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

Version	Date	Changes
1.0.0	2024-03-25	Initial release

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:

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.

Deliverable Catalog

Deliverable	When to use	Minimum content	Format
AI readiness assessment	判断企业是否适合推进 AI	数据、流程、系统、人才、治理、风险成熟度	Markdown / PPT
AI value pool map	寻找高价值机会	收入、成本、风险、决策、知识价值池和证据	PPT / Excel
AI use-case portfolio	选择 AI 场景	用例、用户、数据、模型、价值、风险、优先级	Excel / PPT
Pilot charter	启动试点	目标、范围、用户、数据、指标、时间、评估方式	Markdown / Word
Data readiness checklist	试点前准备	数据源、权限、质量、历史长度、敏感等级、缺口	Markdown / Excel
AI governance framework	管理模型风险	角色、审批、评估、HITL、回滚、供应商和成本治理	Word / PPT
AI transformation roadmap	进入规模化建设	阶段、能力、平台、流程、组织、投资、KPI	PPT
Board AI strategy deck	董事会或高管汇报	AI 战略命题、用例组合、投资收益、治理风险、90 天行动	PPT