By Michelle Miller
Senior Director of AI and Machine Learning
When Starbucks and Little Caesars announced integrations with ChatGPT that let customers discover, customize, and pay for orders inside an AI assistant, the restaurant industry took notice. For an industry that has spent a decade building branded apps and loyalty ecosystems, that is a meaningful shift in where the customer relationship lives.
At Revenue Management Solutions (RMS), we’ve been watching this moment build. The question we’re focused on isn’t whether more chains will follow, it’s what separates the brands that gain an edge from those that won’t.
The Trade-Off in AI Platform Partnerships
Integrating into a platform like ChatGPT offers genuine consumer value. Guests are fatigued by app overload, and an AI assistant that can recommend a meal and complete an order in a single conversation removes friction.
But there’s a tension that brands need to think through carefully.
This also means ceding data ownership and potentially surrendering control over what gets recommended. For restaurant brands that have invested heavily in loyalty ecosystems and first-party transaction data, that’s not a trivial trade-off. The brands that navigate this best will treat AI partnerships as an extension of a broader data strategy, not a shortcut around one.
Will AI Speed Up the Customer Journey?
The potential is real, but it isn’t guaranteed.
AI-driven ordering could meaningfully accelerate the path from craving to cart, particularly for repeat customers whose preferences are well established. But there’s an operational risk on the other side. If AI recommendations drive a surge in highly customized orders, like a viral TikTok drink hitting thousands of locations simultaneously, you can easily slow throughput rather than improve it.
Speed of discovery doesn’t automatically translate to speed of service. Brands will need to think carefully about how AI-generated demand intersects with kitchen capacity and operational execution.
The bigger upside lies in what happens during the ordering moment itself. At RMS, we have long understood that personalization only creates value when it is grounded in accurate, brand-specific data. Brands already personalize digital ordering to a degree, with rules-based add-ons, segment-driven offers and last-order recall. But most of that personalization is shallow. It reacts to what a customer did, not to the full context of what they want in this moment.
AI ordering can change the depth of that signal. AI that draws on a customer’s order history, preferences and profile to make dynamic suggestions in real time has genuine potential to lift check averages and improve margin mix. Making the right suggestion to the right customer at the right time, with full context behind it, is where the return on AI investment lives for brands focused on unit economics.
How RMS Supports Your Pricing Strategy
Understanding how AI recommendations influence guest behavior starts with having insight into your own data. Price Studio helps brands model pricing scenarios and view elasticity before decisions go live.
The Adoption Question: Why ChatGPT Is Different
The industry has navigated AI adoption challenges before. AI-powered drive-thru ordering showed real promise but moved slowly toward widespread adoption because the technology needed to earn trust at scale from operators and guests alike.
The ChatGPT integration model is different in one important way: customers are already using AI assistant platforms in their daily routines. They’re choosing the experience, which removes the friction of forced adoption.
What we’ll likely see is a bifurcation. Brands with strong data infrastructure and clear menu positioning will gain a measurable edge. Brands with underdeveloped digital ecosystems will struggle to translate AI partnerships into measurable outcomes. The technology is only as powerful as the underlying data and strategy supporting it.
Could AI Replace Delivery Platforms?
This is where the strategic implications get most interesting.
Restaurant discovery is where the current delivery platform model is most vulnerable. Consumers do not have deep loyalty to any single delivery app. Their attention is distributed across several platforms, which means an AI assistant with a rich history of a user’s preferences could outperform aggregator-style apps at the discovery and recommendation stage.
If a customer habitually uses ChatGPT to find and order from restaurants, the AI builds a preference model richer than any delivery platform currently maintains. The implications for brand strategy are significant. The next frontier of competitive strategy is optimizing your brand for LLM discovery. Focus on how your menu, positioning and data appear to AI models. At RMS, we already help our clients understand how pricing and menu positioning influence consumer behavior. How those signals translate into AI-driven recommendations is the natural next chapter of that work.
What Restaurant Operators Should Do Now
The Starbucks and Little Caesars announcements aren’t just news items. They’re a signal that the competitive landscape for restaurant discovery and ordering is shifting in a fundamental way. The brands that will come out ahead are those building the data infrastructure today that makes AI partnerships work tomorrow.
Three priorities for operators right now:
1. Know your pricing position before AI starts making recommendations. LLMs and AI ordering platforms don’t surface brands in a vacuum. They factor in price, value perception and category positioning. Brands that don’t have a clear, defensible pricing strategy will find AI recommendations working against them, surfacing competitors as the better-value option.
2. Treat your menu as a data asset, not a marketing artifact. How items are named, structured, categorized and priced influences how AI systems describe and recommend them. Menu engineering used to be about visual hierarchy on a printed menu. It’s now also about how language models parse and summarize your offering.
3. Build the first-party data foundation that AI partnerships depend on. AI-driven personalization is only as good as the data behind it. Brands with fragmented POS, loyalty and digital ordering data will struggle to translate AI partnerships into measurable outcomes. The brands that win will have already done the hard work of connecting these systems.
Want to understand how your pricing and menu strategy will hold up in an AI-driven ordering environment? Schedule a demo with the experts at RMS.
