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The Rise of Artificial Useful Intelligence: A Strategic Shift for Business Leaders
In a landscape often dominated by buzzwords and theoretical capabilities, the focus on Artificial Useful Intelligence (AUI) presents a pragmatic pivot for enterprises looking to harness AI's potential. This focus on utility over grandiose goals like Artificial General Intelligence (AGI) is gaining traction, especially among decision-makers eager to see tangible value from their AI investments.
Decoding Artificial Useful Intelligence
As often emphasized, navigating AI can feel like traversing a maze of acronyms—AI, LLMs, AGI, and now, AUI. But the crux of the conversation should center on utility. While AGI, in all its hypothetical capabilities, captures imaginations and headlines, AUI is grounded in practicality. Its objective is straightforward: to be truly beneficial, an AI system must enhance daily operations and drive productivity, regardless of whether it's labeled as AGI or something else.
The Past: Lessons from Historical AI Milestones
Reflecting on technological milestones shed light on our present trajectory. During the late 90s, AI impressed the world when a machine defeated the reigning chess champion, Garry Kasparov. This was repeated over a decade later with IBM's Watson astonishing viewers on Jeopardy. These breakthroughs were benchmarks, indicating intelligence but also serving as stepping stones for the more nuanced applications we see today.
These achievements highlight a recurring realization: every time technology reaches a pinnacle, the definition of intelligence shifts, underscoring the relevance of continuing to refine our aspirations and applications.
Why AUI Matters Now More Than Ever
In today's rapidly evolving business environment, intelligence is multi-faceted, encompassing IQ (intellectual quotient), EQ (emotional quotient), and RQ (relationship quotient). The focus is shifting towards systems that can complement human capabilities rather than merely emulate them. The ultimate question remains: Is the technology at hand useful? Does it streamline operations or enhance decision-making? AI's utility should be measured by its ability to make life and business processes more fulfilling, productive, and efficient.
The Role of Agentic AI
A potential game-changer lies in the advent of agentic AI, where systems don't just process information but make decisions based on feedback loops, much like intelligent human systems. They break tasks into subtasks and utilize existing tools to optimize outcomes—a marked difference from previous feed-forward systems that relied heavily on static inputs.
Actionable Steps for Business Leaders
Engage Hands-On with AI: Go beyond the hype and interact with AI technologies. Familiarity breeds insight, and real understanding comes from direct engagement.
Strategize for AI Disruption and Reconstruction: Every business must anticipate how AI will both disrupt and rejuvenate its operations. Develop a strategic pathway to navigate this transformation.
Equip Your Team: Ensure your workforce has the necessary skills to adapt to and excel in an AI-enhanced environment. Foster a culture that embraces change and leverages AI to augment human potential.
Conclusion
The emphasis on AUI champions a paradigm in which AI is a tool, not just a trophy. It's an integral part of the business strategy that enhances human effort and enterprise innovation. As AI technologies deepen their roots in everyday operations, the focus should remain steadfast on utility, ensuring that business leaders harness AI for real-world application, driving progress and fulfilling potential beyond theoretical constructs.
Topics Covered in This Episode
- Allocation of AGI Focus vs. AUI (Artificial Useful Intelligence)
- Ruchir Puri’s Background in Automation and AI at IBM
- Discussion of AGI’s Unclear Definition and Historical Milestones (Deep Blue and Watson)
- Breakdown of Intelligence into IQ, EQ, and RQ
- Emphasis on AUI’s Practical Uses in Daily Life and Business
- Evolution of Human Work Due to AI Advancements
- IBM's Software Engineering Agent for Developer Productivity
- Importance of Feedback Systems and Intelligent Agents
- Steps for Business Leaders: Education, Strategy, and Skill Development
Podcast Transcript
Midroll [00:00:00]:
This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life.
Jordan Wilson [00:00:16]:
Sometimes when working with artificial intelligence, it can feel like you're dealing with alphabet soup. Right? Yeah. We're, leveraging AI and and LLMs, large language models for this, Gen AI. Right? And we're all chasing AGI, artificial general intelligence, but is all of that useful? Right? Well, today we're gonna be talking about a type of AI that is probably very useful, and that's the case for artificial useful intelligence and how that's probably more important than what we're all seemingly talking about and focusing on, which is artificial general intelligence. Alright. I'm excited for today's conversation. I hope you are too. If this is your first time here, welcome.
