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Glossary

What is an AI model?

A plain definition of an AI model, the main types, how a model is trained and used in a business process, and how to choose one for a use case.

Updated

Definition

AI model

An AI model is a program trained on data to recognize patterns and make predictions or decisions on its own. It is the part of an AI system that does the thinking. This is what an ai model means for a business: software that learned from examples.

In short: A program trained on data to predict or decide.

What is an AI model in plain words

  1. 01

    Data

    Examples the model learns from.

  2. 02

    Training

    The model finds patterns in the data.

  3. 03

    Input

    A new email, image or record arrives.

  4. 04

    Output

    A prediction, label, answer or decision.

Think of an AI model as a skilled worker who learned a job from thousands of examples. Show it new input, and it applies what it learned to give an output.

IBM defines the ai model by one trait: it makes decisions or predictions autonomously. It does not simply imitate a person.

The same ai model can sit behind many tools. A spam filter, a demand forecast and a writing assistant all rest on models.

Types of AI models

  • Predictive

    Forecasts demand, churn or risk.

  • Generative

    Drafts text, images and summaries.

  • Language

    Understands and writes human language.

  • Vision

    Reads images, scans and video.

Different models suit different jobs. IBM notes that each type is better for specific tasks, where its decision logic fits best.

The four families below cover most business needs. They overlap, and many systems combine them.

How models are trained and used

Model inside a process
  1. Invoice arrives in the inbox
  2. Model reads and extracts the fields
  3. Rules check the numbers
  4. Person reviews the odd cases

Data in the system, ready to pay

Training means feeding a model many examples until it spots the patterns. Large language models, for instance, are trained on huge collections of text such as books, articles and websites, according to Harvard Business School.

In a company, the trained model is one step inside a larger process. It reads an invoice, scores a lead or drafts a reply, and then a system or a person acts on the result.

The model alone is rarely the finished product. It needs your data, your tools and rules for when a person reviews its work. That wiring is the job of custom AI development.

Use at work is already common: a Federal Reserve note counts about 41 percent of the workforce using generative AI on the job by November 2025.

How to choose an AI model

Start from the tool

  • Pick a popular model first
  • Hunt for a problem to fit
  • Skip the data check

Start from the task

  • Name the input and output
  • Match the task to a model type
  • Check data and error cost

Start from the task, not the technology. Name the input, the output and what a good result looks like.

Then match the task to a family. Forecasting points to a predictive model, drafting points to a language model, and reading scans points to vision.

Check your data, the cost of a wrong answer and where a person must approve. For related terms, see machine learning and large language model.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about custom AI development. They named 100 companies, and 80% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named433546
Sites cited mosthatchworks.com (3 of 10)vellum.ai and mindstudio.ai (3 of 10)rtslabs.com and netguru.com (4 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 4 companies appeared in all three. Google showed an AI Overview for 9 of the 10 questions.

FAQ

AI model questions

  • An AI model is a program trained on data to recognize patterns and make predictions or decisions without a person guiding each step. A trained model is the core of most AI tools.

  • The main families are predictive, generative, language and vision models. A large language model is one kind of language model. Generative AI is the content-creating side. See what a foundation model is.

  • AI is the wider field of software that performs tasks needing judgment. Machine learning is one way to build it, where models learn from data. The AI vs machine learning page explains the split.

  • Generative models can produce wrong or invented answers, and they reflect gaps in their training data. Read about AI hallucination and keep a person in the loop for important output.

  • People usually mean a model that learns from data, which is how all trained models work. The process is called training. Machine learning covers the common methods.

  • Not to use one, but wiring a model into your tools takes skill. Connecting a model to your systems is an engineering task, so a developer or specialist partner usually handles it.

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