Outro

Concluding a two-part roundtable discussion, our global heads of Research, Thematic Research and Firmwide AI focus on the human impacts of AI adoption in the workplace.

Read more insights from Morgan Stanley.

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Kathryn Huberty: Welcome to Thoughts in The Market, and to part two of our conversation on AI adoption. I'm Katy Huberty, Morgan Stanley's Global Head of Research. Once again, I'm joined by Stephen Byrd, Global Head of Thematic Research, and Jeff McMillan, Morgan Stanley's Head of Firm-wide AI. 

Today, let's focus on the human level. What this paradigm shift means for individual workers. 

It's Wednesday, November 5th at 10am in New York. 

Kathryn Huberty: Stephen, there's a lot of simultaneous fear and excitement around widespread AI adoption. There's obviously concern that AI could lead to massive job losses. But you seem optimistic about this paradigm shift. Why is that? 

Stephen Byrd: Yeah, as I mentioned in part one, this is the most popular discussion topic with my children. And I would say younger folks are quite concerned about this. There's a lot of angst among young folks thinking about what is that job market really going to look like for them. And admittedly, AI could be quite disruptive. So, we don't want to sugarcoat that. There's clearly going to be impacts across many jobs. Our work showed that around 90 percent of jobs will be impacted in some way. Oh, in the long term, I would guess nearly every job will be impacted in some way. 

The reason we are more optimistic is that what we see is a range of what we would think of as augmentation, where AI can essentially help you do something much better. It can help you expand your capabilities. And it will result in entirely new jobs. 

Now with any new technology, it's always hard to predict exactly what those new jobs are. But examples that I see in my world of energy would be smart grid analysis, predictive maintenance, managing systems in a much more efficient way. Systems that are so complicated that they're really beyond the capability of humans to manage very effectively. So, I'm quite excited there. I'm extremely excited in the life sciences where we could see entire new approaches to curing some of the worst diseases plaguing humankind. So, I am really very excited in terms of those new areas of job creation. 

In terms of job losses, one interesting analysis that a lot of investors are really focused on that we included in our Future of Work report was the ratio – within a job – of augmentation to automation. The lower the ratio, the higher the risk of job loss in the sense that that shows a sign that more of what AI is going to do, is going to replace that type of human work. Examples of that would be in professional services. As I mentioned, you know, one of my former professions, law would be an example of an area where you could see this. But essentially, tasks that don't require a lot of proprietary data, require less creativity. Those are the types of tasks that are more likely to be automated. 

Kathryn Huberty: One theme I hear both in Silicon Valley and in our industry is the value of domain expertise goes up. So, the lawyer that's very good in the courtroom or handling a really complicated situation because they have decades of experience, the value of that labor and talent goes up. And so, when my friends ask me what their kids should pursue in school and as a career, I tell them it's less about what job they pursue. Pick a passion and become a domain expert really quickly. 

Stephen Byrd: I think that's excellent advice. 

Kathryn Huberty: Jeff, how do you see AI changing the skills we'll need at Morgan Stanley and the way that people should think about their careers? 

Jeff McMillan: I think you have to break this down into three pieces – and Stephen sort of alluded to it. One, you have to look at the jobs that are likely to disappear. Two, you have to look at the jobs that are going to change. And then finally, you have to look at the new jobs that are going to actually emerge from this phenomena. You should be thinking right now about how you are going to prepare yourself with the right skills around learning how to prompt and learning how to move into those functions that are not going to be eliminated. 

In terms of jobs that are changing, they're going to require a far, far greater sense of collaboration, creativity. And again, prompting; prompt engineering is sort of the center of that. And I would highly encourage every single person who's listening to this to become the single best prompt engineer in their group, in their friend[s group], in their organization. 

And then in terms of the jobs that are being created, I'm actually pretty optimistic here. As we build agents, there's actually a bull case that we're going to create so much complexity in our environment that we're going to need more people to help manage that. But the skills are not going to be repetitive linear skills. They're going to require real time decision-making, leadership skills, collaboration skills. 

But again, I would go back to every single person: learn how to talk to the machine, learn how to be creative, and practice every day your engagement with this technology. 

Kathryn Huberty: So then how are companies balancing the re-skilling with the inevitable culture shifts that come with any new paradigm? 

Jeff McMillan: So, first of all, I think if you think about this as a tool, you've already lost the plot. I think that number one, you have to remind yourself what your strategy is; whatever that strategy is, this is an enabler of your strategy. 

