If you’re looking for your next big breakthrough, stop waiting for inspiration to strike, says George Newman, a professor of management at the University of Toronto and author of the forthcoming book, How Great Ideas Happen.
While popular ideas of creativity often rely on outmoded ideas of genius—the stereotype of the “lone, usually guy, off in the middle of nowhere, waiting for that lightbulb moment” figure—Newman says great ideas more often come from sustained, collaborative efforts more akin to an archeological dig.
We spoke to Newman about his approach to the creative process, with an eye toward lessons for teams feeling constrained by smaller budgets, tighter timelines, and AI-driven pressures. Here are excerpts from our conversation, edited for length and clarity:
What are the steps of the creative ‘archeological dig’?
The first stage I call surveying. Surveying is all about figuring out where we’re going to dig. It’s being a problem finder—essentially, not just thinking about solutions, but trying to say, ‘well, what are the problems here?’
The next stage is what I call gridding. This is about making your search organized. You can think about the grid lines that an archeologist would put up over a dig site. You’re checking through every square. Have I made sure I’ve looked at everything and noting where I found things and where I didn’t?
The next stage I call digging. Here, the mantra is ‘more is more.’ It’s about making sure I exhaustively searched and have gotten everything out of the ground that I can. There’s some research from a group at Northwestern that identified ‘the creative cliff illusion,’ which is this notion that we think we’re going to run out of ideas pretty quickly. In the experiment, they had people predict first how many ideas they were going to have in minute one and minute two and minute three, et cetera. People thought they were going to run out of ideas, but then when they actually started brainstorming, they just kept on generating more and more and more ideas. These ideas at the end of the session were some of the most successful ones.
One practice I talk about in the book is a Post-it Note challenge. Just take a book of Post-it Notes and commit yourself [or your team] to coming up with an idea for every single Post-it Note. There’s 200 [of them], and you’re like, ‘There’s no way this is going to happen.’ But you’re often surprised at how much you can come up with.
The last stage of the process is called sifting, which is going back and saying, ‘Well, what did I find?’ We’re not always great at knowing when we’ve stumbled on a good idea or a great idea. Part of that has to do with how many successful ideas feel very abstract and foreign to us initially.
At a time when economic uncertainty is putting pressure on teams, how can they use budget constraints to become more innovative?
You can use constraints to your advantage. One of my favorite examples is [the movie] “Jaws,” where they had a pretty limited budget. They built three mechanical sharks, but with the technology at the time, they couldn’t get them to work under water. That forced [Steven] Spielberg to leave a lot of it to the audience’s imagination. But of course that turned out to be a lot of what makes Jaws so terrifying.
The research shows that with many different types of constraints…having a smaller palette to work with can actually lead people to more successful outcomes. There’s a famous study where [the researchers] had people come up with ideas for children’s toys and gave those ideas to a panel of experts who had worked in the industry for a long time.
In one condition they said, ‘Come up with anything. The sky’s the limit,’ and in the most restrictive condition, they gave people five different shapes and said the toy could be whatever they want, but it had to incorporate all of the [shapes] in some way. The problem is now really, really constrained. But people actually came up with better toys—more successful toys—when they had to work inside these very narrow parameters.
AI has made it easier than ever to generate more ideas. How should that change the process of digging and sifting?
You can think about AI as a very powerful excavator. It’s a very powerful tool that helps us to get a lot of stuff out of the ground very quickly. But the caveat there is if you haven’t pointed in the right direction…you’re not going to find anything. If you have no idea [about] the criteria you’re looking for or systematically how you’re going to search, then it is not going to be such a useful tool for you.
The research really supports exactly that. If you just let AI tools run freely, you get very homogenous, boring, uninspired ideas. But if you are able to turn it into a patch of terrain where not so many people are looking, it can actually help you discover the spark of something much faster than you would have otherwise. People are very, very important to this process for talking about these different steps in surveying, gridding, and being able to sift later on.
What are some best practices for becoming better at sifting?
First, separate the digging from the sifting and think of those two as different stages of the process. In work contexts, it’s really easy to start critiquing ideas just at the same time as they’re coming out. The research suggests [you should] first focus on getting every idea out on the table that you can.…Then go back later, and maybe even with a different group of people, to sort through ideas.
I have some research showing [how] we become very attached to our own ideas and have a pretty difficult time evaluating the best [ones]. In an experiment, we offered bonuses to people to give us their best ideas out of a list they came up with. If they gave us ideas that other people say are good, they got a bonus. If they gave us ideas that weren’t so hot, they would start losing money. If it’s their own idea, people submit way more ideas and wind up not making any money. If they’re given somebody else’s list, they do a much better job picking out the good ones.
How Great Ideas Happen is out January 27. Pre-order it on Amazon or Bookshop.
Read our Q&A with Stanford’s Jeremy Utley about how to use AI to spur innovation.
Read our book briefing on Ideaflow by Utley and Perry Klebahn.