Rebecca Hinds (00:00)

Welcome back to Intelligence Real and Imagined from the Work AI Institute at Glean.

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Rebecca Hinds (00:05)

This is the show where we sort through what's real, what's hype, and what actually works with AI at work.

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Rebecca Hinds (00:12)

I'm your host, Rebecca Hinds, and I lead our Work AI Institute here at Glean.

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Rebecca Hinds (00:17)

Today I'm joined by Lauren Pasquarella Daley and Tiffany Hsieh from Jobs for the Future.

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Rebecca Hinds (00:22)

This episode is inspired by our AI Transformation 100 report, and we're talking about what AI leadership looks like.

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Rebecca Hinds (00:31)

How do leaders guide their organizations through this change in ways that expand opportunity, build trust, and prepare their people for what's next?

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Rebecca Hinds (00:42)

Let's go ahead and dive in.

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Lauren Pasquarella Daley (00:46)

Thank you for having me, Rebecca.

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Lauren Pasquarella Daley (00:48)

We're really excited to be here.

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Lauren Pasquarella Daley (00:49)

My name is Lauren Pasquarella Daley, and I'm an Associate Vice President at Jobs for the Future and our employer, Workforce Solutions Practice.

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Lauren Pasquarella Daley (00:57)

For those of you who are unfamiliar with Jobs for the Future, we are a national nonprofit that transforms the US Education and workforce system so people, businesses, and communities can thrive.

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Lauren Pasquarella Daley (01:07)

And I'm really excited to be here today.

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Lauren Pasquarella Daley (01:09)

Been working the future of work for a while and excited to talk about AI and leadership.

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Rebecca Hinds (01:15)

Wonderful.

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Rebecca Hinds (01:16)

And Tiffany, please introduce yourself.

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Tiffany Hsieh (01:19)

Yes.

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Tiffany Hsieh (01:19)

Thanks so much, Rebecca, for having us.

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Tiffany Hsieh (01:21)

I'm Tiffany Hsieh.

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Tiffany Hsieh (01:22)

I am a Senior Director in JFF center for Artificial Intelligence and the Future of Work.

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Tiffany Hsieh (01:27)

We really think hard about how AI is impacting jobs, skills, and work in general, and how we can support the ecosystem to better support workers and learners in this kind of tricky transition period.

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Tiffany Hsieh (01:40)

So excited to dive into this topic.

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Rebecca Hinds (01:43)

Wonderful.

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Rebecca Hinds (01:43)

I am as well.

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Rebecca Hinds (01:44)

I think you both bring such a breadth and depth of expertise and experience.

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Rebecca Hinds (01:50)

So far in this series, you know, we've talked about AI adoption, we've chatted about implementation, we've chatted a little bit about metrics and how to measure success and effectiveness.

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Rebecca Hinds (02:02)

One of the common threads through each of the two episodes so far is leadership.

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Rebecca Hinds (02:07)

You know, what is the role of the leader right now in our AI era?

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Rebecca Hinds (02:13)

What stays the same?

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Rebecca Hinds (02:14)

What changes, and why does it matter?

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Rebecca Hinds (02:16)

Why should we be rethinking leadership in this AI era?

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Rebecca Hinds (02:20)

So, Tiffany, I want to start with you.

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Rebecca Hinds (02:22)

You and Jobs for the Future think a lot about leadership.

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Rebecca Hinds (02:26)

Can you talk to us a little bit about the work you do with employers who are trying to navigate both the opportunity associated with AI as well as the inevitable disruption?

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Tiffany Hsieh (02:39)

Absolutely.

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Tiffany Hsieh (02:40)

I mean, we are living in this messy middle period of AI adoption, right?

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Tiffany Hsieh (02:44)

So we're all sort of figuring it out.

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Tiffany Hsieh (02:46)

And so when it comes to leadership, I think we're really thinking about strong leadership to set a strong vision, but also to support your people in navigating this.

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Tiffany Hsieh (02:55)

And we often talk to employers about two sides of the coin of this.

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Tiffany Hsieh (02:58)

Right?

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Tiffany Hsieh (02:59)

On the one hand, you have the adoption of the tools themselves.

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Tiffany Hsieh (03:02)

Right?

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Tiffany Hsieh (03:02)

What are the right use cases, how should we be using it strategically in a way that drives business value.

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Tiffany Hsieh (03:08)

But also we want to think about then how does that cascade down into the way that we are supporting workers with training?

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Tiffany Hsieh (03:15)

Change management, obviously is a big part of this.

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Tiffany Hsieh (03:18)

And then how does that start to trickle into job design in the way that jobs are shifting and changing and how do we communicate that out to the field?

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Tiffany Hsieh (03:25)

So all of those are really encompassed and needed skills in this moment.

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Rebecca Hinds (03:30)

Definitely.