Jordan Wilson [00:00:59]:
Thank you for listening. My name is Jordan Wilson, and this is Everyday AI. So this is your daily livestream podcast and free daily newsletter helping us all not just keep up with generative AI, but how we can use all of this knowledge to grow our companies and to grow our careers. So if that sounds like what you're doing and what you're trying to do, if you're trying to be the smartest person in AI in your department, it starts here, but then it actually continues at our website. That's youreverydayai.com. So, in our free daily newsletter, we're gonna be giving you all the AI news and tips and tricks and everything like that to keep you up, but we're also gonna be recapping today's conversation, with an amazing guest. So if you didn't catch everything, maybe you're in the car walking your dog, don't worry. We're gonna have all the, takeaways and insights in our free daily newsletter.
Jordan Wilson [00:01:46]:
Also, this is prerecorded. So if you're dropping in for the AI news, that's gonna be in the newsletter as well. Alright. Enough chitchat. I'm excited for today's show and an amazing guest that, you know, a company, in his work, I think we're all gonna all gonna know and probably relate to. And I think, you know, even myself, I do get caught up in this alphabet soup, right, of of all these different types of a AI and AGI and ASI, and, well, let's just make it useful. Alright. So, please help me welcome to the show.
Jordan Wilson [00:02:16]:
We have joining us, Rushier Pierree, who is the chief scientist in IBM Research and IBM Fellow. Rushier, thank you so much for joining the Everyday AI Show.
Ruchir Puri [00:02:26]:
Hey. Thank you, Jordan, and and thanks to all your audience for listening in as well. So
Jordan Wilson [00:02:32]:
Alright. Hey. I'm excited. Before we get into this topic of artificial useful intelligence, can you tell everyone, which might be hard to do it quickly. Right? But can you quickly tell everyone a little bit, about your background and what you do at IBM? Just to kinda set the stage here a little bit.
Ruchir Puri [00:02:47]:
Just I think just from my background point of view, I have focused for last almost four decades, approaching four decades on technologies that relate to automation. At almost every level of abstraction of technology, you can think up think about from potentially two of the most important technologies in the world today, specifically, AI and semiconductors. Both of them are focused pretty much half of my career on semiconductors and its automation, and the second half, last two decades on artificial intelligence. My background is in, optimization and algorithms, and, you know, have been doing computer science for very, very long time. So
Jordan Wilson [00:03:39]:
Yeah. And I'm I'm excited to get in your background and talk about, you know, some of the advancements that, you know, you and your your colleagues at IBM have made over the past couple of year and decades. But before we get into it, let's start at the top. What the heck is artificial useful intelligence?
Ruchir Puri [00:03:57]:
I think you you started the intro very nicely, Jordan, and and I think I'm just gonna pick up from there. And so there's just lot of talk about AGI and the scaremongering that goes together with it regarding, you know, there'll be kind of robots walking around, and we need to start becoming scared and, you know, life is gonna be, no. It's really pretty troublesome. First of all, there is not even a clear definition of general intelligence. And let me just say, every time we thought we have achieved it, we kind of push that can down. Like, I don't think so that's general intelligence. So let me start with something that, you know, we at IBM focused on for long, and that was the pinnacle of intelligence at that time, which is playing chess. And there is no better kind of no.
Ruchir Puri [00:04:56]:
I would say milestone than to defeat the reigning world champion of all times, Garry Kasparov. Well, we built this machine called Deep Blue, and and we had a famous match, and, we defeated Garry Kasparov. The machine defeat defeated Garry Kasparov. And, you know, we we thought, hey. That should be it. Right? We should have surpassed, you know, general intelligence, superintelligence, whatever you call it. Like, I didn't felt very good, but didn't feel like, no, that that was like humankind back. And so, okay.
Ruchir Puri [00:05:29]:
Well, then fast forward another decade, we actually know very famously, played on live TV, which is kind of very, hard, this game called Jeopardy and had a machine called Watson, play that game. And, you know, the the the game is well known now, and we defeated the the reigning champions at that time. And we thought we had achieved general intelligence, not really, actually. And then, you know, the technology continues. And to me, what is general intelligence? I don't even think so in some words, like Sam Altman and others have formulated this regarding when AI can achieve hundred billion dollars of revenue. That's kind of narrowing it too much. I really think so. That's a business lens too.
