EP 323: How AI Is Changing Workplace Productivity

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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. All right, 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. This is your show. This is, 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 going to 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 going to want to read the newsletter as well. So make sure you can find that@your everydayai.com all right, 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. Just started at midnight today following a breakdown in negotiations over AI protections with major game studios. The strike is significant as it marks the second work stoppage for video game voice actors in 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 the shortened version there. So the negotiations, which previously lasted nearly two years, involved major gaming companies such as Activision, Warner Brothers and Walt Disney. While progress has been made on wages and job safety, the two sides could not agree on the regulation of generative AI. All right, our next AI news for the day. Two AI models from Google DeepMind have achieved silver medal performance at the International Math Olympiad. So two 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 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 four out of six 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 two years ago, oh, AI is bad at math. No it's not. It's. I mean that's world class level right there. All right. And then last but not least, if, if you read our newsletter yesterday, we snuck this in there, but we got to talk about it here on the podcast. OpenAI has announced search GPT a new way to search the web. So OpenAI has introduced Search GPT, an advanced AI powered search engine that promises to transform how we find information online, challenging Google's dominance. So Search GPT is designed to understand and respond to user queries with nuanced conversational answers, offering a intuitive search experience. Unlike traditional search engines, Search GPT remembers previous queries, allowing for a seamless flow of information and deeper understanding. The engine is currently in very limited beta release for 10,000 users, with OpenAI aiming to fine tune the system for a broader rollout. So this should be pretty interesting here. So a direct shot at Google. We first reported on this about four 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 going to be be two weeks, two months, we're not sure. 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 open AI did go into partnership with them. So should be pretty interesting there. Direct shot at Google. Direct kind of competitive competitor. Now to perplexity in that regard. All right, 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. All Right. So I'm excited for today's guest. So there we go. We have them on. Please help me welcome on Dean Guida, the founder and CEO of Infragistics. Dean, thank you so much for joining the Everyday AI show.

Yeah, thank you, thank you. Glad to be here.

All right, Dean, tell us a little bit about what Infragistics is and what you all do.

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 really leveraging data and AI to make better, inform, more informed decisions.

Yeah, Dean, for 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 founder and CEO who's been at the game for 30 plus years overall, I mean, how, how do you compare this, this generative AI wave to, to previous, you know, technological innovation, you know, such as the, the, the Internet. Right. Cloud, mobile, web, 2.0, where do you think generative AI falls, you know, when we start to compare it to previous, you know, tech innovation?

Yeah, it's like 10x. 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 10x innovation over all these different transitions we've gone through over the last three or four decades.

Yeah. And hey, as a reminder to our live stream 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 the crux of the topic. Let's answer the question right now. Dean, you know, from your vantage, how is AI changing workplace productivity? I know that's a big question and we'll unwrap it, but overall, what's your take on how AI has impacted productivity?

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 software integrators. And it's not quite really there. So, so just truly understanding how you can use AI and co pilots 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. So it's, it's kind of not there yet. It's not found its equilibrium.

And that's interesting, right, because I've, I 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 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, I've talked about this a little bit recently, but on the educational piece, right, how are you, you know, as the, you know, co founder 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 in software development. You know, how do you kind of navigate, 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?

Yeah, some of it's easy and some of it's hard. So the easy part is like we have websites with large content in it and, and so we'd have to put a lot of people in 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 adoptable adapted in our company and then, but then we even have 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 GitHub co pilot. 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 you have to address. 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 kind of where we're at, like in the middle of everyone rushing to inject this technology into their software. And when it's only right, not all the time, then actually has less confidence than people using it.

I'm curious, you mentioned 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 anthropics Claude 3.5 sonnet, which is amazing at coding. Right. How do you, you know, how can you, as the leader of an organization, keep up with all these advancements, Right? Especially 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 three 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 balance that productivity with, you know, spending a lot of time training, you know, people on a certain system?

Well, we may be a little unique because we're not only users of AI, but we're also building AI. 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 kind of keeping up an area of our development teams. But I think 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 techniques enter the market, how can we leverage that? Where does it make sense? And quite frankly, it's not just value to your customer, it's also, there's always a cost model to it. So as a software company, we looked at OpenAI, then we looked at some smaller models, language models, and we're wanting to deliver the right model and the right value 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. There's also an economic point to it.

You know, one other thing that you mentioned there that I wanted to kind of circle back to, Dean, is this concept of, you know, this heightened expectations. You talked about how there's, you know, resistance even, you know, in the workforce to this. Right. That's one thing I'm always curious to dive more into.