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Rebecca Hinds (03:31)

And one of the things we hear from leaders all the time is the importance of tactical, practical advice.

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Rebecca Hinds (03:39)

You know, when they're facing this massive pressure to move quickly and to help their organizations evolve.

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Rebecca Hinds (03:46)

I've known you both for a couple years now, and in 2023, you created Jobs for the Future's AI Ready Workforce framework.

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Rebecca Hinds (03:57)

It's a really impressive framework.

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Rebecca Hinds (03:59)

Tiffany, what inspired you to develop that framework and what problem were you trying to solve back in 2023 when you did create it?

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Tiffany Hsieh (04:08)

Yeah, it's funny because the problem that we were solving in 2023 is also probably the problem that we're solving for now, which is that, like you said, folks are really looking for tactical actionable frameworks and tools to be able to start to think about this change.

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Tiffany Hsieh (04:24)

Once ChatGPT launched and Generative AI was all in the narrative, there was a lot of uncertainty, and there still is around what are those labor market impacts of AI going to be?

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Tiffany Hsieh (04:34)

How is that actually going to impact jobs and skills?

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Tiffany Hsieh (04:37)

And so we've worked with employer, colleges, workforce organizations with this tool to start to think about what is the impact of AI at a more granular level, as just, you know, 40% of jobs are going to be impacted in some way, but what is that impact?

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Tiffany Hsieh (04:52)

How do we get a little bit deeper into the nuance, and then how do you actually have an actual tool amidst all of this kind of ambiguity about what is really happening to jobs?

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Tiffany Hsieh (05:01)

And, you know, we're going to continue to be in this space for a while.

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Rebecca Hinds (05:05)

Mm.

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Rebecca Hinds (05:06)

It's really important.

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Rebecca Hinds (05:08)

And Lauren, from your side at Jobs for the Future, what are you seeing as top of mind for leaders right now when it comes to AIs?

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Rebecca Hinds (05:16)

Is there something you think is missing from the conversation as well right now?

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Lauren Pasquarella Daley (05:21)

You know what I'm seeing when I'm talking to employers and leaders is that they're feeling this intense pressure to move very quickly and to figure out how to adopt it in their practice, but also with their teams.

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Lauren Pasquarella Daley (05:32)

I think the piece that might be missing is because there's that pressure, there's a lot of short-term thinking right now of how can I solve this in the short term.

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Lauren Pasquarella Daley (05:40)

And I think we want to make sure that yes, we're solving in the short term, we're thinking about how to integrate it into workflows and work with our teams to build those skills.

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Lauren Pasquarella Daley (05:49)

But I also want to make sure that we're thinking long-term of what this looks like as we're designing jobs and processes, and we're not ignoring that long-term impact on the labor market while also making sure that we're listening to our workers.

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Lauren Pasquarella Daley (06:04)

A lot of times, when we think about how to adopt AI, the focus is often on workers who are at a desk, or you know, who have been doing a lot of the kinds of knowledge work tasks.

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Lauren Pasquarella Daley (06:15)

And I think it's important that we also consider how AI is starting to change the roles and skills that are needed across all levels of an organization.

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Lauren Pasquarella Daley (06:25)

So making sure that we're really bringing in a sense of worker voice, that we're talking to employees at all levels, hearing what they see and what they need to be able to build out those processes across the board.

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Rebecca Hinds (06:38)

And Tiffany, I want to double-click on Lauren's great insight there because we are seeing all of these different paradoxes and tensions at play when it comes to implementing AI.

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Rebecca Hinds (06:49)

And one of those big tensions is between democratization of the technology and inequality.

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Rebecca Hinds (06:57)

In some ways we know that AI is democratizing in the sense of putting expert capabilities at people's fingertips.

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Rebecca Hinds (07:06)

But at the same time, many workers haven't been brought into the conversation.

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Rebecca Hinds (07:10)

How do you see that disconnect showing up in organizations today?

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Tiffany Hsieh (07:15)

Yeah, I mean, like you said, AI, especially generative AI in particular, right?

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Tiffany Hsieh (07:20)

Has really democratized access to this tool because AI is not new.

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Tiffany Hsieh (07:23)

AI has been around for over 50 years.

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Tiffany Hsieh (07:26)

But because of the natural language interfaces that these new generative AI tools are using, people can access them and talk to them like we're talking right now.

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Tiffany Hsieh (07:36)

And so because of that, it's kind of grown at a speed that we haven't seen before in the past.

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Tiffany Hsieh (07:41)

And so that's also at a business level, resulted in this sort of BYO AI phenomenon is what we call it.

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Tiffany Hsieh (07:48)

Right.

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Tiffany Hsieh (07:49)

Where people are using it and maybe not necessarily publicly if their organization's policies are not really aligned with that.

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Tiffany Hsieh (07:57)

But that then creates divides in itself within the organization for those who have access to these tools and those who do not.

