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AI in the Workplace: A Quest for Balance
Artificial Intelligence (AI) is an emerging force reshaping the structure of many workplaces. Despite the hype, however, it has yet to reach its promised equilibrium. The challenge is twofold: keeping up with the advancements in AI tools and balancing workforce productivity with the necessary training on these new systems.
Training Employees for the AI Future
Adapting to some features of AI like real-time translation is straightforward, while other aspects require more complex change management efforts. Companies might face resistance, mainly from senior staff who show skepticism towards modifying their processes. However, the fear of AI replacing certain jobs in the near term is mostly dismissed by experts.
AI Tools: A Shift in Skillset Expectations
The future of certain skillsets in the face of AI remains a contentious issue. In fact, some industry thought-leaders argue that certain skills, like coding, may lose relevance due to AI advancement. Regardless of perspective, change seems inevitable, and it's not just about learning new technologies, but about redefining roles, expectations, and performance measures.
The Double-edged Sword of Productivity
As AI tools improve workplace efficiency, they simultaneously increase information consumption. While this can indeed boost productivity, it could also cause burnout if not managed properly. Productivity and burnout are not just AI challenges, but also issues tied closely to self-management.
Rethinking Management Expectations
While management may have high expectations for productivity gains from AI implementation, it’s essential for expectations to align with realistic capabilities. Overemphasis on immediate return on investment could lead to disappointment, and it's important to view AI as a future investment, focusing on its long-term value.
Unlocking Business Growth with AI
AI has the potential to unlock new avenues of business growth through data analysis and the opportunity to leverage business data in ways previously impossible. This creates a competitive edge, provided there's adequate understanding and adjustment within the company and its market ecosystem.
Navigating AI Adoption: The Human Factor
Successful AI implementation goes beyond having the right technology. It requires a growth-oriented human factor: employees who are open-minded, enjoy learning, and problem-solving. Yet, even as AI adoption accelerates, resistance to change remains a challenge that many companies face.
Balancing the Scales: Productivity and AI Adoption
Effective AI deployment goes hand-in-hand with balancing expectations, realistic improvements, and workforce capabilities. While there might be a transitional period, with a clear divide setting forward-looking AI adopters apart from traditionalists, persistence and patience in the transition period are essential for long-term success.
In conclusion, AI’s impact on workplace productivity comes with its challenges and benefits. Despite the roadblocks, the potential that AI possesses to transform traditional business practices is too significant to ignore. The focus should be on understanding AI, its limitations and potential, then tailoring company expectations and strategies to align with this powerful asset.
Implementing AI is an investment in future competitiveness. It's a marathon, not a sprint, and needs to be carefully managed to ensure it delivers its full potential. In this new era, 'patience' is more than a virtue; it's a strategic business investment.
Topics Covered in This Episode
1. Current State of AI
2. AI and Workplace Productivity
3. Implementation and ROI on AI
4. Burnout and Productivity Issues
5. Hiring for AI adoption
Podcast Transcript
AI [00:00:00]:
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.
Jordan Wilson [00:00:16]:
I don't think there's room to deny anymore the fact that generative AI can make us so much more productive in the workplace. But there's pros and cons to that. Right? I don't think productivity is so easy to just pass over that we can just throw a bunch of AI at it and walk away. I think AI is actually changing even what it means to be productive in the workplace and what happens afterwards. Alright. I'm extremely excited to talk about that today, and welcome to everyday AI. What's going on y'all? My name is Jordan Wilson, and I'm the host. And this is for you.
Jordan Wilson [00:00:54]:
This is your show. This is your daily livestream podcast and free daily newsletter, helping everyday people learn generative AI so they can leverage it to grow their companies and grow their careers. So if that sounds like you, whether it's your first time or number 330, thank you for joining us. If you're on the podcast, make sure to check out your show notes. As always, there's gonna be more information on there. We recap our conversation, our interviews every single day in the newsletter, bringing you exclusive insights even more. So if today's episode catches your ear, you're gonna wanna read the newsletter as well. So, make sure you can find that at your everyday ai.com.
