Practical AI Wins: Real-World Use Cases for Marketing, Content and Hybrid AI

In between the hype and the haters are a lot of people experimenting with AI as a tool to help them in their day-to-day work and life. That's the topic of this episode of "Utilizing AI," featuring Dave Graham from MLCommons, Bryan Martin from Signal 65, and myself discussing our own hands-on implementation of AI tools. Welcome to "Utilizing AI," the podcast focused on practical applications of artificial intelligence from the Futurum Group.

Every Wednesday, we explore news and use cases of the ways in which AI is transforming enterprise IT and the industries it serves. I'm your host, Stephen Foskett, president of the Tech Field Day business unit here at Futurum Group. Before we dive into this discussion, let's meet who's on the panel today.

Hi, I'm Bryan Martin, AI data center performance at Signal 65. You can find me on LinkedIn. And I'm Dave Graham.

I'm the director of marketing for MLCommons Association, and you can find me on LinkedIn. Go figure. And as I said, I'm Stephen Foskett.

It's funny, I'm not just the president of Tech Field Day business unit, I'm also an active user of this stuff. And that's why I am really excited to get the three of us together to talk about this, because we have a lot of conversations about AI, and a lot of them are theoretical. A lot of them are based on, I believe that AI could do this.

I believe that OpenClaw might do that. I believe that this is a use case. But Bryan, Dave, and I are actually doing it on a daily basis.

Let me just get this straight at the beginning. I am a believer in the potential of AI as a tool to make me more efficient. AI gives me superpowers.

I use it all the time. But I am a skeptic of overblown statements, and I believe that people have, in many cases, the wrong impression of what this stuff is capable of, simply because they're basing their impressions on rumor and legend and myth, as opposed to actually running the stuff. So Dave and Bryan, like me, are actually running this stuff.

I don't know, raise your hand if you have installed OpenClaw, right? Raise your hand if you used an LLM to do something today that would've taken you an hour, and it took you 30 seconds, right? Absolutely.

Right? Right. We're doing this stuff, and there's this gulf.

So I'm going to throw this to Bryan first. As you said, you're an engineer. You work for Signal 65, which is a part of the Futurum Group, which does actually the testing behind much of what we're talking about here.

You've had your hands on this stuff more than anything. Talk to us a little bit about your own experience with these tools. So thanks, Stephen.

One of the interesting things I've done recently, I spent the last, I would say, eight or nine days trying to create an AI version of myself as a performance engineer. I gave it a task. We had a high-level project.

I'm working on a Dell R770, doing some performance metrics. Started scaffolding up a set of experiments, and was reminded very quickly of an old saying from the storage industry. There's two kinds of people: people who've lost data and people who will.

If you use these AI tools enough, you're going to find out those situations where you didn't think of something, it didn't think of something, something got accidentally deleted. In a lot of ways, it's amazingly like working with a very talented junior engineer. They don't necessarily have the depth of context and experience that I do.

But they're very earnest, they work very hard, and they're very clever. And that has been a very interesting experiment with, I'd say, some very promising results, and a few painful hiccups. Painful hiccups.

You talked about scaffolding, Bryan, and I think I have more scaffolding around this joint than I have anything else at this point. So a lot of what, similar to yourselves, I approach things from a couple different perspectives. Obviously, I live in the marketing space, but I come from the technical side, right?

So- We know you're a nerd ... yeah, it's kind of a distant second. Technical marketing engineer, I guess you could call me at the end of the day on this stuff.

But a lot of this is I have a pragmatic need for using AI for anything, right? So how do we integrate tools that enable us to communicate more effectively? As a sufferer or a useful idiot when it comes to the side of my brain that's ADHD focused, I have to use tools to keep me on track and task, right?

So I spend a lot of time figuring out what's my personal information data store, right? So it's kind of building that out, and so using tools to augment that. Similar to yourselves, again, Bryan, I've spent all weekend and most of the last week doing characterization of Andrej Karpathy's auto research stuff, right?

Sitting down and- Right ... designing experiments around the experiment itself, right? Which, as you know, can blow itself out into large amounts of trouble and turmoil.

But part of this is it's necessary for what I do. And also on the research side, I have a half academic brain here as well. As I started my PhD, one of the things I was interested in was the role of data in society and using AI.

And that's on pause for the time being, but as I kind of rejoin that world again, it's sitting down and looking at the documentation and looking at these things. How does it augment? How does it enable, Stephen, to your point earlier on, how does this enable us to do more?