Ruchir Puri [00:06:17]:
Let's keep that aside. Intelligence in my mind is related to not even just pure what I well, what is known as IQ, actually, which is intelligence quotient. It's also related to a large extent to what is known as emotional quotient. I'll say EQ. It's also related a lot extent to there's a third Q I coined, which is relationship quotient or RQ. So in my mind, intelligence is comprised of IQ, EQ, and RQ. And we tend to focus too much on this sort of very specific IQ part of it. And when we say somebody is intelligent, a human is very intelligent, it's combination of those three factors, sort of generally speaking.
Ruchir Puri [00:07:01]:
And leaving all of that aside, I think what we should really focus on is for your day to day listeners and for your, you know, really people who are decision makers to focus on, is this technology useful? That's all that counts. I really don't care whether we have achieved AGI, ASI, or whatever it might be, AXi. I want this thing to be useful. And usefulness can vary in your perspective. And we can talk a lot about sort of what does usefulness actually mean. But to me, it's about a technology. It's about automation. And is this helpful in your day to day life in the whether you are a enterprise, whether you are a business, whether you are a small business owner? Is this being helpful in helping you accomplish things in day to day life, and is the technology fulfilling? That's all that matters, actually.
Ruchir Puri [00:07:59]:
And that's why I'm a huge fan of AUI rather than AGI, ASI, AXi, whatever it may we may call it exactly. Because that definition is not even clear what that means. And if you go back ten years or even twenty years of history, the definition keeps on evolving all the time, actually.
Jordan Wilson [00:08:16]:
Yeah. Yeah. Nonstop. Right? And I've talked about that on the show, a couple of times just looking at the definition of AGI from fifteen, twenty years ago. It's like, oh, technically, it's already achieved. Right? Like, if you look at definitions from twenty years ago, but, you know, I'm curious. And and and I love how you broke this sound, kind of this this three pillars of intelligence, the the IQ, EQ, and then the, you know, relational, right, RQ. You know, one thing I'm I'm curious about, you know, even as it pertains to to AGI.
Jordan Wilson [00:08:46]:
Right? So that's, you know, oh, when one AI system, you know, can has the ability to understand or learn anything that humans can and perform tasks. But what about, like, it seems and maybe I'm wrong here. It seems like the task that us humans are performing are changing now more than ever. Right? Like, you know, yeah, I've only been working for, I don't know, twenty five years or something like that. But it seems like like, what us humans are expected to do in the last two or three years since large language models has changed very quickly. Do you think that that may be just the the the ever evolving concept of of human work and what we're doing with AI? Is that also kind of changing this fluid definition of AGI?
Ruchir Puri [00:09:31]:
So I think it's a I'm glad you brought it up, Jordan, because if you look at the evolution of sort of just hue we as humans, There was a time to go go back to industry industrial revolution. Now we went through automation technologies, which automated something that was very prized at that time, which was people really building or manufacturing with their hands to machines building those things. Mhmm. There's a profound, actually, revolution and give rise to productivity and consumption, which was literally unprecedented at that time. What was priced? In the new era, post industrial revolution, what was priced? Knowledge worker. Like, if you could create knowledge, if you could analyze knowledge, if you could read, write, all of that, that was priced more and more. And in every revolution in human history, if you just go back literally almost thousands of years and just study that history, one would really conclude that knowledge workers have always been priced more and more and more. It was something that was thought to be almost unachievable, if I if I may say, by machines.
Ruchir Puri [00:10:47]:
Sort of this deep analysis of knowledge, creation of new knowledge as well. I'm gonna keep creation of new knowledge aside just for the time being because we we should discuss it. But this is the first time, I would argue, in human evolution that we are very close to generating language seamlessly, actually. Whether it is spoken language, whether it is analyzing language, and and language of all kind. Whether language happens to be a computer language, code, whether that happens to be a spoken language, we are able to analyze, understand, reproduce, generate, think seamlessly. Now that has profound implications on how society does work. Very similar to the machines that automated, you know, really manufacturing had profound impact on how we did work. But we didn't stop work, by the way.