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Tiffany Hsieh (08:03)

And it also creates divides across organizations where organizations with more open policies around using generative AI may be able to move quicker and be able to use the tools in a way that others who are sort of like more locked down aren't able to.

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Tiffany Hsieh (08:18)

And so that also then trickles down into, from the divide perspective, how are we actually leveraging this democratization?

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Tiffany Hsieh (08:27)

Right.

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Tiffany Hsieh (08:27)

If we know a lot of folks are using these tools, how are we leveraging their capabilities and expertise to be able to consult them around how AI should be used in the workplace?

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Tiffany Hsieh (08:39)

What are the high-value use cases that can really drive business value?

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Tiffany Hsieh (08:43)

And how should jobs start to be reshaped from these use cases?

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Tiffany Hsieh (08:49)

And we're really seeing that employees are not getting the not being consulted in a way that is widespread.

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Tiffany Hsieh (08:59)

We actually just released the results of a survey yesterday that surveyed workers and learners across the United States on their experiences and perceptions of AI.

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Tiffany Hsieh (09:09)

And we found that 56% of workers said that they have not been consulted in any way by their employers.

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Tiffany Hsieh (09:15)

So over half.

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Tiffany Hsieh (09:16)

Right.

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Tiffany Hsieh (09:17)

Almost 60% of folks, which is a lot.

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Tiffany Hsieh (09:19)

And then when we did drill down into the folks who are being consulted, some of them are being consulted in more light-touch ways, like feedback surveys, some of them in a more heavy way.

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Tiffany Hsieh (09:31)

That's probably about 20 or so percent who are involved in, say, standing committees and like, have regular feedback loops.

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Tiffany Hsieh (09:37)

So as we think about the way that we can both leverage the democratization and then make sure that we're involving the right voices at the table, those are the things that we should really be considering.

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Lauren Pasquarella Daley (09:48)

And I'd like to piggyback on that.

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Lauren Pasquarella Daley (09:50)

You know, I think what's important is as we talk about adopting anything, you know, whether it's a new technology like AI, a new process, any sort of change management in an organization, one of the things we believe and our research shows is that, you know, practices that are good for people are good for business.

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Lauren Pasquarella Daley (10:07)

And so I think that's an important reminder for leaders to keep in and keep in mind as they go through this change process, as they're thinking about how to implement and adopt AI, you want to also do this in a way that is good for people.

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Lauren Pasquarella Daley (10:20)

And so in thinking about how you are, what you can automate, also think about what you can augment, what human skills really need to be in the mix.

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Lauren Pasquarella Daley (10:29)

How are you incorporating workers at all levels of the organization and to Tiffany's point about the research, not just asking, but also acting on that feedback to be able to build out those processes.

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Lauren Pasquarella Daley (10:39)

And so I think it's really important for leaders to keep that in mind that they are not only this is like most change management efforts and you want to find a path that actually listens to your people, is good for your people, focuses on the short term, but also doesn't scar or damage your talent for the long term.

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Rebecca Hinds (10:56)

And Aaron had a great comment in the chat.

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Rebecca Hinds (10:59)

AI anxiety is real for a lot of folks within the organization and they're looking for employers to incorporate their voices.

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Rebecca Hinds (11:07)

And Lauren, we've in the past talked about this idea of the IKEA effect and how the more employees feel like they've been brought in and have built something themselves, the more likely they are to value it and feel enthusiasm toward it as well.

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Lauren Pasquarella Daley (11:24)

Yeah, absolutely.

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Rebecca Hinds (11:27)

Tiffany, Lauren, anything we haven't talked about in terms of how do you do this in practice?

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Rebecca Hinds (11:32)

What does it look like on the ground to incorporate workers voices into AI decisions?

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Rebecca Hinds (11:39)

We've talked about surveys.

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Rebecca Hinds (11:41)

Anything else tactically, practically that you recommend to organizations?

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Tiffany Hsieh (11:47)

Yes, I think there are probably three tactical recommendations that we often talk about.

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Tiffany Hsieh (11:53)

One is to leverage that democratization effect and a thousand flowers blooming model where you are giving access to these AI tools to everyone.

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Tiffany Hsieh (12:04)

And secondly also training them on it because AI is a very specific technology that needs to be used responsibly.

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Tiffany Hsieh (12:12)

But if you allow that experimentation and you give that access, oftentimes that will bubble up, use cases that are going to be valuable to the organization as a whole.

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Tiffany Hsieh (12:22)

And that is much more effective than kind of taking a top-down approach that is a bit more slow, that is a bit more, you know, is not leveraging the expertise of workers who know really their workflows on a day to day basis.

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Tiffany Hsieh (12:35)

And we're definitely seeing that training is not being like sufficiently disseminated across organizations either.

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Tiffany Hsieh (12:41)

Only about a third of workers say that they have received training from their employers around AI.

