Generate comprehensive AI strategy reports (15+ slides) in PowerPoint format. Analyzes company's digital readiness, identifies high-value AI use cases, creates implementation roadmaps, and provides ROI projections. Designed for consulting firm quality deliverables.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
[company name] [industry] [focus area] e.g. ABC Manufacturing, Auto Parts, Cost Reduction
description
Generate comprehensive AI strategy reports (15+ slides) in PowerPoint format. Analyzes company's digital readiness, identifies high-value AI use cases, creates implementation roadmaps, and provides ROI projections. Designed for consulting firm quality deliverables.
AI Strategy Report is a comprehensive strategic document that analyzes a company's AI readiness, identifies high-value use cases, and creates a practical implementation roadmap. This skill generates professional-grade PowerPoint presentations of 15+ slides using the KPMG consulting template for consistent, professional formatting.
Key Features:
Complete strategic framework: 15-slide structure covering the full AI transformation journey
Data-driven analysis: Digital maturity assessment and data readiness evaluation
Prioritization matrix: 2×2 value-feasibility matrix for AI opportunities
Deep: 结合客户行业、数据资产、系统架构、组织成熟度、知识库案例和历史项目记忆，形成董事会级 AI 转型方案。
AI Portfolio Logic
每个 AI 用例必须同时评估：业务价值、数据可得性、技术可行性、组织准备度、风险合规、落地周期和可复制性。优先级不能只按“看起来先进”排序。
Quality Gates
AI 用例与客户业务痛点和数据资产匹配。
投资收益有假设、区间和验证方式。
路线图区分数据基础、模型能力、业务流程和组织变革。
风险覆盖数据隐私、模型准确性、合规、采纳和运维。
PPT 输出前已有清晰 storyline，不直接堆幻灯片。
Consulting Excellence Layer
AI Value Pool Logic
AI strategy must quantify value pools before listing use cases. Organize value into:
Value Pool
Typical Levers
Evidence Needed
Revenue growth
Conversion, pricing, cross-sell, retention
Funnel, customer, sales data
Cost reduction
Automation, workload reduction, rework reduction
Process volume, FTE, cycle time
Risk control
Fraud, compliance, quality, safety
Incidents, exceptions, loss data
Decision quality
Forecasting, planning, prioritization
Historical decisions and outcomes
Knowledge leverage
Proposal reuse, case retrieval, expert assistance
Document corpus and usage patterns
Use Case Investment Committee
Every AI use case must be described as an investment case:
Business problem.
User and workflow.
Data required.
Model approach.
Integration point.
Human review point.
Benefit hypothesis.
Risk and control.
Pilot metric.
Scale condition.
Build / Buy / Partner Decision
Condition
Recommended Path
Commodity capability, low differentiation
Buy SaaS or API
Proprietary data and workflow advantage
Build on internal data
Need speed plus domain expertise
Partner / co-build
High compliance or sensitive data
Private deployment or controlled harness
AI Governance Minimum
Deep AI strategy must include:
Model ownership and approval.
Data access and permission rules.
Prompt and output review policy.
Evaluation metrics and regression testing.
Incident response and rollback.
Human-in-the-loop points.
Vendor and cost governance.
Pilot Design Standard
Each pilot must be small enough to run in 8-12 weeks and strong enough to prove business value. Define baseline, target, sample users, process integration, evaluation method, and scale/no-scale decision gate.
