An Intelligence-Driven Theory of Retail

Executive Snapshot

• Figure: Executive Snapshot: An Intelligence-Driven Theory of Retail

Situation Overview

• Figure: Intelligent Retail Converts Data to Decisions

• The Adoption Journey for Intelligent Retail Has Common Threads

• Retail AI Adoption Applies to Specialized Use Cases

• Retail Data Is Massive and Discordant

• Retail Use Cases Have Unique Decisions and Outcomes

• Figure: Intelligent Retail Use Case Types for Higher-Value Use Cases

• Retail AI Methods Are Domain Specific and Operationally Constrained

• Figure: Top Retail AI Methods

• Retailers Rely Mostly on Partners for AI

• Retailers Adopt Intelligent Retail Without Transparency

• Intelligent Retail Drives Business Decisions

• Figure: Intelligent Retail Drives Business Decisions

• Automation Moves Tasks to the Machine

• Scaling Is the Corporate Agility to Handle the Big Stuff

• Interaction Is a Force Multiplier for Augmentation

• Prioritization Ensures Impactful Decisions

• Figure: Retailers Leave Money on the Table at Different Prices/Volumes

• Most Retailers Use AI Apps with Common Characteristics

• Intelligent Retail Is Already Commonplace

• Most Software and Some Hardware Embed AI Capabilities

• Consumers Don't Know (or Care) That AI Is Operating in the Background

• Intelligent Retail Becomes Smarter with Use

• Evolution of Intelligent Retail Will Unify Capabilities

• Retailers Will Adopt Intelligent Retail with Incremental Returns

• Retail Software Will Embed Continuous Learning Everywhere

• Data Siloes Will Convert to Unified Data Streams

• Single-Purpose Apps Will Comprise End-to-End Apps and Product Suites

• AI Will Map Integration to Existing Processes and Applications

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• Related Research

• Synopsis