Ruchir Puri [00:11:46]:
Those became tools through which we did more work, actually. So I'll give simplest of the examples. When somebody was banging nails with a stone, somebody invented a hammer, did we stop banging nails? Like, we really banged more nails. In fact, we did we invented machines that can just, you know, put more nails in, actually, automated machines that put more nails in. And we discovered new sort of usage of those nails, actually, if I may say. We build out more new houses, like, manufacturing grew and so on. I fully expect this revolution to be very similar in perspective. These will become amazing tools through which people will unleash new productivity.
Ruchir Puri [00:12:33]:
I'll give you a very simple example, actually. I happen to be on the campus of MIT this weekend, be with some students, actually. And I was talking to them and says, you know, we used to wait for reading hours, what is known as sort of really reading hours with professors and TAs and so on. And now I can literally take if I don't understand the question, I screenshot. I put that in the in in your lab language model of choice, but, you know, whatever, chat GPT, Claude. And I say, explain this to me. And it doesn't I can read talk to it, and it does an amazing job overall. And I can get to that question and understanding much faster than I would otherwise.
Ruchir Puri [00:13:18]:
Does that mean students have stopped understanding? Not at all. I think they've discovered new ways through which they can understand a whole lot better and debate with the system in a very seamless way. They were waiting for, you know, their turn in the line to talk to someone. Now they can talk to someone as well as they can really debate that internally and be much better prepared for that conversation. I think that's a good example of how these technologies are impacting day to day life and are being useful to these students and other, you know, really people in terms of helping them make their life more fulfilling.
Jordan Wilson [00:13:57]:
So I know one recent, kind of advancement, at least when it comes to large language models, is their ability to reason. Right? So, I know grant you you know, IBM's new Granite three two has a reasoning mode. But essentially now, all of these large language models when it comes to intelligence benchmarks, right, They're they're they're, you know, off the charts of of where we might have expected them to be, you know, four four or five years ago, right, when we were looking at early GPT models. Is so that that, like, leads me to think. Right? Ever since you kind of broke this intelligence down into three categories, now I'm just scratching. Like, is it even useful for us humans to have all of intelligence? Right? The the the IQ side. Is it is it useful for us to have that if we can't actually put it into use on the, you know, emotional side or the relational side. Right? I'm like, sometimes I have all these models that can do anything and everything, and I'm just like, Right? Like, I like, I just get stumped sometimes at the amount of intelligence that I have at my fingertips.
Jordan Wilson [00:15:02]:
So what is actually useful in this in this age of, you know, these large language models on the IQ side are so high?
Ruchir Puri [00:15:11]:
I still definitely think so. I think if you really look at I think what you're bringing up is is is really a profound point on we are mid in the middle of defining the future of work, by the way. I really think so. Like, you are you are at a profine profound point in history where, like industrial revolution, sort of once again at a juncture where you're defining the future of work. And I think it's been said before also, but I I I would capture it again here as well that with reasoning models, these amazing reasoning models, and even more importantly, these reasoning models with tools at their disposal. By tools, I mean, tools that we enterprises and businesses use every day. A database. You know, really, the tools for business processes and so on, which are digital tools that you can call at any time, at any instance, and say, accomplish this subtask.
Ruchir Puri [00:16:11]:
Give me back the results. I'm gonna integrate that back in and continue in my sort of business process. It becomes even more important for us to be able to learn to manage these tools, if I may say. So, you know, people have said it, as I said earlier as well, that if you look at the information technology department of the future, Currently, it's comprised of lots of people, actually. No. I would say information technology department of the future is people who are operating these tools, managing these tools, governing these tools, but will become lot more automated in the future. So it's like people managing these agents, if I may say. These agents happen to be digital agents.
Ruchir Puri [00:17:00]:
Having said that, the value has to be very clear in terms of the business value being delivered. That value is still managed by the people and the relationships among those people as well. We humans are never gonna price less in any way either the emotional part or the relationship part. I think those two parts grow even more in importance if I may say. Because now, so far, it is about people managing people. Then it's it'll be about people, still people, by the way, managing these agents that happen to be digital agent because somebody needs to make sure they're doing the right thing. Because somebody needs to have accountability, by the way. It it all comes down to accountability.