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Tiffany Hsieh (12:46)

So that's really critical as well.

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Tiffany Hsieh (12:48)

And then of course we have to pair that with some of the formal structures we mentioned.

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Tiffany Hsieh (12:52)

Right?

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Tiffany Hsieh (12:52)

What are some standing ways that you can make sure that feedback loops are being incorporated for workers through standing committees.

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Tiffany Hsieh (12:59)

How are you thinking about making sure that then more broadly we are still leveraging employee feedback surveys to get a pulse for what folks are experiencing and feeling on the ground so that we can make adjustments based on that.

Rebecca Hinds (13:13)

That's great.

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Rebecca Hinds (13:14)

I think the combination of the formal and informal is a useful Distinction as well to think about.

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Rebecca Hinds (13:22)

So, Lauren, I want to turn to you in terms of a lot of the components Tiffany has highlighted are they can't be put into place tomorrow necessarily.

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Rebecca Hinds (13:31)

It does require this long-term thinking lens.

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Rebecca Hinds (13:35)

I've heard you in the past on numerous occasions warn leaders about short-term thinking.

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Rebecca Hinds (13:41)

In a world where we are seeing a lot of short-term thinking, and understandably, because of a lot of pressure, a lot of pressure for efficiency, what are you worried most about in terms of that short-term thinking?

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Lauren Pasquarella Daley (13:56)

Yeah.

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Lauren Pasquarella Daley (13:56)

So, you know, I think right now this is definitely something that I'm concerned about, especially as we think of the long-term effects on the labor market.

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Lauren Pasquarella Daley (14:03)

A lot of this is, you know, Tiffany mentioned this in the beginning, and we talked a little bit about it.

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Lauren Pasquarella Daley (14:07)

We just don't know right now.

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Lauren Pasquarella Daley (14:09)

We have some ideas of what the labor market effects are.

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Lauren Pasquarella Daley (14:12)

We have some ideas of how jobs are shifting and changing.

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Lauren Pasquarella Daley (14:15)

I think, you know, for what I'm concerned about is that it's very reactionary and not being intentional in how organizations and leaders are deploying their AI tools and really thinking about how to strategically plan for their workforce in the future.

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Lauren Pasquarella Daley (14:29)

And part of that is we have to understand a lot of pieces that, you know, when there's the pressure of needing to move quickly to adopt and start to think about productivity gains, there's also the, it's easy to kind of move with that without being intentional.

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Lauren Pasquarella Daley (14:43)

And we have to do some work to understand really, you know, what are the skills of our organization.

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Lauren Pasquarella Daley (14:48)

Looking at the skills, intelligence, what skills do we need to invest in?

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Lauren Pasquarella Daley (14:53)

What skills do we already have?

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Lauren Pasquarella Daley (14:55)

And that's not just technical, that's also those human skills that really help us collaborate and be distinct from any of the AI tools that we might be deploying.

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Lauren Pasquarella Daley (15:03)

And so I think there's a lot of pieces as we're thinking long term, we just don't know.

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Lauren Pasquarella Daley (15:08)

And the technology is evolving so quickly that of course that encourages a lot of short term thinking.

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Lauren Pasquarella Daley (15:13)

But it's critical for the long term success of an organization to make sure that they're continuing to invest in their talent, that they are not scarring their talent for the long term and that they're thinking, you know, not just in three month, you know, sometimes even 30 day periods of deploying AI.

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Lauren Pasquarella Daley (15:32)

We want to think about this in one year, three years, five years, and start to do some strategic planning around it.

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Lauren Pasquarella Daley (15:38)

So there's a sense of where the workforce is going and what those skills and what talent needs will be in the future.

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Rebecca Hinds (15:45)

Great Great.

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Rebecca Hinds (15:46)

And you know, one, one way to do that presumably is through frameworks and tools where it less of a cognitive leap to go from now to the future.

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Rebecca Hinds (15:56)

Tiffany, I want to go a level deeper on the AI ready workforce framework because I do think it helps with this.

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Rebecca Hinds (16:05)

Are there general takeaways that you think anyone can glean from the framework in terms of how do you think about the future workforce and how do you start to classify these different roles?

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Tiffany Hsieh (16:18)

Absolutely.

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Tiffany Hsieh (16:19)

So the framework is really built on the premise that jobs are not going to necessarily wholesale change.

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Tiffany Hsieh (16:26)

Right.

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Tiffany Hsieh (16:26)

Because it's kind of intimidating to think from all the proclamations and a lot of the research you see, it's at a high level saying 40% of jobs are going to be displaced or 70% of skills are going to change.

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Tiffany Hsieh (16:39)

But when you get a little bit more granular into a particular occupation, you break that occupation into its component tasks and then analyze the tasks for the impact that AI is having on them.

