AI and the Future of Work

About the Episode

Generative AI is likely here to stay—it’s possible that by 2030, 30% of mundane tasks at work will be fully automated, freeing up many of us to dig deeper into more complex aspects of our jobs. But AI won’t take over our jobs—the human element is irreplaceable. Jesse Bockstedt, professor and Senior Associate Dean at Emory University joins the vodcast to share his perspective on AI, from how it’s changing today’s workplaces to how we’re preparing future generations.

Transcript

Janice Minn: 
Hi Jesse. Thanks for joining us today. So happy you’re here. 

Jesse Bockstedt: 
Hey Janice, I’m pleased to be here. Thanks for having me. 

Janice: 
Do you mind just giving a quick introduction of yourself, your background? 

Jesse: 
Sure, yeah.  

I am a professor at Emory University in the business school, the Goizueta Business School. And my area of expertise is information systems and operations management. And I teach classes on AI, machine learning, data analytics, digital transformation for businesses.  

I’ve been a professor about 16 or 17 years, and prior to that, I worked as a management consultant at Accenture, kind of tail end of the dot com boom, and also in corporate research for IBM.  

Right now I’m actually working as the Senior Associate Dean for graduate programs for the business school. So I’m overseeing our MBA programs, our masters of science and business analytics program, masters of finance program, et cetera.  

So yeah, so that’s my background, a PhD from University of Minnesota. I do a lot of research on how technology, new technologies impact consumers and organizations. And right now everybody wants to talk about AI. 

And I’ve known you for almost 30 years. We met in college and your husband is one of my best friends in the world that I’ve known since I was about 14, I think. So yeah, so happy to talk anytime. 

Janice: 
Yeah, I love it that our professions kind of converge together as a result of technology and generative AI.  

I’m super excited for this conversation. So, let’s get right to it.  

Can you give the listeners just a little bit of overview of what is generative AI, just some examples and what it is? 

Jesse: 
Yeah, sure.  

So generative AI is, you know, I’m sure everybody has heard of, you know, chatGPT and maybe even heard of LLMs, which are large language models, which kind of really burst onto the scene about a year and a half ago, kind of the tail end of 2022 when OpenAI released chatGPT.  

Generative AI is a new kind of class of AI. And when I say AI, I use that word kind of generally, you know, the word AI has been around for a long time has a lot of different means, especially over the years, what it means has kind of changed a little bit.  

Up until about a year and a half ago, when we talked about AI kind of in corporate settings or modern technology settings, we really meant like machine learning, which is the idea that you’ve got lots of data that you can learn patterns from and then use those patterns to make predictions.  

So, for example, in an HR setting, right, you might be wanting to hire someone and you have tons of data on resumes and applicants. And you also know how well people you’ve hired in the past have performed at their job. 

So, you can crunch all those numbers and find patterns and see what types of indicators and resumes are highly correlated with job performance and build a model that helps you then when you see a new resume come in, say, what’s the likelihood that this person’s going to be an over performer and then make a decision.  

So machine learning models, kind of the AI world was really about prediction, doing a lot of prediction. And we kind of call that now we kind of call that discriminative AI. The idea was that you would have these models that learn from lots of data, to be able to discriminate, when you give it a new use, a new case, be able to discriminate whether or not this is X or Y, and then that prediction is useful in a business context.  

You can also think of image recognition software models that you can train. There’s lots of examples of this, you train up a model to be able to tell the difference between a dog and a cat in a photo. So it’s discriminating, you give it a photo, and it’s learned from millions and millions of photos what characteristics in the photo correlate to a dog versus a cat. And so you show it a new picture and it can discriminate whether or not this is a dog or a cat.  

So that’s kind of what AI was predominantly used for and kind of what we meant by AI up until 2022, late 2022. And there’s some other definitions of AI. There’s something called GoFi, good old fashioned AI. And this is like the stuff from back in like the 70s and 80s where we would try to program computers to think like humans. And it was very kind of rule-based and there’s a lot of research done on this. 

Predominantly AI has been data driven for the last 15, 20 years. In 2022, when chatGPT came out, we kind of opened up to the world this idea of generative AI. Now, generative AI wasn’t new. It didn’t start with open AI and chatGPT, but it wasn’t really in kind of the zeitgeist. Like, people didn’t really know what it was. Generative AI takes the same kind of idea of discriminative AI, where you take lots of data and you learn patterns from that data and understand correlations between things and data, and really understand the distribution of how different characteristics in the data kind of fall and fit together.  