Jordan Wilson [00:01:32]:
Alright. Before we get into today's conversation about how AI is changing workplace productivity, which I'm excited about, let's get into the AI news first. So Hollywood video game performers have announced a strike over AI protections. So Hollywood's video game performers are going on strike started that's just started, at midnight today following a breakdown in negotiations over AI production protections with major game studios. The strike is significant as it marks the 2nd work stoppage for video game voice actors and motion capture performers under the Screen Actors Guild, American Federation of Television and Radio Artists. That's a big mouthful. SAG AFTRA, I believe, is is the shortened version there. So the negotiations, which previously lasted nearly 2 years, involved major gaming companies such as Activision, Warner Brothers, and Walt Disney.
Jordan Wilson [00:02:27]:
While progress has been made on wages and job safety, the two sides could not agree on the regulation of generative AI. Alright. Our next AI news for the day, 2 AI models from Google DeepMind have achieved silver medal performance at the International Math Olympiad. So 2 new AI models, alpha proof and alpha geometry 2, have made significant strides in solving advanced mathematical problems, achieving a performance level equivalent to a silver medalist at the International Mathematic Olympiad. So the IMO is a prestigious competition, competition for young mathematicians, challenging participants with complex problems in algebra, geometry, and number theory. So alpha proof is a reinforcement learning based system for formal math reasoning, and alpha geometry 2 is an improved geometry solving system successfully it successfully solved 4 out of 6 IMO problems. The combined AI system scored 28 points out of a possible 42, earning a perfect score on each problem it did solve just one point shy of the gold medal threshold, which is wild. So if if you heard, like, 2 years ago, oh, AI is bad at math.
Jordan Wilson [00:03:40]:
No. It's not. It's I mean, that's world class level right there. Alright. And then last but not least, if if you, read our newsletter yesterday, we snuck this in there, but we gotta talk about it here in the podcast. OpenAI has announced SearchGPT, a new way to search the web. So, OpenAI has introduced SearchGPT, an advanced AI powered search engine that promises to transform how we find information online, challenging Google's dominance. So SearchGPT is designed to understand and respond to user queries with nuanced conversational answers offering a more intuitive search experience.
Jordan Wilson [00:04:14]:
Unlike traditional search engines, search GPT remembers previous queries, allowing for a seamless flow of information and deeper understanding. The engine is currently in a very limited beta release for 10,000 users with OpenAI aiming to fine tune the system before a broader rollout. So this should be pretty interesting here. So a direct shot at Google. We first reported on this about 4 months ago. It was apparently delayed at the time, but now they just rolled the beta out. 10,000 users. So who knows when the rest of the world will get this, whether it's gonna be 2 weeks, 2 months, we're not sure.
Jordan Wilson [00:04:47]:
But you can sign up for the beta, and we will have that link in our newsletter as well. It's interesting too because Reddit also just did announce today that they'll be blocking all AI search engines that it didn't go into a partnership with, but OpenAI did go into part partnership with them. So, should be pretty interesting there. Direct shot at Google, direct, kind of, competitor now to perplexity in that regard. Alright. That's enough for the AI news. We'll have more in the newsletter, but let's talk about how AI is changing workplace productivity. This is something I think about all the time, and I think when we talk about productivity, it's a lot more than meets the eye when it comes to AI.
Jordan Wilson [00:05:24]:
Alright. So, I'm excited for today's guest. So there we go. We have him on. Please help me welcome on Dean Gaida, the founder and CEO of Infragistix. Dean, thank you so much for joining the Everyday AI Show. Yeah.
Dean Guida [00:05:37]:
Thank you. Thank you. Glad to be here.
Jordan Wilson [00:05:39]:
Alright. Dean, tell us a little bit about what Infragistix is and what you all do.
Dean Guida [00:05:43]:
Yes. For the last, 35 years, we've been building tools for designers and developers, that are building commercial applications a lot around the UI and data analytics. And, we have a product called Slingshot, which is an AI data driven work management tool that goes and connects to all your business systems, and then you can have conversational analytics within a, productivity platform that, you can execute and and have all your tasks and have transparency in how you're getting work done and and really leveraging data and AI to, make better inform more informed decisions.
Jordan Wilson [00:06:21]:
Yeah, Deed. For, you you said 30 plus years there. I mean, even when we talk about generative AI, and we'll get into the productivity, you know, topic here in a second. But, you know, for a a founder and CEO who's been in the game for 30 plus years, overall. I mean, how how do you compare this this generative A. I. Wave to previous, you know, technological innovation, you know, such as the the the Internet. Right? Cloud, mobile, web 2 point o.
