## 🗺️ Project Roadmap — Grocery Retail – Driven by Data

**Purpose**
The *Grocery Retail – Driven by Data* roadmap defines how this multi-year project grows from classroom challenges to an open research ecosystem on applied data science and human–AI collaboration.
Each phase expands scope, automation, and interpretability—moving from manual craftsmanship to guided collaboration and, eventually, AI-assisted autonomy.

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### **Phase 1 — Foundations (Year 1)**

**Goal:** Build a shared baseline for structure, trust, and reproducibility.

* Launch the four core challenges covering schema design, data quality, forecasting, and ROI modeling.
* Establish standard file structures, naming conventions, and validation scripts.
* Collect reproducible artifacts (ERDs, ETL pipelines, notebooks, evaluation reports).
* Observe how participants use AI as a **Tutor**—seeking guidance, explanations, and code feedback.

**Outcome:**
A complete, transparent workflow from spreadsheets to structured data, serving as the foundation for all future phases.

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### **Phase 2 — Collaboration (Years 2–3)**

**Goal:** Expand from assisted learning to productive co-creation.

* Transition into the **AI Coder** mode, where AI generates candidate code, models, and diagnostics while humans validate logic and ethics.
* Introduce more complex, multi-store and multi-year datasets with realistic data quality issues.
* Compare human-initiated vs. AI-initiated pipelines for accuracy, interpretability, and reproducibility.
* Partner with universities and research labs to analyze learning patterns, revision strategies, and error correction.

**Outcome:**
Documented design patterns for human–AI collaboration that balance creativity, quality assurance, and accountability.

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### **Phase 3 — Autonomy (Years 4–5)**

**Goal:** Explore responsible automation under human oversight.

* Advance to the **AI Agent** phase—AI executes ingestion, transformation, modeling, and reporting based on human-defined intent.
* Develop reproducibility and explainability metrics to evaluate AI-led pipelines.
* Conduct controlled studies on co-validation, where humans audit AI-generated analyses for logic, bias, and implications.
* Publish governance guidelines for mixed human–AI analytical teams.

**Outcome:**
A transparent benchmark for AI-assisted analytics—showing how automation, validation, and ethics can coexist in real-world data science.

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### **Deliverable Vision**

By the end of the roadmap, the series will provide:

* A longitudinal dataset tracing human–AI collaboration.
* Reproducible benchmarks for schema quality, forecasting accuracy, and ROI modeling.
* Educational modules that map directly into academic and industry training.
* A practical framework for ethical, interpretable, and collaborative AI-assisted analytics.

**Long-term goal:**
Not automation for its own sake—but collaboration.
The project seeks to build shared understanding of how humans and AI co-create insight responsibly, reproducibly, and at scale.