Retail analytics insights
Guides, frameworks, and strategies for retail teams that want to move from dashboards to decisions.
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Shadow AI: Find It in a Week, Then Build the On-Ramp
Every instance of shadow AI is someone who needed an answer badly enough to route around you. Five places to find it, which risks are real, and why blocking first fails.
Human in the Loop: Designing Approval That Is Not a Rubber Stamp
Sixty proposals a day at 90 seconds each is 90 minutes of review nobody has. Four approval designs, what belongs on the screen, and how to read an approval rate.
AI Orchestration Layer vs Point Solutions: When You Need One
Five vendors shipped five AI features and now three of them disagree in the same meeting. What an orchestration layer owns, the four triggers to buy one, and what stays a point tool.
Conversational Analytics: What Breaks When You Point an LLM at a Warehouse
The demo runs on five clean tables. Your warehouse has 480 and the margin column is called gm_amt_net_adj. The three failure modes, and what closes the gap.
Text-to-SQL Accuracy: Why the Benchmarks Do Not Predict Your Schema
Spider and BIRD report accuracy in the high 80s on 15-table databases. Your warehouse has 480 tables named by an ERP vendor. What actually drives accuracy, and how to measure yours.
Agentic Analytics: What Separates an Agent From a Query Bot
Three properties separate an agent from a chat interface on a query engine: it decides what to look at, it takes dependent steps, and it carries state. Most products have the first at best.
The AI Data Analyst: What It Replaces and What It Cannot
Roughly half the analyst job automates well. The half that does not is knowing which question to ask and knowing when the number is wrong, and that half gets more valuable as volume rises.
Self-Service Analytics Failed for a Structural Reason
Adoption has sat at 15 to 25% of licensed users for fifteen years across every generation of tool. A metric that ignores fifteen years of product improvement is not limited by the product.
How to Tell When an AI Analytics Answer Is Wrong
AI analytics rarely hallucinates a fact. It computes a real number from the wrong data with full confidence. The six failure modes, and four checks that catch most of them in two minutes.
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