3486: Augury on Why AI Literacy Is Becoming a Core Skill for Every Worker

[00:00:04] How do you teach an entire workforce to think with AI rather than just use it? That is the question that sits at the heart of today's conversation. And as organizations race to integrate AI into their operations, many are beginning to discover that real transformation doesn't come from tools alone. They actually come from people who know how to collaborate with them. So today I'm going to be joined by the VP of Strategy at a company called Augury.

[00:00:34] We will discuss what true AI literacy means in practice. And he will share with us how why he believes AI fluency is becoming as fundamental as reading and writing in the workplace. And as we're entering a five year adoption cycle faster than any major technology before it, we'll also talk about why the best workers won't be those who master code,

[00:01:00] but those who master curiosity, judgment and emotional intelligence. So whether you're leading teams entering the job market or just wondering how AI will reshape your role, today's episode should help you embrace AI as a trusted thinking partner rather than a distant innovation. But what does it mean to build an AI literate organization? And what happens for those that don't? But before we jump into today's conversation,

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[00:03:14] So thank you for having me again on this podcast, Neil. I think this is the third time. So my name is Artem Krupenev and I head strategy at a company called Augri, where I've been for the last about 10 years. And what we do is provide predictive maintenance machine health solutions for industrial companies. The larger space is industrial AI.

[00:03:39] And we essentially combine hardware sensors that we put on machines and tell maintenance technicians, reliability professionals at large factories around the world exactly what's wrong with their machines, how to fix them when at the level of component. And then they avoid downtime and they're able to keep their factories running and to produce all the wonderful things that we need. So that's what Augri does in a nutshell. And we are a global company.

[00:04:09] We're one of the first unicorns in the industrial AI space. We've been in that space of applying artificial intelligence to these hard engineering problems for well over a decade. And today we work with almost 200 large global and medium sized manufacturers, industrial companies across different segments.

[00:04:36] So anything from medicine to diapers, to food and beverage, to oil and gas, chemicals, building materials, you name it. And we're proud to be able to support, you know, the production of the basic necessities of life by making sure that machines don't fail and that production lines keep running smoothly. And I think we first spoke in 23. Then we spoke again last year.

[00:05:06] So it's great to have you complete your hat trick of appearance here in 2025. Always a pleasure to sit down with you. But I mean, one of the things that's always stood out to me is how you've compared AI literacy to basic reading and writing. And a few years ago, that might have been a stretch, but it certainly isn't now. But what do you think it means right here today in practical terms for today's workforce? Well, I think it's accelerating in many ways.

[00:05:32] But also the tools, the applications and the way we utilize it becoming simpler. Right. So when we talked about Luc4C, maybe a couple of years ago, it was not the same level of ease of use and access as it is today. And those tools and capabilities are becoming much, much better today.

[00:05:53] I think, you know, the way I think about it is that, let's say, for business software or business capabilities spread to things like spreadsheets and word processing from the 1980s throughout the 1990s. And today, you know, it took probably around 15 to 20 years for the adoption of that type of technology within the business world across the board.

[00:06:18] And, you know, there's still some organizations that use paper forms. Right. And don't utilize digital documents, which sounds a little crazy. You know, today, that is that's still the case. And then, you know, you think about the next wave of something like email. Right. Which is Internet based technology. The adoption of email took something like 10 years within the business world, especially within the Western business world.

[00:06:48] And today, that's obviously ubiquitous foundational. There are very few mail rooms around, even though there may be here and there. So it's not, you know, when I think about the adoption of a technology like like what we see today, AI, generative AI, I don't think it's crazy to think that, again, we'll go from, you know, 20 years to 10 years. Now it might be five years or so.

[00:07:14] And the reason for that is not just because, you know, just the general adoption technology is accelerating. It's because the rails, the infrastructure is already there. Right. So whereas for, you know, word processing spreadsheets, you needed to have a computer at every worker's desk to be able to use it. And for email, you actually have to have, you know, computers both in the office and also in the go.

[00:07:43] So you have to have mobile phones to to accelerate that adoption. Mobile phones and computers already exist. Right. For the majority of people, both in kind of the knowledge workspace and more also in the manual space. So the infrastructure is there. So the adoption is going to be much faster. Right.

