AI / Artificial Intelligence
Systems that simulate human intelligence – learning, problem-solving, pattern recognition. Used in business for automation, analysis, chatbots and forecasting.
Artificial intelligence is one of the most transformative technologies. From recommendations and autonomous driving to medical diagnosis – AI is everywhere. For businesses it offers concrete benefits: process automation, data analysis, personalized service and decision support. The question is no longer whether to use AI but where and how.
What is AI / Artificial Intelligence?
Artificial intelligence (AI) is an umbrella term for systems that perform tasks that normally require human intelligence: learning from experience, understanding language, recognizing patterns, making decisions and solving problems. The field includes: Machine Learning (ML) – algorithms that learn from data; Deep Learning – multi-layer neural networks; NLP – language (ChatGPT, translation); Computer Vision – images; and robotics. Generative AI (GenAI) with large language models (LLMs) like GPT-4 has opened a new era since 2023.
How does AI / Artificial Intelligence work?
AI systems learn from data in three main paradigms: Supervised (labelled data: input → expected output), Unsupervised (patterns in unlabelled data: clustering, anomalies), Reinforcement (trial and reward). Deep learning uses many-layer networks: CNNs for images, RNNs for sequences, Transformers for language (GPT, BERT). Typical workflow: collect data → preprocess → feature engineering → train → evaluate → deploy → monitor. MLOps automates this lifecycle.
Practical Examples
AI chatbot with RAG: Answers customer questions from the company knowledge base – many requests resolved automatically.
Predictive maintenance: ML predicts machine failure weeks in advance from sensor data – fewer unplanned outages.
Document processing: AI extracts data from invoices, contracts and forms – large reduction in manual entry.
Fraud detection: ML analyses transaction patterns in real time and flags suspicious activity.
Recommendation system: AI suggests products or content from user behaviour – higher cross-sell.
Typical Use Cases
Customer service: Chatbots, email classification and sentiment analysis
Process automation: Document processing, workflow optimization and RPA with AI
Data analysis: Forecasting, anomaly detection and natural language queries
Quality control: Visual inspection with computer vision
Personalization: Recommendations, dynamic pricing and targeting
Advantages and Disadvantages
Advantages
- Efficiency: Automate repetitive, data-heavy tasks with better quality
- Scale: Handle data volumes that humans cannot
- Insights: ML finds patterns humans miss
- 24/7: AI systems don’t get tired
- Competitive advantage: Early adopters in an industry gain an edge
Disadvantages
- Data dependency: AI is only as good as the training data (garbage in, garbage out)
- Bias: Systems can inherit and amplify bias from data
- Black box: Complex models are hard to interpret
- Cost: Training, infrastructure and expertise need investment
- Regulation: EU AI Act and others add compliance requirements
Frequently Asked Questions about AI / Artificial Intelligence
Where should I start with AI?
What AI strategy do I need?
What does an AI project cost?
Related Terms
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