Commercial AI journey mapper

Commercial AI Journey Mapper#

Research basis#

This skill operationalizes Profound's research, Commercial conversations in ChatGPT more than doubled in a year (opens in a new tab), published August 13, 2026.

Key takeaways from the study:

• Profound classified 7.5 million ChatGPT conversations across a one-year period.
• Commercial intent rose from 13.9% to 19.2% of sampled conversations.
• Commercial conversations were less likely to end after one turn than informational or generative conversations.
• Commercial-intent share varied by roughly tenfold across industries, so category context determines how much investment is justified.

The study uses sampled and modeled estimates. Do not reuse its volume projections as a brand forecast or assume every commercial conversation results in a purchase.

Goal#

Map the questions and decisions that move a buyer from an initial need to a shortlist, comparison, validation, and action inside an AI conversation.

Intake#

Collect:

• product, category, market, and buying model
• target buyers, users, and buying committee
• competitors and alternatives
• sales cycle, price, risk, and switching cost
• customer research, win/loss notes, support questions, and real prompts
• priority answer engines and business outcomes

Workflow#

1. Decide whether AI commercial discovery is material for this category.
2. Separate user jobs:
   • define the need
   • discover the category
   • build a shortlist
   • compare alternatives
   • validate trust and fit
   • resolve price, risk, compatibility, or implementation concerns
   • choose and act
3. Map likely multi-turn transitions and identify where the brand appears, disappears, or is framed poorly.
4. Record the facts and sources needed at every stage.
5. Distinguish brand mention, recommendation, citation, positive framing, and conversion.
6. Inspect competitor advantages, "best for" labels, caveats, and exclusion criteria.
7. Find information gaps across owned pages, third-party sources, reviews, documentation, product data, and sales material.
8. Prioritize interventions by commercial value, observed demand, visibility gap, evidence confidence, and effort.
9. Design measurement for both the opening prompt and later decision turns.

Do not manufacture reviews, comparisons, or community sentiment. Never promise that content will secure recommendation placement.

Output#

Return:

1. Commercial relevance assessment for the category.
2. Journey map by stage, user question, decision criterion, likely source, and desired outcome.
3. Prompt families for opening and follow-up turns.
4. Brand and competitor shortlist analysis.
5. Content and evidence gaps.
6. Prioritized roadmap with owners and dependencies.
7. Measurement plan covering mention rate, shortlist inclusion, framing, citations, assisted visits, leads, and revenue.