Economics of Artificial Intelligence Training Course

 Economics of Artificial Intelligence Training Course 

Introduction 

Artificial Intelligence (AI) is transforming productivity, labor markets, business models, public policy, investment, and global competitiveness. Economics of Artificial Intelligence Training Course provides a practical and strategic understanding of how AI technologies influence economic growth, productivity, employment, wages, innovation, market structures, trade, inequality, and organizational performance. The course integrates AI economics, digital transformation, data-driven decision-making, automation economics, AI investment, productivity analysis, and economic forecasting to help participants evaluate both the opportunities and risks created by rapidly advancing AI systems. 

Participants will examine how governments, corporations, financial institutions, and international organizations can measure the economic value of AI while managing disruption, regulation, ethical risks, and changing workforce requirements. Through global case studies, economic models, policy analysis, scenario planning, and practical applications, learners will develop the ability to assess AI investments, forecast economic impacts, design evidence-based policies, and support strategic decisions in AI-driven economies. The course is suitable for professionals seeking advanced knowledge of the economic implications of generative AI, automation, machine learning, and digital innovation. 

Course Objectives 

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

1. Analyze the economic foundations and drivers of Artificial Intelligence adoption.
2. Evaluate AI's impact on productivity, economic growth, and competitiveness.
3. Assess automation, employment, wages, and future workforce trends.
4. Apply cost-benefit analysis to AI investments and projects.
5. Analyze AI-driven innovation, entrepreneurship, and business models.
6. Evaluate AI market structures, competition, and digital platforms.
7. Assess the economics of data, computing infrastructure, and AI capabilities.
8. Analyze generative AI's impact on industries and value chains.
9. Evaluate AI investment, financing, and economic returns.
10. Examine AI regulation, taxation, and public policy.
11. Assess AI's implications for inequality and inclusive growth.
12. Develop AI economic scenarios and strategic forecasts.
13. Apply evidence-based economic analysis to AI decision-making.

Organizational Benefits
 

• Improved strategic AI investment decisions
• Better understanding of AI-related economic risks
• Stronger productivity and innovation strategies
• Enhanced workforce planning
• Improved technology investment evaluation
• Stronger competitive positioning
• Better AI governance and policy compliance
• More effective economic forecasting
• Improved digital transformation outcomes
• Stronger long-term AI strategy development

Target Audiences
 

1. Economists and economic analysts
2. Government policymakers and regulators
3. Business executives and managers
4. Finance and investment professionals
5. AI and technology professionals
6. Consultants and strategic planners
7. Researchers and academics
8. Entrepreneurs and business owners

Course Duration: 10 days

Course Modules

Module 1: Foundations of AI Economics
 

• Economic principles of Artificial Intelligence
• AI as a general-purpose technology
• Drivers of AI adoption
• Digital transformation and economic value
• AI productivity mechanisms
• Case study: Global AI adoption trends

Module 2: AI and Economic Growth
 

• AI contributions to GDP growth
• Productivity-enhancing technologies
• Capital deepening and AI investment
• Innovation-led economic growth
• Measuring AI economic output
• Case study: AI and productivity in the United States

Module 3: AI, Productivity and Efficiency
 

• Measuring AI-enabled productivity
• Labor and capital productivity
• Process automation and efficiency
• Total factor productivity
• Productivity measurement challenges
• Case study: AI-enabled manufacturing efficiency

Module 4: AI and Labor Markets
 

• Automation and job displacement
• Job creation and augmentation
• Wage effects and skill premiums
• Workforce transition strategies
• Future-of-work economics
• Case study: AI transformation of professional services

Module 5: Economics of Generative AI
 

• Generative AI economic characteristics
• Large language model economics
• AI-assisted knowledge work
• Generative AI productivity gains
• Cost structures of AI services
• Case study: Enterprise adoption of generative AI

Module 6: AI Investment and Cost-Benefit Analysis
 

• AI investment appraisal
• Return on AI investment
• Total cost of ownership
• Risk-adjusted economic evaluation
• AI project prioritization
• Case study: Corporate AI investment decisions

Module 7: AI Innovation and Entrepreneurship
 

• AI-driven innovation economics
• Startup ecosystems and venture capital
• Platform-based AI business models
• Intellectual property economics
• Innovation diffusion
• Case study: Global AI startup ecosystems

Module 8: AI Markets and Competition
 

• Economics of AI market structures
• Network effects and platform economics
• Economies of scale and scope
• Market concentration
• Competition policy challenges
• Case study: Competition in global AI markets

Module 9: Economics of Data and AI Infrastructure
 

• Data as an economic asset
• Data valuation and ownership
• Cloud computing economics
• Semiconductor and computing capacity
• AI infrastructure investment
• Case study: Global data-center expansion

Module 10: AI, Trade and Global Competitiveness
 

• AI and international trade
• Digital trade transformation
• AI-enabled global value chains
• National AI competitiveness
• Technology-driven comparative advantage
• Case study: AI competition among major economies

Module 11: AI, Inequality and Inclusive Growth
 

• Distributional effects of AI
• Income and wealth inequality
• Digital skills gaps
• Access to AI technologies
• Inclusive AI economic strategies
• Case study: AI and inequality across economies

Module 12: AI Policy, Regulation and Taxation
 

• Economics of AI regulation
• Regulatory costs and benefits
• AI taxation considerations
• Public-sector AI policy
• Market-failure correction
• Case study: Emerging global AI regulatory approaches

Module 13: AI in Public Economics
 

• AI and government productivity
• Public-service automation
• AI-enabled fiscal management
• Economic forecasting for governments
• Public investment in AI
• Case study: AI applications in public administration

Module 14: AI Economic Forecasting and Scenario Planning
 

• AI economic forecasting methods
• Scenario development techniques
• Technology adoption forecasting
• Macroeconomic impact assessment
• Risk and uncertainty analysis
• Case study: National AI economic scenarios

Module 15: Strategic Economics of AI
 

• Developing AI economic strategies
• Measuring organizational AI value
• Sustainable AI investment decisions
• Building AI-ready economies
• Executive economic decision-making
• Case study: Designing a national AI competitiveness strategy

Training Methodology
 

• Instructor-led presentations and expert discussions
• Interactive workshops and economic modeling exercises
• Global case studies and industry examples
• Group assignments and strategic problem-solving
• AI investment and cost-benefit simulations
• Scenario planning and economic forecasting exercises
• Policy analysis and regulatory simulations
• Practical demonstrations of AI economic applications
• Peer learning, presentations, and facilitated discussions
• End-of-course assessment and strategic action planning

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.