In conversation with… Professor Gina Neff

Gina Neff: It is the number one mission for leaders right now. Empowering your people to navigate this change is different from a top down demanding that they use the tools in a particular way. If you can get that right, then you're going to create an organization that is learning, that is excited and that is coming along for this journey. They're feeling like they are part of the solution, not that the changes are happening to them.

Lucy: Hello and welcome to the Future of Work Hub's In Conversation podcast. I'm Lucy. I'm a partner in Lewis Silkin's employment team. And whether you're a regular listener or you're joining us for the first time, I'm delighted to have you with us as we explore the trends and challenges shaping the world of work. Today I'm joined by Professor Gina Neve. Uh, Gina is a professor of Responsible AI at Queen Mary University of London and an Executive Director of the Mindaroo Centre, um, for Technology and Democracy at the University of Cambridge. That's uh, an independent research centre that aims to make digital technologies work for people, society and the planet. Gina has been named as one of the hundred most brilliant women in AI ethics. Uh, she's a distinguished sociologist specialising in the ethical and societal implications of digital transformation and emerging technologies. Her research focuses on the effects of the rapid expansion of, uh, our digital information environment on workers and workplaces. And she's advised organizations including UNESCO, the OECD and the Welsh government. Welcome Gina. It's great to have you with us today.

Gina Neff: Thank you so much for having me on.

Lucy: Now your work is so interesting and incredibly impressive, but there are probably people listening that might not know about it. So I thought a really good place to start would be just to explain a little bit about your background and the areas of your research.

Gina Neff: Well, thanks. I'm a sociologist of work and the economy, which means I ask people about their jobs. My day job is to talk to people about what they do and how they make sense of the world. For the last 10 years, I've been looking around data science and AI and how analytics are changing how we work. And that's really led me to think through both people's challenges and resistance to questions of automation, but really how new technologies are made in practice, in, in teams, in companies, in industries. And I would love to get into a little bit more, uh, later on about the new book that we're about to send to the publisher called Negotiating Innovation.

Lucy: That would be great. I'm also hoping that you'll share some thoughts on that and some insights into, um, Some of the challenges around cultural resistance to tech adoption because that's definitely something that we're seeing, there's a sort of fatigue around that. But before we get to that, asking the sort of obvious question or going maybe to the obvious place to start, because there's so much in the media about AI going to result in widespread, uh, job losses, mass unemployment. But it feels like we're coming to a place where actually that picture is a little bit more nuanced than perhaps it was even five years ago. And last year I spoke to Till Leopold. He's one of the authors, um, of the World Economic Forum's Future of um jobs report. Fascinating, because that found that there would be a net increase of 74 million jobs, um, by 2030. So this idea that job roles might change but not necessarily reduce, and I know that that is also consistent with your narrative and your research that AI isn't just going to simply replace human workers. You've described this idea of an automation paradox. So new technologies often create more work because they expose hidden systems, negotiations, all the things that we need to make um, work possible. So it would be great to have your insights on that sort of fundamental question about whether AI is just going to result in mass unemployment.

Gina Neff: Well, first let's just address the economics of the issue. And that is that economists are really mixed. There's two key camps. Uh, one camp, led in, probably most visibly by the Nobel Prize winning economist Darren Acimoglu, is saying that we'll see some productivity growth but we won't see mass unemployment. That tasks will change within jobs quite dramatically. So his notion on the productivity growth is about 0.3% per year in the advanced economies. And that's still great. If we're getting a big bump there, um, from that, that's pretty wonderful. Then on the other side you have Erin Brynjolfsson at Stanford and others who put that number closer to 3% per year in GDP growth. And that's extraordinary, right? The difference is exponential really, that we would see doubling of the economy um, within 12 years. So the gap is really explained, I think, by how these technologies will be able to be harnessed for transformation within companies and organizations. And that's where I come in. Because I'm not working on the economics, I'm really working on what happens inside the firm. I'm really happy on what happens inside the organizations. M and I've spent a lot of time looking at people who work at the coal face of digital change. And my whole career has been on that. And it's easy to talk a big game at the beginning of the dot com boom. It took more than 20 years, years to see that show up in productivity numbers. And what that means is that we're not going to see the kinds of job, dramatic job losses that some predict. I was recently at a workshop in London co sponsored by Anthropic and the Windfall Trust and they were working with economic scenarios that seem feasible. And the scenario was 30% of people in the UK lose their jobs by 2030. Now I want to underscore that it's not necessarily what I believe because uh, we're going to talk a bit about this automation paradox. But very powerful people, very central people within the current debates and discourse around AI and work are uh, saying it's possible that within three years a, ah, third of the country will lose their jobs. And I think we have to get ready for that possibility. So no, the sky is not falling in my assessment. But yes, we still have work to do to prepare people and companies and teams, your listeners and leaders, for the changes that are coming. Because if we want good outcomes, we're going to need to be able to harness these technologies in ways that help us build better products and services for clients, um, while we're growing our economy rather than just build uh, efficiencies through uh, uh, labor redundancies.

