EP 415: Future of Work - When intelligence is democratized, what skills do we need?

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You know one thing that large language models are great at? Knowing a lot of things. That same skill that humans have, we've hung our hat on that, right? Our ability to be intelligent, our ability to know things. You know, I think for so many business leaders out there, it is that intelligence. It is those things, you know, that have paved the way for you to accomplish what you have in your career. So what happens next when we talk about the future of work and artificial intelligence? Large language models, as they get more capable, more robust and they know more things as large language models make intelligence more democratized. What are those skills us human needs? What skills do we need for the future? Well, we're going to be talking about that and a lot more today on Everyday AI. What's going on, y'?

All?

My name is Jordan Wilson and I'm the host of Everyday AI and this thing is for for you. It's your daily live stream, podcast and free daily newsletter helping us all not just learn about AI, but how we can leverage it to grow our companies and our careers. If that sounds like you, you are definitely in the right place. And I'm excited for today's conversation. But before we get started, have to give a quick shout out to our partners at Microsoft Work Lab. So why should you listen to the Worklab podcast from Microsoft? Well, because it's the place to find actionable insights to guide your organization's AI transformation. Tune in to learn how to think more creatively about unlocking the full potential of the technology. That's W O R K L A B no spaces available, wherever you get your podcasts. And speaking of Microsoft, we have to thank them for, you know, sponsoring everyday AI. But also we have a great guest today, today from Microsoft. But if you are tuning in and listening for the AI news, yeah, generally we bring that to you right before we get started every single day technically pre recorded show debuting live. So if you do want that AI news, it's going to be in the newsletter. So make sure to go to your everyday AI.com and sign up for that free daily newsletter. We'll be recapping today's conversation and a whole lot more. So let's talk about that big thing. Well, when intelligence is democratized, what skills do us human needs? What skills do us humans need? Apparently we need communication skills, right? All right, so I'm excited for today's guest. So please help me welcome on the Everyday AI Show. There we go. We have our Alexia Kamban, the Senior Director of Research at Microsoft. Alexia, thank you so much for joining the Everyday AI Show.

Thank you so much for having me, Jordan.

Cat's got my tongue this morning. Alexa, tell everyone a little bit about what you do in your role of senior Director of Research at Microsoft.

Yeah, sure thing. So I head up research for a team at Microsoft that is called the Copilot and Future of Work team. Pretty much what it says on the tin. We essentially take all of the billions of signals that we collect through telemetry, through surveys, through qualitative and quantitative research, and then we try to understand what that means in terms of the future of work. So we can really try to start to predict and project what the employee experience is going to look like. Some of our biggest research deliverables are the Work Trend Index reports. So a little shout out to those. That's where most of our innovative research lives is, in those annual reports.

Yeah. And yeah, you have to shout out that report. We've shared it in our newsletter multiple times over the years. I think it's one of the most all encompassing and best looks at artificial intelligence and its impact on work. So yeah, shout out to everyone that worked on that. But you know, speaking of work, billions of data points, Alexa, like help us better understand, like where the heck do all of these data points come from and how do you and your team use all of these data points to help us hopefully make smarter decisions about the future of work?

Yeah, I mean we're very privileged at Microsoft obviously to have access to a type of data that's called telemetry data. And telemetry data essentially is emitted from all of the products that we create. So that really helps us understand things like time spent in meetings, you know, time spent on email, number of emails read, number of documents edited, all of those signals that essentially make up work. And obviously important to note that we only look at them at the aggregate, but they really allow us to understand how work gets done. And that's obviously important as an observation in the wild, right, Seeing how behavioral patterns look out there in the wild. But we also do a really big global survey that goes out every year to 31,000 people in 31 different countries. And that allows us to get more of the sentiment based data. So really getting a pulse on how people are feeling at work and combining those two data sources is really what makes up the Work Trend Index reports.

So I do want to get back to that, Alexia. But I also want to fast forward to the very end, right? Let's just give away the good stuff. So what skills do we all need? Right? Like, that's. That's what I want to know about the most, right? Because these large language models, they're getting smarter and smarter, and if you know how to use them, it seems like you literally have the world's intelligence at your fingertips, right? Both in multimodality capabilities, voice, all these things. So what skills should us humans be focusing on?

