How Generative AI Is Transforming Customer Experience for Retail & QSR Brands

As of late 2025, the retail and Quick-Service Restaurant (QSR) industries are at a pivotal moment. They aren’t just dabbling in AI anymore; They’re rewiring how discovery, ordering, service, and loyalty work.

With customer expectations rising and competition fierce, brands are searching for new ways to connect with their audience. Generative AI (GenAI) lets brands personalize at scale, compress creative cycles, and turn fragmented data into real-time, one-to-one experiences.

This isn't just a technological upgrade; it's a strategic shift toward hyper-personalization and efficiency that is fundamentally reshaping the customer experience. But outcomes depend directly on the quality of the models, data, and guardrails you deploy. Move fast, but move right.

What is Generative AI?

Generative AI is a type of artificial intelligence that creates new content (like text, images, audio, code, or video) based on patterns it learned from large amounts of existing data.

Instead of just classifying or retrieving information, it predicts what to generate next (e.g., the next word, pixel, or note) to produce original outputs that match a user’s prompt and constraints.

Why Retail and QSR Brands are Leaning into AI

By late 2024, 71% of organizations reported actively using generative AI (GenAI) in at least one area of their business. Smart brands don't just adopt Generative AI for the sake of innovation, they deploy it because real user insights reveal where customers need better guidance, faster answers, or more relevant recommendations.

Backend efficiency is a benefit, but the primary goal is creating contextual interactions that feel natural and genuinely valuable. Get the customer experience right, and profitability follows. Here's a deeper look at the strategy behind this move:

Unprecedented Personalization

Generative AI analyzes customer data, from past purchases and browsing history to location and real-time behavior, to create a comprehensive understanding of each individual. This allows brands to move beyond generic recommendations and deliver truly tailored experiences.

Imagine a QSR app that suggests a custom meal based on your last order, or a clothing retailer that uses a chatbot to help you build a complete outfit for a specific event. This level of personalized engagement fosters a sense of being understood and valued. Customers feel recognized, not targeted.

Enhanced Customer Service

Generative AI-powered chatbots excel at managing high-volume, routine inquiries with human-like conversation. By handling simple, repetitive tasks (like order tracking or answering FAQs) they provide instant, 24/7 support. This frees your human agents to focus where they deliver the most impact: handling complex questions, solving nuanced problems, and building genuine customer relationships. With more time, your team can provide the strategic, high-empathy value that machines can't replicate, turning standard support calls into loyalty-building connections.

Challenging Our Work, Inspiring New Ideas

We believe technology should serve creativity, not dictate it. At Brandience, we use Generative AI as a tool to push our thinking and challenge our work. Instead of asking it to create final content, we use it to brainstorm possibilities, asking "What if?" in countless ways to uncover the most compelling campaign ideas.

These tools help us visualize concepts faster than ever before, allowing our teams to see and react to ideas in near real-time. This iterative process of generating, challenging, and refining ensures our work is more robust and innovative. The final execution is always driven by our team's strategic expertise, but it's sharpened by the limitless exploratory power of AI.

Responsible AI Use: How to Balance Innovation with Human Impact

As Generative AI’s capabilities continue to evolve and automation streamlines repetitive tasks, it will also reshapes roles that were once central to human interaction. For brands committed to human-centered innovation, the challenge isn’t just adopting AI, it’s doing so ethically and remembering to keep the human at the center of everything. That means being transparent about how AI is used, retraining teams to work alongside it, and keeping a clear focus on its impact on both employees and customers. Responsible AI is a tech decision and a CX strategy.

Building Agentic Assistants That Deliver Real Value

Now for the practical part. How do you actually deploy Generative AI in ways that move the needle? The answer is agentic assistants: AI-powered tools that don't just respond to prompts but actively help customers and employees accomplish specific goals. These aren't generic chatbots; they're purpose-built agents designed to guide decisions, solve problems, and take action within defined guardrails.

Here's a few ways to use them.

1) Decision Guidance That Cuts Through Choice Overload

Think of this as your brand-trained advisor for customers. When customers face too many options or don't know where to start, the assistant asks the right questions, narrows choices based on preferences, and explains trade-offs in plain, on-brand language.