Lucy: Yeah, and that feels quite important. It feels like a good segue into this question about integration, how you integrate technologies. Because our experience, partly as advisors, but we've also done some research about clients and contacts, is that there's an expectation of efficiency, an expectation of productivity. That's what you're investing in when you buy um, the tech. But of course it isn't necessarily that straightforward. You can't just assume that the integration follows and there is a bit of a gap there. And I know that you've developed this concept of negotiated innovation. You know, the future of work isn't something that can just happen to us. We've got to negotiate it. You know, every day there are these thousands of small decisions we're making about how to work with new technologies. But how do organizations find the space for all those negotiations to happen as opposed to just acquire the tech, invest in there, put all your money in that bank and think, well, that is now going to fix everything. What does it look like when an organization gets it right versus an organization that gets it wrong?

Gina Neff: Yeah, that's a great question. So to answer that, I'm going to take us back 20 years to the beginning of the rollout of an uh, automating technology in the building services sector called Building Information Management. And this tool, um, is in use today. Um, has indeed been transformative for the industry. And two decades ago it was going to completely get rid of the legal structure in building services in the US There's a hundred year old Supreme Court legal precedent that governs rights and responsibilities, liabilities, who's liable in which part of the process. And the idea from industry leaders was that, you know, if we could only improve information flows between architects, architectural practices, between engineering firms and large scale constructors. Right. If we could only improve that information flow, then we would get productivity gains in, you know, one of the slowest productivity growing sectors in the economies. And so we went into the field to look at how people on construction projects were using these tools. And instead of the mass disruption and mass job losses that ah, we saw, we found we followed three different projects, each for a year where we were embedded in the teams. And it took years for the industry to understand what they could be doing with this tool. And that great insight of the true transformation, the true innovation, not just, you know, tinkering at the edges, but how they were really going to use this new technology to supercharge what they were going to be able to deliver didn't come from the C suite, didn't come from the cio, didn't come from the cto. It came from literally the construction workers on the ground. It came from the people who said, wait a second, we have this new information tool, it will allow us to deliver to the clients something new. I remember the meeting we were in and it was on the third of the projects that we had studied. And that moment was when they said, aha, uh, we know something that the client needs, that they don't even know they need. And we can see it. They could see how to deliver an unplanned but hopefully foreseen new wing of the building. They were building easier, faster, more efficient if they made some changes to the structure now. So building Wing one, that was their job. The client thought they were going to build Wing 2 sometime in the future. And they said, wait a second, we can take a week of work right now and deliver something to the client. Where we say we can make Wing two. We may not even be the people working on it, but we can make sure you will save money on Wing two in that construction if we do this two weeks of work now. And because they did that, they didn't automate away their jobs. They instead said, here's a tool that lets us work in a better, more efficient way. What else could we be doing with that? And that's where we're only at the beginning of this transformation in AI, because right now we're all thinking, okay, how can I do today's tasks more efficiently? But we're not asking the questions, how do teams work differently together because they have new insights? And we're not asking the question, how do project groups, how do multiple companies working on complex projects work together in new ways? Because they have these tools. And we're not saying what can we be doing to transform the products and services we deliver. Right now, we're just in that imagination of how do we use it and how do we use it, and our own work is defined by today. And so that's where I think we've got a real opportunity to reimagine what our companies do, but we're not there yet. And that's where I think we're going to see true transformation. And that's where I see the hopefulness of the future of work.

Lucy: And do you have advice for companies that are investing in this technology? On one hand, so you're. They're making decisions, business decisions to invest it, but on the other hand, they're dealing with a workforce that, as I said, I think at the beginning we are seeing is starting to feel this sense of tech adoption fatigue. It feels like it's happening to us, not with us. How do you bridge the gap? It's not to say the investment in the technology is a bad investment, almost certainly a good one, but as you say, you're not able to realize the advantages of that unless you bring the people in. So do you have advice about how you bridge the gap between the investment and the workers?