[00:08:04] And the other piece is that the way the AI capabilities have been integrated, they've been integrated into the existing ways of working much better than some of those previous technologies. So you had to actually learn how to use a spreadsheet back in the day. Email was more straightforward, but you still had to kind of learn the etiquette and capabilities of how to send back and forth messages and when you're available and so forth.

[00:08:33] So today with the AI, it kind of gets integrated into the existing workflows. Right. So you can, you know, ask a question about the document and then have, you know, have the document written for you or you can start a workflow on some kind of business problem where the same way as you would ask an intern or researcher to do a preliminary brief for you, for instance, you can actually start that workflow with with AI. So I think that's the other reason why we will see adoption much faster.

[00:09:01] And so that level of literacy, being able to use AI as part of your daily workflow is more readily available than I would say any other digital technology before. And therefore, I think it's, it will be vastly accelerated. And there will be many people listening that still see AI as like a specialized skill. So on that side of things, can you just expand on why you believe it's more likely becoming a universal expectation rather than just a differentiator, isn't it?

[00:09:32] Yeah, I don't think, I don't think it's a, it's a specialized skill beyond the realm of people who are actually building AI tools, right? On the user end, it is, it does, what it does require is a slightly different way of approaching problems.

[00:09:48] Um, so when you think about starting to, to work with AI, um, your instinct would traditionally be to do something manually, you know, to think through a problem by yourself, or maybe with, with some colleagues.

[00:10:03] Whereas here you would need to think about AI as not just as a tool, but maybe as a companion in a sense to start making the right types of decisions and, you know, to actually start your workflow with a draft that is, that begins with AI. Right. And so, so I think that type of change is, um, is something that is more of a mindset issue than anything else.

[00:10:28] Um, but being able to do that is not, uh, you know, for somebody who knows that the read and write and, and the environment, it's not that far reach, right? It's not a specialized skill. It is rather more of a mindset change and how you actually start and implement your work workflow with, with AI.

[00:10:50] And one of the things that increasingly concerns me is that for younger people that could be listening out there, they're in education at the moment, studying for jobs that might not even exist yet. And then if they do get out of education, they're looking for a role. Many of those early entry level jobs are disappearing. So how do you see AI changing the nature of, um, entry level work and what kind of tasks are most at risk of automation and what should younger audiences be doing?

[00:11:19] Younger listeners be doing that. Apologies. I've thrown about eight questions in one there, but how do you, how do you see that panning out? Yeah. I think there is, uh, I think the overall pie of knowledge work and jobs in general is going to increase. Yeah. So I don't think that the overall pie is decreasing, but there are different, there's a re-segmentation of the entry level jobs and how that's going to propagate is very difficult to predict today.

[00:11:48] And one of the examples, you know, this is very recent is that there, you know, uh, with AI being highly accurate at radiology, right. So identifying, uh, essentially issues through, uh, radio imaging, um, for internet providing diagnostics based on that AI has become extremely accurate. And there are thousands of models today that do that pretty well.

[00:12:14] And you will think that that would actually reduce the number of radiologists in the market, but that's not the case because they're not open jobs and positions for radiologists is much higher than used to be just, you know, two years ago. So why is that? The reason for that is that when something becomes cheaper and it's an actual needed good, you, you actually use more of it. Right.

[00:12:36] So, you know, one example is, you know, back in the day, 30, 40 years ago, you would think twice about making international phone call because it was so expensive and you will not talk to your relatives potentially in a different country for four months because of that today, you can just FaceTime them and that's free. And so you are much more connected to people abroad because the cost of doing that, that call making that call is essentially has gone to zero. Right.

[00:13:03] And so the same thing happened in radiology where the cost of performing those exams that used to took weeks to do and interpret and so forth now is much, much, much lower. And therefore, the hospitals are ordering many more of those exams because they want to create a much more comprehensive diagnostic for their patients. And therefore, radiologists are more in demand to take those exams.

[00:13:30] So I think, or to provide that type of service. So I think that the the same is going to happen to a lot of entry level jobs that we're kind of are afraid about today. We will see a reshuffling in terms of demand for certain types of activities, but other types of activities are actually going to grow almost exponentially because you are going to need people at the front lines in order to be able to manage, to provide access, to provide something more of a service, to provide judgment.

[00:13:59] There are many different skills that humans will have to provide as the overall pie increases. And if AI inevitably takes over more repetitive and analytical tasks, which I think it will, I'm curious from your vantage point here, what new skills will define value for human workers in the next few years? I would imagine things like curiosity and emotional intelligence are quite high up on that list. But anything else that you see there?

