Key Takeaways

A Slice of the Ordinary

When people debate artificial intelligence (AI), they usually start in Silicon Valley or Shenzhen, as in many forums it becomes about geopolitical competition between the U.S. and China. The conversation revolves around graphics processing units (GPUs), model architectures or the latest release from an AI lab. For instance, we just saw Oracle book more than $330 billion of future performance obligations in a single quarter.¹ Rarely does attention drift to a Midwestern convenience store chain. And yet, that’s exactly where the story of Casey’s—the fifth-largest pizza chain in the U.S.²—becomes instructive.

Casey’s is headquartered in Ankeny, Iowa. On the surface, it looks like a traditional operator: a mix of gas stations, convenience retail and a surprisingly large pizza business. For decades, the company’s bright red signs have dotted small towns across the Midwest. Most customers think of Casey’s as a place to fill up the tank, grab a snack and maybe order a pizza. What they don’t see is the invisible layer of algorithms and AI-driven systems that are increasingly central to how this business runs.

This is what makes Casey’s fascinating. It shows us that AI doesn’t have to be a story about futuristic tech firms. It’s about ordinary companies, in ordinary places, using data and machine learning to quietly rewire how everyday commerce works.

A Quarter That Demands Attention³

The company’s most recent earnings report underscored the strength of this model. Earnings per share (EPS) rose 19% year over year to $5.77. Net income climbed to $215 million, and EBITDA⁴ reached $414 million, both up around 20%. Inside the store, same-store sales grew 4.3%, led by prepared foods and beverages at 5.6%. Even in fuel—a notoriously competitive category—volumes increased 1.7%, despite the broader regional market shrinking by roughly 3%.

Financial discipline matched operational execution. Free cash flow of $262 million was well above the prior year’s $181 million. The balance sheet is in good condition, with $1.4 billion in liquidity and a debt-to-EBITDA ratio of just 1.8 times. This combination of growth and financial flexibility is not what most investors expect from a company best known for selling pizza in rural towns.

But it is precisely that mismatch—the perception of an unremarkable business against the reality of high performance—that makes Casey’s compelling. The explanation lies partly in AI.

Algorithms in the Background

One of the intriguing aspects of the company’s earnings call is that management never used the phrase “artificial intelligence.” There was no self-congratulatory talk about large language models or AI-driven transformation. Instead, the transcript was filled with references to efficiencies in labor hours, improved promotional targeting and smarter fuel pricing. For anyone who reads between the lines, these are AI’s fingerprints.

Take food preparation. Getting pizza to customers on time is a problem of logistics and forecasting. It requires coordinating kitchen activity, anticipating demand surges and adjusting in real time. Algorithms increasingly handle this, allowing Casey’s to produce fresher pizzas with fewer wasted ingredients. Customers may never realize that AI is working in the background, but their satisfaction is a byproduct of it.

The rewards program tells a similar story. With nearly 9.5 million members, Casey’s possesses a dataset large enough to rival some national restaurant chains. Knowing which customers order pizza on Friday nights, which prefer bakery items with their morning coffee and which respond to buy-one-get-one promotions creates an opportunity for personalization. Machine learning models convert raw transaction history into targeted offers that feel surprisingly relevant.

Labor is another subtle example. Management noted that same-store labor hours actually declined, even as traffic rose. That doesn’t happen without optimization. AI-enabled scheduling tools help balance staffing levels, reduce overtime and cut training requirements. For a chain with thousands of locations, saving just a few hours per store per week translates into millions of dollars annually.

Fuel pricing, too, has an AI dimension. Casey’s consistently maintains margins above forty cents per gallon, while peers often struggle to keep volume and profit moving in the same direction. Dynamic pricing models that digest competitive data, local demand and macro signals help Casey’s strike the right balance. The result is a reputation among customers for always being competitively priced—a perception that drives loyalty and traffic.

A Hybrid Model

What emerges is not a simple convenience-store story. Casey’s is best understood as a hybrid business that straddles multiple verticals. It is a fuel retailer gaining market share in a declining industry. It is a grocer that has lifted margins by shifting mix toward higher-margin items like energy drinks and nicotine alternatives. It is a quick-service restaurant that rivals national pizza chains in same-store sales growth. And it is, increasingly, a data company with millions of loyalty members whose behaviors feed back into pricing, promotions and inventory.

