Module I·Article II·~1 min read
Artificial Intelligence in Business: Opportunities and Limitations
Digital Transformation and AI
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What is Modern AI
Modern AI is not a “thinking robot” from science fiction, but a set of statistical methods that enable computers to detect patterns in data and make predictions.
Key technologies:
Machine Learning (ML): algorithms that learn from data without explicit programming. Supervised learning, unsupervised learning, reinforcement learning.
Deep Learning: neural networks with many layers. A revolution in computer vision (object recognition), speech recognition, and language processing.
Generative AI (GenAI): models that create new content — text (GPT-4, Claude), images (Midjourney, DALL-E), code (GitHub Copilot), video (Sora).
Business Applications of AI
Personalization: Recommendation systems of Amazon, Netflix, Spotify — AI chooses what to show each user. Up to 35% of Amazon’s revenue comes from recommendations.
Automation of routine tasks: Document processing (OCR + NLP), email classification, basic customer support (chatbots).
Predictive analytics: Churn prediction, prediction of equipment failures (predictive maintenance), credit scoring.
Optimization: Logistics (delivery routes), pricing (dynamic pricing), supply chain management.
Computer vision: Quality control in manufacturing, facial recognition, medical diagnostics from images.
Limitations of AI
Data: AI requires large amounts of high-quality, labeled data. “Garbage in — garbage out.”
Bias: AI learns from historical data, which reflects past biases. A credit algorithm trained on historical data may discriminate against minorities.
Explainability: Complex neural networks are “black boxes.” Regulators require explainability of decisions (GDPR: the right not to be subject to fully automated decisions).
Hallucinations (LLM): Large language models may confidently generate false information.
Practical Assignment
Choose a function in your organization (marketing, finance, operations, HR). Identify three specific processes that can be improved with AI. For each: (1) What type of AI? (2) What data is needed? (3) What is the expected effect? (4) What are the risks?
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