Well-established quick service restaurant (QSR) brands have several systems each serving a specific purpose. However, each of those systems typically contains a slice of data related to the same thing.
QSR is an industry Adrenalin has worked in for over a decade. The brands we work with have mobile ordering, loyalty, kiosks, payment gateways, engagement platforms, marketing platforms, customer service, and so on. Each one exists to solve for a specific problem or use case, and each one does a great job of what it was designed to do.
Here is a simple – yet typical – stack our clients have in this industry:
Redcat for ordering
Adyen for payments
Zendesk for support
Braze for engagement
Four excellent platforms holding separate views of the same customer. Pulling data together requires technical capabilities, connectors, know-how, or the inefficient use of manual hours to extract data to build a single view of a customer.
In our experience – and through recent experiments – across other industries, we are confident that AI agents can close that gap. We are referring to AI agents that read across systems, understand what the customer wants, take actions on their behalf – or that of the organisation – and improve the customer experience. We are not talking about those simple chatbots that tell a customer what the opening hours are. That's a different thing entirely and fairly basic hygiene these days.
We are referring to agents that can do what a human would, autonomously, confidently, and with clear parameters and escalation rules – all while keeping humans in the loop.
Here are 7 use cases for how food brands and restaurants could leverage AI agents today to build competitive advantage:
1. Complaints that resolve themselves
Scenario: A customer gets home and realises the chips are missing. They open the app, find their last order, click "help", lodge a ticket and wait.
An AI agent can now:
Check with Redcat to confirm the order status, and verify if chips were part of it
Verify with Adyen that payment matched the total order value
Review the customer's complaint against refund policy, what its own limits are, and issue an eligible refund (if the refund value is greater than its limit, it can escalate for human review with justification as to why it should or should not proceed)
Log the case with Zendesk and update the status whilst notifying the restaurant of the mistake (if there is a recurring pattern, it can suggest improvements to the restaurant manager and operations teams to more permanently resolve)
Trigger Braze to send the follow-up to the customer
Manual approaches can take many minutes – but the use of agents saves the human having to switch between four online systems in a browser. Controls stay in place: refund limits, fraud checks, identity verification, and escalation rules are built into the workflow, and humans can review and audit each refund.
Customers love how fast it's fixed – and this speed of recovery decides whether they order again.
2. Failed payments that still convert
Scenario: A customer spends five minutes building an order, then the payment declines at checkout. Rather than the traditional error message with a 'try again' response, an agent reads the failure from Adyen, holds the basket persistent, and offers another payment method on the spot. If the customer has left, Braze sends a push notification instantly (all this done in seconds rather than minutes or hours).
The difference here is the context that QSR brands don't see when an error occurs on a user's screen. An agent can now know what failed for that user, why, how to fix it, and offer a better option than a generic abandoned-cart email – which, note, can still fail.
A 1% lift in completed orders on say 10,000 daily orders – that's 100 extra orders a day. It's not glamorous, but the commercials stack up!
3. Loyalty that knows the customer
Loyalty programs hold rich data: order history, visit frequency, preferred stores and redeemed offers. Despite this, most QSR brands send the same promotions to everyone.
Scenario: Picture a customer who orders the same vegetarian meal every Friday night. They open the app, find past orders, select re-order and follow the checkout process. A few taps to get to the goal.
Instead, an AI agent fires up on app load, checks the time of day, notices repeated behaviours, checks store availability, any eligible rewards, flags a new menu item that matches their preferences – and then offers a one-tap checkout with all those considerations. The basket is built, and the customer just reviews and pays.
Redcat supplies the menu and order data. Braze drives the personalisation. The AI agent joins them together.
Strict rules can apply to this workflow. Meaning, rather than the agent guessing what the menu, dietary and allergen information are, it comes from verified sources only.
This reduces friction, offers more relevant and personalised promotions, and drives higher order frequency. What a way to build customer affinity with a brand that just gets them!
