What Is Generative AI?
Generative artificial intelligence is the branch of AI that creates new content — text, images, audio, code, or video — based on patterns learned from large volumes of data, instead of only classifying or predicting on information that already exists. Unlike a system that answers "this is spam" or "this customer is likely to churn," a generative model produces something that did not exist before: an email written from scratch, a product image, or the summary of a contract.
Tools like ChatGPT, Gemini, and image generators are everyday examples of generative AI that the general public already uses. Inside companies, the same technology automates writing tasks, customer support, document analysis, and report generation, with applications ranging from marketing to technical support.
Generative AI vs. Discriminative AI: The Key Difference
To understand what generative AI actually does, it helps to contrast it with its counterpart: discriminative (or predictive) AI, which was the most common type of artificial intelligence in production until just a few years ago.
- Discriminative AI: learns to tell categories apart or predict a value from input data. Typical examples include a model that flags fraud in a transaction, one that forecasts product demand, or one that approves or rejects a credit application. Its output is a label, a probability, or a number.
- Generative AI: learns the full distribution of the data it was trained on and uses that knowledge to produce new, coherent content — not just a label.
Both approaches coexist in practice: a company might use a discriminative model to flag suspicious transactions while a generative model drafts the notification sent to the customer. If you want to see where generative AI fits in the broader landscape, our guide to the types of artificial intelligence covers the other branches and classifications.
How It Works: Language Models and Diffusion Models
You don't need a data science degree to follow the general logic behind generative AI. Two model families cover almost every use case in production today.
Large Language Models (LLMs)
A large language model (LLM) is trained by reading enormous amounts of text and learning to predict the most likely next word (or word fragment) given the sequence that came before it. Repeated billions of times during training, that simple mechanism produces a system capable of holding a conversation, summarizing a document, translating languages, or writing code. ChatGPT is the best-known application built on top of an LLM; if you want to go deeper on how that specific tool works, we cover it in what is ChatGPT.
Diffusion Models
Diffusion models power most of today's image generators. They work in a way that feels backwards at first: they're trained on images that have noise gradually added to them until they become unrecognizable, and the model learns the reverse process — removing that noise step by step. Once trained, the model can start from pure noise and "clean it up," guided by a text description, until it produces an image that matches that description.
Generative AI Examples in Business: Use Cases in Mexico
Generative AI in business has moved well past the experimental stage. Here are the most common use cases among companies operating in Mexico:
- 24/7 customer support. Virtual agents that answer frequently asked questions, follow up on orders, or book appointments over WhatsApp and other channels, without depending on a human being available at every moment.
- Content generation and editing. Email drafts, product descriptions for online stores, social media posts, and first drafts of internal documents.
- Document summarization and analysis. Pulling the key points out of contracts, policies, or long reports in seconds instead of having a team member read the whole thing.
- Internal assistants grounded in company data. A generative model that answers questions based on an organization's own manuals, policies, or historical records rather than generic internet knowledge — an architecture known as retrieval-augmented generation (RAG).
- Code generation and report automation. Development teams speeding up repetitive tasks, and business teams building narrative reports straight from sales or operations data.
If you're looking for a broader picture of AI use cases beyond generative AI specifically, our guide to examples of artificial intelligence in business rounds up applications by industry.
If your business wants a conversational assistant that answers with your own information instead of generic knowledge, at AISDC we combine AI customer service agents with vector database and RAG architectures so responses stay grounded in your real documents.
Risks of Generative Artificial Intelligence: Hallucinations and Data Privacy
Adopting generative AI without understanding its risks is the fastest way to get a bad result. Two concerns account for most real-world problems.
Hallucinations
A generative model can produce information that sounds convincing but is false: a made-up figure, a quote that doesn't exist, a legal clause the model "believes" it read somewhere. This happens because the model generates the statistically most likely sequence of words, not because it looks up a database of verified facts. That's why any output used in important decisions — legal, financial, medical — needs human review, and techniques like RAG help reduce the problem by grounding responses in verifiable documents instead of the model's memory.
Privacy and Data Handling
Sending customer, employee, or company information to a third-party generative AI service raises questions every organization needs to answer before adopting it: where is that data processed and stored, does the provider use it to retrain its models, and does it meet the contractual confidentiality commitments your business needs? Deciding what information can go into these tools, and what can't, matters as much as choosing the tool itself.
How to Start Using Generative AI in Your Business
You don't need to build your own model to benefit from generative AI. The most common, lowest-risk path looks like this:
- Pick a specific, narrow use case — for example, answering common support questions, not "automate all of customer service."
- Start with a human in the loop. The model produces a draft; a person approves it before it reaches the end customer, at least for the first few weeks.
- Connect the model to your own company data instead of relying only on its general knowledge, to reduce hallucinations and keep responses relevant to your business.
- Measure results — time saved, tickets resolved, perceived quality — before expanding the project to more areas.
Frequently Asked Questions
What is generative AI in simple terms?
It's the type of artificial intelligence that creates new content (text, images, audio, code) based on what it learned during training, instead of only classifying or predicting based on existing data.
How is it different from traditional AI?
Traditional or discriminative AI predicts a category or a value (for example, whether a transaction is fraudulent). Generative AI produces original, coherent content, such as a complete piece of text or an image.
What generative AI examples are businesses in Mexico using today?
The most common ones are conversational agents for customer support, marketing content generation, summarization of documents and contracts, and internal assistants trained on a company's own information.
Is generative AI safe for handling customer data?
It can be, if the company clearly defines what information goes into the tool, reviews the provider's privacy and retention policies, and limits access to sensitive data. It is not safe by default — it requires explicit governance.
How do you reduce the risk of hallucinations in a generative AI project?
The two most effective measures are keeping a human review step before output reaches the end user, and grounding the model's responses in verifiable company documents through retrieval-augmented generation (RAG), instead of relying only on the model's general knowledge.
If your business is evaluating where to apply generative artificial intelligence with less risk and better results, at AISDC we design AI solutions for businesses tailored to your processes and data, not generic implementations. Tell us what process you'd like to automate and we'll work out the right architecture together.