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  "path": "/article/4150697/little-caesars-thinks-big-with-ai.html",
  "publishedAt": "2026-04-06T10:00:00.000Z",
  "site": "https://www.cio.com",
  "tags": [
    "Artificial Intelligence, C-Suite, Careers, CIO, Digital Transformation, Emerging Technology, Generative AI, Innovation, IT Leadership, IT Management, IT Strategy",
    "We roll out carefully",
    "train the AI",
    "speed at scale",
    "innovation"
  ],
  "textContent": "When most people think of innovation, pizza isn’t the first thing that comes to mind. But Little Caesars has long been a quiet pioneer in the restaurant industry. The Detroit-based chain was among the first to adopt conveyor ovens, and decades later, it continues to push boundaries, this time with AI. With thousands of franchisees across the US, innovation is more than a talking point, it’s a business imperative.\n\n“Everything we do is grounded in research and testing,” says CIO Anita Klopfenstein. “We roll out carefully, making sure solutions disrupt in the right way, by creating sales and reducing waste without upsetting the flow of our restaurants.”\n\nThat philosophy underpins the company’s latest experiment, an AI-powered pizza forecaster designed to tell store managers exactly how many pizzas to make at any given time.\n\n## Putting hyperscalers to the test\n\nThe forecaster began with a bake-off challenge between two hyperscalers. Klopfenstein wanted proof, rather than listen to vendors pitch. “I didn’t want them talking at me about capabilities,” she says. “I wanted them to show me.”\n\nEach hyperscaler was asked to build a working prototype of the pizza forecaster, with the winner earning the chain’s broader cloud business. For Klopfenstein, the contest was as much about accountability as technology. “By making them build it first, we knew exactly what we were getting,” she says. “That set the tone for a true partnership.”\n\n## Forecasting the future of pizza\n\nThe winning system can predict demand across Little Caesars multichannel marketplace consisting of walk-in guests, app orders, and third-party platforms. Early results include reduced food waste, more efficient labor scheduling, and reinforcement of the brand’s signature Hot-N-Ready promise, where you can get a hot pizza immediately when walking into a restaurant.\n\nBut what sets the project apart is who gets to train the AI. Rather than relying solely on data scientists, Klopfenstein wants store managers to play a direct role. “This shouldn’t be locked away in a lab,” she says. “Managers can look at the forecast, compare it to what was sold, and track waste. If a snowstorm cuts traffic or two buses suddenly show up, they see it firsthand, and the model learns from that.”\n\nThat frontline feedback loop makes the system more resilient to anomalies and gives employees a sense of ownership. “I tell our teams, just trust the pizza forecaster,” she says. “With their inputs on the ground, it gets sharper and sharper.”\n\nMeanwhile, Little Caesars AI specialists can focus on refining the engine by strengthening models, eliminating errors, and preparing the platform for enterprise scale.\n\n## Balancing Hot-N-Ready with digital growth\n\nKlopfenstein saw the need to balance digital ordering with Hot-N-Ready orders. “We want to grow online ordering, absolutely,” she says. “But we also need to take care of our carry-out guests who’ve been with us from the beginning.”\n\nThe pizza forecaster has become central to that recalibration. By predicting demand with greater accuracy, it ensures Hot-N-Ready pizzas are available when guests arrive, while also giving restaurants the flexibility to fulfill digital orders quickly.\n\n## The necessity of strong fundamentals\n\nAs Klopfenstein is quick to point out, speed at scale doesn’t come from flashy tools alone. It depends on getting the fundamentals right. “Innovation only looks effortless on the surface,” she says. “Behind every breakthrough is a lot of unglamorous work that makes everything else possible.”\n\nFor Little Caesars, that groundwork has meant investment in data governance, including establishing a common data dictionary, clear lineage, and shared definitions so teams know not just where the data lives, but what it actually means. That shared understanding has removed friction from decision making, and created the confidence needed to move faster. At the same time, Little Caesars has built a strong data organization with engineers and data scientists who support advanced initiatives like the pizza forecaster, and enable self-service analytics. By lowering the technical barrier, business users can generate insights on their own and apply AI directly to improve day-to-day operations.\n\nThe company has also leaned into what Klopfenstein calls no-regret uses of AI, particularly in software development. “We’re seeing meaningful gains in throughput without adding headcount,” she says. “That’s what scale really looks like, using technology to amplify your people, not overwhelm them.”\n\nTaken together, those investments form the quiet backbone of the Little Caesars technology engine, ensuring that new capabilities aren’t just impressive, but sustainable.Top of Form\n\n## Innovation as a marathon\n\nFor Klopfenstein, innovation is less about sprinting to the latest trend and more about endurance. A veteran of more than 20 marathons, she sees parallels between distance running and technology transformation. “For retailers, it’s about listening to the data, rolling solutions out slowly, learning along the way, and making adjustments,” she says.\n\nThat philosophy has made Little Caesars a quiet pioneer for decades. The latest wave of digital change, she stresses, isn’t about chasing flashy tools but outcomes: reducing waste, improving speed, and reinforcing the model that’s made the chain successful at scale. “We want technology to make the lives of our franchisees, restaurant colleagues, and guests easier,” she says. “That’s our secret sauce. Innovation is baked into who we are.”",
  "title": "Little Caesars thinks big with AI"
}