Nullius in Verba—take nobody’s word for it—is the Royal Society’s great motto. Proof, not prestige, should be the true persuader. So when a group of academic research psychologists decided to take it seriously, the entire discipline was shaken by the results. The Reproducibility Project found it could substantively replicate the results of fewer than 40% of 100 high-profile experiments published in peer-reviewed journals. One study that failed replication claimed that encouraging people to believe there was no such thing as free will made them cheat more. Another study that failed the test reported on the effect of physical distance on emotional closeness. Volunteers asked to plot two points that were far apart on graph paper later reported weaker emotional attachment to family members, compared with subjects who had graphed points close together.

“The success rate is lower than I would have thought,” Stanford’s John Ioannidis told the Atlantic, who described his Why Most Published Research Findings Are False as “a lightning rod for the reproducibility movement.” “I feel bad to see that some of my predictions have been validated. I wish they’d been proven wrong.”

“This project quite clearly shows that findings reported in the top psychology journals should not be taken at face value,” declared Jelte Wicherts, an associate statistics and methods professor at the University of Tilburg, to Retraction Watch. He added even more damningly, “We knew there were many results that were too good to be true.”

While disconcerting and embarrassing for academe, these unhappy revelations highlight radical new realities and opportunities for business. When it comes to generating breakthrough insights around choice, preference, bias, affinity, creativity and decision—all the psychological elements and ingredients that go into making humans human—industry, not university, is far better positioned to design, develop and deploy replicable experiments that matter. (I’m not talking here about research into “Is this product safe?” or “Does this pill work?” which still needs to come from independent sources.)

Tomorrow’s most important discoveries into why people do what they do will most likely come from business innovation than university research. The best and most rigorous social science experiments will be done for profit.

This shift has, of course already begun. Google, Facebook, Amazon, Microsoft, Netflix, Alibaba and scores of other global enterprises conduct literally thousands of experiments on their networks every day. No doubt, many or most of them are marginal or incremental in design. But with literally billions of measurable customer, client, and channel interactions a year, be sure they’re also testing hypotheses that could lead to profitably disruptive innovations.

“In general, even academics specialized in this area don’t really understand how much of this is already happening,” noted Jim Manzi—the founder and chairman of Applied Predictive Technologies, an experimentation-oriented analytics firm recently acquired by MasterCard for over half a billion dollars—in a recent email exchange. “I think [many businesses] are already doing it at a far larger scale than academics are.” (Full disclosure: I consider Jim a friend; his company’s been a client.)

Manzi also points out the obvious: where an academic experiment is frequently a “one-off” event, business experimentation is part of an ongoing process. What’s more, cost-effective replication is essential to business success; experiments that can’t quickly, easily and confidently replicated “in situ” are worthless. The incentives are such that research academics might be tempted to overstate outcomes in ways post-industrial researchers are not. They’re less likely to be caught fudging results. That’s why business experimentation typically demands higher levels of rigor and accountability than their social sciences counterparts.

This holds as true for the “people analytics” of the workplace as practiced by Google and Amazon as it is for personalization and social graph experimentation of a Facebook and Pinterest. While companies can learn from fast failures in experiment, both inside the enterprise and out, they literally can’t afford to rely on the flawed or fraudulent.

The economics of business experimentation become increasingly favorable as organizations worldwide become more and more networked. The innovation paradigm shifts from R&D (Research & Development) to E&S (Experiment & Scale). Networks dramatically reduce the costs and risks of scaling experiments into value-added products and services. As the author of a book on business experimentation, I see more and more firms recognizing and investing in the new business reality that experimental design and design thinking are opposite sides of the same coin.

In many respects, the ongoing eclipse of academic social science research by post-industrial innovators recalls and recapitulates—replicates?—the historic successes achieved by great corporate laboratories: Heinrich Caro at BASF; Willis Whitney at General Electric; Melvin Kelly at Bell Labs.

In each case and every era, academic distinction and vitality was essential but the dynamism and vibrancy of economic opportunities led to greater insight and impact. In computer science, artificial intelligence and machine learning—the commercial imperatives of the Googles, Facebooks, Amazons and IBMs—have made them the real pacesetters for the deep learning future. Is it any wonder why so many of the very best researchers in these domains go commercial or become entrepreneurs themselves?

This is increasingly true for psychology and the social sciences. See Richard Thaler’s wonderful intellectual memoir Misbehaving for an informal history of how ambitious financial innovators were often more open to the predictive promises of behavioral economics experiments than tenured academics.

There will always be room for the best of the best in academic research in virtually any discipline. But, as the Reproducibility Project results indicate, constant vigilance, scrutiny and skepticism are essential to quality assurance. In the human and social sciences, market forces now drive the elite research agenda. But don’t take my word for it: take a good look at how market leaders are experimenting with experimenting—and are more experimentally creative than “traditional” researchers.