Course Title:

AI Adoption for Strategic Management

Course Description

This course explores how organizations can strategically adopt Artificial Intelligence (AI) to drive competitive advantage, innovation, operational excellence, and long-term growth. Participants will learn how to align AI initiatives with business strategy, manage transformation, assess risks, and build sustainable AI-enabled enterprises.

Course Objectives

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

• Understand AI technologies and their strategic implications
• Identify AI opportunities within business models and value chains
• Develop AI adoption roadmaps aligned with corporate strategy
• Evaluate risks, governance, and ethical considerations
• Lead AI-driven transformation initiatives
• Measure AI performance and strategic impact

Module 1: Foundations of AI in Business Strategy

Topics:

• Introduction to Artificial Intelligence
• Machine Learning, Deep Learning, NLP, Computer Vision
• AI vs Automation vs Analytics
• AI’s role in digital transformation
• Global AI trends and industry disruption

Learning Outcome:

Understand core AI concepts and their relevance to strategic management.

Module 2: Strategic Management in the AI Era

Topics:

• Revisiting strategic management principles
• Competitive advantage in AI-driven markets
• Porter’s Five Forces in AI context
• Blue Ocean Strategy and AI innovation
• Dynamic capabilities and AI agility

Case Studies:

• Amazon AI strategy
• Tesla’s AI competitive model
• Netflix recommendation engine strategy

Module 3: Identifying AI Opportunities

Topics:

• AI use case identification framework
• Value chain analysis for AI integration
• Customer journey optimization using AI
• AI for operational efficiency
• AI-driven decision intelligence

Practical Activity:

• AI opportunity mapping workshop

Module 4: Building an AI Adoption Roadmap

Topics:

• AI maturity assessment
• AI readiness evaluation (people, process, technology, data)
• Build vs Buy vs Partner decision
• Developing AI implementation roadmap
• Budgeting and ROI estimation

Tools:

• AI capability maturity models
• Strategic AI canvas

Module 5: Data Strategy for AI Success

Topics:

• Data governance and data quality
• Data architecture for AI
• Big data management
• Cloud and AI infrastructure
• Data monetization strategies

Module 6: AI Governance, Risk & Ethics

Topics:

• AI governance frameworks
• Responsible AI principles
• Bias, fairness, and transparency
• AI regulatory landscape (GDPR, AI Act)
• Cybersecurity risks in AI systems

Discussion:

Ethical dilemmas in AI-powered decision-making

Module 7: Organizational Change & Leadership

Topics:

• Leading AI transformation
• Change management frameworks (Kotter, ADKAR)
• AI talent strategy
• Upskilling and workforce transformation
• Overcoming resistance to AI adoption

Module 8: AI and Business Model Innovation

Topics:

• AI-enabled business models
• Platform strategy and ecosystems
• AI as a service (AIaaS)
• Monetizing AI capabilities
• AI startups vs legacy enterprises

Module 9: Measuring AI Strategic Impact

Topics:

• KPIs for AI initiatives
• Balanced scorecard in AI environment
• AI ROI measurement
• Performance dashboards
• Continuous optimization

Module 10: Capstone Project

Participants will:

• Select an industry or organization
• Conduct AI readiness assessment
• Identify strategic AI use cases
• Develop an AI adoption roadmap
• Present governance and risk mitigation plan
• Deliver a strategic AI implementation proposal

Teaching Methodology

• Lectures
• Case studies
• Group discussions
• Industry guest sessions
• Hands-on workshops
• Capstone project

Assessment Methods

• Class participation – 10%
• Case study analysis – 20%
• AI opportunity mapping assignment – 20%
• Midterm test – 20%
• Capstone project – 30%

Recommended Tools & Platforms

• ChatGPT / Generative AI tools
• Power BI / Tableau
• Google Cloud AI / Azure AI
• Miro (strategy mapping)
• SWOT & PESTLE frameworks