Concept Taxonomy¶
AI for Investor Relations Transformation¶
This taxonomy organizes the 200 concepts into 12 categorical groups to support structured learning pathways and content organization.
Taxonomy Categories¶
1. IR-FOUND - IR Foundations¶
TaxonomyID: IR-FOUND
Description: Core investor relations concepts, roles, and strategic functions that form the foundation of IR knowledge.
Example Concepts: Investor Relations Function, Corporate Valuation Strategy, Market Communication Strategy, Shareholder Engagement
2. IR-OPS - IR Operations¶
TaxonomyID: IR-OPS
Description: Operational IR activities including earnings reporting, investor targeting, presentations, roadshows, and day-to-day IR workflows.
Example Concepts: Earnings Reporting Process, Investor Targeting Methods, Q&A Preparation Techniques, Roadshow Planning, Earnings Call Scripts
3. INVEST - Investor Types & Analysis¶
TaxonomyID: INVEST
Description: Different categories of investors, analysts, and their characteristics, priorities, and analysis methods.
Example Concepts: Institutional Investors, Retail Investors, Hedge Funds, Buy-Side Analysts, Analyst Coverage Review
4. REG-COMP - Regulatory & Compliance¶
TaxonomyID: REG-COMP
Description: Regulatory frameworks, compliance requirements, SEC filings, and disclosure rules governing investor relations.
Example Concepts: Regulation Fair Disclosure, Reg FD Compliance, Sarbanes-Oxley Act, SEC Filing Requirements, Material Information
5. VALMET - Valuation & Metrics¶
TaxonomyID: VALMET
Description: Financial metrics, valuation methods, market indicators, and performance measurement for IR.
Example Concepts: Stock Price Volatility, Valuation Multiples, P/E Ratio Insights, Market Capitalization, Shareholder Return Metrics
6. AI-TECH - AI Technologies¶
TaxonomyID: AI-TECH
Description: Fundamental AI and machine learning concepts, including generative AI, LLMs, and foundational AI knowledge.
Example Concepts: AI Fundamentals, Machine Learning Basics, Large Language Models, Generative AI Tools, Prompt Engineering Skills
7. AI-CONT - AI Content Creation¶
TaxonomyID: AI-CONT
Description: AI applications for creating investor communications, reports, memos, and maintaining narrative consistency.
Example Concepts: AI for Content Creation, GenAI Earnings Reports, AI-Enhanced Press Releases, Drafting Investor Memos, Narrative Consistency
8. AI-GOV - AI Governance & Risk¶
TaxonomyID: AI-GOV
Description: AI governance frameworks, responsible AI practices, risk management, and ethical considerations for AI in finance.
Example Concepts: AI Governance Models, Responsible AI Practices, Recognizing Hallucinations, Mitigating AI Bias, AI Ethics for Finance
9. ANLYT - Analytics & Insights¶
TaxonomyID: ANLYT
Description: Sentiment analysis, predictive analytics, NLP, data analysis tools, and insights generation for IR.
Example Concepts: Sentiment Analysis Tools, Predictive Analytics, Natural Language Processing, Real-Time Sentiment Data, Trading Pattern Analysis
10. AGENTIC - Agentic AI Systems¶
TaxonomyID: AGENTIC
Description: Autonomous AI agents, agent orchestration, Model Context Protocol (MCP), and agentic workflows for IR.
Example Concepts: Agentic AI Systems, Autonomous AI Agents, Model Context Protocol, MCP Architecture Overview, Agent Orchestration
11. DATA-GOV - Data & Governance¶
TaxonomyID: DATA-GOV
Description: Data governance, quality, security, privacy, compliance monitoring, and risk management frameworks.
Example Concepts: Data Governance Basics, Managing Data Quality, Data Security Standards, Compliance Monitoring, Risk Management Frameworks
12. TRANSFORM - Transformation & Strategy¶
TaxonomyID: TRANSFORM
Description: AI transformation strategy, change management, roadmaps, operating models, talent development, and organizational change.
Example Concepts: AI Transformation Strategy, Change Management Plans, Roadmap Prioritization, Operating Model Design, Talent Strategy Planning
Distribution Guidelines¶
Target distribution (approximate):
- IR-FOUND: ~15 concepts (7.5%)
- IR-OPS: ~20 concepts (10%)
- INVEST: ~15 concepts (7.5%)
- REG-COMP: ~25 concepts (12.5%)
- VALMET: ~15 concepts (7.5%)
- AI-TECH: ~20 concepts (10%)
- AI-CONT: ~15 concepts (7.5%)
- AI-GOV: ~20 concepts (10%)
- ANLYT: ~20 concepts (10%)
- AGENTIC: ~15 concepts (7.5%)
- DATA-GOV: ~20 concepts (10%)
- TRANSFORM: ~20 concepts (10%)
Total: 200 concepts
Taxonomy Assignment Principles¶
- Primary Domain First: Assign concepts to their most relevant primary domain
- Avoid Over-Concentration: No single category should exceed 30% of total concepts
- Clear Boundaries: Categories should have distinct, non-overlapping scopes
- Pedagogical Grouping: Categories should support logical learning progressions
- Use MISC Sparingly: Only for truly cross-cutting concepts that don't fit elsewhere
Taxonomy created for AI for Investor Relations Transformation learning graph