But the goal of generative AI is, instead of making a prediction, instead of giving it a case and then having to make a prediction about that, you’re actually asking it to produce something new, to produce new content based on what it’s learned. So you give it a prompt, and then that prompt instantly causes the model to generate some new content. 

There were some pretty kind of big things that happened in research, you know, several years ago in like 2017 timeframe where some new models were built that really kind of sped up the ability for AI systems to be able to do this kind of generative objective, to be able to generate new things. And so when we think of generative AI now, you know, I think everybody thinks of ChatGPT, you type in a prompt and it generates text, right?  

So you can ask ChatGPT to write a poem for your spouse or something like that. It’s generating content.  

So it’s not doing what we used to do, which was discriminate where you would give it a poem and then ask it, is this a good poem or a bad poem or something like that. You’re actually asking it to generate a poem. We’re still giving it feedback and saying, that was a good poem or a bad poem, and it’s learning and getting better at writing poems, but it’s generating content.  

And there are all sorts of flavors of generative AI.  

There are large language models, which generate text. Because they’re language models, they’re trained on large amounts of text, and so they’re really good at generating text, and generating text that meets a specific prompt.  

And then there are models that generate images. For example, Dali is an image generation model from OpenAI, which creates chatGPT. So we can generate images. And image generation often follows a slightly different underlying model than large language models. 

But it’s the same type of idea. It’s been trained on lots of images, it recognizes patterns, you ask it for something and it generates a new thing. And really where all this is going now with generative AI models is what are called multimodal models.  

So these are large, not just large language models, but LMMs, large multimodal models. And these are models that can do all sorts of different generative tasks. So not just text or not just images, but images and text. And not just text as an input, but also you could give it an image as an input, or you could give it a voice as an input, or you could give it video as an input.  

So we’re starting to see convergence of these things into tools. Underlined, there’s different models that are working, but the tool or the application that your user is using has become multimodal. So there’s a lot of great examples. ChatGPT’s interface is probably the most common people are familiar with, and you can now with the Pro version, give it a document, give it a picture, ask it to create images.  

So it’s starting to become very multimodal, but Google’s Gemini is also doing this. Claude, which is another competitor out there, is doing this. And so that’s kind of where we are in terms of kind of the current state. And right now, most of these models and most of these kind of LLMs are kind of general, right? They’re kind of general knowledge type things in specific industries like HR, engineering, supply chain, marketing, et cetera. 

We’re starting to see verticalized generative AIs where we’re taking the general LLMs and then focusing them on specific context so we can do generative stuff but that’s really good for specific context. So I know that was a lot. You know me, Janice. I like to talk a lot. So hopefully that wasn’t too much. But it’s a really, really interesting area. There’s so much.  

It’s, you know, it reminds me a lot of 1999, 98, 99, and the beginning of the dot com era, when there was a new business model or new online thing every day and you didn’t know what was going to stick and there’s always something interesting new coming out.  

That’s kind of the arrow in right now is there’s every day there’s a new startup, there’s a new tech, there’s a new business that is using this in an interesting way. So we’re seeing that all kind of explode and over the next couple of years that’ll get trimmed down significantly and we’ll really see kind of who the winners are and what the real kind of long-term use cases are. 

Janice: 
Yeah, I mean, we see that definitely in day-to-day life, just in the software we’re using. You see pop-ups that say, hey, we now can leverage generative AI to help you augment your pictures or to be able to scan the documents or even create ticket writing for you. So there’s definitely just the accessibility of it just for the general public and just for business applications is just huge. And yeah, it is a lot like the.com period where it was just you’re getting flooded with information over and over again. 

Jesse: 
Yeah, I mean, by design, these models are like kind of API plug and play type driven. So, you know, the fact that all these tools can be built on top of them, which really neat, you know, there’s not a lot of companies that are out there building their own generative AI large language models, but there are tons of companies that are taking the existing ones and then building them into a feature in a product or building a product on top of them. Really kind of interesting how the ecosystem is evolving. 

Janice: 
Yeah, and I think for me, it’s super exciting because as an organization, we’ve definitely taken advantage of that and using alternative AI to, you know, like you said, doing more personalized content creation, being able to use it to pick up, you know, on sentiment and kind of related to that, you had shared a really funny story with me and Chris [Janice’s husband] a couple of weeks ago about what had happened with you getting your son ready for school and you accidentally leaving your voice recording on with GPT.  

Can you just share that story? I think it’s just, it’s really relatable. 

Jesse: 
Yeah, let me actually, yeah, I got a screenshot of it here because I thought it was so hilarious.  

So, to just give it some context, I use ChatGPT all the time for all sorts of stuff.  

