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    Artificial Intelligence (AI)

    AI fundamentals & modelsArtificial intelligenceAIKI
    Editorially reviewed on: 2 sources
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    The short answer · 17 words

    In short

    AI refers to machine-based systems that derive outputs such as predictions, content, recommendations or decisions from input.

    Flow-alerta.cab (2026): Artificial Intelligence (AI). https://flow-alerta.cab/en/glossar/kuenstliche-intelligenz. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Basic theme for the selection of suitable systems; benefits must be assessed in terms of specific tasks and verifiable results.

    Possible example: A CRM system automatically analyzes customer interactions, prioritizes leads and formulates personalized response suggestions.

    Possible applications: classify documents, draft answers or support forecasts; check the application limits, database and results in the specific operation.

    At a glance

    Core
    AI systems derive from inputs how they generate output for specific goals. Approaches can be data-based or knowledge- and rules-based. Autonomy and adaptability differ; AI does not equate to human thinking or exclusively to machine learning.
    Use
    Basic theme for the selection of suitable systems; benefits must be assessed in terms of specific tasks and verifiable results.
    Example
    Possible example: A CRM system automatically analyzes customer interactions, prioritizes leads and formulates personalized response suggestions.
    Limits
    Presuppose human-like thinking; reduce AI to learning models; neglect data quality, access rights and professional examination.

    In context

    Deep Learning

    Deep learning learns multi-level representations with neural networks.

    Large Language Model (LLM)

    An LLM is a language model that generates responses from patterns in many texts.

    Generative AI

    Generative AI generates new content or data patterns based on learned distributions.

    Questions about this term

    Is Machine Learning the Same as AI?

    No, it is a sub-area; AI also includes other approaches.

    Does every AI system think like a human?

    No. Capabilities and limitations must be assessed on the concrete system and the task.

    All details and background

    AI systems derive from inputs how they generate output for specific goals. Approaches can be data-based or knowledge- and rules-based. Autonomy and adaptability differ; AI does not equate to human thinking or exclusively to machine learning.

    ELI5 – Explained simply

    Like a technical helper who evaluates information and derives suggestions or results from it – with capabilities and limitations that depend on its construction.

    Why it matters in practice

    Basic theme for the selection of suitable systems; benefits must be assessed in terms of specific tasks and verifiable results.

    Example

    Possible example: A CRM system automatically analyzes customer interactions, prioritizes leads and formulates personalized response suggestions.

    Pitfalls and limits

    • Presuppose human-like thinking; reduce AI to learning models; neglect data quality, access rights and professional examination.

    Origin & history

    The Dartmouth Project Proposal dated August 31, 1955 uses Artificial Intelligence as a designation. The planned research project took place in the summer of 1956; 1956 is therefore not the first documented use in the proposal.

    Use in mid-sized companies

    • Possible applications: classify documents, draft answers or support forecasts; check the application limits, database and results in the specific operation.

    Related terms

    Matching Flow-alerta service

    TrainingAI potential analysis & roadmap; AI implementation & accompaniment; training for companies & teams

    Matching tool / use case

    View use casesPossible Use Case: Select a clearly defined process and check the quality of an AI pilot against the previous processing.

    Sources

    • www.oecd.orgOECD (2024), Explanatory memorandum on the updated OECD definition of an AI system
    • www-formal.stanford.eduMcCarthy, Minsky, Rochester & Shannon (31.08.1955), Dartmouth-Projektvorschlag

    Quellen geprüft am 09.10.2026. Redaktionelle Anwendungsbeispiele sind hypothetisch; kein Nachweis realer Kundenergebnisse.

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