More, better, faster, quicker. " Exactly. Yeah.

And that's the thing that I see here too, is that-I was blown away right from the beginning. That's why we started utilizing AI and utilizing tech in the first place back in 2020, because I was finally able to do things faster, better, more flexibly than I could without, once we got these tools. We actually launched this podcast, by the way, before ChatGPT was introduced.

And so it was more theoretical at first, but we were already starting to use LLMs and deep learning and so on, in theory. Mm-hmm. And I saw that things were rapidly improving.

Once the GPTs came, it became very clear that this stuff could be extremely useful. And I use it every day, like you've said. I use it every day in 100 different ways to make me process text better, process audio better.

I will say that one thing I use, not just on a daily basis, but multiple times a day, is AI-based transcription of audio and video files. We produce video at "Techfield Day" at an alarming clip. At "Futurum," we've got daily news programs, daily podcasts.

Sure. I'm feeding those things into a local model that's running on my Mac Studio on my desk. It's able to do a phenomenal job of transcribing that.

I can interact with that transcription. I can find out things that I need to know in order to properly handle the content that we're creating. And it's the same with external people.

So for example, Dave, if you share a link with me on LinkedIn about, "Hey, I was on this podcast," the first thing I'm going to do is take that YouTube video and run it through on MacWhisperer and Parakeet, and then I'm going to talk to you on the podcast using an LLM in order to figure out what's the interesting angle there. Yeah. And this is transformative and cool.

And it's also very cool because it's running locally. But my goal of running locally has not been met beyond things like transcription because frankly, I have not had any success running AI models locally. I literally, on Friday, took this out of a machine because I found that it was not a useful way to run any kind of useful tools locally, and I'm still spending money in the cloud for that.

I think you have more hardware than me. Have you managed to run anything locally? Go ahead, Dave.

Yes and no. So I just recently upgraded to the new M5 Max. So I have 64 gigs of memory, whatever, and 40 GPUs and whatever is in this MacBook Pro.

I do video, I do audio, I do the things, similar to what you do. And a lot of what I've built is that planned obsolescence plan. I know in three years or four years, since that's when my M1, which is sitting behind me somewhere, that one's going the way of the dodo at this point.

So yeah, I have that. I have a Mac Mini behind me. I have an AMD workstation card here.

But I've found I don't have a good small language model. I don't have an SLM that's here- Yeah ... that's really characterized.

I have data repositories and curated data sets, which is great. We all have our Claude MD files. We all have our representative curated who and what we are in the world.

But increasingly, this goes to the Google methodology behind Workspace, why Workspace was created, and all this stuff. Increasingly, my life is almost entirely fixated or located off board. It's not here locally as much.

I have a NAS. I have all that kind of stuff. But really, the things that I operate with and in tend to be around APIs that are interfacing with a, well, my current favorite right now, which is RunPod.

Mm-hmm. They're interfacing with Google services. They're interfacing with...

Yes. You know what I mean? It doesn't- Right ...

end up sitting here. So my need for discrete hardware that's highly optimized, tweaked, and tuned, and like that MSI Armor card I think that you were just showing, I don't need that so much anymore. And I'm also on a Mac, so I couldn't use it anyway if I tried.

But that tends to be the limiting factor. So a lot of what I'm interested in is actually human interface to the technology that I'm doing, much less than I am interested in the hardware interface to my humanity or my digital persona. Brian, over to you on that one.

Yeah. So I've got an AMD Threadripper system with an RTX Pro 6000, which lets me run GPT's OSS 120B locally. I've been pretty happy with that.

5 models locally. I'm in this constant quest for something that feels close enough to the frontier models that I instinctively trust it or I subconsciously trust it. And I've come to accept that there are going to be those scenarios where things go sideways, and I just have to account for that.

But as I get deeper into OpenClaw and its many derivatives, I really want to have a model running locally that is going to be in charge of things that I care about. As I start to share the things that are my personal accounts, my personal documents, my email accounts, those are scary things to put out in the cloud right now. I don't want those models running outside of here yet.

But someday, maybe. But I also feel that the current crop is, well, on one hand, the current crop is very promising, but the frontier models keep moving forward. 6, which is unfair in one respect, but it is what's available.

And then, as Dave said, spending thousands of dollars learning and finding out is part of what we do. It's the nature of the work, but also finding ways to be more balanced, like pushing these things to their limits. I'm advising an aircraft safety startup, 550 Aero, and the guy there has found a way to bring these agents together.