Like I use it at work, you know, in my administrative role, I have to create proposals all the time to get things approved. And so I’ll have the documents, but I don’t have the time to write up a formal proposal. So I’ll ask it to do that kind of stuff or, you know, help me review. 

Janice: 
Yeah, it’s up all the time. 

Jesse: 
I’ve got to read a bunch of papers or something. So like, can you help me extract the, you know, the, the key insights and that type of stuff, but that this morning, my wife, my wife is a veterinarian, Katie, and we’ve been kind of joking about maybe starting a side business related to some veterinary products because she’s a veterinarian expert and I’m the kind of business guy. 

We were asking chatGPT, kind of for fun, to help us design some logos for our idea for the business. And so I was going back and forth to chatGPT while the kids were having fun with it too, like seeing what the logos looked like.  

And I put my phone down because I realized it’s like, oh, we gotta go, you know, I mean, anybody who’s got young kids know like all of a sudden there’s that point where you gotta get everybody ready and get out the door. And I didn’t realize GPT was still running. 

Janice: 
That oh crap moment, yeah. 

Jesse: 
And it recorded my voice as I was talking to it. It’s actually pretty funny. I was, I was first, I responded to GPT. I was like, that’s pretty good. It needs a little work about the logo.  

But then I started talking to my kids and I said, you know, all right, keep your shoes on. They’re over here. Why don’t you have your socks and shoes on? Have you gone to the bathroom yet? Did you really go to the bathroom? When did you go? You know, I kind of had this whole long thing you go through with your kids to get them out the door to make sure they’ve gone to the bathroom, put their shoes on, brush your teeth, all that kind of stuff.  

So it recorded me doing that. And then at the end of it, then it responded and it said, ‘It sounds like you have a busy morning over there. If you need any more adjustments to look or anything else, feel free to ask for whatever when you’re ready.’ And then ‘I hope you get everyone out the door on time’ is what it said to me. 

I was kind of blown away because it was listening to me. I wasn’t talking to it, but it picked up on the sentiment, it picked up on the context, and nowhere in it did I say, we need to go, we need to get out of here on time. But it picked up by the conversation I was having with my kids that what situation I was in.  

So that was kind of an interesting moment. I intimately know what these models can do, but I’d never had that kind of interaction with it, which was pretty eye-opening. 

Janice: 
Yeah, it’s crazy how well it can deduce.  

Even for [Businessolver], we have a service in place where when we have call center recordings with our call center agents and with the members calling in and getting help with their benefits, we are able to summarize the call recordings so that our agents no longer have to do that.  

So that saves them a bunch of time, but then we were able to pull data and you know, information specifically about that call to get more insights. And we can also pull that sentiment in there. And it’s really amazing at how effective these large English models can adapt to it and being able to get that insights that you need and be able to deduce and provide, you know, insights on what happened during that call. Was it good? Was it bad? And that’s exactly what AI achieved for you that morning. 

Jesse: 
Yeah, exactly.  

In the business content, any business context, these tools like start to make you realize how much time people spend on these types of things like just, you know, taking minutes, you know, taking information from a meeting or from a call or from a zoom and being able to like distill it down into key points and distribute that information.  

And once you start to automate some of that stuff or big, big chunk of that stuff with these types of tools, you start to really just immediately start to see what the productivity gains are. So that’s a great example.  

And in your context, of course, for an HR chatbot, but it’s across the board. I know many companies that are using it just internally for their own meeting minutes and those types of things. I was talking with, I did a round table, AI round table on supply chain with some supply chain leaders the other day. 

And some people were talking about how they were using it to interact with suppliers, to kind of manage that whole communication process with suppliers. Anywhere where you’ve got like, you’ve got to take in a bunch of information, you have to organize that information, you have to make sense of that information, distill that information and maybe communicate that information out.  

This is like perfect optimal use case for these types of tools. 

Janice: 
Yeah, it’s crazy. Like I’m an Excel junkie. Like I love Excel and people on my team make fun of me because they’re like, there’s a faster way to do this. But like, even now as you know, I’m kind of going old school, like our ability to be able to just like upload spreadsheets now and being able to look through multiple pieces of data or just looking at code and being able to look at multiple pieces of information and being able to have that, you know, turned around so quickly is just such a huge time saver. 

Jesse: 
It’s crazy.  

I mean, part of my job is to do research. And so we’ll collect big data sets and we’ll analyze them with statistics. And then we’ll write papers about what we find.  

And just for fun of colleague and I were playing with chatGPT and I asked chatGPT to create a synthetic data set. I told them what relationships I wanted to have between the variables. I was trying to have like investment with AI be highly correlated with like, you know, profit or something. And so I told to generate variables, you know, a data set randomly that does that.  