Ruchir Puri [00:17:46]:
Who has the accountability? Finally, where does the no. In in sort of general English, the buck stops where? And if the buck stops at human, then I better know what is going on, actually. Somebody should tell me what is going on. If the buck doesn't stop at me, do whatever, actually. It's fine. But if the buck stops at me, then you should better like, I should have a control over it. I should govern it. I should know how to operate it, and I should be able to manage it as well, actually.
Ruchir Puri [00:18:16]:
So I think that is the most important part. Where does the buck stop? I believe the buck will continue to stop at humans for very long time.
Jordan Wilson [00:18:25]:
Yeah. That's that's a great point. Right? Yeah. Like, even myself as as these models become more and more powerful, like, you feel almost, not pressured, but, like, all of a sudden you're just, like, taking a back seat and, you you know, just kind of marveling at at what AI can accomplish, but it's like, no. You know, the human role in the human of the loop becomes more and more important, you know, as we talk about, you know, agentic AI, reasoning AI, all those things. But, you you know, I I have to ask you this. So, you know, as we, you know, make the case for artificial useful intelligence over AGI, what are you currently finding useful? Right? How are you measuring AUI, whether it's in your own work, in your team's work, you know, at IBM, how are you actually measuring it, and how can you define what's actually useful when using artificial intelligence?
Ruchir Puri [00:19:18]:
So I'll I'll take sort of, I think there are couple of forces that have literally revolutionized our lives in last, I would say, couple of decades. I think it was Mark Anderson, of Anderson Horowitz fame who said, maybe in 2011, if I remember right, software is eating the world. And I think it'll be fair to say software has eaten the world. I think we are in the middle of, you know, an era which is defined by software, to a large extent. And the development of software, the the testing of software has become a major endeavor, and we've got literally millions and millions, tens of millions of software developers. And one thing that we are very focused on from a IBM perspective is how do we make the lives of enterprise developers, business developers much easier? I think I'm I'm gonna sort of lay out a use case in which how we measure this, actually. So given that software is so important to the world and given that software developers are, you know, like, it has been a price commodity, the daily life of a software developer is like this, actually. It's almost like a doctor, although I don't want to compare saving lives to fixing software bugs.
Ruchir Puri [00:20:40]:
But it's almost like you come in and you look at your your you open your tab and say, well, Richard, you've got 40 issues to fix today. Okay. Well, that's kind of gonna be a busy day. And and you start going through the list, and you are fixing issues one after another. You are testing it. You are patching it. You're releasing it. And and and you get to the end of the day.
Ruchir Puri [00:21:02]:
You're about to sign off, and, you know, five more show up. This is urgent. Needs to be fixed. Okay. You'll let you know, you wanna get go home. And you wish at that time there was a technology available for you to help automatically. This is the technology that we are launching, that we are working on today or something called software engineering agent, which is able to look at your complex software, you know, development landscape, you know, hundreds of files, you know, thousands of lines of code, hundred and thousands of lines of code, just description of a English description of a issue that you have. That's it.
Ruchir Puri [00:21:45]:
Pinpoint the issue for me, where it is. Tell me what is the why this issue is there. Second one, suggest a fix and tell me the reasoning for that fix and go fix it. So issue had 40 issues lined up on your plate or in your in your in your list, I fixed, assume for the time being, 15 for you automatically. That's real productivity in your day to day life that you can measure, actually. This is not about how many calls you made to a large language model. I don't care. What I care about is the end value that you deliver to me.
Ruchir Puri [00:22:22]:
The end value is the time consumed in my day to day life, which is going into things that may not be productive, if I may say. By productive, I don't mean let me say fulfilling, actually. I wanted to go home at that time. I wish there was a technology available. There is a technology that's available, actually. Again, the the second part of this could be, you know, fixing vulnerabilities automatically. I mean, the world is full of, at this point in time, cybercrime. Identifying when the cybercrime is gonna happen, where it's gonna happen, if the software has leak on a dynamic basis all the time, you know, what is known as AI for security actually, is, again, in a in a in a world that is full of, you know, risks, if I may say.
Ruchir Puri [00:23:09]:
For every business and every entity in the world, that becomes a extremely useful scenario that there are not enough human hands in the world with the right expertise to be able to identify. You can only minimize the risk. If there's a technology that's available to help you reduce the risk even further, god bless it, actually. So it's not taking away from any human activity. It's just making your risk lower, actually, and your life much better. So those are some very tangible way in which we are measuring things that are impacting day to day activity of sort of normal human endeavor.
