Retail & Zenline Insights

E-Com Podcast "Behind The Click" Hosts Zenline CEO Arber

Arber Sejdiji talking about AI Agents in Retail, why he bets on retail, and the biggest risk for enterprises right now. Click to listen in!

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Modern Retail: AI is now doing parts of merchants' Jobs, managing products - talking about Zenline AI

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Swiss Retail Day: Zenline presents AI Agents in Retail

What does it take to build a shop-worthy assortment in 2025? Here is what Arber told the room at Swiss Retail Day. Click to read

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Zenline AI on Stage at Shoptalk 2026

Chris Walton, President & CEO of Omni Talk Retail, about AI agents in retail from Zenline AI

Listen in

Zenline Q&A with The Retail Bulletin on AI Agents

These agents are already deployed at some of Europe’s largest retailers, supporting margin improvement through agentic assortment and pricing recommendations.

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Zenline AI Gets Recognized by Forbes 30Under30

Out of more than 3,000 applications and nominations, around 3% were selected. Congrats to Arber Sejdiji for his outstanding work in retail.

See Forbes

Zenline's CEO Sejdiji Quoted in "Der Handel" on AI Agents, page 8

Der Handel e-paper

A Conversation with Retail Expert Roberto de Angelis

Where should retailers start their AI journey: Margin, cost, or customer experience? Make or buy: How should executives decide between building in-house and partnerships? Click to read

Case Study Electronics: Cleaning 500k+ SKUs of Electronics Data

Zenline’s Data Cleaning Agent fixed brand and color errors and enriched attributes across 500k+ electronics SKUs using online data only.

What Data Does Zenline Use?

Zenline uses internal and external data for real-time assortment optimization. Learn how our AI works with incomplete data to drive better retail decisions.

Case Study Cosmetics: Finding Private Label Substitutes in Face Creams

We identified true substitutes in face creams by analyzing images, names, descriptions, and attributes, correcting over 50% missing links and 35% wrong pairs and auto-populating substitute modules.

What Zenline's AI Agents Do

Zenline’s AI agents support assortment decisions with margin insights, pricing logic and product signals. Learn how retailers use AI for category planning.

"Every Retailer Will Need AI Agents in the Future"

Zenlines CEO discusses competition with Amazon and Temu. A conversation about why retailers should urgently switch to AI agents.

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Expert Picks

Curated insights on retail, AI, and category strategy.

News & Press
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AI Agents in Retail
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E-Com Podcast "Behind The Click" Hosts Zenline CEO Arber

Arber Sejdiji talking about AI Agents in Retail, why he bets on retail, and the biggest risk for enterprises right now. Click to listen in!

Listen
Category Management
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AI Agents in Retail
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Agentic AI for Retail: Lessons From the Anthropic Founder's Playbook

What an AI-native startup playbook actually means when your users are buyers, merchandisers, and pricing teams.

News & Press
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AI Agents in Retail
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Swiss Retail Day: Zenline presents AI Agents in Retail

What does it take to build a shop-worthy assortment in 2025? Here is what Arber told the room at Swiss Retail Day. Click to read

Read more
Category Management
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News & Press
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AI Agents in Retail
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Zenline at GS1 Webinar on AI Agents in Category Management

Click to review the summary or watch the whole webinar with ECR CatManNetwork, GS1 and Zenline on how purpose-built AI Agents help Category Teams move from data to better commercial decisions.

See more
AI Agents in Retail
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News & Press
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Zenline's CEO Sejdiji Quoted in "Der Handel" on AI Agents, page 8

Read e-paper
AI Agents in Retail
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News & Press
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Sejdiji on AI Assortments as Margin Levers

European retailers are currently facing a double challenge: oversaturated product ranges coupled with increasing margin pressure.

Unternehmer Magazin
AI Agents in Retail
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News & Press
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Zenline's CEO Sejdiji Quoted in "Der Handel" on AI Agents, page 8

Der Handel e-paper
News & Press
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Retail Insights
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Zenline AI on Stage at Shoptalk 2026

Chris Walton, President & CEO of Omni Talk Retail, about AI agents in retail from Zenline AI

Listen in
Retail Insights
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The Hidden Cost of Digital Growth: Why Assortment Data Is the Missing Link

Albertsons Companies reported a gross margin rate of 27.4% in Q3 fiscal 2025. The company's reporting attributed the compression to two primary factors:

Retail Insights
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What Walmart's Q4 Results Tell Us About the Future of Assortment

Walmart itself has highlighted as evidence that AI-assisted discovery is already converting into measurable commercial outcomes.

Retail Insights
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The Grocery AI Race btw. EDEKA and REWE & What It Tells Us

For retail executives, the more useful takeaway: The translation layer, from customer data to assortment quality to commercial outcome, is where ...

Retail Insights
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Why 40% of Merchant Time Is Wasted & What That Means

Retail traditionally used to move in seasons. That cadence made sense a few years ago, but the internet has moved on, and so has the market (& competitors).

