Hey everyone, welcome back to TheAI-4U.com and the second installment of our ‘AI in Action’ series! Following our look at TravelSphere’s journey with sustainable travel features, we’re now diving into how a major retailer tackled fulfillment challenges. If you’re just joining us, be sure to check out the series introduction: Your AI Launchpad: Preparing Your Team for Real-World Results.

We’ve journeyed through the capabilities of Google’s AI suite – exploring the multi-modal power of Gemini, the knowledge consolidation of NotebookLM, the automation magic of Apps Script, and the deep insights from Deep Research. Today, we’re shifting from potential to practice.

Let’s step inside “OmniMart,” a major retailer with a sprawling network of stores and a busy e-commerce site. Their challenge is classic but complex: battling stockouts, optimizing inventory across locations, and speeding up order fulfillment (both shipping and buy-online-pickup-in-store – BOPIS) to meet rising customer expectations. The goal is ambitious: implement an AI-powered system for demand forecasting, intelligent inventory placement, and streamlined fulfillment workflows.

Enter the “Project Streamline” team, an Agile/Scrum powerhouse tasked with this transformation. Their secret weapon? Integrating Google’s AI tools across their entire workflow. Forget siloed data and reactive adjustments; the Streamline team is about to demonstrate an AI-augmented SDLC in action.

Phase 1: Requirements Gathering & Analysis – Forecasting Needs from Data Streams

The Streamline team faces a deluge of information: historical sales data, current inventory levels, WMS reports, supply chain data, customer feedback, and stakeholder goals.

• Product Manager (PM) & Business Analyst (BA): They leverage Google NotebookLM as their central knowledge hub, uploading diverse data sources. Using Gemini within NotebookLM, they query this data for insights like identifying high-stockout SKUs or summarizing customer feedback themes. To ensure alignment, they also use Gemini via Apps Script integrated with Google Meet/Docs to automatically summarize key meeting decisions and action items, potentially drafting Jira task updates. Furthermore, the PM uses Deep Research for Competitor Fulfillment analysis, prompting: “Analyze the top 3 competitors’ strategies for BOPIS implementation and stockout reduction reported in recent earnings calls and industry reports.”
• UX Researcher: Uses Gemini Deep Research to understand best practices for omnichannel fulfillment UI/UX based on user satisfaction drivers and accessibility standards.
• Enhanced Requirements Validation (BA): The BA refines user stories in Google Docs/Sheets using AI insights. Before creating Jira tickets, they use Gemini to cross-reference stories (especially complex ones involving WMS interactions) against technical constraints and API specs documented in NotebookLM, automatically flagging potential feasibility conflicts early.
• BA & Jira Integration: The refined and validated stories are processed by an Apps Script tool enhanced with Gemini to add specific acceptance criteria before automatically populating Jira tickets linked back to NotebookLM.

Phase 2: Design & Architecture – Building the Logistical Blueprint

With clear, AI-validated requirements, the team designs the system architecture.

• Software Architect: Uses Gemini Deep Research for deep comparative analysis of demand forecasting models and legacy WMS integration patterns. To ensure they ask the right questions, the Architect uses Gemini to help refine Deep Research queries, prompting: “Help me formulate a Deep Research prompt to effectively compare middleware vs. direct API integration for an AS/400 WMS, considering data latency and transaction integrity.” The chosen architecture is documented in NotebookLM.
• UX Designer: Uses Gemini to generate UI mockups for warehouse dashboards and customer-facing inventory/BOPIS screens, consolidating designs in NotebookLM.
• Project Manager (PM): Employs the custom Gemini Gem, “SupplyChainRisk Forecaster,” trained on historical data, to identify potential integration risks based on requirements and architecture in NotebookLM.

Phase 3: Coding & Development – Assembling the System with AI Assistance

The Streamline developers build the new systems, integrating AI deeply into coding and review.

• Developers (Backend, Data Scientist/ML, Frontend): Gemini Code Assist accelerates development of forecasting models, API integrations, and dashboards. Crucially, for integrating with OmniMart’s legacy AS/400 WMS, developers upload its often-sparse documentation into NotebookLM and use Gemini for Legacy Code Understanding. They prompt: “Explain this COBOL code snippet handling inventory updates from the uploaded WMS manual” or “Analyze the logic flow for order allocation described in this legacy system document.” This significantly speeds up understanding and reduces integration errors.
• Custom Gemini Gems: Specialized Gems like “DemandModelValidator” and “OMSApiHelper” provide targeted assistance based on context in NotebookLM.
• Apps Script + Gemini API for Integrated Feedback Loops:
  • Automated Documentation & Refactoring Aid: Apps Scripts triggered from Docs/Sheets call Gemini via UrlFetchApp to generate docstrings or suggest refactoring options, pasting results back for consistency.
  • Deliver AI Feedback Directly to Code Repositories: Webhooks trigger an Apps Script on PR submission. The script uses UrlFetchApp to fetch the diff, sends it to Gemini for targeted review (bugs, standards, security), and posts Gemini’s feedback directly as comments on the PR in their Git repository, streamlining the review process.
• Technical Writer: Uses NotebookLM and Gemini to query finalized specs and generate drafts for internal user guides.

