---
language: en
title: "Artificial intelligence"
description: "Artificial intelligence brings together systems that perform tasks such as forecasting, language processing, and decision-making. Learn what to assess in marketing applications and understand their limitations."
canonical: "https://tropk.ai/en/resources/glossary/artificial-intelligence"
last_modified: "2026-10-12T16:13:42.060Z"
---

# Artificial intelligence

Reviewed on 2026-10-06.

## What it means

Artificial intelligence is a field encompassing techniques and systems for performing tasks such as recognizing patterns, interpreting language, planning, and producing forecasts or recommendations. The term covers distinct applications, from classifying messages to generating text. Definitions vary by technical context, but they do not imply that a system has consciousness or human understanding.

## Why it matters for marketing

For marketing, the category is too broad to guide a purchasing decision on its own. You need to identify the task and expected output: classifying contacts, estimating demand, or drafting an initial version of text call for different assessments. Naming the application makes it possible to discuss required data, review, costs, and limitations without attributing an assistant’s capabilities to every system.

## Example in practice

Imagine an events team comparing two solutions described as artificial intelligence. One forecasts the number of registrations using past records; the other writes messages to promote the event. The team realizes it cannot compare them based on writing quality. It defines forecast error as the criterion for the first and alignment with the brief and editorial review for the second.

## How to apply or verify

Ask for a description of the task, the inputs, and the output produced. Assess representative examples of the work, including situations in which the system should refer the decision to a person. Check how data and versions are recorded and who reviews the output. To test a claim, choose an observable criterion and compare it with the current process.

## Important distinctions

Artificial intelligence is not synonymous with [generative AI](/en/resources/glossary/generative-ai), which produces content, or with a single conversational product. [Machine learning](/en/resources/glossary/machine-learning) is one approach within this field. Automating a rule does not, on its own, demonstrate learning. A convincing answer does not certify factual accuracy; assessment should focus on the task, evidence, and specific conditions of use.

## Sources and references

- [artificial intelligence — Glossary](<https://csrc.nist.gov/glossary/term/artificial_intelligence>) — NIST
- [What is Machine Learning?](<https://developers.google.com/machine-learning/intro-to-ml/what-is-ml>) — Google for Developers

## Continue a leitura

- [Machine learning](</en/resources/glossary/machine-learning>)
- [Generative AI](</en/resources/glossary/generative-ai>)
- [LLM](</en/resources/glossary/llm>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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