Cade Massey
Partnership with Wharton Business School
Practice Professor, The Wharton School; Academic Director, Wharton’s Executive Influence Program
The data says one thing, the people who have done the job say another, and too often the loudest voice in the room decides. Cade Massey helps navigate those arguments, from NFL draft war rooms to Ivy League admissions committees, and his research explains how the best answer can get overlooked. He gives audiences a way to cut through the noise and make the decisions that determine whether the next three years go well.
Web
Cade Massey gets brought in at the point where the analysis is finished and nothing has happened. Getting to the right answer is the half you can buy: better data, better models, someone to run them. The half you cannot buy is getting a room of experienced people to act on an answer that contradicts what they already believe. That gap is his subject, and he has worked inside it for twenty-five years.
Massey does more than study how organizations decide. He has built the models and sat with the people who had to use them. His best-known research gave the pattern its name: algorithm aversion. People who saw a model make a mistake abandoned it faster than they abandoned a person who made the same one, even when the model was better overall. It could be right more often and still lose the room. The fix turned out to be small, and it was not a better model. People used it far more once they were allowed to change the answer.
He has consulted with teams across football, baseball, basketball, and hockey, as well as a global technology company and a major pharmaceutical company, on how they choose and evaluate people. He co-created the Massey-Peabody NFL power ranking, published weekly in the Wall Street Journal, which meant putting a forecast in print and getting graded on it every week. He co-founded Wharton People Analytics alongside Adam Grant and Angela Duckworth, helping establish the field of using data to make better decisions about people.
Nothing he says on a stage arrives untested. Massey has taught MBA and Executive MBA students at Duke, Yale and Wharton, and won teaching awards at all three. Wharton has recognized his teaching every year since 2018, and since 2021 in both programs at once, including the Anvil Award that its MBA students nominate. He directs Wharton's Executive Influence program, where the material runs in front of senior audiences from around the world. The people in those rooms have already made the decisions he is describing, and they push back when the research does not match what they have seen.
AI has made the problem sharper in both directions. Organizations still hold good models at arm's length even when the evidence is strong, and analysts trust output past its limits and mistake confidence for accuracy. Massey expects both mistakes to get worse with AI before they get better. A large language model is far too confident and far too crisp, and it will convince anyone who does not already know the answer. What he teaches sits underneath both problems: what a model can tell you, where it falls short, and how to move a room the evidence has not convinced. It is not enough to be right, he tells a room. You have to bring people along with you.
WHY AUDIENCES WILL BE BETTER OFF
- Audiences leave able to get their people using an analytics tool they gave up on, instead of paying for its replacement.
- Audiences leave able to move a decision that more data will not move, by changing how the case gets made.
- Audiences leave able to stop paying a premium for precision their hiring process cannot deliver.
- Audiences leave able to ask what a rating measured before a promotion or a bonus gets attached to it.
- Audiences leave able to stop a model from settling a decision on certainty it has not earned.
Cade Massey’s Speech Topics
Make the Analytics and AI You Already Bought Pay Off
A company buys tools to sharpen its biggest decisions, and the tools sit unused. Massey's research named the reason: after watching an algorithm and a person make the same mistake, people lose faith in the algorithm faster, even when it is measurably better. His follow-up study found that letting people adjust the model even slightly raised the share who used it from 32 percent to 76 percent. They did not need better math, they needed a say. Audiences leave able to restart a tool their team has written off, and to design the next rollout so people actually use it.
Influence That Moves Decisions: Why Being Right Is Not Enough
Massey walked into a football team's facility holding the best data anyone there had, and could not get a single person to act on it. That experience sent him into the study of influence, which he has taught for eighteen years and now directs as a Wharton executive program. After thousands of people have taken his assessment of their own influence tactics, his read is that most people work from too narrow a part of the toolbox. He teaches Aristotle's three paths: logic, emotion, and credibility, and the rooms he works with are almost always strong on the first and thin on the other two. Audiences leave able to name the approach they default to, recognize when more evidence will not help, and reach for the tools they have never used.
Who You Hire and Who You Promote: Stop Paying for Certainty You Never Get
Hiring and promotion are forecasts, and they are rarely graded. One industry grades them in public every year. Massey's paper with his doctoral advisor Richard Thaler, a later Nobel winner, found that a drafted player beats the next one taken at his position 52 percent of the time. Teams pay a fortune to pick first anyway, off a chart one coach drew in 1989, and the same chart shows up in a company as a salary band. Audiences leave able to size a bet on a person to what their process actually knows, and to spot where they are paying for confidence instead of information.
Fix the Performance Review: What a Rating Actually Measures
Massey directs the Wharton People Lab and teaches the statistics of measuring people. His published method for evaluating talent rests on two rules that are easy to state and hard to keep. Keep the judges apart until each has scored independently, and score the parts before scoring the person. A rating produced any other way records an impression and reports it as a measurement. Audiences leave able to ask what a rating measured before a promotion or a bonus gets attached to it, and to redesign a review so its answer is worth having.
And, Not Or: Giving AI More of the Decision Without Losing Control
The question is not whether to trust a model, it is how much of a decision to give it. Massey's answer is a blend that moves: the model earns more of the call as it proves itself, and people keep a standing veto over all of it. He built exactly that into an Ivy League school's MBA admissions process, where the committee overrode fewer of the model's recommendations every year. Either extreme is expensive: an unused model, or unchecked output that sounds certain. Audiences leave able to widen a model's role as it earns the ground, and to keep the call human where the model is least sure.
What other organizations say about Cade Massey
You will finish this course asking yourself, 'How'd I ever survive as a professional without this course?' This course is a must!
Business Conferences / Audiences
Works by Cade Massey
Cade Massey
Wharton Moneyball
Bring This Speaker to Your Stage
Tell us about your event and our team will confirm availability and fees right away.