That blend creates resilience. Even when cigarette sales soften among lower-income cohorts, prepared foods pick up the slack. Even when cheese prices fluctuate, Casey’s hedging strategy and cost discipline keep margins steady. The diversification across categories, all tied together by data-driven decision-making, allows Casey’s to perform in environments where single-category competitors struggle.

Competing with the Big Names

Management has been explicit about benchmarking against quick-service restaurants. On prepared foods, same-store growth of over 5% puts Casey’s in the conversation with Domino’s and Pizza Hut. Sales of whole pies, the highest-margin subcategory, are accelerating. Limited-time offerings like barbecue brisket pizza have resonated with customers. New categories such as wings are being tested and refined.

This matters because it shows that Casey’s isn’t content to be a convenience store that happens to sell pizza. It is positioning itself as a quick service restaurant (QSR) competitor, with AI helping it anticipate demand, stock the right ingredients and price promotions intelligently. The integration of acquisitions like CEFCO will further broaden the footprint, with remodeled stores eventually carrying the full Casey’s food proposition.

For investors, this creates a differentiated play. Casey’s is not a pure QSR, not a pure fuel retailer and not a pure grocer. It is a composite, and that composite is hard to replicate.

The Broader Lesson about AI

Casey’s illustrates a larger point about AI’s diffusion through the economy. The most powerful examples are not always the headline-grabbing announcements from tech companies. They are found in the everyday—in such things as how a store is staffed and how a gallon of fuel is priced.

The AI dividend is not confined to chipmakers and cloud providers. It accrues just as powerfully to companies that use AI to sharpen execution in unglamorous industries. That is what makes Casey’s so valuable as a case study. It’s accessible: everyone understands ordering pizza. And it’s profound: few realize that the experience is shaped by algorithms managing countless small decisions.

Risks and Realities

Of course, there are challenges. Integration of new acquisitions takes time. Remodeling stores and installing kitchens involves permits, construction and capital. Consumer spending patterns show some pressure at the lower end, particularly in cigarettes. Fuel remains a fiercely competitive commodity.

But these are precisely the areas where AI is most useful. It helps forecast demand more accurately, reduces waste, optimizes pricing and ensures that scarce labor is deployed effectively. AI does not eliminate risk, but it tilts the odds. Over time, those incremental advantages accumulate into meaningful performance differences—the kind that showed up in Casey’s latest results.

The Pizza Parable

In the end, Casey’s is a parable about AI’s real-world impact. It demonstrates that AI is not confined to research labs or high-tech industries. It thrives in the ordinary. It is the reason a pizza shows up on time, the shelves are stocked with the right snacks and the fuel prices feel fair.

For investors, Casey’s offers a reminder: the next wave of AI value creation will not be obvious. It will appear in unexpected places, often hidden in plain sight. It will be in the chains that keep America fed and fueled, in companies that rarely show up in tech conversations but are nonetheless transformed by the quiet power of algorithms.

So, the next time someone mentions AI, think not only of silicon chips and data centers. Think of Ankeny, Iowa, and the gas station that sells pizza. Because sometimes the clearest window into the future comes with pepperoni.

As of September 10, 2025, Casey’s General Stores, Inc. was a 1.57% weight in the WisdomTree GeoAlpha Opportunities Fund (GEOA). Holdings subject to change.

By Christopher Gannatti, CFA and Samuel Rines

This article originally appeared on WisdomTree’s website and is reprinted on VettaFi | ETF Trends with permission from the author. For more information, please visit WisdomTree.com.

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¹ Source: K. Weiss & J. Reynolds, “Oracle Corporation: 1Q26 Results – Tectonic Shift in Business Model,” Morgan Stanley Research, 9/10/25.

² Source: Casey’s General Stores, Inc., “About Us — History of Casey’s,” caseys.com.

³ Source for financial and other Casey’s-specific data points in this piece unless otherwise noted: Casey’s General Stores, Inc. “Q1 2026 Earnings Call Transcript,” Bloomberg, 9/9/25.

⁴ EBITDA refers to earnings before interest, taxes, depreciation and amortization.

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