Insights · AI and production readiness in retail

What Dynamic Pricing Actually Requires

Few AI use cases sound more attractive than dynamic pricing — and few place higher demands on your data foundation. Here is what needs to be resolved before the first pricing algorithm goes live, from data quality to guardrails.

approx. 8 Min. read · August 2026 Read the article →

// Datenbasis

The Typical Data Problems in Retail

Duplicate maintenance, dead required fields, silos between store and online. Data problems in retail look remarkably similar from one business to the next. A walkthrough of the six most common patterns, with examples and approaches for assessment.

approx. 7 Min. read · August 2026 Read the article →

// Datenbasis

What Data Does AI Need? Five Questions Before You Start

AI doesn't need perfect data — but it does need the right data in the right condition. Five questions to assess whether your data is sufficient for an AI use case. Before you bring anyone in.

approx. 8 Min. read · August 2026 Read the article →

// Agenten

Why AI Agents Fail in Production

Impressive in testing, switched off in practice. The most common reasons AI agents don't survive day-to-day operations — and how to spot the risk before you invest.

approx. 7 Min. read · August 2026 Read the article →

// Warenfluss

Agentic Replenishment and What It Actually Takes

Replenishment is the retail use case where agentic AI generates returns fastest. It is also the case where poor master data becomes expensive fastest. Here is what an ordering agent truly needs before it is allowed to place orders.

approx. 7 Min. read · Juli 2026 Read the article →

// Term

Foundation Debt

What is foundation debt? The gap between target and actual in your data foundation — and why it accrues interest.

Understand the term → // Measurement

The reliability score

How evidence becomes a number from 0 to 100. A heatmap, not a single figure.

See the method →

What actually moves retail forward with AI.