He's building a data science team purely from AI. And he's been able to automate, and accelerate studies across thousands and thousands of experiments, digital twins flying in virtual reality, to test his system, all driven by AI. So the expertise, I think you mentioned, Steven, what we can do to augment ourselves, accelerate ourselves, is absolutely showing up in a powerful way.

And as long as I'm aware and we're aware that it does have edges, it's not perfect, we keep pushing forward. Yeah, and I think that's one of the things that separates people who are more experimental from either the haters or the lovers, and that's that it's not a question of hating, it's not a question of loving, it's a question of seeing this as a tool that could give us a special capability. And so, for me, that's really kind of been a quest for me.

And like you both, I think, I've been trying out different models based on the hardware that I have. The reason that I chucked this thing is, at first I was under the impression that a GPU could assist in the processing. Ultimately, it didn't.

Ultimately, my Ryzen 5 12-core CPU is actually faster than trying to make use of an older GPU. On the flip side, like you, Dave, I also have a Mac Mini. I've got M4 Pro Mac Mini, unified memory.

That has allowed me to run more models locally. I'm very excited with what you can do with the unified memory. I'm excited with what Apple has been able to do with rolling out NPUs.

I don't yet have an M5, but I'm actually kind of excited about it because I believe that it actually has four times as many NPU processors as the M4, so it ought to be noticeably faster. But I've been trying to run various models. Again, with OpenClaw, I've been trying to run the Qwen models, the Llama models, because I feel like they're a pretty good system.

But unfortunately, I haven't been able to make it run effectively, and I'm back to using online, cloud-hosted APIs of based models for basically everything, simply because it just doesn't generate the tokens fast enough, and it doesn't have the context window to handle any kind of data, really, in order to make any kind of effective use of local resources. Now, that being said, I am excited about what we might be able to do once we get past 64, 128 gigs. So my implementation of OpenClaw is using 10, 15 gigs just for the key value store, which means that my system doesn't have all that much power.

And so I've been thinking about, well, maybe I could have an AI offload, another machine that's actually running the model. But that hasn't been something that I want to do because it just means buying more hardware and deploying more hardware. Have you had any more success than that, Brian?

I was going to jump in and say yeah. So part of the luxury of the job I have is we have testing labs, and when those systems are idle, I can deploy on those. So I occasionally have the luxury of running on an eight by H200 or an eight by MI300.

Well, I've got one of those too. I just haven't started using it. Oh, yeah.

Well, watch out for the power drain. They're loud and expensive to run. Just waiting for National Grid to drop in the three-phase for me, so I mean, hey.

Perfect. We're just bringing online a liquid cooling lab, which is a conversation for another time. But having access to those for comparison and to play with.

5, these are trillion-parameter models. Some of them take 16 high-end GPUs to run. Now we're getting what feels like frontier model quality in the, well, data center, not quite in the office.

But it's local. I can run it there. We can batch it and get incredible response time with incredible accuracy.

So it's fun to watch that sort of, what do you call that, burst out. Trying to... Yeah, I think there's the things, so going back to the experimentation stuff that I've been doing.

Now I'm looking at eight different data sets, and there was the original Climb Mix one from Andre on Hugging Face, and started adding in FindWeb and a couple of these bigger semantic data sets that contain the world plus dog in terms of information, but all in structured formats and stuff like that. And so one thing I experiment on this stuff, and I'm watching my RAM get progressively eaten up. I have an instance on an MI300X right now running in RunPod that's out of the 192 gigs on there, I think is chewing up 128 gigs of memory on that thing, which is ridiculous.

Right. But this is a corpus of data. This is a lived experience of billions of people, right?

When it comes down to it. It's a summation of human thought and thinking across a, whatever, a wide range of texts and whatnot. And so, Steven, you mentioned thisMarching onward, we can talk about flash economics right now, though I'm sure Ryan Chout would want to be a part of that conversation when it comes down to it.

Yeah. Though, when we start to look at this, there's this kind of weird inflection point of economy, right? Like, I bought my M5 because I knew brand pricing was going up.

I got my wife an AI395 from Corsair, right? One of the little mini PCs with 120 gigs of memory right before the memory bubble hit. It was around Christmas time.