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Tiffany Hsieh (16:51)

You can start to see trends around where AI is showing up and how.

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Tiffany Hsieh (16:56)

Right.

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Tiffany Hsieh (16:57)

There's, you know, we, our framework is built off of sort of essentially the automation to augmentation spectrum, which a lot of other research pieces like the Anthropic Economic Index also uses.

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Tiffany Hsieh (17:10)

But you want to think about, right?

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Tiffany Hsieh (17:12)

First classifying a role into its component tasks, then analyzing the AI impact for those tasks.

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Tiffany Hsieh (17:19)

Then you can start to think about for this particular occupation, how can I support my workers if I know, for example, there's a really good use case for say, note taking for my executive assistant that's going to change their role in some way.

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Tiffany Hsieh (17:32)

How do I support them in learning how to use the tool, but then also helping them stretch into some of the more human skills that are going to encompass their time now that they don't have to spend time on more of kind of the menial tasks.

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Tiffany Hsieh (17:45)

Right.

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Tiffany Hsieh (17:45)

And so we don't just ask wholesale is this job going to go away?

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Tiffany Hsieh (17:49)

Because we know that's not the reality.

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Tiffany Hsieh (17:50)

The reality is jobs are going to change at the edges and we're going to start to slowly see those changes.

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Tiffany Hsieh (17:57)

And this helps us capture that in understanding.

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Tiffany Hsieh (18:00)

Right.

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Tiffany Hsieh (18:00)

Is this task going to be replaced?

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Tiffany Hsieh (18:02)

Should it be displaced or reconfigured in a way that changes how it's done?

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Tiffany Hsieh (18:07)

And then how does that then enable you potentially to augment the human skills really at hand and spend more time on that strategic work?

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Rebecca Hinds (18:16)

That's great and really insightful.

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Rebecca Hinds (18:18)

I think the notion of a role being a bundle of tasks and skills, I think is a really important one right now.

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Rebecca Hinds (18:26)

And it's very hard to understand how a role will change or ought to change without understanding the tasks underlying it.

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Rebecca Hinds (18:34)

And I love that the framework really, you know, puts that at the, at the forefront of the analysis.

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Rebecca Hinds (18:41)

So if we are, Tiffany, to go one level deeper, you know, we're hearing a lot of conversation around software development and how the role of the software developer is going to change or has already changed with AI.

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Rebecca Hinds (18:54)

Can you briefly walk us through how you might apply the framework to the role of a software developer?

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Tiffany Hsieh (19:01)

Absolutely.

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Tiffany Hsieh (19:02)

So obviously software development is one of the key use cases for AI right now.

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Tiffany Hsieh (19:08)

That is the place where the frontier labs know the best.

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Tiffany Hsieh (19:11)

And so therefore they've spent a lot of time making sure that these tools work.

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Tiffany Hsieh (19:16)

And they've gotten much better and better, even just in the last week.

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Tiffany Hsieh (19:20)

But if you think about a software developer task, let's break this down.

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Tiffany Hsieh (19:25)

They are generating code, they're testing software, debugging it, but they're also communicating with teams, structuring problems.

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Tiffany Hsieh (19:34)

Those are some of the component tasks that we would be thinking about as we apply this framework.

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Tiffany Hsieh (19:40)

Given the advancements of AI encoding, we think it fundamentally reconfigures the way you might do some of this work.

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Tiffany Hsieh (19:50)

Code generation, for example.

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Tiffany Hsieh (19:52)

Now AI tools are doing the CO generation themselves.

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Tiffany Hsieh (19:57)

The software developer's role is changing and thinking more about how do you structure the problem on the front end and then how do you evaluate the outputs as a human in the loop on the back end.

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Tiffany Hsieh (20:07)

And so that then helps us understand, okay, so if, if that task of CO generation is being more automated by AI, but then it starts to elevate different skill sets.

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Tiffany Hsieh (20:20)

Talking to customers, talking to team members, breaking down problems, critical thinking, problem solving, Right then that elevates the human parts of the job and allows us to think about from an L and D perspective where to really focus training and time.

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Rebecca Hinds (20:37)

And presumably, correct me if I'm wrong, but this presumably, ideally is done in partnership and with support of leaders, organizational leaders, team leaders.

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Rebecca Hinds (20:47)

But presumably you could do it as an individual too, and have a lot of insight and foresight into how might we as individuals be thinking about evolving our roles and jobs as well?

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Tiffany Hsieh (21:00)

Absolutely.

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Tiffany Hsieh (21:01)

Then again, having that feedback loop and communication between the organization and an individual, for an individual to say, this is what I'm seeing on the ground, this is actually where I'm really finding value and this is how it's changing how I'm spending my time, that can really then help craft a new role description or a job description.

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Lauren Pasquarella Daley (21:21)

What's really, really exciting about this is as we start to get a handle on what those skills are and how they're changing and how we want to develop them.

