The Problem
Ecommerce product teams are prioritizing AI in the wrong order. They build chatbots before having structured behavioral data. They implement personalized recommendations before fixing their catalog. They buy generative AI tools before understanding where users drop off.
The result: projects that generate no ROI and create internal skepticism about AI.
The 5 Steps
Step 1 — Data Diagnostics
Prerequisite: none Output: map of where your data is and its quality
Before any AI, map: behavioral events collected, catalog quality (attribute completeness), clean transactional data (no duplicates, consistent IDs), and customer data (cross-device identification).
If less than 70% of your catalog has complete attributes, start here.
Step 2 — Operations Automation
Prerequisite: clean transactional data Output: measurable operational cost reduction
Before improving experience, automate what is repetitive and costly: product categorization, review moderation, fraud detection, inventory forecasting.
Faster ROI and easier to measure. Builds the internal business case for investing in experience AI.
Step 3 — Experience Personalization
Prerequisite: per-session behavioral data, catalog with >80% complete attributes Output: increase in conversion or average order value
Product recommendations, semantic search, behavioral emails, dynamic landing pages. At this stage, pre-trained models (like those from ecommerce platforms) already deliver value without custom training effort.
Step 4 — Conversational Assistance
Prerequisite: structured FAQ, documented product policies, order system integration Output: reduction in support tickets and/or increased checkout conversion
Support agents, purchase copilot, post-sale assistant. The most common mistake here is launching without a structured knowledge base — the agent hallucinates and degrades the experience.
Step 5 — Proprietary Models
Prerequisite: data history >18 months, team with MLOps capability, specific use case with proven value Output: competitive advantage that is hard to replicate
Fine-tuning LLMs for your domain, proprietary propensity models, dynamic pricing systems based on real demand.
How to Use
Identify which step your operation is at today. The prerequisites for each step are your to-do list before advancing. Don’t skip steps — the cost of going back is always higher than completing the prerequisite.