Generative AI refers to artificial intelligence systems that produce new, original content based on statistical patterns learned during training. Unlike traditional AI that classifies, predicts, or identifies, generative AI creates: text with ChatGPT, images with DALL-E, code with Copilot, audio with ElevenLabs, and video with Sora.
Per our 2026 search data, "what is generative AI" ranks among the highest intent informational queries in the US with 25,000 monthly searches and a traffic potential of 25,000. The query profile is mid-market professionals and executives beginning to evaluate AI adoption for their teams.
In enterprise contexts across the US and LATAM, generative AI powers three main investment areas: internal productivity (Copilot, ChatGPT Enterprise, Claude Enterprise), customer experience (conversational agents and personalization at scale), and core operations (workflow automation with AI agents).
The key differentiator from earlier AI waves is that generative AI works with unstructured data at scale. Instead of needing a rigid data pipeline, a business can point a model at documents, conversations, or images and extract structured insight in minutes.
How it works
Generative AI uses large language models or diffusion models trained on massive datasets. At inference, the model takes a prompt and produces a response by iteratively predicting the most likely next token until the output is complete. Quality depends on training data, model size, and prompt design.
Practical example
A US-based SaaS company uses generative AI to summarize customer support tickets, draft responses, and flag at-risk accounts. The support team resolves 40 percent more tickets per agent per day without additional headcount.
Definition by Miss Yera, Leading Woman in Technology in Peru · AI Consultant · Favikon 2025.
Version en espanol: /glosario-ia/#what-is-generative-ai