And then I had it do the analysis.  

And then I had it write up some paragraphs to explain the results.  

And then I had it write a literature review.  

And again, this isn’t something I would submit to a journal or a conference, but all those steps, including the creating fake data was all something that I could do within ChatGPT.  

Now the quality and the level is definitely not something that would fool anybody in my field, but just the fact that it has that breadth, you know, capability to be able to do all these things like write code, write data or do data analysis, generate images, generate text, do communication and synthesis of information.  

It’s pretty amazing. 

Janice: 
I think you touch on a really good point though about the quality of the output. I know that’s one area, right? Where it sounds really convincing or it looks really good, but it may not always be the case.  

So any thoughts there, especially as we have HR folks who we are very risk adverse folk. So how do we dissuade them from being concerned about the quality and the integrity of the information that’s being outputted by journalism? 

Jesse: 
Yeah.  

So, I mean, I think the way to think about this, and this is not my original analogy, I’ve heard this from several different places, but I think the way to think about this is it’s like having a really good high school intern working for you, like one of these tools.  

There’s a lot of stuff you can offset to them, and over time they’ll get better, but there’s a lot of stuff you can outsource to that person and say, hey, can you go do a bunch of research on this and write something up or can you do some initial data analysis of this?  

But you would never take that end work product from your high school intern and just turn it in as yours to your boss or whoever, whatever stakeholder. You would always go through it and double check and make sure.  

So I think the key is that when using these tools can save a lot of time, but think about it as in saving time on the front end of maybe like, you know, prototyping or developing your idea or first draft, rough draft on things. 

But always expect that you’re gonna be spending time going through and making sure that things look factual, that there aren’t any errors or issues. And of course, in different contexts, this is the risk on this side is big. For example, in like medicine and financial services, where there’s regulations and fines and all these types of things, you have to really be aware that, what the possibility of like hallucinations or errors in the output could have. 

In HR, it’s pretty important, but maybe the stakes aren’t quite as high in those. But then there are some things like you’re creating marketing content and stuff like that. The stakes are important for your brand, but you probably aren’t at risk of big issues with regulators and things like that.  

So understanding the risk profile of a business context, I think, is important. And then also making sure employees and people that are using these types of tools don’t see this as just push the button and then take that. That’s it’s really like creating a first draft or maybe it’s helping you final you’ve already got a first draft and you’re wanting to kind of you know make it more concise or something like that.  

It’s an aid. It’s a helper. It’s not it’s not a replacement for what you’re doing. 

Janice: 
Yeah, and I think that’s a great point.  

And even with HR, there are a lot of regulatory constraints, whether it’s at the federal level, the IRS, state municipalities. So there is definitely risk therein.  

And also it’s that risk of giving people the wrong information that’s gonna impact their day-to-day lives. So for us personally, we take a lot of care, making sure that the quality of the, no garbage in, no garbage out, that we’re doing everything we can to prevent those hallucinations. It’s hard. We experiment with it and we iterate on it quickly. 

Jesse: 
Yeah. Yeah, I mean, in the moment, the person interacting should keep an eye. But then I think good practice too is some human role as an auditor who’s taking a look at what’s being used for and what type of stuff has it been used for, and just double checking and making sure that there are no issues. So in the moment, but also just kind of from an audit perspective, I think makes sense too. Yeah. 

Janice: 
Yeah, 100%. 

So I love all those points. 

I think, you know, more specifically, when you think about the explosion of generative AI for us, this is our area. And we get excited about it. We’ll geek out on it.  

But, you know, for a lot of our friends or even Katie, who’s a veterinarian, who’s a little bit more of like, you know, afraid of generative AI, like, where do you see, you know, where organizations should be taking advantage of it? And, you know, either whether it’s skilling up their employees now or just for future, in terms of being able to stay competitive and meet their business objectives? 

Just curious on your thoughts on that. 

Jesse: 
The thing I would want to say is if you went back 25 years and someone said to you, the internet is a fad and nobody’s ever going to buy books on the internet and nobody’s ever going to order dinner on the internet or nobody’s ever going to meet their wife or husband on the internet. Back then there were a lot of people saying that. 

And now you look, if someone said that today, you’d be like, that’s ridiculous, right? It is ingrained part of our culture, part of our society, part of our business.  

AI has got a similar trajectory.  

I think AI is going to be one of these things that is just, it’s going to evolve in such a way that it becomes just part of the background and it’s just going to be enabling things. And it’s not even going to be something that we all think about on a regular basis. It’s just going to be there and it’s just going to be part of the infrastructure. 