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Lauren Pasquarella Daley (21:29)

It allows us to not only develop ourselves and our teams and our larger talent pool, but it allows us to hire in a different way and promote in a different way because we can focus on the skills first that are needed for success in that role and really be able to dial into what that looks like and how we can help develop our staff for the long term.

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Rebecca Hinds (21:52)

That makes sense for sure.

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Rebecca Hinds (21:54)

And Lauren, as we're doing this, as we're thinking about how roles and tasks ought to evolve for the next era, how do you make sure you're integrating workers voices into this framework?

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Rebecca Hinds (22:06)

Robert has a great comment in the chat along the lines of not all software developers want to assume new tasks and new tasks in terms of working with people.

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Rebecca Hinds (22:19)

How do you incorporate workers voices into this equation?

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Lauren Pasquarella Daley (22:23)

I think it's really critical.

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Lauren Pasquarella Daley (22:25)

And the idea of incorporating worker voice we've talked about, there are a lot of different ways of doing that.

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Lauren Pasquarella Daley (22:31)

Whether it's surveys, building from the bottom up and the top down.

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Lauren Pasquarella Daley (22:35)

I think it's important that as you're building out what the tasks are and how the work is changing for the short term and the long term, you're also looking at the skills.

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Lauren Pasquarella Daley (22:43)

And so as roles are changing, and this is the case for many different change management processes, anytime a change comes in, there's a period of people reacting to it and then adapting to it.

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Lauren Pasquarella Daley (22:55)

And one of the skills that I see as being critically important going in now and into the future of work is the comfort with uncertainty and being able to adapt to change.

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Lauren Pasquarella Daley (23:05)

And so yes, not every task is going to be something that a worker is excited about or interested in.

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Lauren Pasquarella Daley (23:11)

And this is where you can really leverage and listen in a way to figure out, okay, if you don't want to do that particular task or develop that particular skill, are there other skills that you've developed and other pathways within our organization where we can deploy you in a different way, not automate your role, but figure out for the long term what makes sense for you, what's good for you, and what opens up access to opportunities that you may not have had before because now you're dialing into new skills and developing them in different ways as the roles change?

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Lauren Pasquarella Daley (23:44)

I think one thing we hear a lot of doom and gloom about the change that's coming, the change that's here.

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Lauren Pasquarella Daley (23:50)

And I always try to focus on, yes, that's the Case I'm not minimizing it, but the ability to adapt is going to be critically important now and into the future and it gives us an opportunity to do things differently.

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Lauren Pasquarella Daley (24:01)

There's a way of figuring out new pathways, new career paths, totally new careers that are going to develop.

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Lauren Pasquarella Daley (24:09)

And so this gives some agency and voice to the workers who are thinking, what do I want for the future?

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Lauren Pasquarella Daley (24:14)

What skills do I want to have as part of my task?

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Lauren Pasquarella Daley (24:18)

And make sure that there's, you know, a two way communication with your employer.

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Lauren Pasquarella Daley (24:21)

I think that's really important,.

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Rebecca Hinds (24:24)

Two way communication before we move on because I do want to spend a little bit of time talking about metrics and how do you think about success through metrics?

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Rebecca Hinds (24:34)

Anything else on that last point in terms of what leaders might be missing right now when it comes to roles, tasks, skills that you think is worth pointing out?

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Lauren Pasquarella Daley (24:48)

I think the importance is, you know, to make sure that you are actually measuring and mapping what the tasks are and what the skills are.

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Lauren Pasquarella Daley (24:55)

If you don't have a fundamental understanding of your skills intelligence in your organization and your team for the different jobs and how they might react and adapt and change with the, with AI, it's hard to be able to do any of what we're talking about.

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Lauren Pasquarella Daley (25:09)

And I think this, that's the piece of you have to measure it, you have to have an understanding of it and then make sure that you're incorporating workers perspective and feedback across all levels of the organization to really build out that skills intelligence map.

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Tiffany Hsieh (25:21)

It's very important.

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Rebecca Hinds (25:22)

That's a great point.

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Rebecca Hinds (25:24)

So metrics, let's talk a little bit about success measurement.

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Rebecca Hinds (25:27)

Tiffany, I'd love your take on this.

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Rebecca Hinds (25:29)

When executives ask how do we measure this, how do we measure success?

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Rebecca Hinds (25:33)

With AI they often default to productivity efficiency.

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Rebecca Hinds (25:38)

From your view, how should they be thinking about metrics right now?

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Tiffany Hsieh (25:43)

Yeah, you're right.

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Tiffany Hsieh (25:44)

Productivity and efficiency are definitely the most common metrics that we hear about.

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Tiffany Hsieh (25:49)

But they are, they kind of lead us down a short term thinking rabbit hole.

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Tiffany Hsieh (25:52)

Right.