Retail Insights
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Revenue Is Growing. Margins Are Shrinking. Promotions Are Why

A category manager responsible for 3,000 SKUs cannot run this analysis manually and continuously. AI Agents can & will improve margin with that significantly.

News & Press
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Retail Insights
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A Conversation with Retail Expert Roberto de Angelis

Where should retailers start their AI journey: Margin, cost, or customer experience? Make or buy: How should executives decide between building in-house and partnerships? Click to read

Category Management
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AI Agents in Retail
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Agentic AI for Retail: Lessons From the Anthropic Founder's Playbook

What an AI-native startup playbook actually means when your users are buyers, merchandisers, and pricing teams.

Category Management
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Agentic AI for Retailers: AI-Native Explains What Works and What Doesn't

Category Management
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News & Press
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AI Agents in Retail
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Zenline at GS1 Webinar on AI Agents in Category Management

Click to review the summary or watch the whole webinar with ECR CatManNetwork, GS1 and Zenline on how purpose-built AI Agents help Category Teams move from data to better commercial decisions.

See more
AI Agents in Retail
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Category Management
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Retail Insights
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Spotlight Interview with Roberto de Angelis

Many AI projects stall at pilot stage. Why do so few deliver real business impact? Recent surveys show that, while many (ca. 80%) companies are already using AI, the actual P&L has been elusive, with about 10% of companies agreeing to have unlocked significant value.

Category Management
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The 5 Most Common Mistakes in Assortment Planning

Here are five common mistakes that continue to limit the effectiveness of assortment planning, and how modern data systems, including AI, help move beyond them.

Category Management
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Why AI Beats Excel for Category Management

For years, Excel has been the tool of choice for category and assortment planning. It is flexible, widely used, and deeply embedded in retail workflows. Most teams still use it to track sales, evaluate product performance, and manage pricing decisions. But the demands on modern assortment management have changed, and Excel can no longer keep up.

AI Agents in Retail
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Category Management
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What Data Does Zenline Use?

Zenline uses internal and external data for real-time assortment optimization. Learn how our AI works with incomplete data to drive better retail decisions.

News & Press
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AI Agents in Retail
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|
|

E-Com Podcast "Behind The Click" Hosts Zenline CEO Arber

Arber Sejdiji talking about AI Agents in Retail, why he bets on retail, and the biggest risk for enterprises right now. Click to listen in!

Listen
News & Press
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|

Modern Retail: AI is now doing parts of merchants' Jobs, managing products - talking about Zenline AI

Read article
News & Press
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Prime Minister Visits - Zenline Drives Expansion

Zenline drives international expansion with new customers and get's attention from Prime Minister Albin Kurti.

Read in
News & Press
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AI Agents in Retail
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Swiss Retail Day: Zenline presents AI Agents in Retail

What does it take to build a shop-worthy assortment in 2025? Here is what Arber told the room at Swiss Retail Day. Click to read

Read more
AI Agents in Retail
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News & Press
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Zenline's CEO Sejdiji Quoted in "Der Handel" on AI Agents, page 8

Der Handel e-paper
News & Press
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Retail Insights
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A Conversation with Retail Expert Roberto de Angelis

Where should retailers start their AI journey: Margin, cost, or customer experience? Make or buy: How should executives decide between building in-house and partnerships? Click to read

News & Press
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Retail Insights
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A Conversation with Retail Expert Markus Schröder

What difference does it make for Category Managers when concept development, forecasting and tracking all come from one integrated source – instead of being fragmented across multiple tools?

Case Studies
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Case Study Furniture: Recovering Gross Margin Through Early Velocity Signals

The retailer deployed Zenline's Margin Agent across its living room and bedroom categories, approximately 3,200 SKUs, to run continuous velocity monitoring against category-adjusted benchmarks.

Click to learn more
Case Studies
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Case Study Grocery: Private Label Mix Optimisation in Dairy

Zenline's Substitutes Agent analysed the full dairy range, approximately 1,400 active SKUs, to build a complete national brand / private label equivalence map based on the attributes that actually drive shopper substitutability

Case Studies
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Case Study Fashion: Fixing Markdowns Through Assortment Design

Most markdown problems aren't a demand problem, they're an overlap problem. How one European retailer turned pre-season planning into a precision allocation decision.

Case Studies
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AI Agents in Retail
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Case Study Electronics: Cleaning 500k+ SKUs of Electronics Data

Zenline’s Data Cleaning Agent fixed brand and color errors and enriched attributes across 500k+ electronics SKUs using online data only.

Case Studies
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AI Agents in Retail
|
|
|

Case Study Cosmetics: Finding Private Label Substitutes in Face Creams

We identified true substitutes in face creams by analyzing images, names, descriptions, and attributes, correcting over 50% missing links and 35% wrong pairs and auto-populating substitute modules.

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