Retail & eCommerce AI Decisioning Automation Assessment on AWS

Retail and eCommerce companies operate in highly dynamic environments where customer expectations, pricing conditions, inventory, and demand signals change in real time. However, many critical decisions—such as pricing adjustments, product recommendations, campaign targeting, and inventory allocation—are still manual, delayed, or driven by disconnected systems.

This limits the ability to react to customer behavior, reducing conversion rates, increasing operational costs, and missing revenue opportunities. As digital competition intensifies, retailers need to make faster, data-driven decisions across customer journeys and commerce operations.

Compass UOL helps retailers assess and modernize their decision-making processes by identifying where AI-driven automation can improve conversion, pricing effectiveness, marketing performance, and operational efficiency. This assessment evaluates current decision workflows, data pipelines, and system integrations to define a scalable decision automation strategy.

Using AWS-native services—including real-time data pipelines, analytics platforms, and AI/ML services such as Amazon Bedrock—Compass UOL defines how to automate key retail decisions at scale. The result is a structured roadmap to enable real-time personalization, dynamic pricing, and intelligent automation across commerce workflows.

Buyer Problem / Business Trigger

Manual or delayed decision-making in pricing, promotions, and personalization Low conversion rates due to weak product discovery and targeting Fragmented customer and commerce data limiting real-time decisions Operational inefficiencies in campaign execution, catalog management, and inventory optimization

Delivery Model

Discovery of commerce workflows and decision points (pricing, campaigns, recommendations) Assessment of data architecture, platforms, and integration gaps Definition of AWS-native AI decisioning architecture Roadmap for automation, scaling, and business impact realization

Assessment / Engagement Scope

Mapping of decision workflows (pricing, promotions, recommendations, inventory allocation) Identification of manual decision points and automation opportunities Evaluation of customer data platforms, analytics, and real-time capabilities Review of AI/ML usage and personalization tools Design of AWS-native architecture (real-time pipelines, AI/ML, decision engines) Prioritization of use cases based on conversion impact and revenue potential

Expected Output / Deliverables

AI decisioning automation assessment report AWS reference architecture for retail decision automation Prioritized use cases (dynamic pricing, recommendations, campaign optimization) Business impact mapping (conversion uplift, revenue opportunities, cost reduction) Implementation roadmap for scaling AI-driven decisioning

Customer Decision Questions This offer helps the customer answer:

Which commerce decisions should be automated to improve conversion and revenue? How can we enable real-time personalization and pricing using AWS? What architecture supports scalable AI decisioning across omnichannel retail?