Reskilling the Workforce With AI: Harvard Business School’s Raffaella Sadun

**Sam Ransbotham** (0:03)
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Stay tuned after the episode as Shervin and I discuss the key points from today's guest. We hear a lot about augmenting humans with AI. But what if a cross-functional team was 100 percent AI? Would companies be more innovative? One researcher shares her conclusions on today's episode.

**Raffaella Sadun** (1:16)
I'm Raffaella Sadun from Harvard Business School, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (1:22)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy Guest Editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (1:40)
I'm Shervin Khodabandeh, Senior Partner with BCG, and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017 Interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Hi, everyone. Thanks for joining us today. We are speaking with Raffaella Sadun, Professor of Business Administration at Harvard Business School. She's also the co-chair of Harvard Business School's Project on Managing the Future of Work and the co-principal investigator of the Digital Reskilling Lab. Raffaella, welcome to the show.

**Raffaella Sadun** (2:27)
Thank you for having me. It's my pleasure to be here.

**Shervin Khodabandeh** (2:30)
It's wonderful to have you. Raffaella, you have a long history of researching and publishing on innovation, technology, AI, and the impact on organization and nature of work. And that last piece, Organization and Nature of Work, is actually one of the most important ones in the work that Sam and I have been doing and talking about quite a lot. We see that most companies struggle to get that part right. And that's where the real, you know, hidden value is in getting full impact from AI. Share with us what you're seeing in your own work and what are some of the key highlights.

**Raffaella Sadun** (3:09)
So let me start by saying I am doing research on AI, but my ideas on AI are also shaped by research that I did prior to the AI era, more generally on technological change and adaptation, how firms adopt new technologies in their organizations. I think a lot of the attention now is focused on the technological layer or the data layer, because it's clear that these are essential prerequisites for AI to work. You need to know and you need to have enough information to be able to start a prediction process, which is most of what these technologies do, and you want to have enough of a technological layer to be able to interpret and understand what the algorithm is doing for you. So that I take for granted. What I'm observing in organizations is that the organization and the change management part is as important and often neglected. And so where do we start? Well, first, you need to understand what is the business application. We know that from the latest surveys, a very large number of US workers are engaging with these technologies. But when it comes to the adoption of these technologies in the firm, you first have to ask yourself, why am I doing it? And what's the value add? That in itself, I think, is one instance where I see tremendous heterogeneity across organizations. Because just to be able to ask that question and answer that question, you have to have in mind how your organization adds value relative to competition. And so you want to understand what is your competitive strength and how this technology may affect your competitive strength.

**Shervin Khodabandeh** (4:52)
Strategy with and for AI, exactly, yes.

**Raffaella Sadun** (4:56)
Exactly. And this is where I come to my second point. My sense is that we are at a point where it is similar to the onset of a new technology paradigm where a lot of the knowledge about how this technology adds value and whether it adds value to specific verticals or specific businesses has not yet been codified. So this is a time in which there is tremendous return from experimentation and in particular tailored experimentation within the firm. And this is where I see organizations diverge. Are you able, as an organization, to formulate an hypothesis, to set up the experimental context that would allow you to test that hypothesis? But also, are you able to codify the learnings that are coming from your experiment and feed it back into the organization? I don't see a ton of attention on this step, but I think that this step is really, really, really important for the adoption process.