The Customer Service Crisis in Brick-and-Mortar Retail

The Customer Service Crisis in Brick-and-Mortar Retail

Customers expect fast, personalized service—but many brick-and-mortar stores are failing to deliver. Despite 80% of retail transactions still happening in physical stores (Gladly.ai), understaffing, slow response times, and fragmented experiences have created a growing service gap.

Frontline staff are overwhelmed. With shrinking teams and rising demands, employees struggle to assist every shopper effectively. The result? Frustrated customers, lost sales, and damaged brand loyalty.

Key pain points include: - Long wait times for basic inquiries like product location or inventory checks
- Inconsistent answers due to lack of real-time data access
- Inability to personalize service at scale
- Poor coordination between online and in-store experiences
- Return or exchange processes that feel cumbersome and outdated

A 2023 NVIDIA survey cited by Shopify found that 94% of retailers reported reduced operating costs with AI, while 87% saw a positive impact on revenue—highlighting the urgency to modernize.

For example, one mid-sized apparel retailer observed that 60% of customer questions in-store were repetitive: “Where’s this in a size medium?” or “Is this on sale online?” Employees spent over 30% of their shifts answering these routine queries—time that could have been spent building relationships or upselling.

This inefficiency isn’t just inconvenient—it’s costly. According to SAP, retailers lose up to 30% of potential revenue due to stockouts, often because staff can’t quickly access inventory data across locations.

Compounding the issue is the omnichannel disconnect. A customer might browse online, visit a store, and expect seamless service—only to find associates unaware of their history or preferences. Yet, Gladly.ai reports that omnichannel customers spend 1.5x more per month, making this gap a missed opportunity.

The crisis isn’t just operational—it’s experiential. Shoppers today compare retail interactions to digital experiences: instant, accurate, and tailored. When physical stores fall short, they risk losing customers to competitors who blend human touch with smart technology.

AI is emerging as a critical bridge between service expectations and reality. By automating routine tasks and arming staff with real-time insights, retailers can close the experience gap—without overburdening employees.

Next, we explore how AI is stepping in to transform in-store customer service from reactive to responsive, efficient, and truly customer-centric.

AI-Powered Solutions: Smarter, Faster, Always On

AI-Powered Solutions: Smarter, Faster, Always On

Customers expect instant answers, seamless service, and personalized experiences—whether online or in-store. AI-powered solutions are meeting this demand by automating responses, delivering real-time support, and integrating across channels to create frictionless retail experiences.

Brick-and-mortar stores are no longer relying solely on staff to answer routine questions. Instead, smart kiosks, AI chatbots, and virtual assistants handle inquiries like product availability, store hours, and return policies—24/7. This shift frees employees to focus on high-value interactions, improving both efficiency and customer satisfaction.

Retailers using AI report measurable gains: - 94% saw reduced operating costs (Shopify, 2023) - 87% experienced positive revenue impact (Shopify, 2023) - 97% plan to increase AI investment in the next year (Shopify, 2023)

These numbers reflect a broader trend: AI is becoming the backbone of modern retail operations.

For example, one UK-based sporting goods retailer deployed AI-powered kiosks to answer common customer questions. The result? A 30% reduction in staff time spent on basic queries and a 15% increase in customer satisfaction scores within three months.

This level of automated, always-on support is now table stakes for competitive retailers.

AI doesn’t just respond—it anticipates. By tapping into real-time inventory systems, customer profiles, and purchase history, AI delivers accurate, context-aware answers instantly.

Imagine a shopper asking, “Do you have size 10 hiking boots in stock?” An AI assistant pulls live inventory data, checks nearby store availability, and even suggests alternatives if out of stock—all in seconds.

Key capabilities enabled by real-time AI: - Instant product locating via in-store maps - Dynamic pricing and promotions based on demand - Back-in-stock alerts triggered automatically - Personalized upsell recommendations at point of inquiry

SAP reports that AI-driven inventory management can reduce stockout-related revenue loss by up to 30% and cut inventory costs by up to 25%—proving that speed and accuracy directly impact the bottom line.

Shoppers move seamlessly between online browsing and in-store visits. AI ensures their experience does too.

With omnichannel AI integration, a customer who viewed a jacket online receives a personalized discount when walking past it in-store. Or, a chatbot continues a conversation started on a mobile app when the user scans a QR code at a kiosk.

Consider this: 80% of retail transactions still happen in physical stores (Gladly.ai), yet omnichannel customers spend 1.5x more monthly than single-channel shoppers (Gladly.ai). AI bridges this gap by unifying data across touchpoints.

Leading retailers are leveraging AI to: - Sync online browsing history with in-store recommendations - Enable buy-online-pickup-in-store (BOPIS) with automated notifications - Offer consistent service across voice, chat, and kiosk interfaces

The future isn’t just digital or physical—it’s integrated, and AI is the connective tissue.

Next, we’ll explore how proactive AI engagement transforms passive interactions into meaningful customer relationships.

Implementing AI in Physical Stores: A Step-by-Step Guide

Retailers who adopt AI in brick-and-mortar locations aren’t just upgrading technology—they’re redefining the customer experience. With 80% of retail transactions still happening in-store, integrating AI offers a powerful opportunity to boost efficiency, reduce costs, and deliver personalized service at scale.

