| name | enterprise-usp-monetization-pack |
| description | Use when applying the monetizable enterprise USP pack for Dataverse and Power Platform: compliance evidence automation, ROI and process value scoring, autonomous remediation playbooks, tenant-specific AI skill generation, governed cross-tenant rollout, business capability mapping, trust scoring for AI actions, autonomous exception management, enterprise change impact AI, and AI-controlled operating cadence. |
Enterprise USP Monetization Pack
Purpose
Turn the product vision into practical, sellable enterprise USPs that create measurable value for Power Platform, Dataverse, governance, operations, and executive stakeholders.
Use this skill when the user asks for:
- monetizable AI USPs
- enterprise differentiation
- market-facing product value
- trust, compliance, and governance features
- Center of Excellence value propositions
- Power Platform production readiness differentiators
- roadmap and MVP planning for advanced product features
USP Catalog
Cover these USPs explicitly:
USP 31 - Compliance Evidence Automation: automatically capture actor, reason, data, rules, approval, change, and evidence for every AI action.USP 32 - ROI and Process Value Scoring: score workflows and automation candidates by time saved, risk reduction, frequency, cost, compliance exposure, and time-to-value.USP 33 - Autonomous Remediation Playbooks: turn detected issues into governed remediation plans for drift, data quality, role mismatch, flow failures, deployment risk, and compliance exceptions.USP 34 - Tenant-Specific AI Skill Factory: generate local AI skills from tenant metadata, terminology, governance rules, approval models, and business processes.USP 35 - Governed Cross-Tenant Rollout: plan and monitor rollout across dev, test, production, regions, business units, and tenants.USP 36 - Business Capability Map: connect Dataverse and Power Platform assets to business capabilities and ownership.USP 37 - Trust Score for AI Actions: score proposed actions by data quality, permissions, policy, approval, reversibility, impact, dependency risk, evidence, and confidence.USP 38 - Autonomous Exception Management: detect, classify, route, and recommend next actions for process exceptions.USP 39 - Enterprise Change Impact AI: explain business and technical impact before changes to tables, flows, roles, processes, integrations, or dashboards.USP 40 - AI-Controlled Operating Cadence: generate briefings, risk reviews, follow-ups, escalation queues, decision agendas, KPI explanations, and owner-specific action lists.
Workflow
- Identify the target buyer or user: CoE, governance, operations, architecture, executive management, sales, service, PMO, or IT.
- Select the relevant USP numbers and clarify the painful job.
- Define the AI mechanism and the Dataverse or cross-system data needed.
- Separate current plugin/MCP capability from future runtime capability.
- Define the trust model: permissions, approvals, audit evidence, reversibility, privacy, and human override.
- Produce an MVP that can be proven with current read-only metadata, simulation, and guidance where possible.
- Define a measurable success signal that supports buying or renewal decisions.
Output Format
Return:
USP focus: selected USP numbers and titles.Target buyer: who would pay or sponsor adoption.Pain solved: operational, governance, risk, cost, or adoption problem.AI mechanism: how AI creates differentiated value.Data required: Dataverse, Power Platform, and cross-system context needed.MVP: first credible version.Trust model: controls needed before production use.Success metric: measurable signal.Commercial angle: how to position it in marketing, sales, or roadmap materials.
