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Ep 139: How To Transition to an AI-First World

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  • 7 Nov, 2023
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Overview

In a rapidly evolving digital landscape, businesses are increasingly embracing artificial intelligence (AI) to stay competitive and drive growth. However, the transition to an AI-first world is not as straightforward as it may seem. While the potential benefits are immense, business owners and decision-makers face both challenges and opportunities along this transformative journey.

Harnessing the Power of Data:

One of the primary challenges in transitioning to an AI-first world is ensuring access to the right quantity and quality of data. Many organizations today lack the necessary data to run AI algorithms effectively. This highlights the importance of building robust data collection mechanisms and unifying data warehouses. Business owners need to work closely with data engineers to establish reliable data pipelines to fuel their AI initiatives.

Addressing Trust and Reliability:

Trust is a critical factor in the adoption of AI technologies. To encourage buy-in from executives and stakeholders, businesses must address any skepticism surrounding AI by showcasing AI’s ability to deliver reliable results. This requires investing in data quality, transparency, and ethical considerations, while ensuring accuracy and compliance with regulatory requirements. Building trust in AI is a gradual process that requires a focus on governance, legal frameworks, and establishing best practices within the organization.

Balancing Novelty and Tangible Outcomes:

While experimenting with AI and exploring its potential is exciting, businesses should not overlook the need for tangible outcomes and real value. Implementing AI purely as a novelty can erode trust and hinder broader adoption. Instead, business owners should emphasize the practical applications of AI that drive improved decision-making, higher productivity, and cost optimization. Demonstrating tangible results is crucial for securing executive support and facilitating the integration of AI into everyday business operations.

Promoting a Culture of Continuous Learning:

Transitioning to an AI-first world requires a culture of continuous learning and upskilling within the organization. Business owners should invest in providing employees with the necessary training and resources to adapt to new AI-driven processes and tools. This includes ensuring that employees understand the capabilities and limitations of AI and encouraging them to engage in ongoing professional development. A workforce equipped with AI literacy will be better positioned to exploit the full potential of AI and drive organizational success.

Conclusion:

While the road to an AI-first world presents challenges, business owners who embrace these challenges can unlock significant opportunities. By prioritizing data quality, establishing trust, focusing on tangible outcomes, and fostering a culture of continuous learning, organizations can position themselves as innovators in their respective industries. The time is ripe for business leaders to navigate this transformative journey, harnessing the power of AI to drive growth, improve decision-making, and stay ahead of the competition.

Topics Covered in This Episode

1. Challenges businesses face in implementing AI
2. The importance of data quality and collection
3. Essential steps in AI implementation
4. Business results and productivity opportunities with AI

Podcast Transcript

Jordan Wilson [00:00:18]:

When will the time come when our work lives are just infused with AI? And it’s everywhere we go in our in our work, in our business, in our companies. Are are we a decade away? Maybe a couple years, months? And then how do we transition to that AI first world. We’re gonna be talking about that today and a lot more on everyday AI. Welcome. My name is Jordan. I’m the host, and I guess your your guide to learning and leveraging, AI. So I’m extremely excited to talk to our guest today about how we can transition to that AI first world. But before we do, as always, we’re gonna go over the AI news for the day.

Daily AI news

Jordan Wilson [00:01:00]:

And, hey, if you’re joining us on the livestream, thank you. Let me know where you’re joining us from. I always like to see that and And, engage with our live audience. If you’re listening on the podcast, thank you as well. Make sure to check out your show notes. We always leave a link to go sign up for our free daily newsletter, as as well as other relevant episodes on today’s topic, so make sure you check that out. Alright. So, normally, we run down a whole list of things in AI news, but pretty big news yesterday.

Jordan Wilson [00:01:29]:

So we’re just gonna focus on actually one thing. So if you wanna get the rest of the news, we got it. Don’t worry. Just go to your everyday .com and sign up for it. But yesterday, big news. So let’s recap it. We had the OpenAI developers day And a ton of news when it comes to, OpenAI and ChatGPT for developers. But I’m gonna break it down real quick in this little quick news segment On what it means for everyday people, because it’s not just for developers.