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Tiffany Hsieh (25:53)

And so we encourage executives to think about how are folks actually using the time that they're saving from a productivity and efficiency standpoint?

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Tiffany Hsieh (26:02)

How are we spending time on new or higher order activities to create new products to lean into more strategic thinking or kind of human durable skills.

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Tiffany Hsieh (26:13)

Right.

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Tiffany Hsieh (26:13)

How does this lead to better use of folks time instead of just saying, oh, we're saving time and we can put out more widgets and then the other thing to think about is job satisfaction.

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Tiffany Hsieh (26:26)

Right.

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Tiffany Hsieh (26:26)

We would assume that this is a really a key Tool as well, AI being the tool that I'm talking about to think about, how does the job design really lean into what people really want to spend their time on?

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Tiffany Hsieh (26:39)

To Robert's point over here, if a software developer doesn't actually want to spend more time with humans, then how are they actually taking the time that they're saving on co generation and thinking about new products to be developing, whereas before maybe they were spending more of their time debugging current products?

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Tiffany Hsieh (26:56)

Is that related to the way in which we can improve job satisfaction for our employees?

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Tiffany Hsieh (27:01)

Helping them find those things that they want to spend their time on?

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Tiffany Hsieh (27:05)

And also potentially in resource constrained environments that we're in right now, maybe leverage that to have better work life balance or have more autonomy over the tasks that they're doing on a day to day basis.

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Rebecca Hinds (27:19)

I'm sure it's not surprising to either of you, but there's not a lot of clarity in most organizations in terms of, okay, we know our employees are using AI.

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Rebecca Hinds (27:29)

We see work being completed at roughly similar quality before where is that time savings going?

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Rebecca Hinds (27:36)

Where is it being redeployed?

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Rebecca Hinds (27:38)

And I think that's essential for any organization to start to understand because that should dictate the strategy moving forward as well.

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Rebecca Hinds (27:48)

So I think it's an incredibly important point.

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Rebecca Hinds (27:52)

What about sentiment around AI?

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Rebecca Hinds (27:55)

Is this something you recommend organizations measure through surveys or other means in terms of do employees actually enjoy using the technology?

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Tiffany Hsieh (28:07)

Yes, I think we're actually seeing a pretty strong correlation between sentiment around AI and use of AI.

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Tiffany Hsieh (28:15)

And AI is interesting because it comes with a lot of more emotional baggage, if you will.

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Tiffany Hsieh (28:21)

Right.

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Tiffany Hsieh (28:22)

We, we hear a lot in the news about the impacts that AI is going to have and that does have an impact on the way that employees are actually experiencing and wanting to use the technology.

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Tiffany Hsieh (28:32)

So we at jff we measure sentiment internally as well as externally in our surveys.

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Tiffany Hsieh (28:37)

And we've found that when people understand how AI works and what it means for them, it feels less scary and they actually have higher rates of use.

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Tiffany Hsieh (28:46)

And so tracking that sentiment across time is actually really important because it also then allows you to see how are your training efforts impacting sentiment and then therefore use of AI.

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Tiffany Hsieh (29:00)

And then how can you, how can you really target capacity building with that sentiment tracking to make sure that your efforts are actually reducing anxiety.

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Tiffany Hsieh (29:08)

So definitely would recommend that as well.

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Rebecca Hinds (29:11)

Wonderful, wonderful.

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Rebecca Hinds (29:13)

So Lauren, I want to, I want to turn to you for this next segment because it's hard to have a conversation about AI and leadership in AI in the AI era.

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Rebecca Hinds (29:23)

Without talking about early career roles and early career hiring, we're seeing more and more of a narrative around cutting early career roles.

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Rebecca Hinds (29:32)

How do you advise leaders think about this?

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Rebecca Hinds (29:35)

Very critical part of the equation.

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Lauren Pasquarella Daley (29:38)

Yes, there's a lot of talk and discussion around this right now.

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Lauren Pasquarella Daley (29:42)

And you know what I'm, what I'm seeing when I'm talking with leaders and executives is that again, there's, there's short term thinking and long term thinking.

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Lauren Pasquarella Daley (29:49)

I think in the short term, a lot of the tasks that people who are starting in an organization at an entry level role can be easily automated.

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Lauren Pasquarella Daley (29:58)

And so I think the short term thinking around that a lot that a lot of leaders may feel compelled to go with is to be like, okay, so we don't need to hire early career talent and then we can automate those tasks and we can save money and then we don't have to worry about having a pipeline there.

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Lauren Pasquarella Daley (30:15)

What I would say is that that's very dangerous to start thinking in that way.

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Lauren Pasquarella Daley (30:19)

Yes, there are many tasks that early career roles can be automated, but the importance of we don't really have a critical handle on all of the skills that are needed there and then the long term impacts of that.

