AI GLOSSARY

Artificial Intelligence

Artificial Intelligence (AI) refers to systems that perform tasks that previously required human intelligence: understanding language, recognizing patterns, and making decisions. Today, it forms the basis of many business applications and is the most important technology trend of our time.

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Subfields
ML, Deep Learning, NLP, Computer Vision, Robotics

Applications
in almost every field

Maturity Levels
From Buzzword to Business Foundation

Best Practices
for Successful AI Implementation

Why Artificial Intelligence Is Becoming the Norm Today

AI is no longer a thing of the future—it’s here now. From chatbots to image recognition to predictive models, it’s shaping nearly every industry. Those who don’t use AI strategically will lose their competitive edge. Those who use it wisely will gain productivity, quality, and new opportunities.

Productivity Gains

Routine tasks are completed faster and more cost-effectively—allowing people to focus on value creation.

New Business Models

AI enables offerings that were previously technically or economically impossible.

Better Decisions

Data-driven analyses complement human experience—errors are reduced.

Enhance the Customer Experience

Personalization, 24/7 availability, quick response—AI improves service.

Competitive Pressure

Competitors are already using AI. Those who wait will lose market share.

Focus on Regulation

AI Act, GDPR — AI is no longer deployed without governance.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence: understanding language, recognizing patterns, interpreting images, making decisions, and learning from experience.

Key subfields: Machine Learning (learning from data), Deep Learning (multi-layer neural networks), Natural Language Processing (NLP), Computer Vision (image and video analysis), Robotics (AI in physical systems), Generative AI (creating new content—text, images, code).

Historical stages of development: Symbolic AI (rule-based, 1950s–1980s), Machine Learning (statistical methods, 1990s–2010s), Deep Learning (neural networks, starting in 2012), Foundation Models (LLMs such as GPT and Claude, since 2020). Each wave brought new opportunities and challenges.

For small and medium-sized businesses, AI today is pragmatic: clear use cases, mature tools, manageable costs. Those who approach it thoughtfully and consider governance from the start can turn AI into a productive tool. Those who try to build everything themselves will burn through their budget without achieving any results.

Machine Learning

Learning from Data Patterns — The Foundation of Modern AI. Classification, Regression, Clustering.

Deep Learning

Deep neural networks. For complex patterns in language, images, and audio.

Natural Language Processing

Processing text and language. Translation, summarization, classification.

Computer Vision

Image and video analysis. Quality control, person recognition, medical imaging.

Generative AI

Generate new content—text, images, code, audio. LLMs, diffusion models.

Reinforcement Learning

Reinforcement Learning — for Robotics, Games, and Optimization.

Expert Systems

Rule-based systems — for well-structured problems involving domain knowledge.

AI Agents

Autonomous systems with tool access. Plans, executes, decides.

Best Practices for AI Projects

These six principles have proven effective:

Start with the business case: AI is a means to an end—not an end in itself.
Start small, learn fast: Run pilots in weeks, not months. Expand iteratively.
Take data quality seriously: Without clean data, there’s no good AI—no tool can replace that.
Incorporate governance from the start: Compliance, ethics, and security from the beginning, not as an afterthought.
Involve people: Change management isn’t an add-on—it determines success.
Stay realistic: AI doesn’t solve everything. Know its limits and use it correctly.

Historical 1

Symbolic AI

Rule- and knowledge-based. The foundation of AI in the 1960s–1980s. Still used today in expert systems.

Rules

History 2

Machine Learning

Statistical methods, learning from data. The dominant trend since the 1990s.

Data

Historical 3

Foundation Models

Large pre-trained models (LLMs). The new standard since 2020.

Models

Common Mistakes in AI Projects

We often see these pitfalls:

Technology-driven: “We need AI” without a clear business case—expensive prototypes with no impact.
Unrealistic expectations: AI is seen as a magic bullet — disappointment follows the initial results.
Data quality underestimated: The model is trained, but the data is poor—results are disappointing.
Governance ImplementedToo Late: Roll out first, then address compliance later—resulting in costly rework.
People forgotten: Change management is missing — users don’t accept the AI.

AI vs. Machine Learning vs. Generative AI

A comparison of three terms:

Artificial Intelligence: Umbrella term. All techniques that generate machine intelligence.
Machine Learning: A subset of AI. Learning from data using statistical methods.
Generative AI: A subfield focused on generating new content—text, images, code.

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