In QSR, that means helping customers build the right meal, mix-and-match bundles, allergen-safe swaps, and "what pairs well with..." suggestions that feel like a helpful recommendation, not a hard sell.

In retail, it's helpful product comparisons that turn endless browsing into confident decisions. The goal isn't just answering questions, it's helping customers find what they actually want, faster.

2) Dynamic Promotional Offers, Grounded in Context

Real-time offer personalization across different parts of the day, weather, location, inventory levels, and loyalty tier. An agent could proactively suggest hyper localized promotional offers by analyzing these factors in real time. A rainy afternoon near a college campus? The assistant suggests a deal for hot drinks and study snacks. Game night in a suburban market? The agent may suggest family meal bundles and party platters move to the front.

These aren't random discounts, they're contextual promotions that align with what customers actually need in that moment, while staying within margin guardrails and respecting store-level stock availability. The assistant learns what drives conversion without burning through profitability.

3) Customer Experience That Earns the Next Visit

Customer service can make or break loyalty, and GenAI agents excel at resolving routine issues fast such as tracking orders, processing returns, and answering product questions. The agent can handle the routine issues, while escalating complex or sensitive cases to human agents with full context. No more forcing customers to repeat themselves.

It's also a great tool to understand common CX frustrations and gather customer feedback at scale. For example, Scott Kelly, Senior Director of Product - Web and eCommerce Experiences at Ford Motor Company explains:

‍"We have a tool that does session replay and session recording, and the AI can actually ingest the video and repeat back what the customer was trying to do when they had this issue. You don't have to go and watch the video. You get a brief description of the issue. So that's helped [Ford] connect to those customers a lot more."

This level of insight empowers store managers with a clear view of both overall CX trends and individual customer experiences. While the AI assistant handles transactions, your team can focus on rebuilding trust and turning a negative moment into a story worth sharing.

4) Content Support That Protects Brand Voice

With locked style guides, approved messaging frameworks, and creative guardrails in place, GenAI helps creative and marketing teams brainstorm concepts, visualize campaign directions, and pressure-test messaging before production. It can help your team explore more ideas faster, challenge assumptions, and maintain consistency across markets. This speeds along the exploration phase, allowing campaigns to move from concept to launch much more efficiently.

5) Store & Associate Intelligence

Frontline teams get AI copilots that assist with scheduling, prep lists, service recovery playbooks, policy Q&A, and inventory insights. New hires ramp faster because they have on-demand access to institutional knowledge. Veterans shed repetitive questions and focus on higher-value tasks like coaching, customer connection, and operational problem-solving.

Scott Kelly, Senior Director of Product - Web and eCommerce Experiences at Ford, has also seen this impact of GenAI firsthand:

"We've started building AI tools for in-dealership that help sales consultants answer questions. It's helped the consultants feel more confident. It's helped them be more knowledgeable to answer questions."

The assistant doesn't replace store expertise, it helps amplify it, giving every team member the tools to perform like a seasoned pro.

Design Principles: Build the Experience, Not Just a Bot

The temptation is to plug in an AI model, flip the switch, and hope for magic. The brands that actually succeed do the opposite: they design the customer experience first, then choose the technology to deliver it. GenAI is a tool, not a strategy and without intentional design principles, even the smartest model will feel clunky, untrustworthy, or off-brand.

As Akhilesh Anakapally, Director of Digital Products at Wendy's, puts it:

"Everyone wants to [implement] AI, but it's very important to define what is the problem you are trying to solve...if it is not making the customer experience better, it is something you should rethink."

Here's how to build agentic assistants that customers actually want to use:

1. Define the core challenge.

What are you actually trying to solve? Speed? Personalization? Efficiency? Access to information? Be specific about the problem before you build the solution. Whether it's reducing wait times, helping customers navigate complex choices, or scaling support across channels, clarity on the challenge guides every design decision that follows.

2. Design for human handoff, not replacement.

Ensure there's a clear and intentional path for when automation should transition to a human. Complex questions, frustrated customers, and nuanced scenarios require judgment and empathy that AI can't replicate. Build escalation triggers that preserve experience quality and pass full context, so customers never start over and repeat themselves.