[00:14:26] Yeah, I think I think that's definitely curiosity, emotional intelligence, the ability to provide judgment and taste. Yeah. So one of the things that I still doesn't do very well is kind of really figure out what is absolutely tasteful, right? Necessary, needed, but also in a way elegant in terms of a solution for for people.

[00:14:53] And people are very good at that in general. And I think that kind of defining not just the ability to perform a task, but also be very tasteful and how the outcome of that task and what great looks like comes out is going to be absolutely critical. The other pieces, of course, of course, creativity.

[00:15:14] Um, it's, you know, thinking, uh, on both the emotional level and also the intellectual level, uh, and skills that are, you know, typically called people skills, right? So emotional intelligence, but, but being able to essentially interact with, with human beings, um, uh, very effectively is going to be important.

[00:15:36] Um, so I think those, those are going to be valued more than highly technical skills, um, uh, in kind of in the workforce overall. Uh, and, and also, I think one of the other, uh, one of the other things that are going to be very important, um, for, for people is collaboration.

[00:15:59] So being able to work both with AI, but also with other people in kind of a way that creates better outcomes, uh, whether it's brainstorming or work groups, or just kind of having back and forth communication that, that, that delivers a more creative outcome for, for work is going to be incredibly highly, highly valued.

[00:16:21] Um, uh, and that also requires not just integration, like I said, of, you know, being able to work with people, but also you have to integrate AI into that process to the effect of essentially making everybody's lives easier and creating something that's, that's brilliant and creative.

[00:16:40] And on the other side of the table, from the employer's perspective, what should their companies be doing when rethinking hiring and training strategies to, to build that genuine AI fluency across their teams? That's, that's needed to succeed ultimately. One of the things that, um, that we're still not seeing, right.

[00:17:01] When we read reports about how effective is AI actually moving the needle for businesses and how the adoption of AI is actually impacting, you know, business outcomes. We're seeing mixed results. And, you know, there were, there have been some studies recently that said, okay, you know, businesses are not actually seeing tremendous improvement in, you know, from an introduction of AI. But we also need to understand that that literacy has to propagate, not just through, uh, the people, right.

[00:17:30] And not just getting the upskilling of the people and changing some of the mindsets. It also needs to propagate eventually through the business processes, through the strategy of the company. And, and, you know, we need to figure out the right metrics to measure it. So I think what we're doing right now is measuring the outcomes of introducing AI into the business world with pre AI metrics, right?

[00:17:54] So we'll need a new set of metrics, new set of KPIs, the business level to really measure, uh, those outcomes and those results. Uh, eventually it will be some form of productivity management and measurement, but it will be different than the way we, I think, um, you know, approach it today. Uh, but, uh, I think businesses fundamentally need to think about this as a core skill, a core capability.

[00:18:19] And I would say, uh, have to integrate AI into the work processes of every employee.

[00:18:27] So there are a number of, um, companies, primarily tech companies out there where the CEOs have come out and said, you know, off the bat, um, for every person, if you're not using, you know, AI as part of your workflow, even if you don't start your workflow with something, a draft that AI can provide you, um, you're behind the curve.

[00:18:53] Right. So start by including AI into your workflow and start thinking about what are the different use cases that you can apply it for, uh, in your day to day, in your day to day decisions and your outputs and your inputs and so forth. And I think that mindset is what, what is absolutely necessary for business leaders, um, everywhere. And then you can start seeing, you know, um, that adoption and that creativity take place within the organization.

[00:19:22] And this is exactly what we did at Augury quite early on. We said that every person needs to allocate time to be able to implement AI in some capacity into what they're doing and then share the results, share the outcomes. And we've seen really, uh, uh, uh, uh, uh, kind of a boom of productivity in terms of people coming with ideas, you know, uh, creating that type of implementation, um, and, and then showing those use cases to others. Right.

[00:19:51] And so for a lot of the employees now at Augury, we have kind of AI as a complement, uh, a, a fundamental part of how they work in some capacity. Uh, but the muscle that we're trying to develop is not just to develop, you know, clear use cases here, the five things that you need to do here. So use it, but rather you have to be creative fundamentally with it as part of your daily work. And, and every day just try to create something new, uh, something that is slightly incrementally more helpful to you.

