Writing MBA E‑Business Reports on Generative AI Product Discovery

Generative AI product discovery is reshaping how shoppers find items and how retailers merchandise at scale. This guide shows MBA students how to design a rigorous academic project around generative AI product discovery, from research scope to KPIs, ethics, and reporting. It explains methods to evaluate conversational search, retrieval‑augmented generation, and recommendation blends.

Project framing for generative AI product discovery

Your report should open with a concise problem statement and a measurable goal tied to the funnel. Example: “Quantify the impact of generative AI product discovery on product views per session and add‑to‑cart rate within a fashion catalog.” Align the goal with one primary business metric and two supporting UX metrics.

Propose a realistic evaluation horizon (4–8 weeks) and an experiment footprint (e.g., 10% traffic) to balance speed, risk, and statistical power. Define success thresholds ex‑ante to avoid p‑hacking.

Scope and modules to implement and assess

Organize the project into clear modules so readers can reproduce your work. Below are typical components that cover technical and managerial dimensions.

Module A: Data and catalog readiness

Detail product data quality, taxonomy, attributes, and media coverage. State the schema and known gaps (missing attributes, synonym drift). Include a plan for cleansing, attribute extraction, and deduplication to support semantic retrieval.

• Construct a minimal product knowledge graph linking categories, attributes, and popularity signals.
• Add synonym dictionaries for domain terms and brand spellings.
• Create a holdout subset for unbiased evaluation.

Module B: Retrieval‑augmented generation (RAG)

Explain how you index product snippets and reviews in a vector database and blend them with keyword search. Compare BM25 baseline to hybrid search (lexical + dense). Record latency, recall at K, and relevance judgments.

• Embed titles, attributes, and top review sentences.
• Use reranking to improve top‑K precision.
• Cache frequent queries to meet SLA targets.

Module C: Conversational assistant and UX

Design a chat‑like interface guiding shoppers from vague intent to purchase‑ready shortlists. Specify handoffs: query understanding, constraint refinement, and product list rendering. Include fallback rules when confidence is low.

• Intent parsing: need‑state (occasion, budget, size) and constraints.
• Clarifying questions limited to one per turn to reduce friction.
• Result cards with badges (fit, sustainability, delivery time).

Module D: Recommendation blending

Blend conversational outputs with collaborative and content‑based recommendations. Justify the weighting logic and guard against popularity bias overshadowing niche relevance.

• Scenario‑based weights: early exploration vs. late‑stage shortlist.
• Diversity constraints to prevent near‑duplicates.
• Cold‑start handling via attribute similarity and newness boosts.

Module E: Governance, safety, and ethics

Document policies for hallucination control, price/availability accuracy, and brand safety. Include human review for sensitive categories and a feedback capture loop for users to flag wrong suggestions.

• Ground all responses in retrieved product facts.
• Timestamp and source every claim displayed to users.
• Redact prohibited content and avoid unfair steering.

Research design and measurement plan

Translate the above modules into a rigorous study capable of supporting managerial recommendations. Use the focus keyphrase generative AI product discovery naturally across your measurements and write‑up.

Primary outcome and KPIs

Choose one primary KPI, such as add‑to‑cart rate or product detail page views per session. Add secondary KPIs: query reformulation rate, time to first relevant result, dwell time, and conversion from assisted sessions.

• Effect size: minimum detectable uplift (e.g., +5% add‑to‑cart).
• Quality: relevance@10 from graded human judgments.
• Efficiency: P95 latency for query and response steps.

Experimental setup

Use an A/B framework: Control uses standard search and recommendations; Variant enables hybrid retrieval and the assistant. Randomize at session level; ensure equal device mix. Define stopping rules and adjust for multiple comparisons.

• Segmentation: new vs. returning users; high vs. low SKU depth.
• Instrumentation: event schema for queries, clarifications, and clicks.
• Bias checks: ensure consistent inventory and price exposure.

Qualitative companion study

Run task‑based usability sessions (n=10–15) to observe how users phrase needs and react to assistant prompts. Code themes: trust cues, comprehension of constraints, and satisfaction with product lists.

Data architecture and reliability notes

Describe a lean architecture: ingestion, feature store, vector index, reranker, generation layer, and analytics sink. Provide SLAs per component with alerts for drift and latency regressions. Keep a change log for models and prompts to attribute effects.

For technical grounding on vector search and hybrid retrieval, cite a relevant primer such as the Deep Learning for Search survey, which helps justify model choices without overclaiming performance.

Evaluation rubric and reporting template

Offer a transparent rubric so graders can verify rigor: data readiness (20%), experimental design (25%), metrics and analysis (25%), ethics and governance (15%), and clarity of managerial insights (15%). Provide a one‑page executive summary and an appendix with event schemas and prompt versions.

Analysis and insight generation

Go beyond uplift. Decompose wins and losses by query intent class, attribute completeness, and catalog freshness. Show counterfactuals: what happens when the assistant is present but reranking is disabled?

Expected learning outcomes for students

Students will learn to frame business questions for AI discovery, set up defensible experiments, blend retrieval and generation, measure relevance and conversion, and articulate governance requirements that translate into maintainable product policies.

Common pitfalls and how to avoid them

Avoid vague objectives, missing baselines, and ungrounded assistant claims. Don’t compare different traffic mixes or ignore latency’s effect on conversion. Pre‑register hypotheses and avoid retrofitting KPIs after peeking at results.

FAQs on generative AI discovery projects

What dataset size is sufficient?

Target at least tens of thousands of queries or sessions for stable estimates; for smaller sites, extend the test window and focus on relevance and UX metrics.

How do I prevent hallucinations?

Use strict grounding via RAG, cite sources, limit generation to summaries of retrieved facts, and implement confidence thresholds with fallbacks to standard search.

Which KPIs matter most?

Pick one primary commercial metric (e.g., add‑to‑cart rate) and two UX metrics (reformulations, time to first relevant result) to balance value and experience.

Can I run this without large budgets?

Yes. Start with open‑source embeddings, a lightweight reranker, and a small inference plan. Focus on measurement discipline rather than complex models.

How to present findings and next steps

Close with a decision memo summarizing the effect of generative AI product discovery on target KPIs, its reliability, and governance readiness. Recommend scale‑up criteria, back‑out conditions, and a roadmap for data quality and latency improvements.

For additional MBA project ideas and evaluation patterns, see the curated MBA E‑Business Reports and a complementary foundation on technical standards in Standards and Specification — an Overview (MBA E‑Business).

Have questions about scoping, data plans, or write‑ups? Reach out via Contact EmptyDoc and our team will help you refine your academic project.