Jordan Wilson [00:01:56]:

But high level, here’s what happened at the OpenAI developer conference. So They announced GPT 4 turbo, which is their newest, and kind of most refreshed model, which is, supposed to be much stronger in a way Longer context. A couple other things is being able to, for developers to have better knowledge. So being able to bring in outside documents, by default in their products that they that they build, as well as new modalities, which we’ve seen these kind of already. So, you know, DALL E 3, vision, The ability to upload, documents and to be able to to, work with those and chat with those. So, you know, now, ChattGPT, developers or GPT developers and Have that, ability to bring that kind of functionality to the products that they build. So a couple things. That is going to affect Just about any GPT related product that you use.

Jordan Wilson [00:02:48]:

So get ready for a lot of changes there, but a couple other I’d say 3, actually, 2 2 big ones for, everyone else. So we have copyright shield, and that’s gonna be mainly for, enterprise and API customers. But, OpenAI is following suit for other companies like Adobe who are offering some of that copyright protection. So if, someone gets sued for using their product, They will offer a sort of legal protection with the copyright shield. And then we have as well, it’s it’s going to be cheaper. Right. That’s the other thing. About 2 to 3 times cheaper.

Jordan Wilson [00:03:23]:

So maybe those products that you use that are very expensive might come down in price. And then last but not least, ChatGPT, some big updates. So, yes, the default mode, for GPT 4 will be turbo. So faster, better, and a much Wider range of memory for ChatGPT. So hopefully fewer hallucinations as well as GPTs, which are custom GPTs that you can build Without code, being able to upload documents, train it to respond a certain way. So very exciting news out of the developer day. And, again, more on that and a lot more on what’s happening in the rest of the AI news world. Go to your everyday AI .com.

About Tina and AI-First Business Podcast

Jordan Wilson [00:04:04]:

But You didn’t come here to hear me rant about ChatGPTe and developer days. You came to learn how to transition. You’re listening because you wanna know how to transition your company or even yourself to an AI first world. So I’m very excited to bring on our guest for today. And please, if I can get it right here, help me welcome to the stage. There we go. We got her. Tina Yazdi is the founder and host of the AI First Business podcast.

Jordan Wilson [00:04:31]:

Tina, thank you for joining the show.

Tina Yazdi [00:04:33]:

Hi, Jordan. Thanks so much for having me on.

Jordan Wilson [00:04:35]:

I I hey. I appreciate you. It’s always fun to have another podcaster on the show. I’ve done it 2 or 3 times. It makes for fun conversation because We, like, just chat about AI all day. Right? So, tell me maybe a little bit, Tina, about, the AI first business podcast and what kind of your focus is there.

Tina Yazdi [00:04:52]:

Yeah. For sure. So the AI First Business podcast came about, as a conjunction of a few, points in my background. One is that I have a philosophy degree specialized in AI. I also worked, for a number of years, selling a product that Implemented into the data stack of, start ups and big tech companies, and that gave me a combination of exposure to Some AI hygiene and also some, real field experience on what the data stack actually looks like behind the scenes of these, like, companies that we put on pedestals. And in the last year, those 2 things kind of collided together, where I’m I I was witnessing a lot of narratives around the Upside down inevitability of the impact of AI on our world, and I don’t agree with that. I think there’s a slightly different narrative that I think might be a bit more and Pragmatic pragmatic and closer to reality, which is that the transition is gonna be fast but slow, and organizations are gonna need a lot of time, and a lot of unsexy admin work to be in a position to truly implement an AI first, habit into organizations.

Challenges business face adding AI

Jordan Wilson [00:05:57]:

Tina, you bring up a great point because I think, people think of AI as something Mhmm. They’re like, oh, yes. Look at this. It’s the balance of wittles. It’s it’s gonna make everything great. But to get there as an organization, It takes unsexy work. You gotta do the admin updating your your governance, your your legal. So talk a little bit about challenges that that you see businesses are facing On getting to that point where you can actually start implementing it.

