Companies Find New Revenue Streams

Generative AI market models are emerging as a means of handling complex tasks in real time. These deep learning models are trained on high-resolution numerical data and designed to analyze, simulate, and predict complex financial patterns. Rather than relying on historical trends or static rules, the market model acts as an AI “brain,” consolidating a variety of data to simulate different market environments and make dynamic commercial decisions, such as pricing, inventory, or revenue management.

“It helps us make better, faster, more granular commercial decisions,” says Dominic Kennedy, senior vice president of revenue management, sales, and e-commerce at Virgin Atlantic. The company is using this technology to drive generative pricing engines in some markets. The system considers, on a real-time basis, a plethora of different inputs, whether it be demand, capacity, or booking. It has a really sophisticated way of evaluating positioning relative to competitors, market conditions, and a whole raft of other things that have significance in how demand is manifested.

For an airline, the sheer volume of logistical variables makes manual or traditional static analysis difficult. An aircraft transports tens of thousands of passengers on hundreds of flights each day, often requiring multiple connections. The company must consider potentially hundreds of variables to price each journey. These inputs include demand, season, time of day, current events, global markets, and competitor airline activity.

Most systems still rely heavily on historical patterns, but newer approaches attempt to break away from those past constraints. A generative model trained on extensive data can theoretically construct a picture of future scenarios based on current inputs. This allows the system to simulate different market environments rather than simply applying a rule to a past trend. The goal is to create a pricing engine that evolves alongside the market.

Implementing such a system creates a stark difference between static pricing and fluid revenue management. When a schedule changes or a competitor lowers a fare, a reactive system might update pricing hours or days later. A generative model, however, processes these shifts instantly. The AI evaluates the new data against its training to determine the optimal price point immediately. This speed allows the airline to capture revenue that would otherwise be lost to market volatility.

From a passenger perspective, the impact of these algorithms is often invisible. The price displayed on a booking screen is rarely static. It shifts constantly as the system recalculates value based on the data it ingests. This creates a more complex purchasing environment. A traveler might see a fare fluctuate several times in a short window, reflecting the underlying mathematical adjustments being made by the system to balance inventory and demand.

Virgin Atlantic uses this technology to drive their generative pricing engines in some markets. The senior vice president noted that the tool provides a sophisticated way of evaluating positioning. By aggregating data points that a human might miss, the model offers a wide perspective of the commercial environment. This helps in making decisions that are both data-driven and responsive.