Artificial intelligence is increasingly being used in organizations around the world and in some cases to be used to set prices across many industries. What began as demand-based pricing in air travel has expanded into automated systems that continuously adjust prices using data and algorithms.
In air travel, early pricing models adjusted fares based on seat availability, booking timing, and projected demand. These approaches proved effective and were gradually adopted in other sectors, including hotels, car rentals, and rail services. In hospitality, centralized systems began adjusting room rates in real time by comparing expected occupancy, historical demand, and seasonal patterns, allowing prices to rise or fall automatically depending on booking conditions.
With the growth of e-commerce, pricing models extended beyond demand signals alone. Online retailers started experimenting with price variations tied to user behavior, such as browsing activity, search history, and purchasing patterns. As digital platforms collected more detailed data—through cookies, location signals, and account histories—pricing systems became capable of estimating how likely a specific user was to complete a purchase and adjusting prices accordingly.
Ride-hailing services further normalized algorithmic pricing by automatically increasing fares when demand exceeded supply. These systems analyzed factors such as driver availability, location, local events, weather conditions, and time of day.
More recently, advances in artificial intelligence have increased the scale and granularity of these practices. Modern pricing systems can process large volumes of data at once, including device type, geographic location, repeated searches, and in-app behavior. Investigations referenced in the video describe cases where identical products or services were shown at different prices to different users at the same time, with variations linked to factors such as zip code, shopping behavior, or device used.
Similar approaches have been reported in grocery delivery and digital retail, where price differences for the same items have been observed across users. In physical stores, the adoption of electronic shelf labels has made it possible to update prices for thousands of products almost instantly, enabling rapid price changes at scale.
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