Jordan Wilson [00:06:50]:
Where do you think generative AI falls, you know, when we start to compare it previous, you know, tech innovation?
Dean Guida [00:06:57]:
Yeah. It's like, 10 x. I mean, seeing all the innovation over 35 years, the the capability and benefit and how software companies can build on top of what's happening in the market is is amazing. So it's really 10 x innovation over all these different transitions we've gone through over the last, 3 or 4 decades.
Jordan Wilson [00:07:18]:
Yeah. And, hey, as a reminder to our livestream audience, we actually got a lot of people in the house today, so thanks for joining us. If you do have questions for Dean, please get them in now. But let's just get to the to to the crux of the topic. Let's answer the question right now, Dean. You know, from your vantage point, how is AI changing workplace productivity? I know that's a big question and will unwrap it. But overall, what's your take on how AI has impacted productivity?
Dean Guida [00:07:44]:
Yeah. I think that the the whole market is trying to figure out an equilibrium where management and the workforce truly understands at what level of productivity you'll get. So you have this heightened expectation from management and then you have, some change management and resistance among the workforce. So, like, we sell a lot of tools to some of the biggest, SIs, you know, Tata, Infosys, Wipro, and we work with some of the largest ISVs and enterprises. And I think the CIOs and the enterprise think that they can get 30% or more productivity from their software development teams and from their partners, their their, software integrators. And and it's not quite really there. So, so just truly understanding, how you can use AI and copilots and, to help aid software development. It's helpful and but it's not quite at the same level of, like, where it's a little disruptive of, in the market of business, both people, you know, hiring people as well as, partnering with companies that build software.
Dean Guida [00:08:49]:
So it it's it's it's kind of not there yet. It's not found its equilibrium.
Jordan Wilson [00:08:54]:
And that's interesting. Right? Because I've I've personally you know, I've interviewed hundreds of people here on the everyday AI show, and I've heard both sides of it. Right? I've I've heard people, you know, just like you say, yeah. We're we're seeing some gains, but it's not quite where everyone's promised. And then I've heard people on the other end say, oh, it's way more than everyone's promised. You know, I'm curious and I've talked about this a little bit recently, but on the educational piece. Right? How are you, you know, as the, you know, cofounder or or or sorry, as the founder and CEO, how are you making sure that that your employees are are properly trained and can use all these tools available to them, especially when you work with, you know, a lot of software companies and there's so many great AI, you know, tools and systems that help in coding and and software development. You know, how do you kind of navigate, you you know, education and making sure that that your employees, you know, have the the right tools and the right training to take advantage of of what is available to them.
Dean Guida [00:09:48]:
Yeah. Some of it's easy and some of it's hard. So the easy part like we have websites with large content in it, and, and so we'd have to put a lot of people and a lot of time to translate to to Japanese, Korean, Spanish. And, we use this product called Woven that's amazing that real time translates our content, for our website. So so there, that that's just there's such huge productivity gains that that's easily adaptable, adapted in our company. And then, but then we even have our our software development teams that, there's a change management there where we have to convince some of our teams to to use Copilot and get a Copilot. And and there's once they start using it, they see the value that's doing a lot of the busy work for them. But there's there is a whole management change management problem, that that you have to address.
Dean Guida [00:10:40]:
And then the other thing, some AI, like, everyone's infusing AI into their product. So generative AI doesn't get it right all the time. So when you're, like even in work management tools where it starts to give you a status update on projects and what tasks matter, it doesn't always get it right. And so they spend the time, using the AI to summarize what's happening, but then you read it and it's not correct. So now you've wasted time reading the summary. It's not correct when you could have just done and gave management an update on where projects are at. So that's kinda where we're at, like, in the middle of everyone rushing to inject this technology into their software. And, and when it's only right some you know, not all the time, then then actually, you know, has less confidence in people using it.