Tina Yazdi [00:06:25]:

Yeah. Absolutely. So my my background is working with enterprise and large, like, multinational groups, in general. So this is kind of more the angle that I’m coming from regarding this. I think the first thing is just having the data available at all. At this moment, I can pretty confidently say that most organizations don’t have the quantity of data to run AI algorithms with any level of reliability. They’re doing it more as a game of novelty, which if that’s the introduction to get your team, like, comfortable with having available in your organization. Like, there there is some merit to that.

Tina Yazdi [00:06:59]:

But in terms of bringing an output that you can actually make decisions on or improve your cogs, like, I don’t think that’s quite what’s gonna be the outcome of that. The danger of that is also it it kind of will already start to erode trust in something that has shaky trust foundations among executives anyways. And I think the other thing is, the the true load of work, like, kind of in combination with that is, like, the data quality and the data collection. That takes a lot of, like, data pipeline building, a lot of unifying your data warehouses, and this means hours and hours spent by Data engineers doing work that isn’t actually AI yet. And I think those are some of the things that I don’t really hear talked about, and I think you can’t really skip those steps, and have, you know, any, like, business results coming from AI. So I I think one of the dangers of this cycle is that, one, it Creates a situation that kind of will lead to an outcome where executives are like, I told you we can’t trust it. I already told you this was useless, and it kind of, like, You know, creates a cycle of despair. And the other is that it leaves such a big opportunity on the table where you can have more productive teams.

Tina Yazdi [00:08:06]:

You can have people working on things that they love to do And do less of, like, the grunchy work that they don’t like to do, but without the foundation. That that transition won’t happen, I don’t think.

Jordan Wilson [00:08:15]:

Yeah. I think we’re all looking forward to less Grunt work and and more Mhmm. Meaningful or more exciting work. And, hey, everyone joining live. Thank you. You know, Shannon said good morning, AI curious world. I I and and I’d like to know also, like, get your questions in for Tina. What do you wanna know about kind of this AI first world and and, you know, becoming an AI first organization, and and maybe let’s start there.

How long until AI is everywhere?

Jordan Wilson [00:08:39]:

I’m gonna rewind. And and, you know, how I start at the top of the show is Talking about when. Right? So when will our business lives? And I’m sure it depends on, you know, where you live. Right? Whether you’re in the US or Europe or elsewhere, you you know, how your business maybe transitions to this. But from your vantage point, in in in in with your background, Tina, what are you seeing? Are are are you seeing it’s it’s very far away until AI is infused in every part of our business day to day, Or do you think maybe it’s coming faster than some might think?

Tina Yazdi [00:09:11]:

It is really scary to answer that question because I need to draw a line in the sand for myself as well. I think I’m gonna be, I’m just such a bit I think we’ll be fast but slow. I think the conversation around AI is unavoidable, and everyone’s talking about it. And thank you, Mike. I he just mentioned that you can’t really skip those steps. I’m kind of, drawing a little my experience rolling out other types of data updates to organizations. And given how those processes go, I’m gonna make some assumptions that it’s gonna be similar with AI transitions. Because AI An AI transition is ultimately a data it’s a subset of your overall data strategy and data, like, you know, vision.

Tina Yazdi [00:09:50]:

What I’m seeing happening is that there is interest in, like, level 1 AI tooling. And in fact, level 1 AI tooling has been around for years. I work in SaaS sales, And we have a number of AI style tools, that have been forced upon us, like, starting 2016 already. I can safely say. So that’s nothing new, actually. And these are things that analyze your recorded calls and give you suggestions on how to improve them. They scrape the data that you have internally on customers and help you make Better, like, either categories or decisions about how to manage those, things like that. Those are becoming a little bit more advanced, and those are the things that are being rolled out right now.

