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What is generative AI? Definition and business implications
Generative AI is a family of artificial intelligence models capable of producing new content (text, image, sound, video, code) from a natural-language instruction, as opposed to traditional AI which was limited to classifying, predicting, or detecting.
Generative AI relies on probabilistic models trained on massive corpora (hundreds of billions of words or images), able to learn the statistical regularities of a domain and to produce new examples consistent with that distribution. The central mechanism is conditional prediction: given a context (your instruction), the model generates, element by element, the most probable continuation according to what it has learned. This mechanism covers several technical families: LLMs (text), diffusion models (image, video), audio models (speech, music). All share one common trait: they do not classify, they produce. That is what sets them apart from predictive or discriminative AI (fraud detection, credit scoring, image classification), which dominated enterprise AI until 2022. Generative AI does not replace predictive AI; it opens a parallel field of uses, wherever the requirement is to produce a deliverable rather than to make a decision.
Concrete example
According to McKinsey's State of AI 2025 report, 88% of organisations now use generative AI in at least one business function, up from 65% in 2024 and 33% in 2023. The expansion is rapid, but the impact remains uneven: only 39% of surveyed companies report a measurable effect on EBIT, and most of those at less than 5%. An identifiable subset of 6% of organisations, the “AI high performers”, captures the majority of the economic value, by redesigning their workflows rather than by layering AI on top of existing tools.
See also
Sources
- The state of AI in 2025: Agents, innovation, and transformation, McKinsey QuantumBlack, November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai