customer-activity-forge

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-grounding
• docs/scenario.html?id=content-understanding-document-workflow
• docs/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.