Tina Yazdi [00:10:25]:

Ad There’s already some blockers there. We just talked about this before the show, which is that, the security and legal teams will probably be the number 1, stumbling block to that, Which is very much their job. Their job is to maintain a level of conservative sober analysis on bringing, you know, new technologies into the organization. But on the other hand, They’re not I I don’t see them being very well prepared to, do something as simple as sign a master Mastercard agreement with an AI service because it just has very variables in it that they’ve never had to deal with before, And there’s no preexisting examples that they can, like, cost you know, copycat to, to have confidence that they’re making the right decisions. And so I feel like there’s a little bit of a cat and mouse game there, and I think that’s gonna prevent companies rolling out these technologies even at the level one stage, to keep up with productivity. I think younger companies and tech start ups and, particularly companies in the US versus, Europe are better positioned To move fast and, get that competitive edge, but I think that’s for, like, larger organizations is for sure gonna be a blocker. And I think the other one is kind of like, I I think companies might underestimate the resources required, and the, the maintenance To get your data and have the right team in place to build, like, sustainable long term algorithms that provide business value for years to come. Like, that will require a lot of foundational work that I don’t know if it’s in the fiscal budget, if it’s in the headcount, if there’s leadership.

Tina Yazdi [00:11:58]:

They can put together a team on, like, a 3 year plan. I don’t know if there’s an appetite to have a long term vision, which you cannot avoid to rule these things out. It’s just gonna have to be a long term game. So I I think those are some of the factors that will influence the, the speed to market of AI first habits in organizations today.

Businesses transitioning to AI-first

Jordan Wilson [00:12:17]:

Yeah. And and, Tina, I love what you said, and I completely agree. The the fast but slow, you know, kind of piece to this because, generative AI has been on the scene for a long time. Right? But it’s been kind of, quote, unquote, mainstream, for a year now, right, since, ChatGPT Was released last November. This kind of opened up a lot of companies’ eyes to the power of generative AI. Mhmm. So what kind of pieces do you see have to fall into place and For this fast but slow, kind of transition to an AI first world to take place. Right? Because we we we talk like, okay, legal team.

Jordan Wilson [00:12:54]:

There’s there’s data. There’s governance. What are kind of some of those key pieces that if if if someone listening to this podcast is Maybe a small business owner or they’re a decision maker in a larger company, and they’re not there. Like, what are those pieces that they have to get moving together To get to the place where they can be ready when they’re going to need the power of generative AI.

Tina Yazdi [00:13:15]:

So in terms of the and slow. I talked about the slow. Let me talk a little bit about the fast, which is bottom up, and I’ll talk a little bit more about the slow, which is top down. What I think will drive the speed here is that Your employees are users in the real world, and they are users of technologies. They are, like, obviously, also the same people who are using chat JDK. I think one of the things that is forcing the hand for organizations to figure things out quicker and is also providing, the Firepower behind things like, you know, IBM’s Watson and, Salesforce’s Einstein is that Whether you like it or not, your employees are gonna try to do things in the most productive way possible, and that means they’re already using generated AI that’s, you know, in the wild right now, and that is causing serious enterprise gap, security gaps and concerns. And I think that kind of forces you to, like, Smart, smart prepared enterprises will have to acknowledge that They’re gonna be using it, so you might as well create a guardrail for them to use it within the, like, liability framework that you’re comfortable with, which means, like, you need to figure out a way to sign agreements to, like, either purchase this and bring it internally or create some kind of guidelines, to operate by. So I think that’s gonna be the where the fast comes from.

Tina Yazdi [00:14:32]:

Like, it’s happening. Whether you like it or not, it’s up to you whether you adapt or not or just, like, put your hand in the sand and, you know, I don’t think that you has historically worked well for organizations. In terms of what I, what I’ve seen the best in class organizations, get into place to have a working, movement towards AI first, one is you need a a dedicated team for AI that has both a short term ROI scope and a long term ROI scope. An example of this could be, like, for example, gaming companies. And what I mean by that is, you know, Transitioning your data and your algorithms to take advantage of AI has an element of, like an element of ambiguity to it. So it is totally, of course, appropriate that you have some short term ROI, that you, you know, Put the team against that, like, you you need to see in in the next year or so. But unless they also have a bit of a scope to Play around and build longer term systems to have ROIs, like, 3 years from now, maybe not immediately in the next year. It’s it might be a little bit hard see the true, potential of AI unleashed for where you’re heading as a business.