And the reason being, it was I saw this kind of trend in here. And I wonder, and part of my wife would actually be a great person to have on this as well, because she's looking at AI usage and digital health, right? And looking at these kind of- Mm ...

the phenomenology of what happens when you start to inject these things into daily living. And a lot of what we're talking about, Brian, you're testing from an infrastructure standpoint, the art of the possible. I'm looking at a pragmatic approach to it for marketing, but also in what we can do with it in order to make our lives.

Steven, you're using it to live your life, right, and make your business more opportunistic. And I think this is one of the really incredible things that we're being offered right now, whether it be on-prem or on-premise, or whether you're going to go after me if I say it wrong. Whether it be on the cloud or here locally, right?

This principle is we're watching that kind of march from it has to be on a hyperscaler, it has to be in a neo cloud, it has to be there, to something where am I able to do enough locally to solve, like, Brian, you want something that's going to run your life. Steven, you want something that's going to run your transcriptions here. Am I able to do enough here?

Like just enough LLM, right? Like getting into that space. Mm-hmm.

And I find that fascinating because this is where we start to see when we do new house builds or new house starts, right? We start to see our infrastructure surround this type of concept. What happens when you have a smart home that integrates small language models that are able to do the proof of concept stuff that we talk about here theoretically and start to pragmatically put this in here.

That's the thing that excites me at the end of the day, and what I'm hoping my testing experimentation leads to more understanding and knowledge. But, yeah. Yeah.

And for that note, that's been a really interesting thing for me is that the more I've tried to run things locally, the more I've realized that it's much more practical just to run them in the cloud. And so with my workflows, for example, I have tried to do local language models to do even simple tasks like summarization. And I've found that it's actually not just easier, but more cost-effective even to just run them using, for example, Gemini on Google, because they have a really well-supported API, and I'm able to just hit that, and it costs not even pennies to do a simple task like summarization on using Google, OpenAI, Anthropic, that sort of thing.

I've also been looking at, I have not yet got it up and running, but for example, Cloudflare has Workers AI. 5. 5 running in Cloudflare on Workers, and I've been thinking now that's a really cool idea because, yes, it's still pay as you go.

Yes, it's still... But it's going to be sips, it's going to be inexpensive, and it's going to be highly scalable and reliable because I've been using Cloudflare Workers for many things in the past, but not so much for AI models. Mm.

What about that, it seems like there's sort of a dichotomy in people's minds that either you're running it locally or you're running it on specifically OpenAI or Anthropic. What about that space in the middle of service providers that are able to provide models that are a little different? So Steven, you get to bring back the hybrid cloud phrase because- Yeah ...

this is going to be the new hybrid Yeah ... in AI. What do I run locally?

What do I run in a Cloudflare or a RunPod? What do I run on the frontier space? And I think part of where we're going to see tools like OpenClaw and the derivatives step up is helping to make those decisions, right?

We're going to have a local router to figure out what goes where, how to optimize, and it's going to be an ongoing process. I can almost envision a set of virtual sliders, like, okay, try this, try that. Okay, I think we're settling in about here.

And then, of course, new models drop, and everything gets, the perturbations happen, and then we settle out into a new norm, and then more new models drop, and maybe it shifts. Yeah. That's exactly what we've been doing, right?

So one of the things we talked about at GTC this past week, it was only last week, was this idea of testing endpoints now all of a sudden, right? So the old model, again, small plug, but for what MLCommons does, but a lot of what we were attempting to do is benchmark and characterize infrastructure. So AI infrastructure, from training to inference to storage to whatever.

And that's all well and good, and we operated in kind of fixed cycles about every six months, alternating between all these major themes. One of the things we kind of determined along the way, and credit where credit's due, some of our competitors or other analysts within the space were hinting at this and putting out data was that because that wheel turns so quickly now, because of the nature of everything, everything is becoming an endpoint. So again, that hybridization, it's not just local, it's not just a neo cloud, but it's also that API that sits in the middle of it that you're wanting to test so that you get- Mm-hmm ...

5 when it launches, or you get what Cloudflare is offering or whatever. So it's this idea that we have to now start testing. It's probably more advantageous for us or one of the more forward-looking things, start testing those endpoints themselves, right?

So you get greater model velocity, you get kind of greater engagement with what people will ultimately end up using, right? Because not everybody's going to want to sit down and provision a RunPod thing. I'm using Jupyter Notebooks, which is enabling my deployments to be a lot quicker.

But I'm not using any of their endpoint API stuff at this point. And so it's that kind of constant re-envisioning and recycling of there's both and then there's the and. Let's use them both together in order to accomplish something.