[00:20:19] And then it just part becomes part and parcel of how you work a day to day. And for individual workers, what would you say are the first practical steps that they should be taking towards developing those fundamental AI literacy skills beyond using tools like, I don't know, chat GPT, copilot, claw, Gemini, et cetera. There's so many different tools they've been around for several years now. So what, what should they, they, they be doing to further develop their AI literacy?

[00:20:48] Well, I think, I think that, um, you know, we have a pretty massive rollout, um, of AI as part of a lot of the different tools that they use, right? Yeah. So, um, and capabilities. So almost every ERP system or, you know, even industrial systems where the world where we, we exist, some part of it is enhanced by AI or even directly affected by AI capabilities.

[00:21:14] So from that perspective, they, a lot of the times they don't really need to change their workflow. Um, but that utilization of AI as a companion, right. And thinking about even as a, as a thinking partner, you know, utilize AI in terms of, well, how do I solve this problem better? Who else should I approach? How else should I approach this problem? What else can we do here? What kind of insight can you provide me? And then if you don't get the right level of insight,

[00:21:40] then you can actually start thinking about what other tools are available for you and for your organization to pull it in. Um, uh, and, and I think we've seen that push for us as well at Augury, you know, giving Augury's example is that we've seen employees starting to show, they can see the limits of what some of the current AI tools that we have, but not just, you know, put their hands up and say, okay, well, this, this thing doesn't do what I actually wanted to do.

[00:22:10] So I'm going to, you know, I'm not, I'm going to give up, but they're saying, well, they're practically looking outside and finding new tools that will solve that problem for them. And we've created a budget for them to be able to do that. Right. So to, to actually experiment with new tools, new capabilities and a simpler, uh, uh, process for, for doing that.

[00:22:30] So if you're an employee, you know, there's easy ways to test out tools and capabilities just through search that will fix a certain problem that your current tools or your current AI capabilities are not able to solve for you. And so you need to be on the lookout for those and then bring those in back into your organization.

[00:22:51] Right. So be very proactive as opposed to, you know, what would happen in the, in the more traditional sense, you would rely on procurement, some kind of centralized planning to be able to provide you with the tools you need for your job. Now, every person, every employee needs to go outside and look for new ways of solving the task of, of, of, or the problems that they're trying to solve by bringing new tools in.

[00:23:16] Yeah. And three years have passed since chat GPT first dropped and many organizations at the time attempted to ban their employees for use from using it. And for very good reasons in many cases, but fast forward three years to present day, what happens to organizations or professionals who, who now fail to adapt? Are we heading towards a, I don't know, a widening gap between AI empowered and AI illiterate workforces? Do you think there's a danger of that happening?

[00:23:46] Absolutely. Yeah. I think, I think that that's, there's a danger of that happening with any technology, especially kind of technology that's massively adopted across the market. Uh, we're going to see a wider and wider gap. Um, and you know, companies will see that from their employees and they'll see that, you know, in terms of are they able to hire the people that used to be able to hire before because they don't have the right.

[00:24:09] They don't have the right capabilities or infrastructure, um, to support their ambitions and, uh, the, the way they would work with, with AI in the modern world. They'd also feel it from their customers because their product does not support those capabilities. Um, and they'll also feel it from competitors and investors, uh, because, you know, competitors will have, you know, some of those capabilities and they'll be able to, to leap ahead and capture more of a market share.

[00:24:36] So I think, I think that is a very clear, uh, gap that that's going to be, uh, that's going to be created. Yeah, completely agree with you there. And finally, as, uh, as yourselves at Augie, as you integrate AI deeply into industrial operations, I've got to ask what lessons have you learned about balancing automation with human expertise that maybe people listening could apply and, or just take away and have a think about that?

[00:25:05] Any big lessons that you've learned? Yeah, I think that, um, there are certain applications like the industrial applications when the world where Augie operates, where it is extremely important to, um, approach it with what we call, you know, or, you know, others call a kind of a full stack approach. Right.

[00:25:26] So, um, where AI is not just given in its rural form, but rather there's, there's a human in the loop, there's a service around it. There's a capability around that can enable adoption. They can also put people ease in terms of anxieties that they have in approaching it that can integrate it into the right workflow and also ensure the right data, the right, right.

[00:25:49] The right foundations, the right integrations, uh, you know, take place, um, all the way down to, to the hardware that's needed in the production for. So thinking about solving a problem with AI and then thinking about how, what's the most effective and most accurate way to do it. And then how do we make sure that we also have people in place to be able to, uh, to drive the adoption of that solution is, I think is the right way to think about it. Right.