Gina Neff: Absolutely. I'm going to give a, uh, call out to Tim Gordon of the Practice Better AI, and he said this really insightful thing. He said, we can't get to real transformation when companies are saying they're adopting AI, but employees are hearing that 20% of you are going to lose your jobs. We've been talking about this adoption as labor saving and not any of the extraordinary other values we could be doing Again, delivery of better, higher quality, more targeted goods and services for clients and customers is the goal. But right now, our imagination has been captured by automating away the work. So how do we create the environments where people working together in teams understand and negotiate what the technology is for and how to change it? Let me take you back to the construction teams. Construction is actually legally, very Complex because, uh, hundreds of companies work on large scale projects and they are governed by the quote, unquote contract. The contract is what everyone told us rules, what they can and cannot do on a construction project. And that story is something that everybody working in construction knows all the way down to the field crews, the installation crews. And so encouraging those teams to think more entrepreneurially and more creatively and to work in a different way, navigate this carrot and the stick, right, the stick of the contract and the legal liability that they own all felt overshadowed by, and the carrot of being able to again improve their company's efficiency and improve the project for the client. How did they balance those tensions that they felt between these changes that would open up new ways of working, but that might not cover their liabilities? So in the interim, they worked on changing how they worked with one another, right? They pushed back on the rules and regulations and in that moment we got to see these wonderful sets of negotiations. That's not necessarily the constraint that every of your clients and your listeners are in, but everybody faces these kinds of challenges between how do we get this particular technology to work in a moment where we're having to imagine and renegotiate what the new rules of engagement will be? And that's where I think real, true innovation can come. Because it's only in those spaces of understanding what the parameters are that we can change and feeling that sense of agency and that sense of negotiation that you get people doing really creative work and really delivering on things that are quite transformative.

Lucy: So taking the conversation in a, I guess, slightly sideways move, but one of the ways that you, um, you can conceive that you might be able to break down this transformation fatigue, let's say, is to be able to illustrate to people that actually there's a value to you, it can improve your, your job, your job satisfaction that can have a knock on effect on, well, being. All the things that we know are increasingly challenging. And I think, you know, certainly our experience is. Despite the noise about job reductions, employers recognise there's still a talent war, particularly for the best talent. Um, and there is a focus on this idea of good work that employers do have a responsibility to make sure that their employee proposition is one where people's jobs are fulfilling. What do you think that idea of good work, and I know there's lots of definitions of that, but more generally, what that idea of good work, what can that look like in an AI enhanced, um, future of work?

Gina Neff: That's a great question. In the conversations about AI when we Talk about good work. It's actually really hard to define. I'm part of a large multi university research project here in the UK called the Digital Good Network that takes a social science view. How do we bring more analytic rigor to thinking about good for whom, good when good under what contexts and what conditions? What that means for the future of work I think is um, we need to get really granular on where good work happens. We need to improve industrial strategy and regional strategy. And I love to joke in the UK that we don't get an AI revolution until we fix the trains. And what I mean by that, what I mean by that, it's so true. You know any of us who want to go between two places, places, um, you know, we shouldn't have to have, have to go through London. We need to be able to connect our growing and vibrant cities and we need to be able to create the places where people are coming together to work on um, these big challenges and exciting problems of the future. So good work for me looks like giving people agency to really be a part of the change, giving them the power and the environment to negotiate how to use these technologies and what they can imagine doing with them. So for example um, a lot of universities are rushing headfirst into AI tools for teaching, the delivery of teaching and instruction. We without bringing in the people who are doing the uh, teaching and instruction. And that's both disempowering but it's also risking locking out the people with the most creative ideas because they're the ones doing the job. The good ideas aren't just going to come from the top. I think also and I mentioned regions, there's a um, recent um, very large scale review that's recently wrapped called the Pisserides Review that was co led by um, Christopher Pisserides, the Nobel Prize winning economist at lse. And that review really doubles down into thinking about what the future of good work looks like, is making sure that we're investing in regions. I fear the way we're talking about AI in the UK right now has been about the development of frontier AI tech and the development of that tech in London and not about how small and medium sized enterprises are going to be the ones who are going to use, adapt, negotiate around, develop new kinds of ways and applications for building on frontier models. So I think there's a real interesting tension that we're going to have where our image of innovation has been what's going on in Silicon Valley, but what's really going to spark the next wave of AI innovation is what's happening in our existing companies, what's happening in our small companies, and what's happening from all of those good ideas. Once people get their heads wrapped around what they might be able to, what they might be able to do with AI, they're going to see new and powerful ways to deliver exciting new products and services.

Lucy: Thanks. That's really, really useful. A sort of related point, but one I wanted to ask you about because one of the things that I think people worry about um, is the extent to which AI might entrench, um, existing inequalities, might um, entrench existing biases. They want to feel satisfied that uh, if we're going to buy into this technological revolution, that it is going to be one closes those gaps rather than allows them to become widened. And I know that um, you've been very vocal about the impact of AI on women's working lives. So as I had you, I wondered if there's reassurance you can provide to people that worry about that.

