# Commercial AI journey mapper Human Guide

## What This Is For
Maps how buyers discover, compare, validate, and choose brands across multi-turn AI conversations, then identifies content and measurement gaps. It gives the agent a clearer input/output frame for commercial AI journey mapper: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Commercial AI journey mapper agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for commercial AI journey mapper.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Commercial AI journey mapper guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- 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.
- 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
- Decide whether AI commercial discovery is material for this category.
- resolve price, risk, compatibility, or implementation concerns

## Decision Points And Nuance
The original skill emphasizes: Research basis, Goal, Intake, Workflow, Output.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

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

## Copy-And-Paste Prompt
```text
Use the Commercial AI journey mapper human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the Profound skill entry for `commercial-ai-journey-mapper`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Commercial AI Journey Mapper

## Research basis

This skill operationalizes Profound's research, [Commercial conversations in ChatGPT more than doubled in a year](https://www.tryprofound.com/blog/commercial-conversations-in-chatgpt-more-than-doubled-in-a-year), 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.