Generative AI refers to models that produce new content — text, images, code, audio — based on what they have learned. For a company, the piece that matters most is large language models (LLMs) like GPT, Claude, and Gemini. They can read, summarize, write, and reason in natural language.
A large language model is trained on billions of text documents to predict the next word. That simple objective produces a surprisingly broad capability: models answer questions, draft contracts, interpret tables, translate languages, and do limited logical reasoning. The model does not, however, "know" anything about your company unless you feed it. This is the most consequential architectural choice for a business: how do you connect your own knowledge to the model so answers are correct and traceable.
The technical patterns for connecting an LLM to your data reduce to three. They are not mutually exclusive — a mature deployment uses all three.
These are use cases that repeat across industries and typically show positive ROI within the first six months.
Risks do not mean adoption should be delayed. They mean adoption needs an architecture.
Practical guidance that has worked in dozens of mid-sized and large companies.