Many future-of-work researchers argue that artificial intelligence has the properties of a general-purpose technology—a category of technologies that are defined by their ubiquity, their potential to be improved upon over time, and their ability to give rise to other innovations. General-purpose technologies aren’t invented every day. Other examples include electricity and the internet.

In an essay in Academy of Management Discoveries, a team of researchers argues that, similar to other general-purpose technologies, artificial intelligence will only provide value to companies that create the right infrastructure to support it—or as they explain, the ones that invest in “complementary assets,” such as talent and data.

We spoke with Robert Seamans, one of the essay’s authors and an associate professor of management and organizations at New York University’s Stern School of Business, about the importance of complementary assets to AI adoption. Here are excerpts from our conversation, lightly edited for length and clarity:

You argue that the companies that reap the greatest reward from general-purpose technologies like AI are the ones that invest in complementary assets. Can you share an example of how that’s played out in the past?

One of the best examples is a historical case study by Paul David, who was an economist at Stanford University, about manufacturing firms that relied on steam power to power everything that they did and then switched over to electricity. He found that it took firms years before they saw any productivity gains from that switch.

Which begs the question, why? He dug into that and found that the way that these production processes are set up, they’re optimized to run on steam. If you switch out the steam, performance is going to drop. So each of the manufacturing firms had to tinker with their production process and figure out a new way to set everything up to take advantage of what the electricity can do.

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How should organizations identify the types of complementary assets they need to invest in?

What makes investments in complementary assets hard is that you don’t necessarily know what they are ahead of time. It’s going to involve incurring additional expenses through trial and error. I’ve visited manufacturing firms that are part of the automotive supply chain, and I’ve observed firms that are starting to put robots in place. It’s a similar type of thing, where you can’t just take a human out and stick a robot in. There are things a human does well, and there are things a robot does well, but they’re not exactly the same.

What you have to do is rearrange your production line in a way that takes advantage of the things that the robot can do. For example, if it’s a stamper—where the robot will be feeding metal into the stamp and pulling away the stamp—in the past, a human would visually inspect it, but the robot can’t do that. In order for that inspection to happen, you have to purchase sensors, and you have to wire that stuff up to a computer system. You have to purchase a new bunch of software that will analyze it. Typically, this also involves hiring people that have very specific human capital around how the robot works and how the production process works, but also interpreting everything that is being fed to them via that software.

How should organizations incorporate investments in complementary assets into the way they think about hiring and upskilling?

There’s the short term, and then there’s thinking a little bit longer term. What makes the most sense longer-term is having a strategy that’s continuously updated. It’s not trying to cycle through different workers that have different skills. It’s a company trying to find the workers it’s most comfortable with, that share some values that the firm thinks are important. And then over time, it’s making sure the workers have the skills that are needed to take advantage of whatever the new technologies are. You mentioned generative AI, but there might be something different a year from now. Moreover, what matters now in terms of generative AI is almost certainly going to be very different a few years from now.

You don’t want to constantly have to chase folks that have these skills. Ideally, you’ve got a set of employees that are totally bought into what you’re doing as a company, and then you’re training and retraining them over time to have the skills that are needed to take advantage of the technology and work well for you as a company.

What are some of the most important complementary assets for AI that’s being used in the context of knowledge work?

There’s human capital, physical capital, and then digital capital. I don’t think the physical capital matters as much here, but for sure the human capital does, and the digital capital does as well. It’s rarely the case that a firm is just adopting AI. They’re typically also starting to digitize a lot of the records that they have. At a minimum, you have to purchase some software to help with digitization. You have to think about how you’re going to store information, how you’re going to retrieve it, and who you’re going to partner with. I would think of that as all the digital capital that one invests in.

In terms of specialized human capital, some of your existing managers might be able to handle some of that, but you’re going to need to train up people in terms of the new systems that you’re putting in place. You also have to start hiring folks that know how the AI itself works and know what other types of assets need to be invested in.

You wrote in your paper that people often consider a new technology and its implications as something never seen before, when in fact the underlying dynamics are the same as for earlier technologies. What would you say about AI adoption in that context?

This point about complementary assets has mattered for all the other technologies that have come before AI, and it’s going to matter for AI as well. AI can drive productivity improvements in a firm, make the firm more profitable, do whatever it is that they’re doing better—but it’s not the case that you can just buy AI and sprinkle it on the firm, and you’ll get these productivity improvements. It involves a lot of learning by doing, about how you need to change what your firm does to take advantage of what AI can do.

We were talking about robots before—the average cost of a robotics arm in a production setting is about $30,000. The average cost of it with all of those complementary assets that we talked about before is about $90,000. So we’re not talking about just purchasing a few other little things here and there. If you’re making a decision to invest in robots, it’s this huge investment that involves investing in all these other complementary assets. Firms should have in the back of their head that whatever it costs to in order to purchase AI, at a minimum, you should expect to spend at least that amount in terms of all the other investments that you have to make.

Read a full transcript of our conversation, including more on the jobs most exposed to AI and AI-focused hiring strategies.

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