How home décor leaders are using AI: a product development playbook
AI adoption in home décor isn't theory anymore. These five leaders are deploying it across design, content, forecasting, and customer experience - and they're getting faster to market with tighter margins.
If you build products, the pattern is clear: automate the slow parts, keep humans on taste and judgment, and wire your asset pipeline for speed. Here's what they're doing and how to adapt it to your roadmap.
Howard Elliott - Brian Berk
The team is using AI to produce silhouette images, line drawings, and environmental shots for e-commerce. That reduces photo bottlenecks and compresses time-to-market.
They're also spinning up AI-assisted catalog creation and beta testing AI for order entry, with more data processing on deck. For product dev, this means earlier visibility of assets and fewer delays between sample, photography, and sell-in.
Phillips Collection - Jason Phillips
AI is positioned as an accelerator for creativity. It supports product storytelling, visual content generation, and data organization across design, marketing, and operations.
Next moves include product development analytics, digital asset management, and customer personalization. They're exploring AI-driven design visualization so clients can see pieces in their spaces - a direct boost to spec confidence and iteration speed. If your asset library is scattered, start by tightening digital asset management.
The RW Collective - Rebekah Osborn
AI is embedded through core systems - accounting, ERP, and purchasing - to analyze sales trends, profitability, and demand. On the ground, ChatGPT supports product descriptions and marketing, Gemini generates lifestyle imagery, and Wiz Commerce creates lifestyle composites with fabric options.
The company is clear: AI is not replacing people. It's freeing time for higher-value work and scaling output. They've set targets for 2026 that every product org can borrow:
- Increase demand forecast accuracy by 20%-25%
- Reduce product content creation time by 40%-50%
- Improve gross margin by 1-2 percentage points
- Increase sales rep productivity by 25%
- Lower operational costs by 5%-8%
- Improve customer experience metrics
Uttermost - Mac Cooper
AI powers site search that understands intent, learns from behavior, and adapts results. It's also used to summarize quality data and claims, helping teams spot patterns fast.
For product development, AI supports early exploration and room-scene generation. Coming next: a customer service chatbot and item-level demand prediction to push in-stock rates from 90% toward 95% across distribution centers - the kind of operational reliability that protects launch dates.
Zuo Modern - Miguel Gonzalez
Zuo merges accurate 3D replicas into real-world scenes to generate lifestyle imagery that preserves size, shape, textures, and colors. Internal tools recommend similar items from a photo sample, while enhanced search understands natural language and product imagery.
Sales teams get purchasing-history insights to propose better assortments. The company plans to expose select internal AI tools to customers, reducing friction in discovery and speeding up confident decisions. For product teams, it's a cue to maintain high-fidelity 3D "twins" and consistent material libraries.
What product development teams can ship this quarter
- Stand up a lightweight synthetic imagery pipeline for concept, silhouette, and simple lifestyle shots. Measure cycle time from sample arrival to publish.
- Create a centralized DAM with versioned assets (3D, textures, copy, compliance). Tie it to your PIM/PLM so updates cascade automatically.
- Adopt semantic search across your catalog and briefs so designers, merch, and sales find "the right thing" without keyword guessing.
- Link ERP sell-through and returns to a basic forecasting model to guide line planning and MOQ decisions. Start with high-velocity SKUs.
- Build a 3D-first habit: every new product gets an accurate model and material profile. It pays off across imagery, AR, packaging, and instructions.
- Set two KPIs now: content lead time (down 40% target) and forecast error (down 20% target). Review weekly and iterate your prompts, data, and workflows.
- Deploy a simple chatbot on your site or sales portal using existing FAQs, spec sheets, and policy docs. Keep a human-in-the-loop for edge cases.
Helpful references
The signal across all five companies is the same: compress feedback loops, standardize assets, and let AI handle the repetitive work. Keep your team focused on taste, material choices, and final calls - the parts that win categories.
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