Dean Guida [00:11:28]:
You
Jordan Wilson [00:11:29]:
know, I'm curious. You know, you mentioned, you know, GitHub copilots. Right? You know, a great, you know, piece of piece of AI software to help people code and I'd say it was one of the earliest, you know, focus on a specific task. Right? And it did it very well. Right? But now you have, you know, just as an example, you know, you have you have, Infropix, Claude, 3.5 SONNET, which is amazing at coding. Right? How do you, you know, how can you as as the leader of an organization keep up with all these advancements? Right? Especially if if maybe you have to win people over and finally get them in a system. And then all of a sudden, you know, it seems like every week, even this week, we've seen 3 literally groundbreaking tools that are, you know, benchmarking through the charts on on things like coding, on things like, you know, creative writing, everything else. How do you keep up with all of these advancements and and balance that productivity with, you know, spending a lot of time training, you know, people on a certain system?
Dean Guida [00:12:30]:
Well, we may be a little unique because we're not only users of AI, but we're also building AI. So so we're constantly looking at different new models and, how we're leveraging it inside of our, data analytics and conversational analytics products. So that's kinda keeping up, an area of our development teams. But I I think it's, like, it's all of our job to do it. At least we're a software company, so it's our job to, get at a baseline of understanding where the market is and then constantly, as you said, as new models and, new new techniques enter the market, you know, how can we leverage that? Where does it make sense? And quite frankly, it's not just that, you know, value to your customer. It's also there's a there's always a cost model to it. So, like, as a software company, you know, we, like, we looked at OpenAI, then we looked at some smaller models, language models, and we're we're wanting to deliver the right model and the right value at at the cost structure we can afford to pass on to our customers. So it's like it's not just about innovation and, and the model's capability.
Dean Guida [00:13:34]:
There's also an economic, point to it.
Jordan Wilson [00:13:38]:
You know, one other thing that you mentioned there that I wanted to kind of circle back to, Dean, is is this concept of, you know, this heightened expectations. You talked about how there's, you know, resistance even, you know, in in the workforce to this. Right? That's one thing I'm I'm always curious to dive more into. Right? If if you're introduced, you know, if if if you're a manual knowledge worker, which so many of us are. Right? We're working in front of a computer, doing certain skills, doing certain tasks over and over. And there's a new tool that comes in that can, you know, in theory, if you use it correctly, it can automate a good chunk of this. Why do you think that there's resistance in the workforce, in the workforce to these generative AI systems that can really help alleviate some of these conflict some of these time consuming mundane tasks.
Dean Guida [00:14:27]:
Yeah. I mean, it's when it's time consuming and mundane task, I think you get adoption, but then you get, like, these senior architects and these senior developers and and just and maybe just even developers where they're they're setting their ways. They're they're they're amazing at what they do, and, it takes a lot to get them to change and do something different when they're they've had such success at building software in the past. And so it it it's just a human nature where I mean, it's not just software developers, but it's human nature where people change their process and and and they're skeptical of it. So, but so, yeah, you have to, like you know, there is some management and there's some cajoling involved here. And then, and then when they, like no one like, what AI is doing now is a a lot of work that no developer likes to do. But then you have this hype hype out there saying that in 3 year, like, great, you know, rate you know, that 3 years or or in this decade, you won't have any more software developers. I just don't believe that that there's too much complexity in the enterprise.
Dean Guida [00:15:29]:
There's too much technology. There's too much data. There's too much legacy. It it's complex. I mean, so I I I just so so I think you have some of that as well.
Jordan Wilson [00:15:41]:
Yeah. And and let's let's dive into that a little bit more because, again, you hear people on on both sides of this issue. Right? We've had multiple people on the podcast who, you know, deemed very much like what you just said. Like, no, it's it's it's much more intricate than that. And, you know, maybe famously or infamously, you know, you had NVIDIA CEO Jensen Huang kind of suggest like, oh, no. Kids probably shouldn't be learning coding because it's not gonna really be needed in the future. Right? For for for some of those and we don't have to just talk about, you know, software development and coding. But, you know, when it comes to, you know, you're wanting to build up certain skill sets in your team.