Jordan Wilson [00:15:44]:

I love that point, and there’s there’s such a disparity there. Be yeah. Because I feel like for for, you know, medium sized businesses, especially, planning for Long term, ROI is is the norm, but, you know, preparing for short term ROI. If I’m being honest, yeah, I’ve been a, you know, a and Digital dork now for 20 years. I’ve been working in different, you know, marketing and tech and comms roles professionally for 20 years. And I haven’t seen anything, not even the Internet, that can provide such a short term ROI. It’s hard to measure, I feel. Like, Tina, like and and I know this is a tough question.

Jordan Wilson [00:16:22]:

Right? It’s it’s the $1,000,000,000,000 question, but, I mean, what are ways that companies can can kind of even gauge that, like, short term ROI from generative AI because it’s so fast, and we don’t have really a road map to follow necessarily.

Tina Yazdi [00:16:36]:

Yeah. I would say, like, I think one of the classic use cases or business cases for AI is just the automation one where, you can just take, This goes back to, like, the grunt work. Right? So there’s, like, something that has a lot of grunt work or a lot of manual lift from particularly your developers and technical team. And can you do something with AI that increases the, accuracy of the of the results that you’re making decisions on and reduces the hours event, and then the ROI is, like, pretty basic. It’s like the hour like, the, the cost per hour for the employees in question and, like, reducing that. That’s a really simple calculation that you can do, but also I’m now thinking a little bit, like, in a cybersecurity space. Like, if there’s, for example, a certain service that you run To audit something for your cybersecurity clients. Right? Improving the accuracy, like, 1% or 2%.

Tina Yazdi [00:17:28]:

Like, what is the business value to your client? Like, what kind of risk Does that reduce? What is the cost of that risk? I think those if you break it down into its components, those are some ways you can calculate, and holds AI teams accountable to short term ROI.

Will AI help balance competition?

Jordan Wilson [00:17:42]:

And and, you know, some actually fantastic questions here. Hoping hope we can get to a couple. So, Michael here with a great question. Thanks for your question, Michael. So he said, how do you see this disrupting the digital divide? So saying, With everyone having access to ChatGPT and pro versions for pretty cheap, do you think it will empower the low end, or do you see it empowering the big guys to Dominate and drive up costs in the divide increases. That’s a great question. Tina, what’s what’s your thought on this? Is this going to, you know, Going to this AI first business world, is that gonna drive that divide or help close it?

Tina Yazdi [00:18:19]:

I think a little bit of both. I think what’s really, Funny about this, I have some real examples, but I will not name any names, is that AI tools like ChatJMP, And the pro versions as well can be lumped under the general category of productivity tools like Asana. Right? And in this sense, when you look at it in this way, which some organizations do, They become a commodity. They become a very low value, thing that is actually really hard. Again, I have a sales background, so I’m looking at this from a sales perspective. It’s actually really hard to command budget from it. And in fact, the the the way that you can actually influence organizations to invest in tools like that, At a leadership level is simply by pointing out the and kind of exploring a little bit the danger of using open source tools to the organization and the liability that cost and the chance that your information leaks into the Internet. So that’s actually one of the only ways at the moment you can, like, get budget for stuff like that.

Tina Yazdi [00:19:17]:

In terms of empowering, like, I think that it improves per like, per like, personal productivity and productivity per employee, and I think that’s really powerful. The the way the different organizations value that ranges quite a lot. So I think it depends a little bit on the organization. I think and Nimble, fast moving, organizations, tech companies, series 8 ID types of companies, they really will understand the point here. I think maybe some, more traditional larger organizations will need some time to see the value that that can bring, and feel like they’re falling behind competition by not making the right investments here.