I mean, essentially, the key question you're asking there, Jordan, I think the most important question is what skills, skills will AI make rare? What skills will AI rarefy? Because AI is going to bring to the table whole new skill sets that we can use and adopt from AI, but that also means that it will probably rarefy other skill sets that perhaps aren't considered with as much esteem today. So that's a really interesting question to sort of dig into. I think we should also look at what skills will we need in the short term and what skills will we need in the long term? In the short term, very much we need AI aptitude, like, far beyond, you know, this. This Future era in 5, 10, 15 years, where all of us are AI experts and it's just interwoven into how we work right now. All of us are building the aptitude to actually use AI effectively. And that is a hugely important skill set for everyone to be. To be acquiring. And we see that, you know, within the LinkedIn research, for example, that people are adding AI skills to their profiles in the long term, the question then becomes, okay, so if AI enables you to write better, if AI enables you to create better, if AI enables you to code, if AI is giving you all of these technical skills that are part of your learned experience, then what are the skills that we rely on humans to give us to contribute to the team? And so my hypothesis here is that essentially, with AI giving us a lot of those skills that we would acquire through learned experience, the skills that humans will essentially possess that are rarer are skills from our lived experience. And so those are all of the types of skills that we acquire in our personal lives as well as our professional. So our ability to, for example, coach a soccer team, a kid's soccer team, that really builds your resilience, your patience, your empathy. You know, maybe some of you have a neurodivergent family member, right? So you have acquired skills that allow you to create really important routine structures that have allowed you to develop really important communication skills. And those are all the skills that you wouldn't necessarily get through AI or through a degree. Right. They are skills that you have acquired through your lived experience. And essentially that is the more human skills that we possess. So my hypothesis would be that those are the ones that become rarer and therefore more valuable.

And that's, it's interesting, right, because sometimes when I think about the future of work, it seems very robotic to me. Right. It's, it's this, you know, a single person orchestrating an agentic ecosphere. Right. But it seems like maybe that's not the most important or, or the most, you know, skill that could be rarefied by AI. So you're saying it's maybe more of not interpersonal skills, but some interpersonal skills and, but soft skills, empathy and patience. Why do you think those skills in the long run are maybe more important than, you know, being able to, you know, prompt engineer the heck out of copilot?

Yeah, I mean, the prompt engineering skills in the short term for sure will be important. The skills you mentioned around, you know, a single person managing an agentic workforce, that is a management skill, right? A lot of our managing skills do come from our personal experiences, they do come from our lived experiences. And that is probably a future job somewhere, right? The job of AI manager. And if you think about what that job would entail right now, managers of humans, they hopefully have the types of skills that enable humans to progress in their career. So empathy, to your point, patience, kindness, investing in your career, AI doesn't need you to invest in its career. Like AI doesn't care about climbing the corporate ladder. Right. So the manager of an AI agent, or a group of AI agents, or even an AI agent workforce probably needs fundamentally different skills from the manager of a human. And so that then becomes very interesting as we will now be blowing up the definition of a manager and maybe opening up the role of a manager to people who normally wouldn't gravitate towards being a human manager. So, you know, I think those, a lot of those soft skills, they often get a bad rap, right? Soft skills, it's like, it sounds like it's unimportant. It's not. Those are the things AI can't give you, like AI, you know, and maybe one day they will, I don't know. But right now, if you want to learn how to be empathetic and kind and patient, it probably comes through coaching your kids soccer team or, you know, those more lived experiences that make up most of our lives.

You know, Alexia, you, you brought up A super interesting point about how, and, and you know, part of this is, you know, I'm putting, you know, two and two together that hopefully equal four. But you know, I'm thinking, you know, maybe some people that rise up to management, you know, maybe they're not the best, right? Maybe they're, you know, they have a certain charisma or persuasion skills, but then once they're managing people, maybe not the best. Might we see a, a future of work in the medium or where you have these smaller companies that can maybe accomplish much more because you have a different human, you have a different human pilot that maybe would not have been considered for a leadership role to historically how we kind of judge management. Is that off or is that like a thing?