Jordan Wilson [00:16:19]:
Right? You know, in your workforce. How do you balance that as a CEO? Right? When there's some of these, you know, AI systems and tools, even consumer facing ones like ChatGPT and Claude, you know, Gemini, etcetera, that can do certain tasks so so well. How do you manage that? Right? Do you go out and, you know, just try to hire people who just have an AI first mentality only, or do you still try to hire people who still want to, you know, develop, develop and, quote, unquote, use those kind of, like, and I chuckle when I say this, like, these old school skill sets. Like, how do you balance hiring the right people who have the right mindset on what they wanna learn and know?
Dean Guida [00:16:57]:
Yeah. I mean, for us, we always wanna hire this kind of growth mindset people. We always wanna hire people that are really focused on, enjoy learning and problem solving. And so for us, it's not a change from that point of view where when you have these growth minded people, what any kind of productivity and, ability to be creative or solve problems faster, they're they're into it. But even with that said, in our company with growth mindset at people, we still have, you know, resistance to change too. So it's not, it's not perfect world. And so, I think we're just at this point in time where we're talking about this, but, as the the months years go on, we won't talk about this. We'll talk about understanding, okay, what, you know, what models are really doing well, where to watch out over time, what models are, you know, not doing so well.
Dean Guida [00:17:48]:
And and and so I think we'll this is a conversation at a point in time in history that, we will we won't have this in the future.
Jordan Wilson [00:17:56]:
Yeah. And, you know, one thing that, you know, even we're very small. Right? We have a small team here. But, you know, I tell our team, hey, we have to use this AI system for this task. No more doing it like we used to. You know, do you put those kind of directives down? Like, is there a certain, you know, kind of governing structure where, you know, you say, hey, when we're doing these tasks, we need to use, you know, GitHub Copilot or we need to use this system or do you just kind of leave it up to individuals and teams? Right. Like especially, you know, you said that you guys work a lot in the in the software industry where there's a lot of of hand coding, I'm sure, and, you know, creating other AI products for other companies. How do you actually manage that? Can you say, oh, only use these AI tools for these tasks?
Dean Guida [00:18:44]:
I mean, we we do say it, but then you have to be realistic that not everyone will follow it. So we do oh, we've always promoted improved process, always promoted use of software and technology. And and now that we have AI, of course, we're promoting that. And, so we don't we don't, like, reprimand. Like, it's so it's it's a level of of enforcing it. We don't reprimand people, but we're like, look. You you know, here's the gain. So it's a constant reinforcement.
Dean Guida [00:19:12]:
We're like, here's this team's getting the gains. Here's how you could do less busy work. Here's how you could do things faster. And so it for for us, it's it we do, like, state it and mandate it, but, like, the level of enforcement, we we don't do.
Jordan Wilson [00:19:28]:
Yeah. And, you know, I'm wondering and you don't have to talk about this specifically with, you know, your company. Maybe you can talk about it in the broader scope. But do you think that there's maybe a potential for divide? Right. Like I tell people, AI implementation is actually about change management more than it's actually about artificial intelligence or large language models. Do you think that there is maybe a divide right among those who are using it and advocates and and finding great productivity gains versus those who kind of maybe are a little more, you know, stuck in their ways, kinda like what you said earlier. Is there a potential for a growing divide in a company, and could maybe an AI tool or system do more harm than good?
Dean Guida [00:20:11]:
I think there is. It's always a change management problem. I don't think it'll be so, like, two sides saying we're harming good because, like, if if you use some of these generative AI features in software and workflow tools and, you know, design tools, you you find out that, like, it doesn't remember right now. All like, you're trying to iterate through some, creative visual concept, and it doesn't remember what it got right. And then, and then you try to change the pieces it got wrong, and then you start iterating on it. And then all of a sudden, it lost what you like and so then people become frustrated, but that doesn't mean throw the tool out. It means, you know, okay. It's that's just the state.
Dean Guida [00:20:51]:
We'll keep improving, the output and the capability of, these different models. So I I don't see it as a lined up divide. I I think you do see it in industries like you reported in your news that, you know, it could really take out, like, future opportunity for industries and, and so then you will see that divide. But internally, I think we're all going through a transition of good value, Forgive it when it doesn't do something good, but, like, leverage it where it does do good and, and understand the, you know, the the value of of the or the capability of the tool to get your job done.