Jordan Wilson [00:19:56]:

It’s, and such I’m curious, by

Tina Yazdi [00:19:58]:

the way, what what you think about this question. I’m sure you have some observations also.

Jordan Wilson [00:20:02]:

Yeah. Oh, thank you. Yeah. Normally, people don’t turn the tables on me because I might go on a rant. I’d say I don’t think necessarily that business leaders are looking at ChatGPT, as a productivity tool. I think, originally, they were. But I think as you see this push toward enterprise and then when you see, you know, which we haven’t even talked about much, on on this show, when you see Microsoft Copilot, I think the smart forward thinking business leaders are looking at generative AI as less, Less of a productivity tool and Mhmm. More as an essential in a new way to do business.

Jordan Wilson [00:20:42]:

But I I like, that’s a very common, I think, you know, not not argument, but, yeah, people think, okay. Is this just something That’s going to increase efficiency, you you know, productivity, or is this the new way that we do work in general? And I think that’s probably, Tina, what we’re gonna find out over the next couple of months, you know, as Microsoft Copilot is starting to roll out here over the last week, and I think it’s gonna take a couple of months, but, I mean, yeah, what do you even think about that? What do you kinda think about my take on that? We’ll go back and forth here.

Tina Yazdi [00:21:11]:

I think I think both are true. I think this is not an or, but it’s more of an and. I I totally agree with you. And, again, like, coming back to the sales perspective, like, I think, Professionally, my, what I find really fun to do is figuring out, like, how do you take something like this and pull out, like, the core narrative that fits, like, the particular, you know, organization that you’re speaking to. And even if both our points are right, you have to figure out which one is gonna resonate in this situation, and I think that’s where where things get really interesting.

Jordan Wilson [00:21:41]:

Yeah. Yeah.

Tina Yazdi [00:21:42]:

Yeah. And I and I think this is, like, moving in real time. Right? I don’t think organizations like, There is no answer or playbook on how people are thinking about this. I think it’s being formed through, you know, listening to podcasts like this one, Through everything you’re reading every day, through conversations with your colleagues, and it is not in any way concrete. It’s very much water still. So Oh, yeah.

AI benefits for enterprises

Jordan Wilson [00:22:04]:

As as as soon as you feel, you know, as a business leader, maybe an entrepreneur, small small, you know, business owners, I I feel as soon as you feel you You have it. Like, you haven’t understood massive updates, like, with what happened yesterday with with OpenAI’s developer day. That Shakes up that that long term plan, at at times as well. So maybe maybe we’ll focus on this. Something, a great question for Mike here. Mike, thanks for your question. Thanks for joining us. So, Mike, saying, Tina, please share AI benefits in enterprise that are low hanging.

Jordan Wilson [00:22:34]:

Right? Because sometimes when you think of that long term and you See all these new tools and advancements and this and that. It’s like, oh my gosh. How are we gonna implement this? But maybe for those at enterprise that are maybe looking for That shorter ROI. What are maybe some tools, tips, or processes that can address more of that low hanging fruit that you can really start to measure?

Tina Yazdi [00:22:53]:

For sure. And, again, like, I’m not saying this is the answer. This is just an example that comes to mind. And, in a year from now, I might look back and be like, that was a terrible answer. What I have for you right now is that I think bringing in something as simple as a generative AI copilot into your organization today, is ad Predigestible. It’s not super risky, and it also derisks the reality that your employees are using generated by tools whether or not they are honest about it in the in the wild anyways. And I think because it’s kind of like a like, most people are, like, kind of Able to get their head around generated AI and may have tried it themselves. It’s digestible enough that it’s a good, sandbox to, give that 1st pass of how are you gonna get it through the door.

