like Cold War-esque, but just think about it if you're testing something.

And even if you're testing someone home or a new model you got on your phone or the new massive video generation thing, try to break it. And I've always loved that. And red teaming is basically like adversarial testing. Try and find how it doesn't work. Because if you find where it doesn't work, you're also going to find where it does work. So it's a win-win situation.

And so part of this is, if you're going to go into 2026, is figure out what it is that you can automate safely. And then what are those integrations? Because the AI, I think we're, there's the investments there the momentum is there but it's also the integrations are the coin of sale system's going to be able to keep up are the that you've been using and invest it in and there's no you know you're basically this is what you're you have to use if you have a proprietary

system what changes need to be made are your vendors going to be you know AI first or AI native at some point in time right it's like make sure that all the the scaffolding is built up the Foundation is strong for being able to say, we're going to do AI and we're going to do it right. Jim just hit us with the quad-fecta ending, some of my favorite things, evals, breaking things, responsible automation and integrations. Love it. So thank you, Jim, so much for taking time out of your day to join the Everyday AI show. We really appreciate your time and insights.

Yeah, thank you, Jordan. That was fun. All right. If you miss anything, don't worry. We're going to be recapping it all in today's newsletter. So if you haven't already, go to your EverydayAI.com. Sign up for the free daily newsletter. Thanks for tuning in.

Hope to see you back later 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.

Stay in 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.