How does generative AI work?
A generative model is trained on very large volumes of text, images or code until it absorbs the statistical patterns in them, then produces new output one token at a time in response to a prompt. The transformer architecture behind most of these models is what made that leap possible. The model does not look answers up, it predicts what a plausible answer looks like, which explains both the fluency and the occasional confident error. Companies rarely train models themselves; they rent one through an API and adapt it with prompts, their own data, or light fine-tuning.
What are examples of generative AI in a business?
The everyday examples are already on most desks: assistants that draft emails and reports, conversational AI answering customer questions, document AI reading contracts and invoices, code assistants in the IT team, and image generators in marketing. Even an AI website builder is generative AI applied to one narrow, well-understood artifact. The pattern across all of them is the same: unstructured language in, useful draft out, with a person deciding what actually ships.
Generative AI vs agentic AI: what is the difference?
Generative AI produces content when asked; agentic AI uses that same ability to plan and act toward a goal with less supervision. The generative model is the engine, and the agent is the vehicle built around it. A drafting assistant that waits for a prompt is generative; a system that reads an inbox, drafts replies and files the results is agentic. Most of the value companies report today comes from the second shape, because it completes work instead of accelerating fragments of it.
Where does generative AI create business value?
Not evenly. The reliable wins are language-heavy, repetitive work: support answers, document processing, first drafts, internal search. The common failure is buying a general tool, skipping AI adoption work, and producing plausible text nobody checks, complete with hallucinations. Value follows the workflow, not the model, so the useful question is which process changes, not which LLM to pick. Newer multimodal models extend the same trade to images, audio and video.
What are the risks of generative AI for a company?
Three recur. Output risk: fluent text that is wrong, so every workflow needs review steps sized to the stakes. Data risk: employees pasting confidential material into public tools, the pattern known as shadow AI. Accountability risk: nobody being able to say afterwards which decisions AI touched. All three respond to the same basics, an AI usage policy people can actually follow and lightweight AI governance that assigns owners. The risks are organizational before they are technical, which is why buying a safer tool never substitutes for deciding how tools get used.