No, I think you're absolutely right to be questioning what are the types of people that will be in which types of, of roles. We're already seeing some, you know, some insights that suggest that people who are very good at delegating and very good at managing are actually really good with AI. Because that's essentially what AI right now in its form as a personal assistant is, right? You are delegating tasks to it. So those skills serve those people really, really well. I think in future what will be interesting is how will that dynamic play out in terms of giving opportunities to people who might not otherwise have had easy access to them. And beyond the management and the leadership question, one of the things I've been thinking a lot about is, you know, the idea economy, essentially the ideas economy. If every great contribution in art, in literature, in music, you know, in history, every great contribution from humans towards society has required two things. It's required the idea and then it's required, you know, the knowledge, the resources and the skills to bring that idea to life. So if you think about, for example, Mozart, right, and his symphonies, he had to have the idea for the symphony, but then he also had to have, you know, the 10 years experience and the genius and the resourcing to bring the symphony to life, to actually play it right. And in a world in which AI is giving you a lot of those skills, it's essentially decoupling the idea from the application of the idea. And so now you could have a great idea for a song and you could hear it in your head and you could know what the lyrics are, but you wouldn't know how to actually write it, you wouldn't know how to play the instrument to do it. And you can now go to an AI music generator and it will allow you to bring that to life. So that will probably really create huge openings for people who've always had great ideas, but who haven't had the resources or the skills or, you know, the experience to bring those ideas to life. And that is when we start to see the, the idea become one of your most valuable commodities as a human.

That got me. That got me, like, weirdly, like, pumped up, right? Kind of this, this decoupling of, of required skill sets. You know, it really is a new era of work. But, you know, could you take us back to where are we at right now? Right? Like, where's the state of AI? I think, like, I know things have changed very much, and they change quickly, right? Anyone listening to this podcast knows that I was just at Microsoft Ignite, and I, I, I think, you know, the things that you all, you know, at Microsoft have been working on for many months, the world just sees them now. And I, you know, the future of work, when I think about it, my thoughts are so much different than they were maybe two or three weeks ago. So where are we at right now, Alexia? What is the state of AI at work?

Yeah, great question. So we see the large majority of information workers, over 70% say they're using AI at work. So we know that AI has arrived at work for sure. But within that population, we also see that over 70% of people are using tools that weren't provided to them by their organization, not exclusively. They probably are also using tools provided by their organization, but they're also using tools that weren't. And so that's creating this phenomenon that, you know, is dubbed BYO AI. Bring your own AI, which is essentially also known as shadow AI, which is when your workforce is using AI on the sly. And that creates a whole degree of risk for the company. Right? So that also shows that employees are moving very, very quickly. And that contrasts a little bit with what we're seeing on the leadership side, where leaders are perhaps lagging a little bit and are very worried about how to bring this into the organization and about the risks that presents and really wanting to take things much more slowly. And so that is creating a bit of a dichotomy between leaders and employees, employees who want to go fast and leaders who are taking a little bit more slowly. So I think that number will only increase over time. I think when we run the survey again next year, we'll probably see that number has jumped massively. And we're also seeing that employees who have tried out AI and who have been using it for a consistent amount of time don't want to go back to a time in which they didn't use it. And it's kind of, I think, the comparison of like, once you've learned how to, you know, write on a typewriter or on a laptop, you don't want to go back to writing things manually by hand. Right. You just can't, you can't grasp the annoyance of having to spend time and energy on something that could be done a lot easier and a lot quicker through technology.

I, I feel that. Right, because sometimes I accidentally find myself like reading 10 different tabs and 10 different web pages and I'm like, why didn't I just use, you know, Copilot or Chat GBT for this? So, yeah, that, that one hits, hits home. So, so you know what you told us and we'll make sure to include this in our newsletter because great things from the Work Trend Index annual report from work lab. But. So 75 or over 75% of global knowledge workers are using AI. Is, is it working? Are, are people more productive? Are companies making more money? Is AI working so far?

Yeah. So I think one, what's interesting is the speed at which AI has been adopted is pretty unparalleled in terms of the history of technology. So within two years to see this amount of people using it, it's pretty incredible. What we also know about AI and the way people are using it is that they are using it primarily as a personal assistant. And as a personal assistant, it's giving them, you know, an uplift in productivity across certain universal tasks. So we know there are, you know, tasks that create a lot of stress and really pose a barrier to productivity for all information workers. Things like time spent searching for information. Right? You, you would not believe how much time we all spend searching for information on our day to day that does not yield a lot of ROI in terms of your finite energy and time. How you are spending that budget, I wouldn't want you spending it on search. Right? And AI's ability to retrieve information quickly and easily is very important. But, and I will say this very emphatically, AI is not a search engine. And I think this is one of the issues that we're finding is that we look at that search box that Copilot presents or chatgpt presents and our brain automatically thinks, oh, search engine. And that's how we've been treating it. And we know that AI is not a search engine. AI is not a command based tool. It is a conversation based tool. And when you treat it as a partner, a collaborator, you Know when you use it to think through ideas and brainstorm, you actually get a lot more of the value than when you're just using it as a tool or a search engine. So I think that's the big shift we have to move towards now is in this early era of it being a personal assistant and helping you find documents and helping you catch up on meetings and you know, helping you write first drafts, all of that's amazing, provides enormous productivity uplift for employees who desperately need it, by the way. But the real exciting transformation, the place that it's really going to disrupt your workflows ways of working, is when we treat it as a thought partner, a collaborator, a game changing addition to your team, if you will.

