Course Description¶

Title¶

AI for Investor Relations Transformation

Overview¶

In an era where artificial intelligence (AI), agentic systems, and data analytics are reshaping capital markets, the investor relations (IR) function is undergoing rapid transformation. This self-paced executive course equips senior leaders—especially Chief Data & AI Officers (CDAO), CFOs, and strategic advisors—with the frameworks, tools, and governance models required to lead AI-powered IR modernization efforts.

Built on Wharton-caliber instructional rigor and drawn from Fortune 100 best practices, the course explores how advanced AI—particularly generative and agentic architectures—can enhance investor communications, regulatory alignment, stakeholder analysis, and IR strategy. Through case studies, hands-on exercises, and applied projects, learners will build the strategic literacy and operational insight necessary to drive responsible and high-impact AI adoption in the IR domain.

Target Audience¶

Executive leaders (CDAO, CFO, CIO) driving AI transformation in finance and communications
Heads of investor relations and corporate strategy teams
Strategic advisors and consultants working with public companies on market engagement
Experienced AI/ML professionals new to the investor relations domain

Prerequisites¶

Working knowledge of corporate financial statements and capital markets
Basic understanding of investor relations roles and disclosures (e.g., Reg FD, earnings calls)
Familiarity with AI/ML concepts (no programming required)
Executive-level experience in digital, data, or innovation functions

Topics Covered¶

Foundations of Modern IR
Strategic role of IR in market communication and corporate valuation
Core IR workflows: earnings reporting, investor targeting, and Q&A preparation
AI-Augmented IR Communications
Generative AI tools for drafting earnings materials and investor memos
Tone and compliance considerations for AI-generated content
Investor Sentiment & Predictive Analytics
Sentiment modeling from filings, media, and social channels
Forecasting market responses to IR narratives
Agentic and Autonomous AI Systems
Agent orchestration for live data retrieval and briefing generation
Model Context Protocol (MCP) as a secure AI integration standard
Data Governance and Compliance in IR
Regulatory frameworks including Reg FD, SOX, and their implications for AI-assisted disclosures
Preventing selective disclosure through AI-generated content
Managing risks such as AI hallucinations, bias, and drift in financial communications
AI Transformation Strategy for IR
IR operating model redesign and roadmap planning
Talent, tooling, and governance alignment
C-Suite Communication and Change Management
Storytelling for data transformation in IR
Building cross-functional buy-in for AI use in market-facing functions

Topics NOT Covered¶

Deep learning architectures or model development
Proprietary algorithmic trading or high-frequency strategies
Securities law and financial auditing practices
Hands-on Python or programming-based AI implementation
Technical accounting or GAAP-focused instruction

Learning Outcomes¶

By the end of this course, learners will be able to:

Remember¶

List the strategic functions of a modern IR team
Identify key regulatory frameworks impacting IR (e.g., Reg FD, SOX)
Recall major types of institutional investors and their priorities
Name common AI tools used in investor communications

Understand¶

Explain how generative AI supports IR messaging and narrative consistency
Describe how algorithmic trading affects investor perception and timing of disclosures
Interpret key sentiment signals and engagement metrics
Summarize ethical risks in AI-assisted IR workflows
Explain how Reg FD governs public disclosures and why it’s critical in AI-generated communication

Apply¶

Use GenAI tools to draft investor-ready documents with governance safeguards
Apply sentiment analysis to investor feedback, analyst reports, and social commentary
Build AI-enhanced dashboards to monitor investor engagement KPIs
Deploy AI assistants for summarizing financial data and Q&A prep

Analyze¶

Analyze the effectiveness of AI-generated investor narratives across channels
Examine AI vendor offerings for IR fit, risk, and regulatory alignment
Assess internal readiness for adopting agentic AI across IR touchpoints
Investigate data quality and bias in market analytics pipelines

Evaluate¶

Compare AI-driven IR strategies for shareholder reporting and perception management
Judge the risks of over-automation and hallucination in sensitive disclosures
Evaluate governance frameworks for responsible AI use in market communications
Evaluate vendor solutions and internal processes for their ability to ensure Reg FD compliance

Create¶

Design a transformation roadmap for AI-powered IR—including tech stack, workflows, and governance
Develop responsible AI policies aligned with IR regulations and brand reputation
Create an agent-enabled IR assistant prototype using an MCP-compliant architecture

Capstone Project: Develop a comprehensive AI-Enhanced IR Transformation Plan that includes:

Strategic vision and transformation objectives
AI tooling architecture mapped against regulatory requirements (e.g., Reg FD, SOX)
Compliance protocols for reviewing and auditing AI outputs before public release
Governance model for responsible AI, including role-based review, escalation processes, and audit trails
Change enablement and C-suite alignment for secure rollout

Course Design Philosophy¶

Bloom’s Level	Instructional Strategy
Remember/Understand	Multimedia lectures, foundational readings
Apply	AI sandbox labs, prompt templates, guided tools
Analyze	Case study deconstruction, sentiment deep dives
Evaluate	Governance simulations, vendor solution critiques
Create	Capstone roadmap, IR AI implementation plans

Format & Assessment¶

Delivery: 100% self-paced, asynchronous online
Modules: 6–8 modules + 1 capstone project
Assessments: Reflection prompts, AI labs, case study analyses, roadmap presentation
Credential: Certificate of completion verifying AI-in-IR strategic fluency

Instructors & Contributors¶

Instruction by senior AI, IR, and capital markets leaders from Fortune 100 enterprises, top-tier advisory firms, and major academic institutions. Guest contributions from AI governance experts, GenAI builders, and regulatory advisors.

Next Step¶

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