Jordan Wilson [00:23:49]:
Yeah. I think I think the day to day activity of of what, you know, knowledge workers normally do, it's alright. Like, that's that's where, you know, AI, I think, is truly useful. Right? When you can get time back, when you can get focus back, when you can get creativity back. Right? All of those things. But, you know, I wanna hit rewind here real quick. You you know? Because we briefly mentioned, you know, some of your your background and, you know, IBM's, achievements in the field of artificial intelligence. Right? It's been around for many, many decades, and it's been useful as well for many, many decades.
Jordan Wilson [00:24:22]:
Right? So, maybe the timing here, it's it's it's kind of, interesting. Right? Because you had Deep Blue. I believe that was, you know, around '97. Then you had you you know, becoming the chess champion, and then you had, you know, Watson on on Jeopardy about fourteen years later. You know, both of those two, very useful, right, in terms of of where the artificial intelligence, you know, is is at and where it's going. And here we are now fourteen years later. So fourteen years between each one. So, you you know, what's that next big kind of landmark? Right? So first, it was, you know, beating the smartest, chess player, then it's winning Jeopardy.
Jordan Wilson [00:25:00]:
What's that next big milestone with everything that we have in AI right now? You know, what's that next big thing that you're like, oh, okay. This just opens up a whole another echelon of AI being useful.
Ruchir Puri [00:25:12]:
I think that word has been overused, abused, but I'll I'll really clarify why I'm so excited about that technology because that's a profound shift in technology, which is what I'll say agents. And I describe what I mean because it's been really lot is written on it, kind of abused in many ways. So far, we've been working with systems that in engineering terms is called feed forward systems. Feed forward systems are you give that system an input, it gives you output. If you don't like the output, you as human don't like the output, what do you do? You as human change the input. Mhmm. That's called prompt engineering. You don't like the prompt you don't like the output of a, you know, charge GPT, you change the prompt.
Ruchir Puri [00:26:05]:
You change the prompt. Agents take it at a whole different level, actually, like exponentially smarter. They say, you know, you give me input. I'm gonna give you output. I'm gonna analyze that output. I machine is gonna analyze that output, compare it to your intent of the input, and continue to iterate into no. Internally until I get it right. That actually entails we were talking about reasoning earlier, deep reasoning of these AI systems.
Ruchir Puri [00:26:40]:
In particular, another step they take this is not just the only step they take. Another step they take is, you know, it it's okay now, actually, in chat GPT, but go back around a year back, and there were many, you know, Twitter posts on it as well that if you give chat GPT two numbers to add, just give it two kind of random numbers to add, little bit larger, It's likely to get the addition wrong because adding two numbers is not a what in, you know, LLM terms or large language model terms is known as next token prediction problem. Mhmm. Adding can somebody tell ChargePD to please use a tool called calculator that we have used for, you know, thousands of years, not just decades, thousands of years? Yeah. Like, please use it. Please don't use it as a language problem. This is not a language problem. This is a math problem.
Ruchir Puri [00:27:30]:
Can somebody use a calculator? Okay. I think then realizing when to call that tool. You gave me English problem and say, oh, that's an addition problem. I should call a calculator.
Jordan Wilson [00:27:41]:
Mhmm.
Ruchir Puri [00:27:42]:
And then you call a calculator, look at the result, plug the result back in in that English words and then your addition is x, and you continue actually from there. So this ability to be able to take a task, break it down into subtasks, call the right set of tools for the right subtasks, integrate all of that together, reflect on the results, and continue until I accomplish that task is what in sort of no. The the next level of technology is called agents. Mhmm. And it takes the word from what is known as feed forward systems to feedback systems. All intelligent systems in the world are feedback systems. I'll give you simplest of example where it will make very, very clear, actually. As you know, for the time being, you are trying to send a rocket to the moon.
Ruchir Puri [00:28:37]:
If you were point zero zero zero zero zero one degrees off launching from the earth, you ain't going to the moon. You're going somewhere else.
