From Public Voice to Board Action: A Structured Decision-Making Process
We’ve all faced decisions where both sides seem reasonable. One moment, you’re sure of the right path; the next, someone presents a compelling counterpoint that leaves you second-guessing.
Maybe you’re calling the shots on whether prospective employees must meet specific academic qualifications. Or you’re spearheading a public policy project that the entire community has an opinion on. It could even be something as basic as choosing new workplace software.
No matter the field, good decision-making requires diverse perspectives and reliable methods to synthesize them. Getting input from colleagues, community members, and even strangers online can help steer you in the right direction. But once you’ve gathered all that feedback, the real question becomes, what do you do with it?
In this article, I’ll share how TDL turns mountains of feedback into actionable insights. Our test case involved a professional board deciding whether a college degree was required for a specialized role. The result? An easier, clearer way to understand what’s most important to everyone involved. If you've ever felt overwhelmed by opposing ideas, spreadsheets, or counsel from all sides, this technique can turn chaos into clarity.
Gathering public input
Collecting community voices
In theory, we all understand the importance of entertaining different perspectives before making a critical decision. But in practice, when you receive a couple hundred (or more) comments, it’s easy to feel like you’ve taken on too much.
You know that feeling when your inbox is jam-packed, Slack notifications won’t stop, and you’re wondering if you’ll ever make it through the work piling up right in front of your eyes. That’s pretty much where we landed after reviewing over 250 people’s perspectives on the question: “Should a college degree be required for a particular specialized role?”
Some folks insisted that a formal degree is the gold standard for building trust and ensuring quality. Others pointed out that hands-on experience can be just as valuable, sometimes even more useful. And, of course, all sorts of people stood in between. However, when it comes to research, gathering feedback is easy; everyone has an opinion. The real challenge lies in making sense of it all.
Using AI to craft summaries
So how do you process so many opinions without getting lost in the weeds? We leveraged the power and efficiency of AI to classify each opinion, kind of like a personal research assistant. Every comment went into a ChatGPT-powered tool, which identified the core arguments and tagged them as either “in favor” or “against” requiring a college degree.
Using AI helped us avoid overlooking a great argument buried in an overwhelming set of opinions. And it’s not just a time-saver; this efficiency translates into real value. By reducing the hours spent manually coding feedback, teams can cut costs dramatically and refocus on strategic decisions rather than administrative tasks.
Of course, AI isn’t a silver bullet. While an algorithm can power through vast stacks of feedback—handy for everything from software rollouts to curriculum design—it can’t automatically catch all those subtle, human nuances. To keep things accurate and fair, we ran randomized quality checks. But even with that extra step, the time savings were significant.
Filtering and prioritizing arguments
After gathering all the arguments, from strong support for a mandatory degree to outright opposition, we asked ourselves: What themes keep showing up, and what ideas stand out? To find out, we used AI to sort each argument into existing categories or create new ones to capture unique ideas. Grouping related arguments helped us cut through the noise while ensuring each perspective got a fair hearing.
For each argument group, we captured four key data points:
- Frequency: How often an argument was tied to an overarching theme.
- Direction: Whether the argument supported or opposed requiring a college degree for the specific specialized role.
- Representative Quote: A direct excerpt from the original public letter illustrating the argument.
- Counterargument: An AI-generated response providing an opposing perspective.
References
- Sawtooth Software, Inc. (n.d.). MaxDiff (Best–Worst) Scaling. Retrieved June 30, 2025, from https://sawtoothsoftware.com/maxdiff
- Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
About the Author
Jerónimo Kanahuati
Jero is a Consultant at The Decision Lab with a passion for artificial intelligence and behavioral science. Prior to joining The Decision Lab he founded a startup in Mexico to develop apps for kids to encourage education, and developing web scraping bots. He also worked at Google as an account manager and technical specialist focused on ad placement across Google's products. Jero has a bachelor's degree in engineering and a postgraduate specialty degree in operations from Universidad Panamericana in Mexico City.
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