So ignoring it and putting your head in the sand and saying like, this isn’t right, we shouldn’t. That’s a little bit of a kind of a luddite type view that I think, you know, in the long run is not going to be beneficial. And companies during the internet boom that waited too long to kind of adopt internet technology and digital transformation, you know, they suffered. They suffered in the marketplace. Companies that were leaders in that, they took some risks and yeah, there were some. But ultimately gave them a competitive advantage.  

And so I think companies have to think about how AI is going to augment and improve their business processes. I think leaders need to think about how these types of tools can help generate more value for their customers or reduce costs for their operations. I’m not saying you go out tomorrow and say, I got to find an AI provider and sign some big multi-million dollar contract, but you have to start paying attention.  

You definitely have to start paying attention to what the capabilities are and how companies are doing it. Because the companies I’ve talked to, as a professor and as I do a lot of executive education stuff as well, it’s clear there’s a huge spectrum of how companies are using it. Some of them are dipping their toe in and saying, well, we’re letting our employees play around with some of this stuff. And then there’s some that are going full in and you can automatically see how they’re gonna get a competitive advantage. 

Everybody will catch up and eventually everybody will be using it, you know, you know, extensively, just like that happened with internet technology, but there will be, and least in the short term, some opportunities for some competitive advantage gained by, by taking risks and trying these things out.  

So, I think companies need to think a lot about how it fits into their work processes and into their business. Think about how it how it creates it, how you can use it to create value for your customers.  

So example, in your HR setting, you’re better serving your customers through chatbots. Maybe they’re getting answers quicker. You’re able to service them more quickly. They’re more satisfied because they’re getting the information more quickly than they might with a human agent. You’re able to handle a bigger load, a higher number of customers at a given time. So they’re not waiting as long. That’s all creating value for your customers.  

And on the cost side, it’s probably reducing the headcount in terms of that, or maybe not reducing the headcount, but not requiring you to expand the headcount to deal with increased demand. So that helps you with the cost side as well.  

So that kind of perspective is, where do these things fit in? And then when you go in to start talking to companies about buying technology, you wanna go in as the skeptical view because there are so many companies trying to sell so many products on the market right now.  

So companies that have a good analytics and technology team, I think will have an advantage as well because they’ll be able to understand what the real value is in these tools and things that are being offered on the market. A lot of companies, I think your company, I think you mentioned, built some stuff, homegrown stuff that they were able to build using some open-source models and things like that. 

You know, so companies that have a mature kind of technology and analytics type of group and mindset will be able to move more quickly and take advantage.  

But you know, one last thing on that, but some of these technologies too, it’s very interesting. I’ve talked to some companies that are able to leapfrog, like instead of spending tons of money on data infrastructure and data governance and all these things in order to get to the level where you need to be to kind of build your own stuff, they’re going with third party providers and able to leapfrog that and just ignore building all that stuff because they’re able to go directly to the solution.  

So it’s a really interesting time. I know that was maybe kind of a non-answer all over the place, but I think that the key is really don’t ignore it. You know go into this kind of marketplace of all these tools with a you know an open mind, but you know a skeptics mind like that you know is this something that really will do something for me and understand how it can affect your business in terms of creating value for your customers or reducing costs in your organization. Don’t just go buy shiny things, really think about how it impacts the bottom line. 

Janice: 
Yeah, I mean, I think that’s fair. I think it’s, you know, your business best.  

So look at where there’s areas of opportunity to automate, streamlined tasks, you know, and then being able to look at like, do you build or do you buy?  

And I think you hit a really important point that we didn’t talk about in the past, but like the access to that data and being able to really leverage that data to propel you forward. I think that’s really key and something that a lot of organizations haven’t been really great at, at being able to get access to that data to get the insights that they need to make decisions or to leverage it to train these models to be able to achieve their business results. 

Jesse: 
Yeah, just on that too, I think it’s interesting too. We’ve talked about data as the new oil for over a decade and that like all this value in data. Yes, but now it really is right. Especially in certain contexts, because you have these, you have these large language models that are trained on open, open data, right?  

You know, the, the most of them are trained on what’s called the common crawl, which is a common crawl of the open public data web internet. But you can start to think about your company as its own external data that is not out in the world and what value that has and how that data can be used to augment these language models so that you can provide a unique value in the marketplace.  

For example, Reddit, right? They stopped all data scraping and they’ve created a whole new model where you have to pay to be able to use their data to train models because Reddit has, if you don’t know Reddit, it’s a collection of web forums where people talk about all sorts of stuff.  