AI is no longer a futuristic concept. Nearly 90% of retailers are already using or evaluating AI, and 97% plan to increase spending on it within the year. The most successful implementations follow a structured, phased approach—from pilot testing to full-scale deployment.

Before deploying AI, retailers must evaluate their infrastructure, data quality, and team readiness. A clear strategy ensures alignment with business goals like improving response times or reducing stockouts.

Key steps include: - Audit existing systems (POS, inventory, CRM) - Identify high-impact customer pain points - Set measurable KPIs (e.g., query resolution time, staff workload reduction) - Ensure data privacy compliance (GDPR, CCPA)

For example, Slazenger achieved a 49x ROI by aligning AI deployment with specific service gaps in their UK stores. Their focus was on automating product location queries, which freed staff for higher-value interactions.

Start with a narrow use case—like AI-powered kiosks for FAQs—before expanding across locations.

The market offers diverse AI tools, from no-code platforms to local open-source models. The best choice depends on technical capacity, security needs, and scalability.

Retailers using real-time inventory integrations report up to 30% reduction in stockout-related revenue loss (SAP). Choose solutions that connect seamlessly with your systems.

Pro tip: Use Smart Triggers to activate AI when customers linger in aisles or approach service desks—enabling proactive support.

Launch a small-scale pilot in one store or department to test performance and gather feedback. This minimizes risk and provides real-world insights.

Best practices: - Deploy AI kiosks or mobile chatbots for common queries (“Where is aisle 5?”) - Monitor accuracy, response time, and customer engagement - Train staff to supervise and escalate when needed - Collect data on query volume, resolution rate, and staff time saved

One retailer reduced customer wait times by 40% during peak hours after piloting an AI assistant linked to floor maps and inventory.

Use the pilot to refine prompts, integrations, and handoff protocols before scaling.

After validating success, expand AI across multiple locations. But scaling requires more than replication—it demands robust integration and security.

Critical actions: - Ensure OAuth 2.1 authentication for all API connections - Implement input validation and sandboxed code execution - Integrate with Customer Data Platforms (CDPs) for omnichannel personalization - Maintain fact validation systems to prevent hallucinations

Alarmingly, 492 MCP servers were found exposed without authentication—putting CRM and payment systems at risk (Reddit security research). Secure deployments are non-negotiable.

Retailers leveraging AI for omnichannel personalization see customers spend 1.5x more monthly (Gladly.ai).

AI isn’t “set and forget.” Continuous optimization based on user behavior and performance data drives long-term success.

Monitor: - Customer satisfaction (CSAT) with AI interactions - Accuracy of product and inventory responses - Frequency of human agent handoffs - Impact on operational costs

Use insights to: - Refine AI training data - Adjust engagement triggers - Expand use cases (e.g., returns processing, loyalty rewards)

The future belongs to agentic AI systems—autonomous assistants that learn, anticipate needs, and act across channels.

By following this roadmap, retailers turn AI from a pilot project into a strategic advantage—blending automation with human empathy to elevate service where it matters most: in the store.

Best Practices for Human-AI Collaboration

AI is transforming retail customer service, but success hinges on balancing automation with human empathy. While AI handles routine tasks, human staff deliver emotional connection—proving that the future of retail lies in collaboration, not replacement.

Nearly 90% of retailers are using or evaluating AI, and 97% plan to increase investment in the next year (Shopify, 2023). Yet, 80% of retail transactions still happen in-store (Gladly.ai), where personal interaction drives loyalty.

This shift demands a strategic approach to human-AI teamwork.

AI excels at speed and scale. Humans excel at empathy and judgment. Together, they create seamless, satisfying experiences.

Use AI to: - Answer FAQs like “Where’s aisle 7?” - Check real-time inventory - Process returns via kiosk - Suggest products based on purchase history - Notify staff when a customer needs help

For example, a major electronics retailer deployed in-store AI kiosks to handle basic queries, freeing associates to assist with technical setup and complex purchases. The result? 25% faster service times and a 15% increase in upsell conversions.

When AI flags a frustrated customer lingering near checkout, it can alert a manager—blending data-driven insight with human intervention.

AI handles the “what,” humans handle the “why.”

Customers distrust AI when it gives wrong answers or feels impersonal. To maintain trust: - Disclose when a customer is interacting with AI - Ensure responses are fact-validated using real-time data - Allow seamless handoff to human agents - Avoid overpromising (e.g., “I can fix that” when unable to)

A study found 94% of retailers reported reduced operating costs with AI, but accuracy remains a concern—especially with open-source models lacking live data access (Shopify, 2023).

Platforms with RAG + Knowledge Graph integration reduce hallucinations by grounding responses in verified business data.

One clothing chain reduced misdirected queries by 40% after syncing its AI to live inventory and store maps—ensuring “in-stock” answers were always up to date.

Employees may fear AI will replace them. Proactive change management turns skepticism into partnership.

Best practices include: - Co-designing AI tools with frontline staff - Training teams to interpret AI suggestions - Rewarding employees who use AI to improve service - Creating “AI buddy” roles to support adoption

At a grocery chain pilot, staff trained to use AI-powered shelf scanners reported 30% less time on inventory checks and higher job satisfaction—because they could focus on helping customers instead of counting stock.

Empowered employees + smart tools = better customer outcomes.

The key is treating AI as a teammate, not a supervisor.