3. Ground responses in trusted sources.

Every answer should trace back to verified, up-to-date information; your menu, pricing, policies, inventory, hours. Use systems like Retrieval-Augmented Generation (RAG) to pull from authoritative data rather than relying on the model's training. When the assistant doesn't know something, it should say so, not guess.

4. Establish brand voice with guardrails.

Set tone and style guidelines that reflect your brand while allowing flexibility for different contexts. Define what topics are off-limits and how the assistant should handle edge cases. Your AI should sound unmistakably like your brand, whether it's helping someone order lunch or resolving a complaint.

5. Build feedback loops that matter.

Track meaningful metrics, such as conversion rates, resolution time, customer satisfaction, cost to serve. Use that data to refine the experience over time. Chat length doesn't matter. What matters is whether customers accomplished their goal and felt good about the interaction. Let real outcomes drive iteration.

A hard truth: If the model is slow, the data is stale, or guardrails are thin, customers feel it immediately. Tool quality = experience quality.

Pitfalls Worth Avoiding

Even with strong design principles, there are common traps that can undermine your AI strategy.

• Hallucinations: Never let models free-wheel on facts. Ground and cite.

• Over-automation: Don’t rely on bots. Your team of people will always be your strength, especially when it comes to edge cases and scenarios for empathy.

• One-size-fits-all: Localize your experience by market, daypart, and even weather.

• Vanity metrics: Optimize for conversion, CSAT, time-to-resolution, and cost-to-serve, not chat length.

Generative AI in Action: Real-World Examples

These principles aren't theoretical. Across the QSR and retail sectors, forward-thinking companies are already deploying Generative AI to create these unique customer experiences.

Wendy's and Taco Bell: The Voice AI Drive-Thru

Drive-thru lanes, a critical touchpoint for QSRs, are being revolutionized by voice AI. Companies like Wendy's and Taco Bell have been testing and rolling out AI-powered drive-thru systems. These systems use advanced language models to accurately understand customer orders, even with complex customizations.

The goal is to reduce human error, speed up service times, and allow employees to focus on food preparation and customer interactions at the window. Early results show that these systems are improving order accuracy and service speed, which are crucial metrics in the fast-food industry.

See Taco Bell's voice AI system in action.

Sephora: Virtual Skincare Analysis

In the retail world, personalization is key, and Sephora is a leader in using Generative AI to meet customer needs. Their app features a "Smart Skin Scan" tool that allows customers to take a photo of their face and receive a real-time analysis of their skin. The AI then uses this information to generate a hyper-personalized skincare routine and product recommendations.

This provides a valuable, high-touch experience from the convenience of a customer's home, driving sales and building consumer confidence.

The Home Depot: AI-Powered Customer Assistance with "Magic Apron"

Generative AI's impact extends beyond in-store operations to digital customer service. The Home Depot launched "Magic Apron" in March 2025, a proprietary suite of generative AI tools embedded into millions of product pages on their website and mobile app. This 24/7 assistant helps customers answer how-to questions, compare products, and get guidance on home improvement projects by synthesizing information from Home Depot's vast product catalog, instructional content, and real-time inventory data.

By providing instant, expert-level guidance at the moment of need, The Home Depot is making complex purchasing decisions easier and building customer confidence in their ability to tackle projects themselves.

The Future of CX Is Human-Centered, AI-Enhanced

Generative AI is no longer a futuristic concept, it’s a present-day advantage for retail and QSR brands ready to rethink how they serve, support, and connect with customers. The brands that win won’t just adopt AI tools; they’ll design experiences that feel intuitive, personal, and genuinely helpful. By grounding innovation in strategy, ethics, and empathy, companies can unlock smarter service, stronger loyalty, and a customer experience that keeps people coming back not just for the product, but for their experience.

About the Author:

Ryan Grant is a Digital Media Associate at Brandience, where he focuses on driving performance and efficiency across programmatic advertising and paid social campaigns. With a deep understanding of audience targeting, data-driven optimization, and platform-specific strategies, Ryan plays a key role in helping clients reach the right consumers at the right time through scalable digital media solutions. Also a passionate AI enthusiast, he also co-leads the AI Hub at Brandience, a cross-disciplinary team of strategists, creatives, and analysts who ensure that artificial intelligence is thoughtfully integrated across every department. Connect with him on LinkedIn here: http://linkedin.com/in/ryan-n-grant/