Tina Yazdi [00:23:39]:

So it’s a good, like, maybe target practice if you can maybe look at it in this way on How will your how will your legal team look at this? How will your security team look at this? How will your employees accept this? Like, I think the other thing that I don’t see acknowledged explicitly enough is that People don’t trust AI at all. It causes mass anxiety. There is big feelings about it, and I think it’s important to acknowledge that and understand why. Is it because people are scared to take their jobs? Are they, they just don’t don’t get it, and they’re suspicious of engaging with it because it’s like, The end is not. I don’t there’s a lot of big feelings about it. I don’t know what it is for each individual. But I think it’s important to acknowledge how your employees feel about it because you might make this huge investment and see, like, The reason it it’s implemented, it got approval, you got budget, all of it looks good, it’s been rolled out through the entire in infrastructure of your organization, and, like, no one uses it. Completely just refuse to engage with it.

Tina Yazdi [00:24:30]:

Like, those are things you can’t really predict per organization, and I think, like, to summarize, like, bringing in a pretty, like, simple generative AI copilot that’s built on your internal systems, and seeing what happens is a good low hanging, safe ish way to to test, what it might look like for your organization and where the gaps are for for your group.

Tina’s final advice

Jordan Wilson [00:24:49]:

Yeah. I think there’s gonna be a lot of copiloting. Right? Whether Yes. Whether companies actually using Microsoft copilot or bringing, you know, someone on in a role that can just help them from top to bottom is is so important, to transition to this this AI first world. Right? Because, if you just throw this responsibility onto someone that’s probably already overwhelmed, It may not it may not turn out very well. So so so, Tina, we’ve we’ve talked about a lot, in today’s show so far. So everything from, approval and budgeting for generative AI, unsexy work of implementing it, you know, measuring the long term for short term ROI. But Maybe if there’s 1, you you know, as we wrap up today’s show, maybe if there’s 1 takeaway, that you really want people to be able to, you know, take that and to be able to use it and And to help them really transition into this this new AI first business world.

Jordan Wilson [00:25:41]:

What is that kind of one major takeaway that you want people, to have?

Tina Yazdi [00:25:45]:

Yeah. I think as an individual in in regards to your career, like, be curious and just try stuff. Like, get the free version, Timebox 30 minutes and just try it, because even if it’s scary for you or you’re suspicious of it, just having more of a playful mindset in a way that’s maybe not At work, but something at home like chat GDP, is worth the investment because these tools are not gonna go anywhere anytime soon. And if you are a business leader, maybe, I would say, like, there needs to be a little bit more conversation between technical teams and leadership about what it what’s needed from bottom up and top down to, You know, if if, like, bottom up your engineering team, for example, is upset with you that you’re, like, not understanding the need for, like, this amazing algorithm that they built, like, Maybe having an honest conversation about, like, what is the executive point of view, and what is the trust gap that’s, like, blocking the final, like, headcount approval that they need from you. And in terms of the other way, like, you know, what do the, executive team need to be aware of to make the right investments to have a long term ROI from AI? Like, You know, sitting down with your technical team and being like, you know, let let’s get real. Like, how many months do you need to get our data cleaned up? Like, how many how many months of data collection are we missing to To be able to run an algorithm with any value. I think those are some conversations I see only starting to take place very slowly, but I think they’re the hygiene that might be missing to make any real transition.

Jordan Wilson [00:27:08]:

Well, I think whatever the case, the transition is coming fast, I think. Right? Slow slow but fast. I I I I love, kind of that we’ve been able to talk about that multiple times. But, Tina, thank you. Thank you so much for joining the Everyday AI Show. Really appreciate your time and your insights. Thank you for coming on.

Tina Yazdi [00:27:28]:

Thanks Thanks for having me, Jordan. It was great to connect with you.

Jordan Wilson [00:27:31]:

Alright. And, hey, as a reminder, there’s always more in the newsletter. We’re gonna break down a lot more on today’s conversation. You can check a little bit more of, Tina’s work as well, so make sure you go to your everyday AI.com. Sign up for that free daily newsletter, and we hope That you can join us back again tomorrow and every day for more everyday AI. Thanks y’all.

Tina Yazdi [00:27:50]:

Thank you. Bye bye.

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