Right.

If, if you're introduced, you know, 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 con. Like some of these time consuming, mundane tasks?

Yeah, I mean, it's, when it's time consuming and mundane tasks, 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 set in their ways, they're, they're, they're amazing at what they do. And it takes a lot to get them to change, to do something different when they, they've had such success at building software in the past. And so 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 they're skeptical of it. So, but so yeah, you have to like, you know, there is some management and there's some controlling involved here. And then, and then when they like no one, like what AI is doing now is a lot of work that no developer likes to do, but then you have this hype hype out there saying that in three year, like great, you know, rate, you know, that three years or in this decade you won't have any more software developers. I just don't believe that there's too much complexity in the enterprise. There's too much technology, there's too much data, there's too much legacy. It, it's complex. I mean, so I just, so I think you have some of that as well.

Yeah, 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 in the podcast who you know, Dean, very much like what you just said, like, no, it's, it's much more intricate than that. And you know, maybe famously or infamously, you know, you had Nvidia CEO Jensen Wong kind of suggest like, ah, no, kids probably shouldn't be learning coding because it's not going to really be needed in the future. Right. 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, 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 Chat, GPT and Claude, you know, Gemini etc 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 want to learn and know?

Yeah, I mean for us we always want to hire this kind of growth mindset people. We always want to 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, 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 months and 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. And so I think this is a conversation at a point in time in history that we won't have this in the future.

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?

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 on. So we don't, we don't like, reprimand, like, 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. We're like, here's this team's getting the gains. Here's how you can do less busy work. Here's how you could do things faster. And so for us, we do, like, state it and mandate it, but, like, the level of enforcement we don't do.

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 in finding great productivity gains versus those who kind of Maybe are a little more, you know, stuck in their ways. Kind of 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?

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 you use some of these generative AI features in software and workflow tools and you know, design tools, 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. We'll keep improving the output and the capability of these different models. So I don't see it as a lined up divide. 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 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 understand the value or the capability of the tool to get your job done.

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You know what, speaking 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 thousands 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.

Yeah. I mean, there's a heightened expectation and then there's a moving productivity delivery from the people building out these software systems and AI systems. So, so it's one of these things where you can set a high goal, think you're going to get 30% productivity, and then all of a sudden you're overworking your teams. And so it's really about listening to your teams 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 going to help us better here. But I think we're in this very transitional stage in history where there's both real true value being delivered and then there's heightened expectation and finding that balance is going to be, you 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 a lot of jobs.

Yeah. And, 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 8am you know, my time as I'm talking with you, Dean, I've already used at least five different AI systems. I've already done the work in, you know, two hours today. That would normally take me 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, 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 two days worth of work and days of work in one, now people think you can do three 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 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.

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 kind of 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 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 new business growth. How do you manage these new AI initiatives that you implement in your organization?

Well, I think I come from it, from being a tech CEO point of view, which is that we always have to over invest in the future of technology so much to stay ahead and be competitive. So we have a lot of patience of over investing 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 over invest and be patient about that. And I think Microsoft and others are saying that because there's so much money flowing in to AI and there's such a big shift, everyone's worried about where all the chess pieces shift. Who, 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, now I'm leading Microsoft, kind of talked about, oh, AI is a new digital species. Well, that's just going to scare the crap out of everybody. Like it's not a new species. You know, it's like the, the tech and the, and where we're at with these LLMs is going to get us really far, but not enough to AGI level. And so, but with this heightened expectations because there's trillions and billions of dollars going in here. Like I was amazed anthropic getting 6 billion 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. 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.

Yeah. What would you say? 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 in an AI move that you've made recently that has paid the biggest dividends already in terms of new business growth?

Yeah, well, we've seen amazing returns 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 Hubs 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 even though they may have reporting and analytics capability, it's still locked up in these silos. And so this huge value we're achieving is by training an AI on all of your business data and then the user experience of just asking questions in a conversation to understand customer acquisition cost or 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's been a huge game changer for us.

Where, where are you looking at next. Right. I think, I think you're in a unique position. I 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?

I mean, for us it's, it's also getting more accurate. Like for, for me, I really want to see an ITER 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 already you like. Like when we get to over that hump, it's going to be so much more productive for everyone. And that's kind of a generic statement, you know, whatever content you're outputting.

All right, 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 got to 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 organ, within their own organization?

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.

And you know what, 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.

Yeah, thank you for having me.

All right, everyone, There was a lot there and there's a lot more. As always, we're going to 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 everyday AI.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.

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