Gina Neff: Yeah. A few years ago I co authored with UNESCO, the OECD and the Interamerica Development Bank a report on AI and the impact on women's work. And that was a uh, launch for International Women's Day. And you know, it was interesting because we launched this report pre ChatGPT, so pre the mainstream understanding that AI tools were going to be in many more people's hands. And there were a couple of things to be uh, concerned about. First, in the uk, the us, uh, most European countries, we have fewer women in STEM jobs, we have fewer women in computing jobs, and we have in the AI sector even fewer still. So somewhere between 13 and 15% of the workforce in developing Frontier AI are women, even, even a smaller number than in computer science technology in general. And that's concerning. Right? So literally women are not at uh, the table in developing and delivering the frontier of the technology. We also are concerned that many of these services are coming first for the kinds of jobs that women have held and the kinds of jobs that women have held when they've had career breaks. So for example, clerical jobs, assistant jobs, marketing jobs, um, some of the lower uh, rungs on white collar work jobs that we're really worried that these technologies will automate first. So I think there is reason to be concerned. However, the hopefulness we had in that report I think still stands in that there is an enormous amount of, of high touch work that remains, will continue to remain and that have traditionally been women's jobs. So I don't think we're going to automate away teaching or nursing anytime soon. Most economists agree with that. I don't think we're going to automate away the jobs that help people understand and make sense of what many people are doing and saying. So um, let, what do I mean by that? Molly kinder from, from the Brookings Institute recently said this wonderful thing that if your job is mainly being in meetings all day, it's pretty safe from AI. And what she meant by that was if your job is just sitting in front of a computer, there are pieces of that that can be automated away. But if your job is to help make decisions about what we should do next, then those are kinds of jobs that involve a high degree of emotional intelligence, a high degree of uh, communication skills, high degree of interpersonal skills. And we know that women have developed those skills in the workplace. So again it's difficult because the future is uncertain. But there are these possibilities that I think we can develop when we give people the space to tap into their creativity and their ability to do human work.

Lucy: There's another group I think that um, are ah, feeling very directly impacted, maybe disenfranchised by this and that's younger people. It's the pipeline of talent, um, coming through. Entry level jobs have been a big focus of the media, particularly around whether AI might replace or reshape them. But it feels to me that um, that maybe just isn't the point. I m mean I'm the mum of um, two boys that are reaching the end of school. They look at the world, they can see it's changing around them but they know they're not being provided with the skills that will equip them for the future. That's not to say what they're doing in school isn't useful, but they're worried about this pipeline um issue. What can employers be doing to make sure that they are developing future professionals, that we're not inadvertently losing those learning opportunities that, that have always been part of the career structure.

Gina Neff: There's a couple of ways I'd like to take this. First, as a mom to 17 year old boys, you know, I too am looking at this landscape and thinking how do I best prepare them for what's out there? What we tell young people now is get any job first, get into employment. And that's hard advice for those of us who think about smooth career paths in white collar professional work. But you know, those summer jobs, those seemingly disconnected jobs help to land the skills that demonstrate how they can get into better jobs and with the better Jobs, we transition to career pathways. So the ABCs, right, of the new workforce, is any job then a better job than a career job? I think the mistake that we're making with young people is they look at LinkedIn and all those resumes that are being automatically generated in the career jobs and they're going to need a few more steps in order to get into that, um, that role. I think the second thing that I would tell employers is that there's a real spark around negotiating innovation. That's the key. How do we get people to negotiate around these changes and how do they create the environments? So that involves building the teams, the psychological safety, what do they need to do to make sure that they're getting everyone to come along with them on the journey and to make sure that they're enrolling people into that excitement rather than, um, people feeling like the technology is hanging to them. So if we're going to negotiate innovation, we're going to do it from our own career pathways and our organizations are going to need to be able to be better set up for that.

Lucy: Thanks, Gina. It's been such an interesting conversation. We're starting to get to the end, but before we finish, I wanted to ask you the question I've been asking to all the guests on this podcast series this year. If you had one piece of advice for leaders that want to build their organization's future resilience in the face of constant change and pressure, what would it be?

Gina Neff: Yeah, I'm going to pick, uh, build an environment where people can negotiate around innovation. That means ensuring that you have the data teams in place where people can make their voices heard, that you have the environment where they feel safe to do that. And you are empowering people and giving them the agency to think through, be entrepreneurial and creative on the organization's behalf. That's really what I think is just so central. It is the number one mission for leaders right now. Empowering your people to navigate this change is different from a top down demanding that they use the tools in a particular way. If you can get that right, then you're going to create an organization that is learning, that is excited and that is coming along for this journey. They're feeling like they are part of the solution, not that the changes are happening to them.

Lucy: Thank you, Gina. That's excellent, really excellent advice. And thank you to everybody for joining us. If you'd like to hear more conversations like this, you can, um, sign up on our website or subscribe through your usual channels. And, um, look forward to having your company again soon. So until next time. Goodbye.