Yeah, that's such an overlooked aspect. I think six or so months ago I dedicated an entire episode to that very concept. So yeah, we'll make sure to put that in the show notes as well. But I want to get back to something that we were talking about a little bit earlier. Right. So having access to these billions of data points, not one individual, it's just a collective in seeing these trends. What are maybe a few things that you've been able to identify that have changed with generative AI? Right, because I'm sure Microsoft has had historically access to a ton of data. But what are some of these big shifts that we're seeing across the board? Not saying that we're in a post generative AI world, but since November 2022, what are those big things that the data is already telling us that's it's shifting?

Yeah. Well first if we think, if we look back to the early days of AI or right before AI joined the scene and we look at what the problems were there. You know, the way I categorize it in my brain is we had big problems with email, we had big problems with meetings, and we had big problems with like document and collaboration type activities. And so I'll give you an example for emails. You know, we saw in some data a couple of years ago that 85% percent of emails are read in under 15 seconds and the typical person has to read about four emails for every one they send. So like we, we saw this idea of digital debt, you know, that you are just submerged by information constantly and you know, if you can imagine, you know, just the emails like piling on top of you and you trying to like gradually like move your way on top of it, that feeling is what people experienced at work and it bed out in the data. So we were very interested to See, well, what happens with emails once you give people access to AI. And so we did this very important study. It was done as a randomized control trial. For those of you who don't know what an RCT is. It's the way pharmaceutical companies test out new drugs. So it's the most scientific robust way of doing any sort of comparative AB test. And we essentially partnered with 58 CoPilot customers to essentially look at their data over a period of 12 months and to compare data from their copilot users with data from their non copilot users. And the copilot licenses were randomly allocated. So there was no risk of bias. There was. We could be completely confident that the differences we were seeing were because of Copilot as a result. And the greatest effect we saw was definitely on email. Like, we saw a significant reduction in time spent on email for these companies. And that just had me really intrigued about if we're seeing that already in like year one of these employees having access to copilot, what does that mean in five years? Like, do we, are we just all spending no time in our inboxes in five years, do we have an agent, like tapping us on the shoulder and saying, look, here are the things from your inbox that you need to be aware of. Because copilot's ability essentially to help you retrieve the most important information you need from an email to summarize a super long chain, you know, to write a first draft of an email. All of those, all of those innovations essentially are changing our relationship to email altogether, which is hugely exciting.

All right, I have one or two more questions for you that I can't wait to get to. But first have to give a very quick another thank you to our partners at Microsoft. So why should you listen to the Worklab podcast? Podcast from Microsoft? It explores the questions business leaders are asking. How can they guide their organization's AI adoption journey? How can AI help them maximize value and create new products and business models? How should they help their teams reskill for the new era of work? And why is it important to be completely transparent about when and how you use AI? So find the answers on WorkLab. That's W O R K L A B, no spaces available wherever you get your podcast. So thanks again to our friends at WorkLab for sponsoring, you know, everyday AI. So one thing that I want to get to. Right, so we, we already talked about this a little bit, Alexia, but you know, kind of the, the skills that become important in the short term and the long term, right to Oversimplify it, we said, hey, you know, working with AI and AI agents going to be very important in, in the short term and you know, maybe some of these more, you know, interpersonal skills in the long term. But I'm curious for you, someone that, you know, knows the, the future of work probably better than, you know, 99.9% of anyone else out there, what skills are you focused on building?

