AI Strategy Report

Overview

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
• Implementation roadmap: 3-phase timeline (0-6m/6-18m/18-36m)
• Financial projections: Investment breakdown and ROI calculations
• Risk assessment: Technical, organizational, and compliance risks with mitigations

Output Format: PowerPoint (.pptx) with professional styling and structured content.

When to Use This Skill

This skill should be used when:

• Developing AI transformation strategy for enterprises
• Evaluating AI opportunities and prioritizing use cases
• Creating implementation roadmaps for digital transformation
• Building business cases for AI investments
• Assessing organizational readiness for AI adoption
• Planning talent and capability building for AI teams
• Preparing board-level presentations on AI strategy
• Supporting M&A due diligence for AI capabilities
• Planning cloud migration and data infrastructure
• Creating vendor selection criteria for AI platforms

Report Structure (15 Slides)

Slide 1:  Cover Page
Slide 2:  Executive Summary
Slide 3-4: Current State Assessment
Slide 5-6: AI Opportunity Map (2×2 Matrix)
Slide 7-9: Top 3 Use Cases Deep Dive
Slide 10-11: Implementation Roadmap (3 Phases)
Slide 12: Investment & ROI Analysis
Slide 13: Organizational Capabilities
Slide 14: Risk Assessment & Mitigation
Slide 15: Next Steps & Action Items

Input Requirements

Required Information

**Company Basics**
- Company Name: [Name]
- Industry: [Industry Sector]
- Company Size: [Employees] / [Revenue]
- Digital Maturity: [Beginner/Intermediate/Advanced]

**Business Context**
- Core Business: [Description]
- Key Challenges: [List 2-3 major pain points]
- AI Objectives: [What problems to solve with AI]

**Strategic Priorities** (Select all that apply)
- [ ] Cost Reduction & Efficiency
- [ ] Revenue Growth
- [ ] Customer Experience
- [ ] Innovation & New Products
- [ ] Risk Management

Optional Information

**Data Assets**
- Existing data types: [Customer/Operational/IoT/etc]
- Data history: [Years of historical data]

**Technology Stack**
- Cloud platform: [AWS/Azure/GCP/Alibaba/etc]
- Existing systems: [ERP/CRM/MES/etc]

**Constraints**
- Budget range: [Amount]
- Timeline: [Expected delivery]
- Special restrictions: [Data privacy/etc]

Workflow

Phase 1: Analysis (Internal)

Analyze the input information and determine:

1. Digital maturity level based on described systems and processes
2. Data readiness for each potential AI use case
3. Priority ranking of AI opportunities (value × feasibility)
4. Implementation complexity for each phase

Phase 2: Content Generation

Generate structured content for each slide:

Slide 2 - Executive Summary:

• 3-5 key conclusions
• Investment overview
• Expected ROI
• Critical milestones

Slide 5-6 - AI Opportunity Map: Create a 2×2 matrix categorizing opportunities:

• Quick Wins (High Value, High Feasibility): Immediate start
• Strategic Bets (High Value, Low Feasibility): Long-term planning
• Low Priority (Low Value): Defer or discard

Slide 10-11 - Roadmap: Define 3 phases:

• Phase 1 (0-6 months): Foundation + Pilot
• Phase 2 (6-18 months): Scale + Capability Building
• Phase 3 (18-36 months): Optimization + Innovation

Phase 3: Tool Execution

Call generate_ppt_from_skill tool with structured slide content:

{
  "skill_name": "ai-strategy-report",
  "title": "[Company] AI Strategy Report",
  "subtitle": "Digital Transformation Roadmap",
  "slides": [
    {
      "type": "title",
      "title": "Cover Title",
      "content": "Subtitle"
    },
    {
      "type": "content",
      "title": "Slide Title",
      "content": "Bullet points and analysis"
    },
    {
      "type": "two_column",
      "title": "Comparison Slide",
      "left_content": "Current State",
      "right_content": "Future State"
    }
  ]
}

Phase 4: Optional Data Export

If user needs editable data, call save_json:

{
  "filename": "[Company]_AI_Strategy_Data",
  "data": {
    "scenarios": [...],
    "roadmap": {...},
    "financial": {...}
  }
}

Tool Configuration

Tool 1: generate_ppt_from_skill

Purpose: Generate PowerPoint using the KPMG template bundled with this skill

When to Call: After content generation is complete, always call this tool to create the deliverable.