[00:26:17] Um, but there are, there are other industries where, you know, that level of accuracy, that, that level of capabilities are not as important. Whereas, you know, maybe, maybe creating more, uh, more creative solutions would, uh, would apply. So I think, I think the lessons are that, um, when we balance automation and human expertise and we introduce a powerful new AI enabled tool, we have to think about holistically full stack end to end.

[00:26:46] What does it mean to plug that in into an organization and then to drive the adoption of it many years forward? So if you're in the corporate world and you're driving strategy for your organization, you need to think about it that same way. Right. Right. And if you're thinking about a company or vendor, that's providing that for you, well, do they have that same mindset and approach? Right. And can it prove that they've been able to, to do that?

[00:27:09] Um, I think that that is, that is kind of the biggest lesson that we've had and, and that's, what's actually made our customers successful so far. And I'm conscious. We've been very forward thinking today in our conversation, you passing on your expertise, the lessons that you've learned along the way. And what to look out for in the future. But to finish, I'm going to take you back in time.

[00:27:33] Now I want to learn who helped you get you where you are, because I think none of us are able to achieve any degree of success without a little help along the way. So is that a particular person that you're grateful towards who maybe helped you get you where you are? And we can give a little shout out to today. Who would that be? And why? Well, can I give, can I name four people? Go on then. Why not? Let's go with a four.

[00:27:57] So there are two people that were kind of earlier on, uh, helping me, uh, get, get where I am and really, uh, brought me to, uh, an incredible school for, uh, for creating ventures, for building startups, for building product. Uh, this is David Kidder and Anne Berkovich, who are the founders of company Bionic. That was very fortunate to join early on.

[00:28:20] And then I would say also the, the two co-founders at, at Augury, uh, Gal Shaul and Saryoskovic, who, um, have been friends of mine for a very long time. And also have, you know, I've joined, uh, about a decade ago in order to be able to help build Augury. And I'm immensely grateful to, to those people because they have really brought me to where I am today and I could not have done it without them. Well, a quick shout out to all four there.

[00:28:49] They're probably blissfully unaware on the scale of the impact that they've had on you and your career. Yeah. And for everybody else listening, they want to find out more information about where we're heading, the future, how you might be able to help a little bit more about anything we talked about today. Where would you like to point everyone?

[00:29:04] Oh, well, absolutely. Uh, come check us out at Augury.com. Uh, and you can also check out my LinkedIn profile at Artem Krupenev, um, to see, you know, follow, um, uh, if you want more info and kind of more thoughts on industrial AI industry in general, uh, product, things like that. Yeah. Fantastic. Well, same as the previous two times, I want to add links to absolutely everything.

[00:29:30] A pleasure to get you back on the podcast and so many big topics today because workforce costs are high. The job market across every sector is continuing to recalibrate. And instead of thinking, uh, of AI literacy as a differentiator, we should be, as you said, there, be looking at it as a fundamental skill, just like basic reading and writing. So much food for thought, but thank you as always for, uh, shining a light on this.

[00:29:58] And I'll see you in 2026 for hopefully your fourth appearance, but thank you for joining me today. Yeah, it's always a pleasure. Thank you so much. I think after that conversation, my big takeaway is that AI literacy isn't about learning to code or mastering prompts. It's more about re-imagining how we think, how we collaborate, how we create value. And as he explained today, the most forward thinking companies aren't just adopting AI.

[00:30:23] They're embedding it into the natural rhythm of daily work, almost encouraging experimentation and redefining success through new metrics. And again, the goal isn't replacement here. It's amplification of human creativity and judgment. And I think his examples of Augury, they show that what can happen when people and AI evolve together?

[00:30:49] Productivity rises, innovation compounds, and fear slowly gives way to curiosity. And I think it offers a glimpse into the next chapter of what work could look like, especially if we approach it with open minds and practical optimism. But as always, I'd love to hear your thoughts. I'm the eternal optimist. You may have a different view. How do you see AI reshaping the way we learn, the way we work? And are we all ready for this new kind of literacy?

[00:31:17] Join the conversation, techtalksnetwork.com. You'll find all the information you need there on how to get hold of me and even leave an audio message. But that's it for today. So thank you for listening as always. And I'll speak with you again tomorrow. Bye for now.