But the language in there is so interesting because it’s a lot of conversations back and forth about a lot of different topics. And so Reddit has seized that as a really critical asset now. And New York Times too, right? And New York Times has been in some lawsuits with OpenAI because some of their archive stuff was used to train the original model. 

And they see that as now a really big asset. Their data is a big asset. So thinking about data as an asset really is becoming an important thing. 

Janice: 
Yeah, that’s conversations we’ve definitely had internally of, you know, the access to the data and being able to use it and what insights we can provide back that is, you know, more readily accessible now because we are doing call recordings, we can transcribe it, we can pull it, you know, and share it back with our clients.  

So related to that, you know, we talked about, you know, organizations shouldn’t be ignoring it. Deloitte had like a skills based survey where they said that corporations that took advantage and like re-skilled employees to take advantage of generative AI were 63% more likely to achieve results.  

So when you look at like specific skill sets, so like for me, I have a couple of openings on my team, you know, I’m hiring some new folks, like what are some of the skills that you would think you would wanna look for in an individual person to be able to make sure that they can, you know, really help your team grow and thrive in this environment. 

Jesse: 
Yeah, I mean, you just generally awareness of this technology is important. I’m not saying you need to have super technical people that can write code and that kind of stuff, but kind of the awareness of this, like people that are using these types of tools in their daily lives are ready, are going to fit in well and understand how to use them.  

But beyond that, like the types of skills you’re looking for, you know, Deloitte’s talking about upskilling. There’s different degrees of upskilling, right? There’s taking your employees and training them all how to code, right? And then there’s taking your employees and getting them up to speed on the latest tools and just what they are and how they’re so that they’re still aware of what’s available.  

That’s all great. But I think as the workforce evolves to be using generative AI as, you know, in kind of an assistant type mode for employees, but also using it in products and services that are, you know, that is running things automatically, you know, critical thinking is become, I mean, it’s always important, but it’s become even more important because of what we were talking about earlier, right? 

Like, how do I trust this output from this AI? How can I discern between this is good output, this is bad output? How do I make sure that I’m using this in the right context? When do I decide to use the AI versus use my own expertise? How do I marry those two things?  

So understanding kind of critical thinking skills to be able to kind of think through that, I think will be really important. I think organizational management skills will be important too, because I think people’s jobs are gonna become…A lot of the mundane stuff we do on a daily basis, we’re gonna be able to offload to these types of tools.  

So being able to kind of figure out where I focus my time and my attention for the biggest productivity gains or the biggest bang for the buck. So having good organizational skills, I think, it’s always important, but being able to kind of have that, executive functioning, organizational things, to be able to do multiple things and more things because now you’ve got this tool that will help you be more productive will be important. 

I also think kind of like bigger picture strategy type thinking is important too, because understanding how these tools can augment business processes, how they can make you more productive, how the bigger picture kind of fits in, I think is important because things are going to change. I mean, all the boats are going to rise as the sea of AI increases, but it’s like which boats rise first and is going to give companies an advantage. So kind of being able to kind of see that. 

And I heard, I mentioned I was in this roundtable, a bunch of supply chain managers a few days ago, and one of the, I can’t disclose who these people were, but one of the high ups at a big Fortune 500 company, probably Fortune 25 company, said that they’re seeing a trend of what they call the rise of the gray hairs, which I thought was interesting.  

Janice: 
Yeah, I don’t know how I feel that you’re calling us gray hairs, even though I do cover up my gray, but yeah. 

Jesse: 
Yeah. Well, I mean, I’m just, you know, I don’t think of myself as a gray hair, but I mean, when you look at the, when I look at the numbers, I’m getting close, but yeah.  

And the idea was that they see an increasing demand for older people, like our age, that have a lot of work experience and have seen a lot of different work environments and situations and contexts because they have that bigger picture and lots of experience so that when they’re put in this situation, they’re able to kind of know what to do. And so that seems to be like a skill that’s becoming more valuable again, is kind of having that real experience so that you can kind of interpret the value that’s being created by using these tools.  

So I thought that was interesting. 

And he was, he was older, so maybe he was just, you know, trying to make us all feel better, but you know, that it’s not just going to be a bunch of young people. So, you know, so I think it’s important, you know, technical skills are always going to be important, but, um, you know, a lot of those, a lot of things that required a lot of technical skill, like, you know, Excel, coding, statistical analysis, we can offload to the AI.  

But, I think it’s still important to think that, you know, if you just blindly offload, you don’t know when errors are being made. And so, so again, think of these things as assistants that make people better at their jobs. There’s a great, great quote. You know, and I am, this is not my unique quote. I’ve heard it many times from many different people, but it’s, you know, you’re not going to be replaced by AI. You’ll be replaced by somebody who’s using AI. 