Parameters:

{
  "skill_name": "ai-strategy-report",
  "title": "Company AI Strategy Report",
  "subtitle": "Digital Transformation Roadmap (2024-2027)",
  "slides": [
    {
      "type": "title|content|two_column",
      "title": "Action-oriented title (verb-first)",
      "content": "Markdown formatted content with bullet points",
      "left_content": "For two-column layout",
      "right_content": "For two-column layout"
    }
  ]
}

Content Guidelines:

• Use action-oriented titles ("Drive Efficiency Through AI-Powered Quality Control")
• Format with Markdown: - Bullet points, **Bold highlights**
• Keep bullet points concise (1-2 lines each)
• Use color coding: 🔴 High Risk, 🟡 Medium Risk, 🟢 Low Risk

Tool 2: save_json (Optional)

Purpose: Export structured data for further editing or integration

When to Call: When user explicitly asks for editable data or mentions integrating with other systems.

Parameters:

{
  "filename": "Company_AI_Strategy_Data",
  "data": {
    "company": "Company Name",
    "industry": "Industry Sector", 
    "scenarios": [...],
    "roadmap": {...},
    "financial": {...},
    "organization": {...},
    "risks": [...]
  }
}

Quality Standards

Content Requirements

• Specificity: All recommendations must be specific to the company's industry and stated challenges
• Quantification: Include estimated savings/returns where possible (mark as "estimated" if not precise)
• Feasibility: Only recommend AI use cases that match the described data availability
• Actionability: Every recommendation must have clear next steps

Slide Content Standards

Executive Summary (Slide 2):

• Max 5 conclusions
• Include 1-line ROI summary
• List 3 critical milestones

Opportunity Map (Slide 5-6):

• Minimum 4 opportunities mapped
• Clear rationale for each quadrant placement
• Prioritization within each quadrant

Use Case Deep Dive (Slide 7-9): For each of top 3 use cases:

• Business pain point (2-3 sentences)
• AI solution approach (high-level)
• Quantified expected benefit
• Implementation complexity rating

Roadmap (Slide 10-11):

• Each phase has clear deliverables
• Logical dependencies between phases
• Resource requirements specified

ROI Analysis (Slide 12):

• 3-year investment breakdown
• Year-by-year savings projection
• Payback period calculation
• Key assumptions listed

Prohibited Content

• Do NOT specify specific vendors (e.g., "use AWS SageMaker")
• Do NOT make unrealistic claims (e.g., "100% automation")
• Do NOT ignore stated constraints (e.g., data privacy requirements)
• Do NOT provide implementation details beyond strategic level

Example Output

See examples/manufacturing_example.md for a complete input-output example.

Best Practices

For High-Quality Output

1. Encourage detailed input: If user input is vague, ask clarifying questions
2. Be conservative with estimates: Better to under-promise than over-promise
3. Highlight risks explicitly: Don't hide implementation challenges
4. Emphasize data readiness: Make clear when data preparation is needed
5. Provide alternatives: Offer options when ideal path isn't feasible

Industry Customization

Manufacturing:

• Focus: Predictive maintenance, quality control, supply chain
• Key metrics: OEE, defect rates, inventory turnover

Retail/E-commerce:

• Focus: Demand forecasting, personalization, pricing
• Key metrics: Conversion rate, customer LTV, inventory accuracy

Financial Services:

• Focus: Risk modeling, fraud detection, customer service
• Key metrics: False positive rate, processing time, compliance score

Healthcare:

• Focus: Diagnostic imaging, patient triage, resource optimization
• Key metrics: Diagnostic accuracy, wait times, resource utilization

Dependencies

Required Backend Tools

• generate_ppt - python-pptx 1.0.2
• save_json - Python built-in json

System Requirements

• AriaAI Backend >= 1.0.0
• Function Calling support enabled

Version History

Maintenance

• Maintainer: AriaAI Team
• Update Cycle: Quarterly review
• Feedback: Submit via Issue or contact admin

Capability Upgrade

Mode Selection

• Quick: 输出 AI 机会清单、优先级和 90 天试点建议。
• Standard: 输出完整 AI 战略报告、用例组合、路线图、投资和组织能力建议。
• 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:

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

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.

Deliverable Catalog

Diagnostic Intake

先确认行业、决策问题、现状资料、读者、预算/人力约束和交付日期。 页数与格式沿用用户要求；默认结构只能用于未指定的场景。 对每个 AI 场景确认业务负责人、可获得的数据、权限和质量基线。 缺少成本或收益依据时，输出假设范围和敏感性分析，不承诺 ROI。 区分试点指标、规模化条件和停止条件，并为关键数字记录来源日期。 验收时检查建议是否可以由负责人执行、衡量和复盘。