Jordan Wilson [00:28:45]:
Yeah. You're messing where you're going,
Ruchir Puri [00:28:47]:
but you're not going to the moon, actually. So the whole point is for rocket launching systems is not just to have the strongest possible rocket. Think of that as a very strong model. But to have the ability to be able to correct every time, you realize, oh, I'm not going there. Let me correct. I'm not going there. Let me correct. This ability to be able to correct is so important in intelligent systems that it can mean the difference between landing no.
Ruchir Puri [00:29:17]:
Not going anywhere to really going where you want to go. I think that's a that's a good analogy to what agents are in this new era to what technology was before, actually. And that's why I'm so amazingly excited about sort of agents and what they can do to the world.
Jordan Wilson [00:29:32]:
So speaking of that, right, and and and tying it back to this, you know, artificial useful intelligence, when we talk about, agentic AI, you know, AI that can reason, you know, agentic AI that can reason that has tool access and knows when to use the right tools. Right? It seems like we're on the precipice of all these things coming together. So speaking of useful, what's useful right now, for business leaders to be focusing their own time on aside from, you know, using the right system. Alright? And and, actually, you know, AI strategy and AI implementation. But what about on their own EQ in our cube side? Right? What are those useful human skills that we need to be learning and practicing to properly take advantage of AI's, you know, nonstop, you know, development that's happening seemingly on its own. Right? What do we need to be preparing for to actually make good use out of all of this AI?
Ruchir Puri [00:30:35]:
So I think I encourage every business owner, every business decision maker, every business strategist, number one, to get and it's sort of it's almost like, you know, by default, should be true, but get educated beyond the hype. Even start like, I encourage everyone to be hands on. You don't need to build a software. You don't need to be a software developer. But please play with the technology yourself. The technology is available at your fingertips. Please play with the technology yourself, and that will give you a notion of the power of the technology. Second, I would suggest will be, for everyone, have a strategy and a plan of how AI is gonna disrupt and reconstruct your business.
Ruchir Puri [00:31:34]:
It's like mandatory, actually. It's like, you know, everybody has a plan for if the if I lose electricity, I'm gonna have a backup generator or something. Yeah. Yeah. It's almost like that. It's like electricity, actually. Have a plan of how AI is gonna disrupt and reconstruct your business. I don't mean just that's why I didn't stop at the disruption part.
Ruchir Puri [00:31:54]:
And the third one will be, make sure your employees, your teams have the right set of skills. Because we are in the middle of a transition, as I said. No. You it can be very scary for people who are sort of don't have the right skills. Because people can be very scared of technology if they don't know how to transition into the new world, actually. So those three factors together have opinions that are grounded in sort of hands on activity. Second one, really have a plan at a business level, at a decision maker level. And the third one, bring your teams together, which is the part on the EQ and the RQ.
Ruchir Puri [00:32:41]:
If you leave it, then it it becomes cultural, actually. They're gonna resist it. You can shove it down their throat. It just you know how it goes, actually, in general. It's not the the the best fulfilling activity a decision maker wants to have, actually.
Jordan Wilson [00:32:55]:
Right. That that was such a good way to end today's show with that, you know, on the fly, by the way. My gosh. Right? We didn't we didn't talk that one out beforehand, but I love that. Just the education, the strategy, you know, to, not just, you know, deal with the disruption, but the reconstruction is huge and then making sure employees have the right set of skills. Amazing. Today's conversation was a fantastic one. So, thank you so much, Roshir, for joining the Everyday AI Show.
Jordan Wilson [00:33:26]:
We very much appreciate your time and your insights.
Ruchir Puri [00:33:29]:
Thank you, Jordan. It's a pleasure.
Jordan Wilson [00:33:31]:
Alright, y'all. That was a lot. I'm excited. I'm I'm gonna go listen to this show, and I'm gonna type up this newsletter. This is one that I think you need to relisten to at least twice, because Rushear dropped a lot of great information, on our heads live, unscripted. Love to see it. So thank you for tuning in. If this was helpful, please go to youreverydayai.com.
Jordan Wilson [00:33:52]:
Sign up for that free daily newsletter. Thanks for tuning in. We'll see you back tomorrow and everyday for more everyday AI. Thanks, y'all.
Midroll [00:34:00]:
And that's a wrap for today's edition of everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll
Jordan Wilson [00:34:19]:
see
Midroll [00:34:19]:
you next time.