I think for me, if I think about what is the unique value that I provide Microsoft, right in my job and what is the unique value that when, you know, AI is able to do a lot of the tasks that I do either autonomously as an agent or, you know, with heavy assistance from myself in five years, what is the thing that AI will not be able to do? And one should never compare oneself to Taylor Swift, ever. And so I don't want listeners to think that is what I'm doing here. But I was thinking recently about what is the difference? What would the difference be in my own personal experience between listening to a song that I knew AI had written 100% and listening to a song that Taylor Swift had written? And regardless of quality, right, let's assume both are really high quality songs. The difference is when I listen to Taylor Swift, the thing that goes through my brain as you know, a 34 year old female who, you know, who can see herself in Taylor Swift in many ways is, oh, wow, she went through that and I went through that and she knows exactly how I feel and there is someone else who knows exactly how I feel and I don't feel alone. And that I will never get from AI because AI is not a human, is not a sentient being, and I'm not going to get that sense from an AI written song. And so as I think about, you know, my own career and what it is I do, there's the data and there's the crunching of the numbers and then there's the looking for the patterns, there's the trying to dissect what is happening. All of that, I bring enormous value to all of that. I've been using AI to help me do better, right? But the thing that hopefully Microsoft will never outsource fully to an AI agent is my ability to storytell and to be on a podcast with you right now and to tell you about my experiences and my hypotheses, and all of those have come from my unique experiences in life, right? And you know, I've been having this conversation with a bunch of people I work with who also feel the existential Threat. I think it's quite natural when you see, you know, this, this incredibly powerful intelligence generator and curator suddenly doing all of these tasks, you start to wonder what is my unique value as a human. And it is all of the personal, creative, human based contributions that we provide to the organization that cannot be automated. So you know, my data scientist, who I work with very closely, who does all of the data analysis, who does all the coding, you know, his ability to look at all the patterns and relate to the story that he's seeing between the lines and to figure out, well, this might be the cause that all comes from his experience. And that is really important and valuable to me. You know, I don't, I don't want to get rid of that. That is something I really need on my team. And so that's again, going back to this hypothesis of it's humans lived experiences that will really count in future. It's those skills, the storytelling, the creativity that will become more important, will become rarer. That's where I'm hopefully investing in my future career, is becoming a better storyteller, becoming better at using, using my experience to spot the patterns and project the future.

It's a deep, that's a deep answer. I like, I love that. Right. I'm just, I'm just sitting here and thinking about this, this concept of, you know, rarefied skills and human connection and you know, kind of this maybe brighter in more energy giving side of AI that I don't think a lot of us spend time looking at. But you know, as, as we, you know, you know, kind of transition here toward the end, I'm wondering, so in the same way I asked you like, hey, what skills are you working on? What skills should everyone else be working on? Right. And I know that's, you know, you can't give a blanket answer and we kind of already did talk about this, you know, short term versus long term. But you know, if someone's listening out there and they're like, wow, you know, the future of work, I have a completely different viewpoint on it. What should they be focusing on?

Yeah, well, I would say build your AI aptitude. Do not neglect that. Do not wait to see if it's a passing hype. I don't think it is. Invest the time and the patience and the energy in becoming really good at AI that I don't think you can wait on. And then I think narrow down the things that you personally are really good at and the things that you really enjoy and really sharpen those. Because if we're Moving into an era where your idea is one of your most important contributions. Because remember, like prompting AI, you are putting in the seed. Like the seed comes from you. You are the person who is creating the. The flow of work that the AI will do. And so your ideas as a human will become your most valuable commodity. And so essentially sharpen the things that you are already good at and that you already enjoy. Because that's probably where your best ideas are coming from. And if you can hone and develop that over time, then you will have a lot to offer and a lot to contribute in an era where your ideas are the most unique aspects to your work and the rarest and most in demand aspect of you as an employee. And again, saying this with 90% of what futurists say turns out to be false. So I am not pretending that this is the actual reality that will for sure 100% happen. Right? This is all my own personal hypotheses based on the research that we've done so far. But that would be what I would tell my imaginary kids.

Well, I think a lot of us feel uncertain about the future of work, but Alexia, I think you helped a lot of us, myself included, feel a lot better. So thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate your time and your insights.

My pleasure. Thank you so much for having me for the great chat.

All right, y'. All. And that was a lot. So many. Like, maybe you can hear me typing over here, but I'm typing over things. I'm like, that is amazing. I've done this thing, you know, 400 plus times and still getting great insights that you haven't even heard here on the show before. So if you haven't already, please make sure to go to your everyday AI.com Sign up for the free daily newsletter. We will be recapping all of the great advice that Alexia just gave us, links to the Work Work Trend Index, Annual Report, and a whole lot more. Thank you for tuning in. Please join us tomorrow and every day for more Everyday AI. Thanks, y'.

All. 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 your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.