Jordan Wilson [00:21:28]:
You know what? Speaking of of recent news, I think we talked about this on the show yesterday, but there was a recent study on productivity in AI from the Upwork Research Institute. So it said that despite 96% of c suite leaders expecting AI to boost productivity, nearly half of employees using AI feel that they don't know how to meet those productivity gains expected by their employers. You know, Dean, I'm sure you have thoughts on, on this, but where do you stand on kind of this, you know, this c suite expectation for increased productivity? Right? Because sometimes companies are investing, you know, tens of 1,000 of dollars a month or more just on software licenses. Right? Or sometimes a lot more than that. So where do you stand on this, you know, C suite productivity demands versus, like, workers being like, I can't do that?
Dean Guida [00:22:21]:
Yeah. I mean, there's a heightened expectation, and then there's a a moving productivity delivery from the people building out these software systems and AI systems. So so it it's one of these things where, you can set a high goal, think you're gonna get 30% productivity and then all of a sudden you're overworking your teams. And so it's it's really about listening to your teams and, and and and truly listening to them. Like, hey. We're getting overworked. You're giving us too much. We're not getting the productivity level, or we're having a level here, or maybe the next release of some model is gonna help us better here.
Dean Guida [00:22:56]:
But I think we're in this very transitional stage in in history where there there's both real true value being delivered and then there's heightened expectation. And finding that balance is gonna be, know, shaken out. It's it's like I was saying earlier in our conversation, you have some of these very big CIOs in the enterprise thinking that they can use the same workforce and get 30% more output. Therefore, they don't need more staff, and and and that's just not quite there yet. You know? There's just so much more complexity in in a in a lot of jobs.
Jordan Wilson [00:23:32]:
Yeah. And, you you know, I think, ultimately, for those companies or, groups that can experience that that 30% more output as an example, I think it can lead to burnout. Right? This is something even myself I experience often that I didn't experience before generative AI. Right here. Here it is. It's not even 8 a. M, you know, my time as I'm talking with you, Dean. I've already used, at least 5 different AI systems.
Jordan Wilson [00:24:01]:
I've already done the work in, you know, 2 hours today that would normally take me a a day or more. You know, I personally often feel just fatigued from, you know, consuming way more information than I'm normal, you know, normally would. Is is there a downside to employee productivity as well in feeling this burnout because you always have a tool that can help you do more and there's an expectation to do more? Yeah.
Dean Guida [00:24:25]:
I think for, like, a personality such as yourself or people that are driven, they're gonna leverage tools and, push it more. So if you used to be able to get, 2 days worth of work and, days of work in 1, now people think you can do 3 and you're driven then to hit that goal. I mean so I don't think it's an AI problem. I think it's, you know, just a, self management problem. But, yeah. I mean, we it's more about management understanding, the real level of productivity and expectation so that people feel at the end of the day they were successful and management's happy.
Jordan Wilson [00:25:04]:
You you know, speaking of management being happy, I think a lot of that, you know, obviously comes from driving business value. Right? Ultimately, you know, you want to implement some sort of AI and you want to see some sort of business growth. Had a global, AI leader from Microsoft on last week who she just kinda said, hey. Don't worry about your return on investment just yet. How do you tackle that, Dean? Kind of like looking at, you know, creating business value and and finding that nice balance with with productivity. How do you kind of steer that? And I I mean, when you implement a a a new AI, system into your workflow, is there a direct expectation that, hey. This much must lead to x level of productivity, x, dollars of of new business growth? How do you manage these new AI initiatives that you implement in in your organization?
Dean Guida [00:25:56]:
Well, I think I come from a from being a tech CEO point of view, which is that we always have to overinvest in the future of technology so much to to, you know, stay ahead and be competitive. So, we have a lot of patience of over investing and and being patient about the ROI and return. But if you don't do that when the market's ready and then you're you're too far behind on on on your software or your tech. So so we're I guess my answer is a little probably a little bit different than maybe others or maybe it's not, but, we we have to overinvest and and be patient about that. I I think Microsoft and others are saying that because there's so much money flowing into AI and there's such a big shift. Everyone's worried about where all the, chess pieces shift. Who who now is the, you know, the king and queen? And, and and then there's such great promise and such heightened promise. Like, I was at, TED and, you know, one of the heads of DeepMind.
