Case study – Select the AI tool for business challenges

Designing and Implementing AI Workloads for a Global Retail Organization

Tailwind Traders is a mid-sized, global retail organization operating both e-commerce and physical storefronts across North America and Europe. The company is experiencing rapid growth and is under increasing pressure to improve customer experiences, streamline internal operations, and optimize inventory management—all while meeting strict security, governance, and compliance requirements.

Leadership approves a multi-phase AI adoption initiative aligned with the Cloud Adoption Framework for AI. The goal is to deliver quick productivity wins while building a scalable, governed foundation for more advanced AI workloads over time.

Tailwind Traders must design AI solutions that balance speed, customization, cost, and control. The organization chooses a blended approach that uses Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) Azure AI offerings, selecting the right tool for each business problem.

Business challenges

Tailwind Traders faces several common enterprise AI challenges:

  1. Employees spend excessive time searching for information, summarizing documents, and responding to routine communications.

  2. Customers expect real-time, conversational assistance on the e-commerce site, but current chat solutions are rule-based and inflexible.

  3. Inventory forecasting relies on historical rules rather than predictive modeling, leading to overstocking and stockouts.

Requirement

[!IMPORTANT] STOP HERE TO DISCUSS – SOLUTION FOLLOWS













Solution Challenge 1: Employee productivity with SaaS AI

Business problem

Employees across sales, marketing, HR, and finance report spending considerable time on routine tasks such as drafting emails, summarizing meetings, and locating internal documents.

Selected solution: Microsoft 365 Copilot (SaaS)

Tailwind Traders begins its AI journey by adopting Microsoft 365 Copilot, a turnkey SaaS generative AI solution integrated directly into familiar Microsoft 365 applications.

Why SaaS?

  • Fast outcomes: No infrastructure or model management required.

  • Low skill barrier: Employees use AI within tools they already know.

  • Built-in governance: Access is governed through Microsoft Entra ID and existing Microsoft 365 permissions.

  • Responsible AI by default: Data remains within the Microsoft 365 tenant and respects existing compliance controls.

Outcome

Within weeks, employees report measurable productivity gains, including faster document creation, improved meeting summaries, and reduced time spent searching for information. This early success builds organizational confidence and momentum for broader AI adoption.

Solution Challenge 2: Customer-facing chat with PaaS AI

Business problem

Tailwind Traders e-commerce site lacks an intelligent, scalable chat feature capable of answering customer questions about products, returns, and order status.

Selected solution: Azure AI Foundry with Azure OpenAI (PaaS)

To support a custom, customer-facing solution, Tailwind Traders builds a retrieval-augmented generation (RAG) chatbot with Azure AI Foundry and Azure OpenAI models.

Why PaaS?

  • Customization: The chatbot is grounded in Tailwind Traders product catalog, FAQs, and policy documents.

  • Managed operations: Azure handles scaling, availability, and patching.

  • Security: Models and data are deployed within Tailwind Traders Azure subscription.

  • Flexibility: Supports both generative and nongenerative AI APIs.

Architecture highlights

  • Azure OpenAI for natural language responses.

  • Azure AI Search to ground responses in approved product and policy data.

  • Azure Container Apps to host the chat application.

  • Private endpoints and controlled network access for secure data flow.

Outcome

Customers receive faster, more accurate responses, leading to increased satisfaction and improved conversion rates—all without exposing sensitive data to public endpoints.

Solution Challenge 3: Inventory forecasting with machine learning

Business problem

Inventory levels are managed with static rules based on historical data, resulting in inefficiencies and missed revenue opportunities.

Selected solution: Azure Machine Learning (PaaS)

Tailwind Traders chooses Azure Machine Learning to build predictive models that forecast demand with historical sales, seasonal trends, and promotional data.

Why Azure Machine Learning?

  • End-to-end ML lifecycle: Data preparation, training, deployment, and monitoring.

  • Supports nongenerative AI: Ideal for prediction and forecasting use cases.

  • Enterprise readiness: Role-based access, versioned models, and experiment tracking.

  • Integration: Works with existing Azure data services.

Outcome

The organization achieves more accurate forecasts and reduces excess inventory.