GenAI Embed at NRF 2026: Retail's Big Show

Retail has always been about storytelling, but most digital experiences still look like spreadsheets: long grids of SKUs, filters, and endless scrolls. Shoppers, however, think in outfits, rooms, routines, and ideas. They want to see how products come together, not just how they look alone.

At NRF 2026: Retail’s Big Show, GenAI Embed is attending to shine a spotlight on PaletteAI – our collection-led merchandising engine built specifically for this gap. PaletteAI helps retailers turn large catalogs into clear, narrative collections that are easy to understand, easy to shop, and easy to reuse across channels.

Instead of asking customers to assemble everything themselves, PaletteAI gives them complete looks, room-ready layouts, and curated routines that feel intentional and relevant. At NRF, we are showcasing five high-value use cases that show how PaletteAI can support merchandising, clienteling, content, and post-purchase experience in one unified approach.

If you lead ecommerce, merchandising, digital, or CX, PaletteAI is designed to help your teams move faster and tell better product stories, while giving shoppers a more intuitive way to explore what you sell.

What Is PaletteAI?

PaletteAI helps retailers move from SKU-first to story-first merchandising.

Instead of presenting products as isolated items, PaletteAI enables you to showcase:

• Complete outfits instead of single garments

• Room-ready layouts instead of standalone furniture pieces

• Full beauty routines instead of one-off products

Collections are built around real-life contexts such as work, weekend, home, travel, gifting, and daily essentials. This makes it easier for shoppers to see how products come together, make confident choices, and discover more of your assortment.

At NRF 2026, we are focusing on five practical use cases where PaletteAI delivers clear and measurable value.

1. Narrative Buying & Curated Collections

Shoppers respond to stories, not lists. Many retail journeys still start with flat category pages where customers are left to imagine how items might work together.

PaletteAI turns this into narrative-led collections.

Retailers can quickly build sets such as:

• “Weekend City Break Outfit”

• “Cozy Living Room Refresh”

• “Minimal Workspace Setup”

• “Everyday Makeup Essentials”

Each collection is anchored in a clear idea or lifestyle moment, so shoppers immediately understand the context. Rather than browsing disconnected SKUs, they see ready-to-shop combinations that feel complete and purposeful.

This approach supports:

• Deeper browsing as shoppers move through collections instead of single products

• Higher basket sizes as complementary products are surfaced together

• A more premium, editorial feel across digital touchpoints

2. Intelligent Clienteling & Customer Engagement

In-store associates, chat agents, and digital stylists are constantly asked:

• “What goes with this?”

• “Can you help me style this space?”

• “What else do I need for a complete routine?”

When teams rely on memory or manual searching, answers can be slow or inconsistent.

PaletteAI gives clienteling and engagement teams a ready library of curated looks and setups.

Teams can:

• Pull complete outfits or room concepts while speaking with customers

• Share collections as tailored recommendations via email, chat, or SMS

• Use curated sets as starting points for styling appointments and consultations

Guidance becomes faster, more confident, and more polished. Customers experience a higher level of support, while teams save time and effort by working from structured collections rather than starting from zero each time.

3. Predictive Merchandising & Demand Foresight

Shopper interest shifts quickly. Search trends, campaign performance, and social buzz can change the products and themes customers care about within days or even hours. Static merchandising setups struggle to stay aligned with these shifts.

PaletteAI helps keep collections in sync with what customers are actively exploring.

Using signals from search behavior, performance data, and trend inputs, teams can:

• Refresh collections around themes that are gaining momentum

• Highlight combinations that reflect emerging styles and preferences

• Rotate products within collections to align with interest and inventory priorities

Example scenarios include:

• “Winter Office Looks” as cold-weather workwear gains traction

• “Neutral Living Room Essentials” as minimal interior styles perform well

• “Skinimalism Routine” aligned with interest in clean, simplified beauty regimens

PaletteAI gives merchandisers a faster way to reflect real demand in the way products are grouped and presented.

4. AI-Driven Storytelling & Content Creation

Content and marketing teams need to ship campaigns, landing pages, and on-site experiences that feel unified and on-brand. Building everything item by item is slow and fragmented.

PaletteAI provides a structured foundation for storytelling across channels.

Teams can:

• Start with PaletteAI collections as the base for homepage hero modules and lookbooks

• Build campaign narratives such as “Back to Work”, “Weekend At Home”, or “Soft Neutrals” around curated sets

• Keep visuals, copy, and merchandising aligned across web, email, and paid media

Because products are already grouped into meaningful collections, it becomes much easier to:

• Brief creative teams on the visual direction

• Write consistent copy linked to strong product stories

• Reuse themes across multiple touchpoints without reinventing every layout

PaletteAI reduces friction in content production while improving consistency and impact.

5. Post-Purchase Clarity & Return Reduction

Returns often happen when there is a gap between what customers imagined and what arrives. Seeing a single product on a plain background rarely conveys how it will look and feel in real life or how it works with other items.

PaletteAI helps customers understand the complete picture before buying.

With curated collections and contextual setups, shoppers can:

• See garments in full outfits rather than guessing what matches

• Understand scale and style when furniture is shown in realistic room layouts

• Visualize step-by-step routines for beauty and personal care

When customers have a clearer expectation at checkout:

• Satisfaction increases

• Avoidable returns decrease

• The overall brand experience feels more considered and honest

PaletteAI supports more accurate decision-making by showing products in realistic, lived-in contexts.

How PaletteAI Brings All Five Use Cases Together

Across narrative buying, clienteling, merchandising, content, and post-purchase experience, PaletteAI acts as a shared engine for curated, story-led retail.

It helps retailers:

• Build curated collections in minutes instead of hours

• Create consistent product stories across site, app, email, and store environments

• Communicate seasonal and category direction clearly to internal teams

• Give shoppers an immediate understanding of what works together

• Reduce manual effort for merchandising, clienteling, and content teams

In short, PaletteAI introduces structure and clarity into every stage of the retail journey, turning large catalogs into experiences that feel guided, cohesive, and easy to shop.

Visit GenAI Embed at NRF 2026

You can see all five PaletteAI use cases live at:

GenAI Embed at NRF 2026: Retail’s Big Show
Booth 2938, Level 1
Jacob K. Javits Center, New York
January 11 to 13, 2026

Our team will provide live walkthroughs of PaletteAI, highlighting how it supports:

• Merchandising and assortment presentation

• Clienteling and personalized engagement

• Content and experience design for modern retail strategies

Bring your own categories, campaigns, and challenges. We will be ready to explore how PaletteAI can fit into your tech stack and support your plans for 2026.

Book a dedicated session with the GenAI Embed team at NRF to see PaletteAI applied to your brand and assortment.
Booking link:
https://outlook.office.com/book/NRF2026BoothVisitors@genaiembed.onmicrosoft.com/?ismsaljsauthenabled=true