Artificial intelligence is reshaping retail by closing the gap between digital convenience and in-store service, a divide that has long favored national chains with large technology budgets.
For local and regional retailers, the growing threat is “response latency,” the inability to engage customers in real time across digital channels while most purchases still occur in showrooms. AI built for brick-and-mortar environments unifies customer data across texts, chats, email, voice and purchase histories, giving retailers a persistent memory and a single source of truth for inventory, pricing and policies.
In high-touch categories such as furniture, AI supports rather than replaces the showroom by capturing online intent, recommending products based on local inventory, escalating to human staff when frustration arises, and automating routine service tasks that improve margins and staffing efficiency.
AiPRL Assist is an AI technology company developed specifically for local and regional retailers to help them compete with national chains. Founded by longtime furniture marketing executive JD Camden, the company blends artificial intelligence with retail expertise to deliver an always-on, Amazon-like customer experience tailored to physical stores.
Its platform creates unified customer profiles across every interaction and translates digital inquiries into more productive in-store appointments and higher conversion rates. In conversation with DNN, Camden explains how AI is shifting from a marketing tool to essential retail infrastructure, how data must serve as a single source of operational truth and how better customer intelligence can strengthen collaboration with designers and manufacturers while extending, not replacing, the role of sales associates.
AI adoption
DNN: Where are local and regional retailers most at risk of falling behind national chains when it comes to AI adoption?
Camden: Regional retailers face the greatest risk in the “Customer Experience Gap” the inability to provide 24/7 digital responsiveness while 84% of commerce still happens in-store. They are vulnerable to “response latency,” where leads decay overnight while national chains use autonomous agents to qualify demand. Furthermore, many local retailers suffer from “silent websites” that are static and invisible to agentic search engines used by modern consumers.
DNN: Why should retailers think of data infrastructure as foundational to AI strategy, not simply a marketing tool?
Camden: Data infrastructure serves as the “Unified AI Brain” that provides persistent memory across every customer touchpoint. It must move beyond marketing to become a foundational operating layer that integrates ERP, PIM, and POS systems into a “Store-Level Knowledge Graph”. This ensures that AI agents have a single source of truth regarding local inventory, pricing, and policies, preventing the “hallucinations” common in generic models.
Collaborating with designers and manufacturers
DNN: How does better customer intelligence change the way retailers collaborate with designers and manufacturers on assortment, customization, and lead times?
Camden: It transforms collaboration from manual updates to real-time synchronization. AI acts as the central layer connecting manufacturer ERP and PIM systems directly to dealer workflows. This allows for authenticated B2B views where designers can access tiered pricing and real-time inventory securely. Manufacturers can also share fabrics and finishes as digital assets, which AI uses to generate photorealistic visualizations for customers instantly.
DNN: Furniture remains a high-touch, in-person category. What AI capabilities most directly support the showroom experience rather than competing with it?
Camden: AI supports the showroom by handling the “robotic” and administrative tasks. Capabilities include virtual room visualizers that remove “will it fit” anxiety before a visit and logistics calculators that compute complex shipping fees based on specific truck routes and zip codes. In more advanced stages, AI provides “physical context” by using in-store sensors and heatmaps to help associates “see the aisle” and assist customers based on what they are currently viewing.
DNN: How should retailers be using AI to translate online experiences into more productive, higher-conversion in-store appointments?
Camden: Retailers should use AI to capture “Top-of-Funnel” intent analyzing what customers are asking for before they visit. By using context-retention tools like “Magic Links,” retailers can ensure that when a customer moves from a digital chat to an in-store appointment, the associate already knows their preferences and history. This eliminates “cold starts” and can drive a 25% to 40% lift in conversion rates.
Data-driven decision making
DNN: National chains use AI to deliver Amazon-like personalization. What specific AI-driven personalization strategies are now realistically accessible to independent retailers?
Camden: Independent retailers can now access a “Agent-Driven Retail OS” platform that offers Multi-LLM Orchestration, which routes queries to the most efficient model for cost and speed. They can implement branded personas that match their local store’s “voice” and provide grounded product recommendations based on real-time local inventory rather than generic national trends.
DNN: Beyond customer-facing tools, where should retailers be applying AI behind the scenes to improve margins, inventory flow, and staffing efficiency?
Camden: Behind the scenes, retailers should apply “AgentOps” to internal operations. This includes “Whisper Mode,” where AI suggests responses to staff during live interactions to increase productivity. Additionally, AI can automate routine logistics inquiries and EDI (Electronic Data Interchange) flows directly into manufacturer pipelines, reducing the labor required for manual order management.
DNN: What signals should retailers and manufacturers be paying attention to when AI surfaces customer sentiment, hesitation or frustration?
Camden: They should monitor “Affect Detection” and “Intent-Sentiment Fusion”. For example, if a customer repeatedly asks about delivery lead times, the AI flags this as a high-frustration signal. Dashboards should track when an agent’s internal “confidence” drops, which should trigger an automatic “warm transfer” to a human supervisor to preserve the customer relationship.
DNN: In what ways can AI-driven insights inform more relevant showroom vignettes and floor sets?
Camden: By integrating Wi-Fi, sensors and heatmaps, AI can track “dwell time” and customer paths through the showroom. This data allows retailers to identify which vignettes effectively capture attention and which floor sets lead to higher engagement, allowing for data-driven merchandising decisions.
DNN: How can AI help manufacturers and retailers align more closely on demand forecasting, regional preferences and customization?
Camden: AI captures pre-purchase signals, such as which fabrics or finishes are most frequently “swapped” in virtual visualizers. This conversational intent acts as a leading indicator of demand, allowing manufacturers to adjust production based on real-time regional preferences rather than lagging sales history.
Managing a digital workforce
DNN: What cultural or organizational shifts are required for AI strategies to succeed in furniture retail?
Camden: The primary shift is moving from “buying software” to “managing a digital workforce.” This requires the CIO to act as a “Chief Agent Officer” who governs the “Agent Charter,” defining where AI can act and when it must escalate to a human. There must also be an organizational commitment to an “always-on” service culture.
Setting yourself apart
DNN: As AI becomes ubiquitous, what will distinguish retailers who use it as a true competitive advantage?
Camden: The winners will be distinguished by “Omnichannel Memory,” the ability to recognize and remember a customer across every visit and channel. They will also maintain proprietary “Knowledge Graphs” to ensure they own their data moats rather than letting that intelligence accrue to a general AI vendor.
DNN: And lastly, how should designers, retailers and manufacturers be thinking collectively about AI as shared infrastructure rather than isolated technology?
Camden: They should view AI as a central operating layer that synchronizes the entire industry. This includes shared creative assets, live-syncing ERP systems for inventory accuracy, and continuous learning loops where insights from manufacturer support calls are fed back into the retailer’s knowledge base to improve the overall customer experience.