Phase 4: Testing & Quality Assurance – Validating Accuracy and Flow

QA is critical for ensuring the reliability of fulfillment systems.

• QA Engineer: Uses Gemini to generate diverse functional, performance, and scenario-based test cases (e.g., holiday peaks, inventory discrepancies). They also use Gemini, potentially triggered via Apps Script from a results Sheet, to analyze complex test results or prediction accuracy reports.
• Automated Test Environment Setup: To handle testing across different warehouse configurations or WMS instances, the team uses Apps Script triggered by Jira. When a specific type of test case is moved to ‘Ready for Testing’ in Jira, an Apps Script uses UrlFetchApp to call cloud provisioning APIs or internal tools to set up the required test environment configuration automatically.
• Automated Jira Updates via Apps Script: When Gemini assists in analyzing failed tests or generates test case ideas, Apps Script integrated with the Jira API automatically creates or updates corresponding bug or test case tickets in Jira, ensuring findings are immediately tracked and linked.
• NotebookLM: Remains the central repository for all testing artifacts, including links to Jira tickets and environment configurations.

Phase 5: Deployment & Operations – Rolling Out Efficiency

The new systems are rolled out carefully.

• DevOps/SRE Engineer: Uses Apps Script + Gemini API for targeted internal communications about the rollout to specific warehouses or staff roles. They also use Gemini for Deployment Risk Analysis during the rollout; by feeding Gemini current monitoring data snippets, deployment logs, and initial feedback from pilot sites, they can ask: “Analyze this data for potential risks related to the WMS integration performance under partial load that might impact broader rollout.” This provides dynamic risk assessment beyond pre-defined checks. Gemini Deep Research informs the overall phased rollout strategy.

Phase 6: Maintenance & Monitoring – Sustaining Optimal Flow

Ongoing monitoring ensures system health and accuracy.

• Support Engineer (Internal): Uses NotebookLM for troubleshooting guides and the “FeedbackSummarizer” Gem via Apps Script + Gemini API to analyze internal user feedback trends.
• Predictive Monitoring with Gemini: The SRE team uses Apps Script to periodically pull key monitoring trends (API latency, queue lengths, forecast accuracy metrics). This data is fed to Gemini with prompts like: “Analyze these WMS API latency trends over the past month and predict potential bottlenecks during the upcoming peak season.” This proactive analysis helps prevent future operational issues.
• SRE & Data Scientist/ML: Use Gemini for analyzing performance/drift. Use proactive Apps Script + Gemini alerting triggered by monitoring thresholds for potential issues.

The Agile & Management Layer: AI-Driven Oversight & Onboarding

Guiding the Streamline team involves strategic oversight and effective enablement.

• Scrum Master: Uses Gemini to analyze retrospective notes in NotebookLM. They also leverage NotebookLM’s ability to synthesize across projects; by querying retrospectives from past logistics projects stored there, they ask Gemini: “Identify recurring organizational challenges related to data pipeline management based on post-mortems from Project X and Project Y.” This provides Synthesized Cross-Project Learnings.
• People Manager/Software Development Manager (SDM): Uses automated Apps Script + Gemini API reports for velocity tracking. Uses NotebookLM for skill matrices. Uses Gemini Deep Research for management best practices.
• Enhanced New Developer Onboarding: The interactive “Onboarding Podcast” in NotebookLM provides a great baseline. Additionally, the SDM uses Gemini for Onboarding Path Generation. By analyzing the current codebase structure, documentation in NotebookLM, and relevant open Jira tasks for beginners, Gemini generates a personalized task list and learning resource recommendations for the new hire, tailored to their specific initial assignments on the complex fulfillment system.

The AI Synergy: Orchestrating Retail Efficiency

OmniMart’s Project Streamline wasn’t just about deploying AI; it was about orchestrating it. NotebookLM provided the shared knowledge context across roles, projects, and even legacy systems. Gemini delivered analysis, code understanding, content generation, risk assessment, and predictive insights. Code Assist and custom Gems accelerated specialized tasks. Deep Research informed strategic decisions with external context, aided by Gemini for prompt refinement. Apps Script automated critical workflows, connecting Jira, code repositories, monitoring tools, test environments, and communication channels.

The result? OmniMart achieved a significant reduction in stockouts, faster fulfillment, optimized inventory, and empowered staff, all driven by a more efficient and intelligent SDLC.

OmniMart’s story further underscores how orchestrating Google’s AI suite can address complex, core business operations. By embedding AI across the SDLC, they achieved tangible results. This concludes our initial ‘AI in Action’ case studies, but the exploration doesn’t stop here! Revisit the key skills and mindsets needed to start your own AI integration efforts.

Your Turn to Streamline with AIThe OmniMart story highlights how Google’s AI suite can tackle core challenges in traditional retail and e-commerce by augmenting expertise, automating intelligently, and embedding predictive capabilities. How could these enhanced AI synergies transform your operational or e-commerce platform challenges? Share your thoughts below!