So a hybridization model of OpenCloud plus OpenRouter to determine where data's going to be shunted or the calls that need to be made, or Brian, the stuff that you're testing in your lab, if it's running on your RTX 6000 Pro, but then you need to augment it with an 8 by V300, which you probably don't have at this point. I can bounce out to ScaleWay or CoreWeave or some of these other things and be able to hybridize this thing together. The end cost is probably less than you trying to consume the services directly yourselves in that case as well, which is an important concept.

We're privileged because we have the income business opportunity in order to do this, but when we start to look at the ubiquity of using these things, the hybridization model probably makes the most sense for that, putting the reference to Altman, but that universal basic compute concept that Altman tossed out there as a cheeky aside to UBI, which is inherently more useful by the way, don't do UBC. But that kind of concept so... Right.

Computers eat tokens. People don't eat tokens. Yeah.

And they certainly do eat tokens. If Jensen, like Jensen said, if he has a 500K staffer that's not spending 250K of tokens per year, they're not doing their job. I mean, Jensen, I urge you to give me those $250,000 worth of tokens- Tokens ...

and I will absolutely exploit that to the ends of the earth. Well, yeah, that's actually a real good point that you make because even though this is still pretty cutting edge, and even though some of these models are still moderately expensive, I am hard pressed to spend that much money on tokens in the cloud, and that's actually been one thing that's holding me back from local models in that, I look at it, and my budget is not many thousands of dollars a month. In fact, I would be surprised, I don't actually know because I'd need to look at that.

But I would be shocked if I'm spending more than a few hundred dollars a month on tokens, despite the fact that I am aggressively trying to deploy these tools. And that's made me kind of question whether I need to go out and run out and buy an M5 Max with 128 gigs of RAM or whatever it is, to run this stuff locally, because frankly, it's still pretty cheap to run it in the cloud, even though it does eat up tokens. Yeah.

I had heartburn going from the Claude $20 a month plan to the $100 a month Max plan. Mm-hmm. However, I looked at my usage patterns.

" Cursor was a great example. I used Cursor a lot last year to the point where I blew $500 in a month on Cursor, and I went, "I'm not a developer. I should not be spending-" But you're not blowing $5,000 a month.

No, I'm not. You're not. Right.

No. Thank God. My wife and other people in my life would absolutely pillory me for that one, and for good reason.

But it was one of those situations where the tipping point, to your point, Steven, was what am I using this for? Is there a comfortable kind of offset where $100 a month actually makes a lot more sense, where it's not all you can eat, but it's enough that I can eat, in terms of the things I do? And I've looked at my usage patterns over the past month on Claude and Claude Desktop and the stuff that I'm doing, and I'm hitting 25% utilization of my credits per day or my usability window, and that's good.

And I'm seeing the outputs of that, and I'm able to work within that particular space. So I have no need to do the $200 a month plan or any of these type of things. I think the flip side of that, not to be negative about it, but the flip side of that is I've turned into a SaaS monkey when it comes to actually spend.

Oh, yeah. com and all that kind of fun stuff. There still is that argument to be made, and I think there still is, we're talking about it loosely here, there is that rollover where at some point it becomes more tenable to run in cloud or more tenable to run locally depending on what you do.

If I had Brian's hardware, I'd be running locally all the time, so... Not that I'm jealous or anything. But the problem is that, Brian, your hardware is not your hardware.

It's the lab, and so you can't use it all the time, right? Exactly correct, Steven. This is the challenge, right?

I really wanted to like, okay, I want the quality of a frontier model. I would trust my version of Kimi K2I running my stuff locally. It's not locally, I can't count on it, so I need to find something else in that space.

Dave, you just need to be running more in parallel. One of the things that starts to uplift, I watched my cloud, I spent over $500 one month on cloud- Yeah ... last year, and that's when I went to the $200 Max plan for all you can eat when I was driving it hard.

But as we start spinning up multiple agents in parallel, and I think Jensen's really looking at in the data science space. If you look at what Andre was saying, he's running these experiments, he's running them in parallel, he's running hundreds of them, thousands of them. The ability to go wide then starts to tax me is how much can I keep track of?

Where do I start losing operational control of what's going on? And then the next step is how do I learn and embrace delegation for that? And that comes incrementally with trust.

