| name | customer-activity-forge |
| description | Research a customer and industry from public sources, then generate ranked AI-application ideas mapped to the three customer-delivery scenario playbooks. |
| argument-hint | Company name and industry are required. Optional: region/segment and known pain points. |
Context
Use this skill when a participant has a customer or industry but no bounded AI opportunity. It
bridges “we should do something with AI” and a useful customer conversation: research public facts,
propose approximately ten achievable ideas, and map the best candidates to a scenario playbook.
This is an intake tool, not an architecture approval. The next step after choosing an idea is a
scenario playbook conversation that validates source ownership, access, environment, operating
model, and the evidence needed for the next decision.
Input
Required
customer_name — company or business unit.industry — sector, such as retail, healthcare, financial services, or manufacturing.
Optional
region_or_segment — geography or sub-segment.known_pain_points — customer signals to verify during research.
If either required input is missing, ask for it before proceeding.
Process
1. Research public sources only
Use web_search and web_fetch to collect:
- the company’s products, services, customer segments, recent announcements, and stated priorities;
- public evidence of operating activities from investor relations, annual reports, earnings material,
leadership posts, or official press releases;
- current industry pressures and AI/automation trends from freely accessible industry bodies,
analysts, or government sources.
Cite every company or industry claim with a URL and retrieval date. Mark unavailable facts with
⚠️ rather than guessing. Do not use account plans, CRM data, confidential material, or non-public
information.
2. Generate approximately ten ideas
Every idea must be tied to the research, safe enough for a first demonstration, and described with:
| Field | Guidance |
|---|
| Title | Outcome-first, 4–8 words |
| Description | What improves, how the experience works, and the first tangible output |
| Target user | The role benefiting from the result |
| Business outcome | What becomes faster, safer, cheaper, or more reliable |
| Scenario direction | One primary playbook plus any relevant secondary capability |
| First decision | The question to take into the selected scenario playbook |
| Effort | Starter, Core, or Stretch |
| Research fit | Why the idea fits this customer, with citation |
| Safe representative context | Candidate documents, data product, approved content, or sample to use in a demonstration |
| Evidence | The routine, edge, refusal, review, or access case that proves the first outcome |
Use these exact primary scenario labels:
| Scenario direction | Use when |
|---|
| AI Grounding / IQ | Trusted answers require the right mix of enterprise knowledge and operational context. This can include Foundry IQ, Fabric IQ, Work IQ, Web IQ, SharePoint, or a Copilot Studio discussion. |
| Content Understanding and Document Workflow | Business content needs SME-authored understanding, extraction, review, and handoff into a process. |
| Avatar Scenario | Approved learning, communications, onboarding, or support content needs an accessible, governed semi-automated avatar-led presentation. |
Visual input, structured data, actions, evaluation, tracing, and deployment are capabilities—not
competing top-level scenarios. Mention them only when they are necessary to the proposed proof.
3. Calibrate scope
| Effort | Meaning |
|---|
Starter | A narrow demonstrator for one user and one customer decision: a safe sample, an explicit owner, and a small evidence set. |
Core | A credible customer workshop or event-day proof: one bounded outcome, an evaluation/golden-data slice, and a clear review or operating decision. |
Stretch | A follow-on proof requiring multiple systems, a richer integration, multiple interfaces, or a more mature operating model. State what is intentionally deferred. |
Apply these guardrails:
- Do not propose a generic chatbot, a broad autonomous workflow, or a production integration that
cannot be demonstrated safely.
- Do not assume that every idea needs RAG, a new landing zone, or a Foundry-only implementation.
- Do not use file counts as an architecture decision. Start with ownership, access, freshness,
quality, and the evidence needed.
- Every idea needs an approved or synthetic representative sample. If customer data is not ready,
name the gap and use a safe sample only for the conversation.
4. Produce the result
Return the following sections, in this order:
Part A — Research summary
Three to five sentences with inline citations and explicit coverage gaps.
Part B — Ranked summary
| # | Title | Effort | Scenario direction | Why it fits |
|---|
| 1 | … | Core | AI Grounding / IQ | … |
Rank by customer fit, achievable first proof, and differentiation.
Part C — Idea details
Give all ten fields from step 2 for every idea.
Part D — Recommended top three
Name the top three and give a one-sentence reason for each.
Part E — Scenario handoff
For the top idea, pre-fill this handoff. Clearly mark information the customer must confirm.
| Scenario input | Pre-filled direction |
|---|
| Customer outcome | … |
| Target users and access boundary | … |
| Context and source owner | … |
| Existing environment | … |
| Ownership model | … |
| Recommended scenario | … |
| Golden-dataset / evidence starter | … |
| First customer decision | … |
End with the recommended scenario-playbook URL:
docs/scenario.html?id=ai-groundingdocs/scenario.html?id=content-understanding-document-workflowdocs/scenario.html?id=avatar-scenario
Anti-patterns
- Fabricating company facts or leaving research claims uncited.
- Treating the output as an approved architecture or a product recommendation.
- Prescribing Foundry, Copilot Studio, SharePoint, Fabric, or an IQ flavor before the customer’s
data ownership, access, licensing, and operating constraints are discussed.
- Suggesting a landing zone, broad infrastructure baseline, or production deployment for a first
demonstration.
- Omitting the customer owner, safe representative context, or first evidence case.