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Lauren Pasquarella Daley (30:33)

So if we think about this and kind of play it out, if you eliminate your early career roles and automate those, give those over to AI, how do you develop and train your next level of leaders or your middle managers?

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Lauren Pasquarella Daley (30:46)

How do you start to build some of that company sentiment or organization knowledge?

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Lauren Pasquarella Daley (30:51)

How do you build the skills that make for success at the next level and then beyond?

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Lauren Pasquarella Daley (30:55)

And so there has to be a way of continuing to hire early career talent and think about doing it differently.

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Lauren Pasquarella Daley (31:01)

What skills?

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Lauren Pasquarella Daley (31:01)

If you're automating some tasks, what skills really do you want to lean into to build that next pipeline of leaders in your organization or beyond?

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Lauren Pasquarella Daley (31:10)

So I think that's a really critical point for a lot of leaders think about is how do we do this for the long term, getting short term gains?

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Lauren Pasquarella Daley (31:16)

I'm not saying that, you know, it's one or the other, but thinking about those early career roles slightly differently, automating some tasks, but making sure that you're augmenting and building out the tasks you need for success as someone would move through your organization or progress, you know, through their talent pipeline going forward.

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Lauren Pasquarella Daley (31:33)

And that's really why understanding those skills pieces are so important and thinking about what this looks like.

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Lauren Pasquarella Daley (31:38)

There are some organizations that are really leaning into this and saying, you know, we're going to hire more early career roles because we know how critical this is, that we get it right at this moment in time.

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Lauren Pasquarella Daley (31:48)

And so I would encourage a lot of leaders to think about the importance of that right now.

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Lauren Pasquarella Daley (31:53)

Make sure that we're not going with a short term gain that can actually harm the organization for the long term and harm your talent.

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Lauren Pasquarella Daley (32:00)

You know, if you start to do that now, will people want to work for your company, your organization in the future if there's not a pathway, if you can't bring people in?

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Lauren Pasquarella Daley (32:08)

And so I think it's really important to think about that for the long term as well.

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Tiffany Hsieh (32:12)

And I think it to that because, you know, this is a really big topic for us too in the center for AI.

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Tiffany Hsieh (32:19)

I think two things.

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Tiffany Hsieh (32:20)

One is that, you know, we often also see a strong correlation between expertise as well as the ability to use AI tools well.

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Tiffany Hsieh (32:29)

And so how do you actually develop expertise when you don't have the reps at the beginning of your career to really build that critical foundation?

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Tiffany Hsieh (32:36)

So again, just to underscore Lauren's point that it's actually really important from a talent pipeline perspective to invest in early career workers.

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Tiffany Hsieh (32:43)

And then the second thing that I would mention is just for employers to also think about what are new models and ways of investing in early career talent development.

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Tiffany Hsieh (32:53)

Both, you know, the responsibility also lies with employers and how they can facilitate that.

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Tiffany Hsieh (32:58)

But also what are partnerships that you can form with other organizations, training organizations, community colleges, you know, education institutions across the learn to work ecosystem that can help support, support the development of these early career workers.

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Tiffany Hsieh (33:10)

And what does that interplay look like and how do we think about new ways of doing that rather than just kind of the typical like A into B roles?

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Lauren Pasquarella Daley (33:20)

Yes, we spend a lot of time at JF thinking about those partnerships and how we can innovate around building those pathways across early career talent and for incumbent workers as well.

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Lauren Pasquarella Daley (33:30)

So I think that's a really critical point that Tiffany just brought up.

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Rebecca Hinds (33:34)

Yeah, it's really important.

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Rebecca Hinds (33:35)

I think it's no coincidence as well that many of the organizations that are leaning in most to AI are also the ones doubling down on early career hiring.

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Rebecca Hinds (33:43)

And I see more and more of a recognition of the importance of that group for rethinking processes.

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Rebecca Hinds (33:49)

You know, often we're ingrained in our ways of working and it's difficult to see beyond that.

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Rebecca Hinds (33:53)

And we know that so many of our workflows need to be fundamentally rewired for the AI era.

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Rebecca Hinds (33:59)

Often a new grad will come in with less bias and less, you know, ingrained ways of working that can allow them to think fundamentally and in new ways.

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Rebecca Hinds (34:10)

Well, Lauren, Tiffany, we're out of time.

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Rebecca Hinds (34:12)

I've learned so much.

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Rebecca Hinds (34:13)

Thank you so much for joining us.

‍

Rebecca Hinds (34:15)

Thank you everyone for tuning in.

‍

Rebecca Hinds (34:17)

If you enjoyed this conversation, be sure to subscribe so you don't miss the next episode of Intelligence Real and Imagined.

‍

Rebecca Hinds (34:24)

You can find the link to our AI Transformation 100 report in our show notes as well.

‍

Rebecca Hinds (34:29)

Thanks for listening and we'll see you next time.