GenAI — IT definition
Generative AI: the family of models that produce text, code, images, audio, or video from a natural-language instruction.
GenAI (Generative AI) refers to the family of artificial intelligence models that produce original content — text, code, images, audio, video, 3D, molecules — from a natural-language instruction. It contrasts with discriminative AI, which classifies or predicts (fraud detection, scoring, computer vision).
The mass take-off dates to November 2022 (ChatGPT): per Bloomberg Intelligence, the GenAI market grew from $67B in 2024 toward more than $1.3T by 2032. For a CIO, it is the fastest technology shift ever observed — and one of the main engines of enterprise AI alongside AI agents.
Main families of GenAI models
- •LLMs (text): ChatGPT, Claude, Gemini, Mistral, Llama. See LLM.
- •Image models: DALL·E, Midjourney, Stable Diffusion, Imagen.
- •Video models: Sora, Veo, Runway, Kling.
- •Audio models: ElevenLabs, Suno, Udio.
- •Code models: Codex, Claude Code, GitHub Copilot.
- •Multimodal models: GPT-4o, Gemini 2.0, Claude 4 — text + image + audio in and out.
How it works
A GenAI model is trained on massive corpora (web, books, code, images) from which it learns statistical distributions. At inference time, it samples that space to produce a coherent response — without "understanding" in the human sense. Hence the frequent hallucinations.
Dominant architectures:
- •Transformers: GPT, BERT, Llama — dominate text.
- •Diffusion: Stable Diffusion, DALL·E 3 — dominate image generation.
- •Mixture of Experts (MoE): Mixtral, GPT-4 — several specialized models selectively activated.
Enterprise use cases
- •Productivity: drafting, summarizing, translating, email generation.
- •Customer support: chatbots, conversational agents.
- •Marketing: content, visual, and video generation.
- •Engineering: code assist, test generation, refactoring.
- •Knowledge management: RAG over internal corpora.
- •Data: synthetic data generation.
GenAI-specific challenges
- •Hallucinations: confidently produced false information.
- •Confidentiality: sending company data to a public model may breach GDPR.
- •Copyright: gray area on training corpora and generated works.
- •Carbon footprint: training and inference at scale consume significant power.
- •Sovereignty: dependence on US hyperscalers is a strategic European issue.
- •[Shadow AI](/en/glossary/shadow-ai): massive adoption without governance.
Governed GenAI vs Shadow AI
Governed GenAI combines:
- •An enterprise license (ChatGPT Enterprise, Copilot for M365, Claude for Work).
- •DPAs guaranteeing that prompts won't be reused for training.
- •SSO and logging enabled.
- •A clear acceptable-use policy and training.
- •Compliance with ISO 42001 and the EU AI Act.
Without that frame, Shadow AI takes over — bringing data leaks, IP loss, and regulatory risk.
Kabeen automatically detects GenAI usage in the IT estate (accounts, spend, integrations) to give the CIO the visibility required to govern.
Frequently asked questions
What is GenAI?
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GenAI (Generative AI) is the family of AI models that produce original content — text, code, image, audio, video — from a natural-language instruction. It contrasts with discriminative AI which just classifies or predicts (fraud, scoring, vision). The mass take-off dates to November 2022 with ChatGPT.
What is the difference between GenAI, an LLM, and an AI agent?
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GenAI is the general family of generative models (text, image, audio, video). An LLM (Large Language Model) is a specific kind of GenAI focused on text. An AI agent is a software system that uses an LLM to plan and execute concrete actions. The three nest: AI agent ⊂ LLM ⊂ GenAI.
How do you govern enterprise GenAI use?
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Four pillars: (1) deploy enterprise licenses (ChatGPT Enterprise, Copilot M365, Claude for Work) with DPAs that prevent prompt reuse for training, (2) enable SSO and logging for traceability, (3) write a clear use policy and train employees, (4) align with ISO 42001 and the EU AI Act. Without this frame, Shadow AI takes over.
What are the main GenAI risks?
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Five major risks: hallucinations (confidently false output), confidentiality (sensitive data leaking to public models), copyright (gray area on corpora and generated works), carbon footprint (training and inference power), and sovereignty (dependence on US hyperscalers). Shadow AI amplifies all of them.
All terms
5R Method
A strategy used during application rationalization to determine the best approach for managing applications.
8R Method
An extended version of the 5R method used in application portfolio management and migration strategies.
Application
A computer program or set of programs designed to automate a business process or deliver value to end users.
Architecture
Refers to the structure and behavior of IT systems, processes, and infrastructure within an organization.
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