Christopher Wilson: The Polling Revolution: How AI Is Reshaping Public

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A few months before the recent election, I was invited to join a select group—said to include Elon Musk—to preview what was touted as a groundbreaking innovation in public opinion research: Brad Parscale’s EyesOver platform. To be candid, having seen countless technologies crash under the weight of their promises during my three decades in polling, I was skeptical of its claims. But this was different—what I witnessed shattered my expectations.

Before detailing the platform’s remarkable accuracy, it’s important to understand why it matters. “Dewey Defeats Truman” haunts every pollster’s nightmares. In 2024, we lived it again—only worse because, this time, nearly every major polling firm had issues at both the national and state levels. Ann Selzer, once considered the nation’s “gold-standard” in polling, missed Iowa by 16 points—a state she was thought to understand better than anyone.

Even The New York Times, rated as America’s top polling shop, stumbled, missing outcomes in Nevada and Arizona by about eight points each. Marist, another highly-rated firm, projected a four-point popular vote win for Kamala Harris, only for Donald Trump to win by nearly two points. Meanwhile, Quinnipiac was off by approximately seven points in Wisconsin and Pennsylvania. More modern online polling had the same issues— in the 17 polls Morning Consult released since Harris joined the race, not one showed Trump with a popular vote lead. Private polling I was privy to faced similar challenges.

This is where the platform I tested breaks the mold. Unlike conventional polling, where respondents are asked to take surveys, it leverages artificial intelligence to gauge public sentiment from social media interactions, news consumption, and other online behaviors in real-time. It’s like having millions of virtual canvassers working around the clock.

The results? Parscale’s platform not only accurately predicted the winner in every swing state but also correctly forecasted Trump winning the national popular vote and identified the winner in 49 out of 50 states, barely missing New Hampshire. Take Pennsylvania: while traditional polls claimed Harris was surging after the now-infamous Puerto Rico joke at a Trump rally, EyesOver captured real-time sentiment shifts across the Rust Belt—toward Trump.

In fairness, as Nate Silver and others have noted, legacy polling made some progress this year, with the average poll in the final weeks missing by just 2.94 points—an improvement over past cycles. But here’s the catch: much of those improvements came from right-aligned pollsters routinely dismissed as “biased” and “flooding the zone.” In fact, as my analysis of RealClearPolitics’ final 20 polls shows, without data firms Atlas Intel and Rasmussen, Harris’s national lead in the aggregator would have been inflated by nearly a full percentage point.

I take no pleasure in saying this—after all, this is an industry I’ve helped build. But the issues pollsters experienced this year weren’t minor missteps, they were unacceptable systemic failures in an era where elections increasingly hinge on fractions of a percent. This should not be surprising — expecting 20th-century polling methods to capture voter sentiment in the digital age is like trying to chart a hurricane with a weathervane.

It should, however, serve as a wake-up call. Our responsibility as pollsters is to measure public opinion using the best available tools. Methodologies are means to an end. Traditional polling remains valuable for message testing, but it’s clear that our industry requires new ways to deliver real-time insights, broader demographic coverage, and nuanced sentiment tracking.

The integration of AI in polling is a necessary evolution at a time when the speed of communication dramatically outpaces traditional survey methods. This technology is already reshaping industries—from tracking consumer preferences to advancing flight safety and healthcare. It was only a matter of time before political campaigns began to harness its potential.

The stakes couldn’t be higher for the next phase of public opinion research. Leaders, organizations, and businesses depend on our industry to provide clarity and precision to guide critical decisions. It’s not enough to deliver accurate data—it must also be timely, actionable, and reflective of how people engage today. Hiding behind arcane methodological disclaimers and margins of error is no longer going to cut it. If we refuse to evolve, we risk irrelevance.

The future of polling is here. The question isn’t if AI will overhaul polling, but how quickly we can adapt to harness its full potential. Either we adapt and lead, or we will become as obsolete as the rotary phones we used to call.