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Offer Optimization in Times of Turbulence: Navigating Tariffs and Demand Shifts with AI

Airlines are no strangers to uncertainty. COVID was only a few years ago, causing more uncertainty in the industry than has been seen in the last two decades. Lately, this uncertainty has resurfaced in the airline industry through macroeconomic instability, particularly the threat or implementation of international tariffs. This has led to evolving consumer patterns across industries, with the airline sector experiencing some of the greatest impacts. With airlines relying on both business and leisure passengers, they must understand these macroeconomic dynamics, translate them to the micro level—down to individual O&Ds and markets—and shape a strategy that ultimately supports macro-level corporate goals. That’s no easy task for revenue managers, who must not only react to what has already happened but also anticipate the next change and forecast future demand.

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In conversations with our airline partners, one theme keeps coming up: speed and adaptability matter now more than ever. The environment is too dynamic for static strategies. Instead, success depends on the ability to detect change early, understand its implications, and act with precision—often in real time.

A recent quote from one of our global partners captures this mindset well:

“We can change our revenue management algorithms to be much more open to U.S. inbound traffic and as well U.S. sixth-freedom traffic, which going into Q2 and Q3 look very favorable,"
— Mark Galardo, Chief Commercial Officer, Air Canada as quoted in The Airline Observer1

This importance of POS-based strategy is backed up by recent analysis from Visual Approach highlights a key asymmetry in international air travel recovery: while inbound visitors to the U.S. have declined, U.S. carriers have maintained strong international performance by selling the majority of those seats—about 80%—to U.S.-based travelers2. This discrepancy illustrates the strategic advantage of capturing demand from more resilient point-of-sale regions. For non-U.S. carriers facing weaker local demand, this may be a signal to reorient strategies: identify and prioritize POS regions showing stronger demand signals and adjust inventory and pricing accordingly. Revenue management systems that support POS-level optimization give airlines the flexibility to execute this kind of pivot in real time.

We’ve seen similar moves from other carriers—some shifting capacity away from tariff-impacted markets, others experimenting with promotional strategies based on point-of-sale elasticity. On the capacity side, these decisions are typically made by the network planning team, but the revenue managers must react to these changes as well. Reducing capacity in one market can shift booking curves, impact overall demand levels, and change passenger willingness-to-pay.

Consumer sentiment around airline pricing has become increasingly sensitive to headlines about tariffs and trade disputes. When tariffs are imposed on aircraft parts, fuel, or goods between key markets, the public often anticipates fare volatility, even before airlines adjust prices. This perception alone can lead to early booking hesitancy or shifting travel choices, especially for leisure travelers who are more price conscious. On the flip side, if tariffs cause economic uncertainty, business travelers may cut back, leading to softening demand that forces airlines to lower prices temporarily or launch targeted promotions to stimulate bookings.

Because of this, airlines must balance real cost pressures with customer expectations, something that underscores the importance of dynamic offer optimization. Airlines using AI-powered RM and Pricing systems can more effectively manage this tightrope, adjusting prices not only in response to actual cost changes but also in anticipation of demand sentiment shifts driven by tariff news.

These kinds of adjustments rely on systems that don’t just monitor demand—they learn from it. The PROS forecasting methodology is designed to capture demand patterns in real-time, allowing for rapid and data-driven adjustments to pricing and availability. It’s critical to capture the demand patterns quickly and then apply those through the optimization process.

After that optimization has run, the next step is applying the actual availability or price decision. The use of dynamic availability or price strategies is critical to this. It allows analysts to quickly adapt to market dynamics and apply the strategy. It also allows for adjusting from a strategy quickly, all done at the individual market level.

The broader takeaway is this: flexibility is no longer optional. The pace of disruption has outstripped what legacy processes can handle. Airlines that invest in intelligent, adaptive systems are in a much stronger position to navigate shocks—whether they come from tariffs, geopolitics, or shifting consumer sentiment. Like everyone, airlines are closely following the news on what’s next. And whether you’re predicting the stock market reaction to these macroeconomic decisions or forecasting passenger demand, there’s a lot of uncertainty to go around. For the airline side, the key is being able to have the right tools to react.

In this uncertain landscape, the future of revenue management isn’t just about better forecasting; it’s about building systems that empower faster, smarter decision-making at every level of the airline.


1The Airline Observer, The State of North American Transborder Demand

2Visual Approach, International Visitors Down – U.S. Airlines…Up?

About the Author

Justin Jander is Senior Director, Product Management at PROS, leading the vision, strategy, and execution behind the company's travel portfolio of products. Over his 15+ year career at PROS, he has played a pivotal role in shaping the evolution of revenue management and offer optimization solutions for airlines worldwide. Throughout his tenure, Justin has worked closely with airline partners across the globe, translating industry needs into innovative product features that drive business value. Justin holds a Bachelor of Science degree in Mathematics from Stephen F. Austin State University and a Master of Science degree in Statistical Science from Southern Methodist University.

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