Dean Guida [00:26:56]:
Now I'm leading Microsoft kinda talked about, oh, AI is a new digital species. Well, that's just gonna scare the the crap out of everybody. Like, it's not a new species. You know? It's like the the the tech and the, and where we're at with these LLMs is gonna get us really far, but not enough to AGI level. And, so but with this heightened expectations because there's 1,000,000,000,000 and 1,000,000,000 of dollars going in here. Like, I was amazed anthropic getting 6 1,000,000,000 in funding when they had, 50 people and no revenue. And it's just that's just the insaneness of what's happening at this point in time. But there's real value there.
Dean Guida [00:27:33]:
So it's insane because it's never been that much funding. But then the promise of what you you know, the problems you can solve on top of of these technologies is really great.
Jordan Wilson [00:27:43]:
Yeah. What would you say, you you know, because you talked about not only are you all using AI internally, but you're also building AI solutions for other clients. But, you know, I'm wondering for yourselves internally, where have you found the maybe either best productivity kind of returns on an AI investment internally or something that you think an AI move that you've made recently that has paid the biggest dividends already in terms of new business growth?
Dean Guida [00:28:13]:
Yeah. What we've seen amazingly turns and a great use case for AI is where we're training these models on all our business systems. So, you know, we use Salesforce. We use, HubSpot sometimes. We use all these account based marketing systems. We we use so many systems at our company, but all this data is locked up in these different business systems. And and even though they may have reporting and analytics capability, it's still locked up in these silos. And so this huge value we're we're achieving is by training an AI on all of your business data and then the user experience of just asking questions and in a conversation to understand customer acquisition cost or a best performing campaign or whatever question you have is a game changer for us and, and for a lot of businesses that, you know, when you can unlock your business data to tell you what's happening so that you can kind of hypothesize and create experiments and improve business outcomes, that that's been a huge game changer for us.
Jordan Wilson [00:29:22]:
Where where are you looking at next? Right? I I think I think you're in a unique position. I'd love talking to people in your position, Dean, that, you know, both build AI solutions for others and are using other, AI solutions internally. Where are you looking next?
Dean Guida [00:29:38]:
I mean, for us, it's it's also getting more accurate. Like, for for me, I really wanna see, an inter working with an AI to iteratively create something, whether it's content, a visual, code, whatever. It I just feel that it's not quite there with, changing pieces of its output and iterating on the pieces you want change and not, affecting pieces of what the output that's or you like. Like like, when we get to over that hump, it's gonna be so much more productive for everyone. And and that's kind of a generic statement, you know, whatever content you're outputting.
Jordan Wilson [00:30:17]:
Alright. So so, Dean, we've covered a lot in in about, 30 minutes. So, you know, we've talked about the pros and the cons of of productivity. We've talked about how AI and in, productivity in the workplace is more change management than than anything else. But maybe, you know, as we wrap up here, what is the one best piece of advice that you have for others? Maybe, you know, something you said really struck a chord with someone and they're like, we gotta get this whole productivity thing with AI balanced and figured out. What is your one best piece of advice or takeaway for those business leaders that are moving the pieces within their own over within their own organization?
Dean Guida [00:30:54]:
Yeah. I think it's just constantly being open minded and understanding the state of all of your, all the innovations happening. So and then prioritizing what's most important to your business to stay even closer to it. And then, just being realistic about, the outcome and, you know, once you deploy it and use it. And know that everything's iterative, that everything's always improving. So I think it I think if you start at the top of expectation, you'll help the whole organization adopt and deliver and be more harm you know, better execution.
Jordan Wilson [00:31:29]:
And you you know what? I I think there is so much great value in today's show. So, you know, thank you so much, Dean, for joining the Everyday AI Show and helping us all better understand, kind of how to balance and find this workplace productivity, with AI. We appreciate your time and your insights.
Dean Guida [00:31:47]:
Yeah. Thank thank you for having me.
Jordan Wilson [00:31:49]:
Alright, everyone. There was a lot there, and there's a lot more as always. We're gonna be recapping today's conversation as well as giving you the most up to date what's going on in the world of generative AI, fresh finds from across the Internet, everything in our newsletter. So please go to your everydayai.com. Sign up for that free daily newsletter. We hope to see you back next weekend every day for more everyday AI. Thanks, y'all.
AI [00:32:13]:
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.