Illustration by: iStock.com/Eoneren
Innovation may not be quite as novel as you’d think: Great ideas often comprise pieces of tried-and-true concepts that have come before. Take Henry Ford’s car company, for example. His assembly lines were based on moving rails in Chicago slaughterhouses; the company’s dealer franchise networks were inspired by Isaac Singer’s sewing machine sales model; and the profit-sharing system his workers enjoyed had been pioneered at Procter & Gamble more than a decade earlier.
Ford attributed his success to the ability to apply relevant past innovations to his company’s specific needs. The precedents thinking innovation methodology, developed by Stefanos Zenios, faculty director of Stanford Graduate School of Business’s Center for Entrepreneurial Studies, and Ken Favaro, MBA ’83, is based on similar principles. It provides an organized process that increases the likelihood of landing on a viable idea, according to Zenios, the Investment Group of Santa Barbara Professor of Entrepreneurship and Professor of Operations, Information & Technology at Stanford GSB, who teaches in Executive Education programs.
Combining Past Precedents to Solve Modern Problems
Zenios and Favaro had both been working on a more systematic way to spur innovation when they joined forces about five years ago to create the precedents thinking method. They wanted to establish a precise, repeatable process that would allow entrepreneurs to quickly and easily combine ideas from past innovations to address today’s challenges. “It’s an approach that is also consistent with how innovation happens,” says Zenios.
Precedents thinking involves three steps: First, frame the challenge. Identify a problem that needs to be solved, creating a brief but clear statement that defines it. Then, search for past innovations. Brainstorming, interviewing industry experts, and even asking AI can surface examples of applicable precedents. The precedents should be highly relevant, be more than common best practices, and demonstrate strong success. Finally, combine elements of the most promising precedents. After thoroughly evaluating each precedent for applicability, feasibility, and impact, consider how to creatively combine parts of them to generate the best actionable solutions to the challenge.
From Theory to Practice
Zenios was interested in how precedents thinking could be applied to larger systemic problems in the healthcare industry. He teamed up with his Stanford GSB colleague Kevin Schulman, who is also a professor at Stanford School of Medicine, to use precedents thinking to find a solution to administrative waste in the U.S. healthcare system, where unnecessary bureaucracy, duplication, and inefficiency cost more than $265 billion annually. A research team of six students from Stanford GSB and the medical school worked with Zenios, Favaro, and Schulman to understand the precedents thinking process and frame the challenge with a single statement: “How to create the standardization and infrastructure that’s needed to reduce administrative waste in healthcare.”
From there, the research team spent four months searching for and compiling 82 potential precedents, including the 1040 tax form, ticketing at Disney World, and 18th-century British banking clearinghouses. The team evaluated each one carefully, and eventually chose 26 for more detailed research. In the end, the researchers took pieces from several precedents and combined them into two potential solutions: The first was modularized machine-readable contracts, and the second was the construction of a uniform digital transaction program.
The researchers then shared their proposals with policymakers and industry leaders. Because the ideas are based on examples of past successes, Schulman thinks they’ll be considered seriously. “When we say, ‘Here’s how these markets have done it,’ it really changes the conversation,” he says. “That’s the power of the precedent model.”
Putting Insights into Action
The precedents thinking method offers an organized, proactive approach to innovation — and it’s just as appealing for large risk-averse industries or corporations as it is for small startups. Here are some best practices to apply it to challenges your organization might like to solve:
- Frame your challenge. Research thoroughly to clearly identify and distill the problem. It might help to examine the problem from different angles for a fresh perspective, or to break it down into smaller elements.
- Search for precedents. Look outside your own industry, and take advantage of AI tools to speed up and broaden the search for precedents — just be sure to review the results for accuracy. Start with dozens of possibilities, and then evaluate and narrow the list, keeping only precedents that are highly relevant to specific elements of your problem.
- Combine the most useful parts. You’re unlikely to find a single precedent that’s a complete match to your particular challenge, so you’ll need to break your top choices down and assemble elements from each into a viable solution.