4. Fixing problems before the complaint
Customer service is reactive by design. When something breaks, a customer raises a complaint, and the business responds.
Scenario: Let's say a click-and-collect order is running 10 minutes late. The agent can spot the delay in the operational data and is the first one to tell the customer. The agent can:
Share a revised pickup time
Offer an approved goodwill reward (depending on the delay)
Open a Zendesk case if a human needs to step in
Imagine you were dining at a restaurant, and your food took longer than expected. Would you rather the staff came to your table, apologised for the delay, and offered something complimentary to make up for it? Or hear nothing and be the one to raise a complaint?
Having agents that get ahead of a problem before the customer escalates it resets the expectation and prevents frustration. Of course, this only works when you have reliable data (in this example, order-status and prep-time data) to make this work.
5. Retention that reads the signals
Brands spend heavily to acquire customers, then rely on generic, blunt rules to keep them.
We have seen QSR brands offer automatic discounts if a customer did not order in the last 30 days. But if that customer left due to a bad experience, then a discount just misses the point.
An agent, on the other hand, can automatically combine purchase history, Zendesk interactions, loyalty activity, and engagement data to work out why their behaviour changed:
Service issue – respond with a targeted follow-up
Drifted away – send a personalised recommendation
Unlikely to respond – do not offer – escalate any common issue to ops teams to prevent further churn
Braze can run the approved journeys, and the agent picks the right one. The result is a customised way to bring back past customers while protecting margins.
6. An assistant for restaurant managers
Brands are often uncertain how AI agents will speak to customers without human control. We get it – and we do not recommend building such use cases without a proper workflow design that goes through a shadow mode first. That being said, as a starting point – and to build internal trust in the agent's capabilities – we suggest agents that support internal staff.
For example, a restaurant manager asks: "How did we go yesterday, and what should I watch for today?"
The agent pulls sales, ordering, payment, past complaints/tickets, and support data and surfaces the issues (not just issues at that store, but across the entire network for network-wide learnings):
A spike in cancelled orders at one store
Payment failures above baseline
Repeat complaints about one menu item
Across 100 or 500 restaurants, a priority list of what needs attention replaces hours of dashboard-checking.
7. When customers stop opening your app
This is the one we are watching very closely.
We've spent a decade building bespoke apps and loyalty programs. But customers, starting in the US, are beginning to use their own AI assistants.
"Order my usual lunch from Juicy Burgers, under $20, ready for pickup at 12:30."
No app. No browser. Just words (or voice) and their own AI agent does the work (e.g. using ChatGPT, Claude, etc.).
To make this work, QSR brands need their menu, pricing, availability, ordering rules and checkout available through secure, authorised integrations – agentic commerce. The customer simply approves the order before payment.
Think about this like Apple Pay or Google Pay on steroids. When you select that payment method, you don't need to type your name, address, etc. In this case, the agent takes it one step further – and all of it is done without any branded interface whatsoever.
This is the kicker: your QSR brand and platform need to be agentic ready for this to work.
Also – importantly – your customers may not name your brand in the prompt – leaving AI to do the heavy lifting on which brand to select for its human. That means you need to be discoverable as well as transactable via agentic commerce to capture this audience – otherwise we can assure you that your competition will!
It is worth noting that none of the above requires replacing your stack – it all stays the same:
Redcat still runs ordering
Adyen still runs payments
Zendesk still runs support
Braze still runs engagement
What changes is how the systems connect, which wasn't practical or affordable before. Agentic workflows change that, and unlock more value than ever.
You can build a demo agent in a week, but enterprise-grade integration, customer identity, security, data quality, business rules, API access and governance are all very important to building agents that run autonomously and accurately within their defined limits.
Customers will judge QSRs on three things: how easy ordering is, how fast problems are identified and fixed, and whether the brand remembers them well. We believe QSRs that lean into AI agents to develop these new experiences will build themselves a competitive advantage in market.
Learn from us
Join thousands of other Product Design experts who depend on Adrenalin for insights
