The AI Operations Innovation Award recognizes retailers that are using artificial intelligence to optimize internal operations and core business processes. 

This award celebrates applied AI initiatives such as predictive analytics, machine learning, automation, machine vision, robotics, generative AI, demand forecasting, supply chain optimization and more.  The goal is to improve efficiency, accuracy, scalability, and decision-making across supply chain, inventory management, pricing, merchandising, and enterprise operations. Successful submissions will demonstrate measurable operational impact and responsible AI implementation that advances the retail business.

CONTENT REQUIREMENTS FOR CASE STUDY:

Introduction (100 Words)

Please include the following in your response:

• Name of retailer
• Name of AI initiative
• Area of application (e.g. supply chain, demand forecasting, inventory optimization, pricing, merchandising, store operations)
• AI technologies or platforms used (high-level)
• Brief overview of the initiative and its operational focus

Needs and Objectives (300-500 Words) – 20%

Please include the following in your response:

• The internal business challenge or operational inefficiency addressed
• Why AI was the most appropriate solution compared to traditional approaches
• Target users or beneficiaries (e.g. internal teams, associates, planners, merchants, supply chain partners)
• Objectives and intended outcomes, such as improved accuracy, speed, scalability, cost reduction, or decision support

Program Details & Results (600-1000 Words) – 70%

Please include the following in your response:

• Overview of the initiative from concept through deployment
• How AI was designed, trained, tested, implemented, and integrated into existing systems or workflows
• How the initiative improved operational outcomes, such as:
  • Forecasting accuracy and demand planning
  • Inventory optimization and supply chain efficiency
  • Pricing, promotions, or assortment planning
  • Merchandising or category performance
  • Store operations or associate productivity
  • Governance considerations (e.g. data quality, bias mitigation, model oversight, privacy, transparency, responsible AI practices)
• Internal and external collaboration (cross-functional teams, technology partners, vendors)
• Resources, budget, and implementation timelines (high-level)
• How success was measured and evaluated
• Results achieved, including (as applicable):
  • Cost savings or margin improvement
  • Efficiency or productivity gains
  • Accuracy, speed, or performance improvements
  • Employee adoption or satisfaction
  • Key learnings, scalability, and future roadmap for the initiative

Supporting Evidence: Judged – 10%

Provide relevant examples with brief descriptions (e.g. dashboards, model outputs, forecasting accuracy improvements, workflow diagrams, performance metrics).

FORMATTING REQUIREMENTS FOR CASE STUDY:

1. Title Page
   (Must include Award Category, Name of Company and Title of Submission)
2. Table of Contents
3. Content Pages
   • Introduction
   • Needs and Objectives
   • Program Details & Results
4. Appendices (if applicable) and Supporting Evidence with description