24 Mar 2026

Behind the Research: Jacqueline Ng Lane

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by Shona Simkin

Harvard Business School assistant professor Jacqueline (Jackie) Ng Lane studies how new ideas take shape. We talked with Lane about how innovation is both structured and messy, what her research with organizations like NASA reveals about the roles humans and AI can play in generating breakthroughs, finding awe in nature, and more.

What is your area of research?
I am an innovation scholar; I really care about how early ideas take shape, in terms of how we identify problems that are worth solving, how we come up with different solutions to solve these problems—and once we come up with options, how to figure out which ones are worth implementing and pursuing.

I think of the world in terms of processes, but innovation is sort of a messy process—how do we make sense of something that can seem non-scientific? Many people imagine a creative genius who just comes up with an idea while strolling down the street or some serendipitous discovery in their science lab. But I think there’s a lot of structure that goes into it. If we want to find more breakthroughs and high-impact ideas and innovations, how do we support that process?

There’s a concept that really resonates in the first year TOM [Technology and Operations Management] class that I teach. In the first half, we talk about efficiency and productivity—trying to minimize variance so your operating process is predictable and optimized. The second half of the class is about innovation—suddenly you want to blow up the variance to find the “best” ideas, and this part of the course is about how to structure the creative process to support the emergence of these innovative ideas. What’s really exciting to me is the processes to put in place so that this can happen.

How did you get interested in this topic?
There’s an obvious answer, which is that I worked at Microsoft during the Windows 8 launch—an innovative time when the company was preparing for the first Surface tablet launch. It was cool to see a big firm trying to innovate and do something new, so I got excited about that. But more fundamentally, I like to take some risks, so maybe there is something fundamental about innovation and it being uncertain that resonates with me about how to manage and support risk-taking, and perhaps that’s more apparent in the choices that I have made, and in my career.

Tell us about those risks.
I studied operations research and financial engineering in undergrad, and back then, much of my coursework was on financial engineering and managing heavy-tailed distributions. My undergraduate senior thesis was about pricing crash options—if you could buy an option that could protect you from big financial crises, how would you model these market crashes? In my thesis, I developed a little “jump” diffusion model and applied it to the 1997 Asian financial crisis. I really enjoyed that work, but I didn't really think about grad school and took a job on Wall Street trading US government bonds, which gave me firsthand experience in managing a lot of risk. The subprime mortgage crisis hit a few months after I started, and suddenly, government bonds went from one of the most boring to the most in-demand financial instruments. I traded government bonds during the last financial crisis, and then spent a year supporting the company’s co-president. I saw the entire operating committee and how one interacts with different people and integrates their viewpoints.

That got me out of the weeds to think about how to plan and how to run a business, and importantly, how to make decisions under uncertainty to turn a company around that had been substantially weakened by the financial crisis. Things were never perfect because we never had enough time—that made me wonder if I could study these things systematically.

But I didn’t really connect the dots until I was working in an innovation context at Microsoft, and I realized that this was something I could study—the people who work on the products, the timing of innovations and making fast decisions. What kind of expertise is needed in different parts of the innovation process? It seemed like something that could be mapped out.

What are some examples of that?
One study that has been particularly formative in my development as a scholar is a field experiment I ran with NASA. They were creating robotic arm designs, and we wanted to understand how NASA could expand their expertise pool of evaluators by bringing in outsiders, namely the crowd. They were interested in the feasibility of the designs but also in identifying novelty, as that combination improved the performance of the designs. The people with deep domain expertise looked solely at whether they would work in space or on the International Space Station. The people who had T-shaped expertise—those with expertise in one discipline and a broad understanding of other knowledge domains—also noticed when designs had a novel function or mechanism that would improve the design’s longevity and performance. Working on that made me realize that a theme of my work is appreciating alternative perspectives and the role of people who see things differently.

That’s the case with my current work on hybrid teams of humans and artificial intelligence (AI). In one project, we’re looking at the core idea behind crowdsourcing: bringing together many different perspectives increases the likelihood that at least one will produce a particularly promising or creative idea. Now that generative AI can produce large numbers of ideas almost instantly, we’re facing an important question: What does this mean for the diversity of perspectives that traditionally came from the crowd? Do we still need the diversity of human contributors?

To test this systematically, we compared crowdsourced human ideas with AI ideas—an independent, external human rated them for creativity. We saw that AI is very good at doing things rapidly at scale, and finding ideas that are feasible and valuable, whereas humans came up with more disparate, innovative ideas. I think that speaks to the complementarity of using AI with human creativity—disparate human insight and experience enable novel, creative thinking.

More broadly, I believe the concept of T-shaped knowledge will become increasingly important for organizing and designing hybrid human-AI teams. As technologies like AI make deeper specialization possible, the need grows for individuals—or systems—that can understand the architecture of problems and solutions and integrate insights across domains.

What does your work look like, day to day?
Much of my work is with the D^3 LISH lab, the Lab for Innovation Science at Harvard. We have a team of people who work at the intersection of AI, firms and innovation.

One of the projects I am particularly excited about is identifying which problems are worth solving. We’re trying to create a map of all the different kinds of problems that exist with early-stage startups. What are the kinds of problems they’re tackling and, importantly, what human needs are they seeking to meet for their customers? We are also interested in how AI may be reshaping what needs entrepreneurs choose to target. This is still very early-stage research, but it has been especially exciting to collaborate with a multidisciplinary team as we push ourselves to learn how to leverage the sophisticated capabilities of large language models to ask new research questions and make sense of unstructured textual data.

I feel very fortunate to be at HBS. I’ve always liked exploring a diversity of perspectives and

working with people from different disciplines. Sometimes it can be more challenging, but you learn so much more. It makes each day fun.

What do you like to do outside of work?
I love to ski, and I love what skiing embodies for our family—I have two kids, they’re eight and four, and they both love to ski. It’s a fun activity for our family. I'm Canadian, I grew up in Vancouver—Whistler was a few hours from our house. So we go back every year; it’s a little place of childhood that still lives in us all.

I also like to travel; we try to take a trip every year. One of the most beautiful places we went to recently was the Golden Circle in Iceland. We saw geysers, craters, dramatic waterfalls, and many majestic places that have been there for thousands of years—it really makes you feel like nature is wonderful. Day to day, I’m thinking a lot about AI and other emerging technologies, and it’s sometimes nice to be away from that and appreciate nature’s amazing creations.

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