And I’ve seen a similar quote recently, a lot that says, you know, businesses are not going to lose out, lose to AI. Businesses are going to lose to businesses that are using AI. And so it’s this idea of like, how do I use it effectively to make, you know, to make my job better, my organization better, my company better. 

Janice: 
Yeah. I think the whole point about grey hairs is really interesting too, as like, you know, there’s a labor shortage as well.  

There’s a lot of individuals that are retiring where they might be looking for a second career and we could leverage from their expertise and their higher functioning skills, like you were saying, to be able to, you know, find success in an organization.  

And I think the one important thing to think through as we talk about generative AI is we talk about value back to the business, value back to the customer. 

But I think what’s really important is the value back to your employees as well, because you’re offloading these mundane skills and being able to let people work on more value added, more critical thinking types of skills. And, you know, I think there’s always that concern about how it’s going to impact more of the entry level positions or, you know, the lower paid salaries. But I think there’s an opportunity there where you’re providing more meaningful work for that segment of the workforce population as well. 

Jesse: 
I think so, yeah. And also, from an HR perspective, too, I think your communication strategy with your employees is really important around this kind of stuff. Because there are a lot of people out there that are scared they’re going to lose their job because of these types of tools. And communicating that, hey, we’re not planning to replace you with AI, but we want you to be able to use AI to become better at your job or more efficient at your job or be able to take on more responsibility.  

I think it’s kind of important.  

And thinking about implementing this type of stuff for your employees to support them in their jobs. I don’t think it’s a wise strategy to just be like, let’s get everybody chatGPT accounts and be like, go for it. You really want to think about what their work process is and what the workflow looks like and then start identifying specific steps in the workflow that you can automate or offset with support from a generative AI tool or something like that.  

So that it’s structured in how you’re kind of adopting it instead of just leaving it up to the employee. If you just kind of leave it up to the employee, say, here’s this new technology, see if you can use it to make yourself more effective, that’s not going to work. But thinking through how it fits into individual people’s workflows, I think, is the way to go. 

Janice: 
Yeah, I agree. And I think that’s important for HR leaders is to look at, you know, what is our philosophy around it? How are we gonna use it? How are we gonna skill up our workforce? A change management that you brought up is really important just to not instill that fear of being replacement. But I think the key here is it’s really, like you said before, augmenting the work that we’re doing. It’s the value add. 

Jesse: 
Yeah, so like an example, I think is really interesting. Like in the restaurant industry, we’re starting to see a lot of AI and robots, robotics being used in, in the restaurant industry. And, you know, serve you survey, like restaurant employees or like, you know, fast casual employees, they hate doing mundane stuff, right? They hate doing the cash register stuff. They, they hate, you know, putting together the sandwich, but they, a lot of them actually like to be chefs, right. Or it’d be cooks, right. I heard a, I actually heard the CEO of sweet green being interviewed about this recently. 

And the idea is that you’re taking away the stuff about the job they don’t like so that they can focus on the stuff about the job they do like. And I think that’s a great way to communicate it, you know, for all, not just in restaurant, but in all parts of, you know, in all parts of the industry, all parts, all industries, all jobs, et cetera. So yeah. 

Janice: 
Yeah, like I think for us, like our call center agents, not having to like think through taking notes that automatically happening for them, they can really focus on the quality of call and really provide that excellent customer service. That’s that value added. So just kind of selfishly, and I think it’s because I get a lot of friends that are like fretting right now, right? About what schools should my kids attend?  

And you know, I know you’re a team of students for graduate programs, but I think you’re like in a really unique situation, you know, as we’re trying to figure out what skills do we instill in our kids, you know, to get them ready for the future of work. When you’re looking at, you know, admitting students or, you know, how we train this like, gen alpha generation and, you know, our kids. 

I’m just curious, like, what tips would you give parents or teachers or anybody else that, you know, might be interested in how we’re preparing our younger generation for the future. 

Jesse: 
It’s tough. I mean, I go back forth on this myself. You know, I have two kids and, you know, I’ve been in two engineering degrees and a PhD and like, I was going to have them coding by age eight and, you know, doing calculus by age 10. I don’t know if I need to push so hard on that, you know, anymore because we’re able to offload a lot of that stuff now. But, I do think critical thinking skills are so important.  

And so rather than looking at things like teaching your kids to code because you want them to be a software engineer, think about teaching your kids to code because that teaches them critical thinking skills. It teaches them how to take a problem, dissect it, break it into different parts, attack it and, you know, and focus on this part and this part and this part and then figure out how to put it all back together. So, you know, even though we think about like, you know, generative AI maybe eliminating a lot of coding jobs because we can do a lot of things automatically. 

