The End of Guesswork: AI That Predicts Your Next Move Is Here

NEW YORK, NY – May 28, 2026 – The advertising industry has long chased a holy grail: knowing not just what consumers have done, but what they will do next. Today, GroundTruth, a ZeroToOne.AI company, announced a major leap in that pursuit with the launch of Dynamic Intent Prediction, the first large-scale deployment of a new kind of artificial intelligence designed to forecast future human behavior.

This new technology, powered by ZeroToOne.AI's patent-pending Large Behavioral Model (LBM), moves beyond the industry's standard practices. For years, advertisers have relied on 'predictive' models that are, in reality, sophisticated analyses of the past. These models build 'lookalike' audiences by finding people who resemble past customers, essentially refining historical data into a static list. GroundTruth argues this is guessing, not true prediction.

Dynamic Intent Prediction promises to change the game by identifying who is likely to act, what they are likely to do, and when they are most likely to do it, with predictions that refresh every 24 hours. It’s a shift from targeting consumers who might convert someday to engaging those who are in-market right now.

"The industry has been describing every refinement of historical data as 'predictive.' It isn't," said Rosie O'Meara, CEO of GroundTruth, in a statement. "With Dynamic Intent Prediction, we're giving advertisers audiences that tell you who will convert and when at a scale nothing else in market can match. This is the first of many innovations coming from the GroundTruth and ZeroToOne.AI combination."

Under the Hood of the Behavioral Crystal Ball

The engine driving this new capability is the Large Behavioral Model (LBM), developed by a team of AI researchers recruited from Carnegie Mellon University, the nation's top-ranked AI program. While its architecture is similar to the Large Language Models (LLMs) like ChatGPT that predict the next word in a sentence, the LBM is trained on a different kind of data for a different purpose: predicting the next human action.

The model ingests a staggering volume of information, processing over 15,000 distinct behavioral signals gathered from more than 7 million points of interest across the United States. It retrains itself on a weekly basis and scores over 2 billion mobile advertising IDs daily, generating forward-looking intent predictions for more than 9,000 categories and brands. This allows the system to forecast purchase and visit intent over windows ranging from the next 24 hours to the next 30 days.

This constant refresh is a critical differentiator. While traditional audience segments can become stale quickly, the LBM updates its predictions at the individual device level every 24 hours, ensuring that advertisers are always working with the most current understanding of consumer intent.

From Theory to Profit: Early Results Showcase Massive Gains

The promises of new ad technology are common, but the initial results reported from pilot deployments of the LBM are turning heads. According to the company, Dynamic Intent Prediction audiences have delivered a footprint nearly 50 times larger than standard audiences, reaching a vast portion of the targetable consumer universe with daily-refreshed predictive segments.

More importantly, the predictions are proving highly effective. The model's highest-confidence predictions are reportedly 8 to 9 times more likely to convert than a standard audience. For advertisers, this translates directly to less wasted spend and more budget allocated to impressions that drive real results.

Case studies from early enterprise adopters highlight the potential financial impact:

• A global quick-service restaurant chain deployed the LBM across various use cases, from competitive conquesting to app growth. The results included the identification of $100 million in wasted media spend, a 27% increase in store visits, and a 3x increase in app downloads, all while improving overall media efficiency by 30%.

• A leading global automotive brand used the model to boost media efficiency and conquest sales from competitors before expanding its use to regional dealer activation and financial services. The company reported a 30% increase in media efficiency, a 400% increase in website traffic, and a 10% lift in financial services revenue.

GroundTruth is making these predictive audiences immediately available to all its customers, from large media agencies and enterprise brands to small and medium-sized businesses using its self-serve Ads Manager platform. The audiences can be activated across a full range of media channels, including CTV, mobile, streaming audio, and desktop.

Navigating Privacy in an Age of Prediction

While the efficiency gains are compelling, the ability to predict human behavior at this scale inevitably raises important questions about data privacy and ethics. The LBM's reliance on billions of daily interactions and thousands of behavioral signals places it directly in the center of a global conversation about how personal data is collected and used.

GroundTruth states it operates a privacy-conscious infrastructure and adheres to the Digital Advertising Alliance's (DAA) self-regulatory principles. Its privacy policies note that it does not use or disclose sensitive personal information for purposes other than those permitted under regulations like the California Consumer Privacy Act (CCPA). ZeroToOne.AI similarly emphasizes that its models are trained on anonymized data.

However, the launch comes as regulatory scrutiny of AI and data collection intensifies worldwide, with frameworks like Europe's GDPR and AI Act setting new standards for transparency, consent, and automated decision-making. The challenge for innovators like ZeroToOne.AI will be to balance the power of predictive technology with the growing demand for consumer privacy and ethical data stewardship.

For now, the focus is on the transformative potential for businesses. The technology represents a move from historical analytics to forward-looking intelligence, a shift that could extend far beyond advertising.

"Predicting human behavior is one of the hardest problems in AI, and the most consequential," said Naseer Hashim, Co-Founder and CEO of ZeroToOne.AI. "ZeroToOne.AI's Large Behavioral Model was built to solve it, not just for advertising, but for any enterprise decision that depends on understanding what people will do next. Dynamic Intent Prediction is the first product bringing that capability to market at scale. It's the beginning of a much larger platform."