The second point I'd make is that you have to go from both – the top down, in terms of leadership messaging that this change is here, it's important and it needs to be embraced. And then it's a bottoms-up because you have to empower people with the right tools and the technology to transform their own work. 

Because if you're trying to tell people that this is the path that they have to follow. You don't get the buy-in that you need. You really want to empower people to leverage these tools. And what excites me most is when people walk into my office and say, ‘Hey Jeff, let me show you what I built today.’ And it could be some 22-year-old who; it's their first month on the job. 

And what's exciting about this technology is you do not need a technology background. You need to be smart; you need to be creative. And if you've got those skills, you can build things that are really innovative. And I think that's what's exciting. So, if you can combine the top down that this is important and the bottoms up with giving people the skills and the technology and the motivation – that's the secret sauce. 

Kathryn Huberty: Jeff, what's your advice for the next generation college students, recent college graduates as they're thinking about navigating the early parts of their career in this environment? 

Jeff McMillan: Well, Katy, I first of all, I'd agree with what you say. You know, everyone's like, ‘What should I study?’ And the answer is – I don't actually know the answer to that question. But I would study what you care about. I would do something that you're passionate about. 

And the second point, and I hate to be a broken record on this. But I would be the single best user of GenerativeAI at your college. Volunteer with some nonprofit, build a use case with your friends. When you walk into your first job, impress in your interview that you are able to use this technology in really effective ways – because that will make a difference, in your first job. 

Kathryn Huberty: And I'm curious, are there areas where you think humans will always beat AI, whether it's in financial services or other industries? 

Jeff McMillan: I like to think that we are human and that gives us the ability to build trust and emotional relationships. And I think not only are we going to be better at that than machines are. But I think that's something that we as humans will always want. I think that there may be some individuals in the society that may feel differently. But I think as a general rule, the human-to-human relationship is something that's really important. And I like to think that it will be a differentiator for a long time to come. 

So, Katy, from where you sit as the Head of Global Research, how has GenAI changed the way research is being done? 

Kathryn Huberty: With the help of your team, Jeff, we have now embedded AI through the life cycle of investigating a hypothesis, doing the analysis, writing the research in a concise, effective way. Pushing that through our publishing process, developing digital content in our analysts’ voice, in the local language of the client. 

And now we're working on a client engagement tool that helps direct our research team's time. And so, the impact here is it reduces the time to market to get a alpha generating idea to our clients and, you know, and it's freeing up time for our teams. 

Stephen Byrd: So, Katy, I want to build on that. Productivity is a big theme. And away from the research itself, from a management perspective, how are you and your team using AI? And what do you see as the benefits? And how are you spending the extra time that's freed up by AI? 

Kathryn Huberty: I like to say that the research AI strategy is less about the tools. I mean, those are critical and foundational. But it's more about how we're evolving workflow and how our teams are spending time. And so, the savings are being reinvested in actually your area – thematic research – which takes a lot more coordination, collaboration. A global cross-asset view, which just takes more time to develop, and test a hypothesis, and debate internally, and get those reports to market. 

But it's critical for our core strategy, which is to help our clients generate alpha. When you look at equity markets over the past 30 years, a very small number of stocks drive all of the alpha. And they tend to link to themes. And so, we're reinvesting time in identifying those themes earlier than the market to allow our clients to capture that alpha. 

And then the other piece is when we look at our analyst teams, they spend about a quarter of their time with clients because they have to meet with experts in the industry. They need to do the analysis, they have to build the financial forecast, manage their teams. You know, we have internal activities, build culture. And with the ability to leverage these tools to speed up some of those tasks, we think we can double the amount of time that our analysts are spending with clients. And if we're putting thought-provoking, you know, often thematic global collaborative content into the market, our clients want to spend more time with us. And so, that's the ultimate impact. 

On a personal level, and I think both of you can relate. I think a lot of the freed-up time right now is just following the fast pace of change in AI and keeping up with the latest technology, the latest vendors. But long term, my hope is that this frees up time for more human activities on a personal level. Learning the arts, staying active. 

So, this could be potentially very beneficial to society if we reinvest that time in both productive activities that have impact in business. But also productive, rewarding activities outside of the office.

As we wrap up, it's clear that the influence of AI is expanding rapidly, not just in digital- and knowledge-based sectors, but increasingly in tangible real-world applications. As these innovations unfold, the way we interact with both technology and our environments will continue to evolve – both on the job and elsewhere in our lives. 

Jeff, Stephen, thank you both for sharing your insights. And to our listeners, thank you for joining us. If you enjoy the show, please leave us a review wherever you listen, and share Thoughts on the Market with a friend and colleague today.