Things like learning how to code or science and technology type stuff is still important because it teaches those essential critical thinking skills, right? You know, the idea that your kid is going to need to be able to recite the date of the Magna Carta or something like that, you know, they’re never going to have to know that stuff in the future. They don’t even have to know it now because you can Google that kind of stuff like historical facts.  

But understanding context and really understanding you know, how things kind of happen and why they happen a certain way and be able to kind of think critically about how things happen, I think is always going to be extremely important. I think social personal skills are going to become, I mean, they’re always extremely important, but they’re going to become even more important because we’re going to have so much more of our society and in business, communication is going to be happening through these types of AI channels and that genuine, unique human interaction is going to become more and more and more valuable, right? 

You know, in admissions now, you know, I oversee our graduate programs, which means I, you know, I don’t do admissions personally, but I oversee the group that does admissions. You know, and one of the things we have to deal with now is admissions essays in the age of chatGPT, right? So you almost have to discount, you know, I’m not saying we don’t care about admissions essays anymore, but you know, you have to go into every one of them you read, like, is it possible that this was just written by, written by an AI, right? But what we do is we do get, you know, potential MBA students on the phone and talk to them and do a zoom with them and interact with them and have a real conversation with them. And that becomes more important when the when the essay is something that you know, you can just have chatGPT spit out in 30 seconds.  

So I think that interpersonal stuff is really important. So critical thinking skills and interpersonal, you know, kind of communication skills are great. And then again, related to what we’re talking about earlier about employees, I think like time management and organization is also key to because I think the future worker is going to have a lot of stuff on their plate because they’re going to have these tools that offset the mundane things that take up most of their day of their job.  

So they’re going to have a lot of different responsibilities and a lot of different things to keep track of and think about. So executive functioning and organizational skills I think become critical. 

Janice: 
Yeah, our output’s going to be so much higher, but how do you prioritize that output to deliver the biggest value?  

Jesse: 
Yeah. I mean, and this is just a continued trajectory. Like if you look at like agrarian culture through the industrial revolution, through the information revolution, it’s all the same trajectory, right? You know, very few people, their job nowadays is to go out and do one single focus task the whole day long. You know, in order to, in order to make a living, in order to create food for them to eat, you know, at each stage, we’ve become more complex and the types of things we have to do. And I think AI is just going to keep doing that. 

The alpha generation is going to be the AI natives, right? Like we call Gen Zs the digital natives because they’ve never known the world without the internet. This alpha generation and the gamma generation following it will be the AI natives because they’re never going to know the world without AI. And so they’ll figure it out. You know, things will evolve naturally. It’s just, you know, relying on any one specific type of profession now. Thinking that your kid’s gonna be X in 15, 20 years, I think that’s a good strategy, because you don’t know how these things are all gonna change with these new technologies. 

Janice: 
So what I’m hearing is I should tell my friends to calm down about school admittance and really help their kids.  

Jesse: 
Yeah, I mean, I would, yes, exactly. Calm down. You know, I mean, the fact that your friends, you know, people care and worry about the stuff anyways, means that those kids are already in a better spot.  

I wouldn’t be suggesting, ‘Hey, let’s make a career out of being a photographer or a copywriter.’ That’s probably not, you know, those are types of things that obviously are probably, you know, from an artistic standpoint, yes, pursue those types of things. But the idea of some of these types of trades that are obviously easily replaceable by this, you know, you have to kind of keep an eye on. 

Janice: 
Oh, 100%. Yeah. 

Jesse: 
But generally, I think critical thinking, organizational skills, and kind of socio-emotional communication skills are always going to be important. 

Janice: 
I love it. Well, thank you so much, Jesse. It was so fun to be able to talk shop with you and connect on a personal level as well. Any last insights you want to share or? 

Jesse: 
No, I think we covered it all. I mean, from my perspective, I do what I do because I love technology and I love seeing new technology and technological innovation. And this is a really cool time. To be alive for this, to see this kind of happen is a really, really cool thing. I’m highly biased because that’s what I do and that’s what I love. But as someone who’s around for the internet, boom, and this, it’s a really cool time.  

So go play with these tools, go explore with them, even if they scare you a little bit, learn about them and play around with them and see what they can do because it’s really eye opening some of the stuff you can do with these things. 

Janice: 
Well, thank you so much, Jesse. I appreciate it. Have a great day. Bye. 

Jesse:  
Thanks, Janice. Yeah, you too, take care. Bye.