So that's the next frontier for me is delegation, distribution, and starting to build an environment there that is reliable. Yep. And that's the real challenge, I think, is that unfortunately, too, I'm looking at what I'm doing, and some of it looks, if you'll forgive the phrase, pretty janky.

I've got some frameworks that are not exactly bulletproof. And you're talking to somebody who was very deeply into business process automation and so on before the AI boom hit. I'm a Zapier power user.

I like to say I have features in Zapier named after me because I pushed their product so far to the limit that they came back to me and said, "Man, okay, we'll develop something that will meet your need," back five years ago. Now, here we are in the future, and a lot of that stuff is still running pretty good, and my AI stuff is still just duct tape and bailing wire, because I just haven't gotten to the point of maturity. Dave, take us home here a little bit.

How effectively are you using AI right now, and when do you suppose you're going to be able to say, "Yeah, I've got some good AI-powered processes helping me develop superpowers"? Yeah. As the cautious optimist or the pessimistic optimist, I'm not a full Luddite.

Again, I come out of a background being social work and background being psychology and background being therapy. And I came out of a very humanities-focused kind of space. That was my ontology for decades.

Still I'm in that space, but now, kind of leaning in it. " Things that I would devote a lot of brain power to. I still love, just like you like watches, I like pens.

I still have my pen and paper- ... that I jot notes down. And it's good because it's an analog kind of...

There's a tactile feel to doing this stuff. But when I look at it, the summation of what I do, a lot of what I'm spending time on, are these digital assets where I'm building things out, like market requirement stocks or looking at the kind of grand scheme of how I communicate this stuff. Those are, I won't say they're easy, but they're easy enough that you can frame it, you can hit that 90% good enough, I can evaluate, and then I can pass on.

I think, Brian, you mentioned trust. This is where I'm at. Yeah.

I'm still building that basis of trust. Trust but verify, right? It's that kind of basis.

When I flip into my academic side, the paper that I'm writing with Patrick Hughes right now, the idea is everything that gets reported back, I'm having to double-check the data. Why? Because I don't inherently trust what is going on and what's being reported back.

I need to see and I need to understand the basis for that. As my understanding grows, as my model usage grows, as my understanding of those characterizations grow, I think that's where it leads me to integrate more and more of it into my life. I drive a car that has AI in it, which I disable a lot of those features at this point, but again, I characterize, I try to understand.

That is my job, my day job with ML Commons is that idea of characterization. It's understanding what it is and what we do. And so I think that's the baseline.

And then joining stuff with y'all here at Tech Field Day stuff, I'll be doing AI Field Day in May and participating in that kind of stuff. And again, you can pay attention, I try to make everything open and audible for what I do, because again, because I'm trying to engender that trust that I expect from the models back to me, and then from me to the greater community out there as well. Got it.

Great. Yeah, for me, just trying to keep pushing the limits forward as I can look at what runs locally, what runs balanced, how far we can push this system. A lot of work lately with Dell and Dell Infrastructure.

In that vein, you can see me at Dell Tech World, or find me on LinkedIn, and let's keep pushing the limits of AI. Yeah, and thanks for that. Dave, Brian.

Dave, I'm looking forward to seeing you at AI Field Day. Brian, we just saw you at AI Infrastructure Field Day. Thank you for hopping out for that.

com, you'll be able to see these. This podcast lives as well on the Techstrong side. ai, you can see the home of the podcast, and you'll be able to see a lot more of what we're talking about and doing here in terms of video.

But thank you both for joining us so much. This has been a lot of fun conversation. I wish we could talk, well, we probably could talk for hours on these subjects.

Let me know if you find anything that really, really works. And to the listeners as well, I hope that you hear what we're saying, which is not that these tools are just magical, not that these tools are evil, just that these are tools. And we're figuring out how to use them, we're figuring out how to integrate them with our workflows.

Mm-hmm. And I think a lot of our listeners are in that same situation. They see the power of agentic AI, they see the power of generative AI, and they see that these are things that can help them do their jobs.

And now the question is more, when and where and how should I use it, and how can I make this most effective? And for me, that's the whole ballgame. " This podcast is available on YouTube, which is the best place to subscribe.

Or you can find us in your favorite podcast application. And of course, we would love if you would give us a comment, a rating, a review. This podcast is brought to you by the analysts and experts from the Futurum Group, where insights meet AI.

ai, the "Utilizing AI" YouTube channel, or Techstrong TV app on your streaming device. Thanks for listening, and we'll catch you next week.