Rise of Marketing AI and What It Means for Businesses

Marketing once relied on surveys, focus groups, and last quarter’s sales numbers to understand the market - information which could be months old by the time it was acted on. Now, software can analyze what someone was looking at moments ago, recommend a product before they’ve had a chance to search for it, and hold a conversation with hundreds of thousands of people at once. Marketing has become one of the most visible arenas in which artificial intelligence has entered the fray, if only because the sheer volume of clicks and views in the digital space has long since exceeded anything a human could handle.

Typical marketing software is built on rules defined by its human operators. Marketing AI learns from data: what headlines resonate, what discounts induce clicking or buying, and what products tend to be chosen together, adapting its assumptions each time a customer takes an action. The result is seen in recommendation engines, in which users’ behavior is compared against similar metrics from every other person who has viewed or purchased the same item, enabling each customer to be a tiny focus group for themselves. Deep learning takes this process further, using layered algorithms that recognize patterns in images, text, or sound at multiple levels, allowing the technology to identify visual scenes, the emotional tone of a review, or the intent behind a conversation. The reason such systems have become common is less because of any one innovation and more because of three forces combined: a flood of digital data feeding into marketing software, chips designed for video gaming that could perform mathematical operations necessary for these algorithms, and iterative improvements in training processes, allowing moderately large companies to apply fairly sophisticated technologies.

A marketing AI’s ability to make assumptions based on past behavior is impressive, but its most valuable applications may lie in its ability to move from reacting to results to anticipating the future. Predictive analytics enable a company to identify which customers are likely to engage in a particular behavior, such as unsubscribing from a mailing list, and respond accordingly. More practically, predictive models can be used to personalize offers to individuals, directing a cashback promotion to one customer and a premium upgrade offer to another, based on which audience each prospective buyer falls into and what actions might convince them to remain a customer.

The same principles have been applied to pricing, where the technology has caused its first major public confrontation with regulators. As of July 2024, the Federal Trade Commission (FTC) is investigating several firms that provide surveillance-pricing software for retailers, including Mastercard, Accenture, PROS, Bloomreach, and McKinsey, demanding a detailed explanation of how such tools function and whether they engage in deceptive or abusive practices. According to the initial staff report released by the FTC in January 2025, such surveillance enables retailers to identify which customers are most sensitive to price changes, using signals as varied as the shopper’s location, browsing history, and even the movement of a cursor or items left in the shopping cart. One example given in the report was a retail chain that recognized a new parent from purchased baby goods and leveraged this information to alter prices for other goods. The issue of surveillance pricing has since entered the public consciousness as a genuine policy concern, with the FTC voting along party lines to publish the staff report in early 2025. The agency’s new chair has since tempered the report’s findings and held back additional investigations. At the state level, California AG Rob Bonta launched a similar probe into surveillance pricing practices of grocery, hospitality, and retail chains in early 2026, while a New York privacy law went into effect in November 2025, banning similar practices or mandating disclosure, and similar legislation was proposed in other states. Finally, Congress is also considering the issue, with the Oversight Committee holding hearings with major travel and social media firms in March 2026. The situation is not set in stone, and any organization making extensive use of dynamic pricing should be aware that the legal environment is anything but settled.

Chatbots have broken through the limitations of traditional call center software, and the most advanced examples of conversational AI distinguish themselves from older systems in a crucial way: understanding. Modern systems perform language parsing at a level comparable to human capability, extracting meaning from phrasing and context, rather than relying on keyword matching, and learning from the conversations in order to improve. From a marketing perspective, the technology has value both in terms of engaging with the customers on a more individual level and analyzing those conversations for common themes, effectively replacing traditional word-of-mouth research. Meanwhile, virtual assistants provide yet another avenue for customer outreach, requiring brands to consider voice searches alongside traditional text queries.

In practice, that means that the assumptions embedded in the code may reflect prejudices that the developers were not even aware of. The most prominent example of this issue involved Facebook’s ad platform, which was accused by the Department of Housing and Urban Development of allowing advertisers to engage in discriminatory practices by automatically excluding certain protected groups from seeing housing-related posts. Facebook eventually settled the lawsuit by removing the option to target or exclude users on the basis of gender, race, or other protected characteristics. However, the issue did not end there: the fundamental advertising algorithm continued to direct housing ads to users on the basis of the same characteristics, meaning that people searching for apartments continued to see discriminatory targeting, even if it was unintentional. It took another lawsuit from the Justice Department, and a resulting $1.3 million settlement for the largest housing discrimination case ever brought under the Fair Housing Act, before Meta Platforms Inc. - the parent company of Facebook - was able to update its advertising policies to include explicit consideration for these factors and remove their influence on ad targeting. The lesson learned from the Facebook scandal was simple: even if algorithmic biases originate with human actors, the responsibility for correcting them ultimately falls on the companies employing these technologies.

It is important to note that marketing AI systems are not a substitute for human judgment, at least not yet: the nuance of brand-building and brand management remains beyond the reach of software, and most marketing decisions still require human oversight. What the systems do provide is an explosion of options at the lowest levels of marketing execution, from predicting which customers are most likely to make a repeat purchase to generating personalized suggestions for each user. The best companies utilizing these systems are ones that recognize the value of such minute-level personalization and are aware of the potential pitfalls of using such data, whether it concerns discriminatory pricing or the ability of algorithms to make human labor redundant.