AI in Business 2600-DMdzAIBf

The foundations of artificial intelligence and its place in business
Block 1: History and Basic Concepts of AI
• The origin of artificial intelligence : Alan Turing, The Turing Test , Dartmouth Conference.
• Main stages of development: expert systems, machine learning, deep learning.
• Basic concepts: algorithm, model, training data, neural network.
• Differences between AI, ML, DL, Generative AI.
Block 2: Key AI Technologies
• Machine learning – supervised and unsupervised learning.
• Deep learning – multi-layer neural networks.
• Natural Language Processing – language analysis and generation.
• Generative AI – models that create content (text, images, sounds, videos).
Block 3: AI Application Areas in Business
• Marketing and sales – recommendations, predictions, personalization.
• Finance – credit scoring , fraud detection.
• HR – recruitment and competency analysis.
• Logistics – demand forecasting, route optimization.
• Customer service – chatbots , voicebots .
Block 4: AI and the labor market and business
• AI as support vs AI as threat.
• The concept of augmented intelligence .
• Changing employee competencies in the AI era.
AI in Marketing and Customer Experience
Block 1: Recommendation algorithms
• Mechanisms recommendations : collaborative filtering, content-based filtering.
• Examples: Amazon, Netflix , Spotify .
• The impact of recommendations on sales and customer loyalty.
Block 2: Personalization and Dynamic Pricing
• Content personalization: mailing, websites, online advertising.
• Dynamic pricing – operating logic, examples (Uber, airlines).
• Challenges and controversies of dynamic pricing .
Block 3: Communication automation – chatbots and voicebots
• Role in customer service – 24/7 availability, cost reduction.
• NLP technologies in practice.
• Advantages and limitations of chatbots in customer communication.
Block 4: AI in marketing campaigns
• Programmatic advertising – automation of media buying.
• Predictive targeting – predicting customer behavior .
• Automatic optimization of advertising campaigns.
Data and machine learning in business practice
Block 1: The role of data in artificial intelligence
• Data sources in enterprises (CRM, e-commerce, social media).
• Big data vs smart data.
• Data quality as a condition for the effectiveness of AI models.
Block 2: Machine learning mechanisms
• Supervised learning: classification, regression.
• Unsupervised learning: clustering , segmentation.
• workflow : data → training → model → evaluation.
Block 3: Customer Segmentation and Predictive Analytics
• Demographic segmentation vs behavioral segmentation.
• churn and value prediction ( Customer Lifetime Value).
• Sentiment analysis in social media.
Block 4: Application Case Studies
• E-commerce: recommendations and personalization (Amazon, Allegro).
• Banking : fraud detection, credit scoring .
• Retail and FMCG: demand forecasting, inventory management.

Regulation, Ethics, and the Future of AI in Business
Block 1: Legal regulations regarding AI
• EU AI Act – risk assessment system.
• GDPR and restrictions on customer profiling.
• Copyright and intellectual property in the context of AI.
• Regulations in the US and Asia – differences in approaches.
Block 2: Ethical Challenges of AI
• Bias and discrimination in algorithms.
• Transparency and accountability of AI systems.
• Deepfake , fake news and their impact on business and society.
Block 3: Business threats and risks
• Risk of incorrect forecasts and decisions.
• Cybersecurity and attacks on AI models.
• Reputational risks associated with the use of AI in marketing.
Block 4: The Future of AI in Business
• AI as a tool supporting creativity.
• Trend: AI agents and autonomous decision-making systems.
• AI in the concept of Marketing 5.0, 6.0 ( Kotler et al.).
• Directions of further development and implications for company strategies.

Basic literature
Russell, S., Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Marr, B. (2021). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems . Wiley .
Kotler , P., Kartajaya , H., Setiawan , I. (2021). Marketing 5.0: Technology for Humanity . Wiley.
Mazurek, G. (2019). Digital transformation – marketing, business, consumers . Poltext .
Additional literature
Chaffey, D., Ellis-Chadwick, F. (2022). Digital Marketing (8th ed.). Pearson.
Brynjolfsson, E., McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future . W. W. Norton & Company.
McKinsey Global Institute (2022). The State of AI in 2022 . Industry report.
Błażewicz, J., Perek, Ł., Szczęch, A. (2022). Artificial intelligence in business and management [in:] E-mentor , no. 2(94), SGH