What is the difference between traditional AI and generative AI?

Traditional AI analyzes data and recognizes patterns, while generative AI creates new content. Traditional AI is good at predicting outcomes, such as recommending products or recognizing images. Generative AI uses prompts to create original content, such as text, images, or video.

What is an example of generative AI?

Image generation systems like DALL-E and Midjourney are examples of generative AI. These programs create original images based on user prompts, and their ability to create new content classifies them as generative AI.

Is ChatGPT a generative AI?

ChatGPT is a generative AI, since it creates new content based on user input. As a large language model, ChatGPT can understand language and generate original text and images in response to user prompts.

What are foundation models in generative AI?

Foundational models are neural networks that have already been trained on large data sets. Organizations can adapt these models to different tasks instead of training a new AI from scratch. Foundation models make generative AI easier to use and adapt quickly to new tasks.

What is generative AI?

By: Reese Lopez, Edited by: Gabriela Pérez Jordán, Reviewed by: Jeff Le

Published: May 23, 2025

Closeup of a businesswoman using a generative AI app on her smartphone to assist her while working on her laptop in an office park.

While other types of AI can analyze or interpret existing content, generative AI can create new content. Programs like ChatGPT, DALL-E, Claude, and Gemini have already reshaped how users create original media like text, images, and music.

AI has quickly found its way into most industries, affecting how companies do business and how schools educate students. These tools will only become more widespread and essential for business leadership as the technology evolves.

Explore our guide to generative AI and find online courses that can help you master this influential new technology.

How does generative AI work?

Artificial intelligence analyzes large amounts of data to predict patterns.

Generative AI takes this a step further, predicting the pattern to create new content. An AI program like ChatGPT processes enormous amounts of text and finds patterns, allowing it to "learn" the rules of language and generate new text.

Generative AI is trained on huge sets of data, eventually allowing it to create new content with limited supervision. This process uses neural networks in which millions of digital nodes communicate and categorize data, similar to a human brain.

Benefits of generative AI

Increases productivity by automating repetitive tasks.
Can be programmed for tasks in almost any industry.
Remembers user preferences, allowing AI to improve its efficiency.

Limitations of generative AI

May plagiarize or copy without proper citation.
May generate hallucinations, false statements, or information.
May reproduce biases found in its source data, including hate speech.

Use cases for generative AI by industry

Similar to the advent of electricity or computers, generative AI is changing entire industries, including education, business, and medicine.

Generative AI can optimize productivity in almost any field, helping workers save time on simple tasks. As a research tool, generative AI can also assist in skilled professions such as law and medicine.

But what is generative AI's impact on specific career paths? Explore how five industries use AI below.

Use cases for generative AI by industry
Industry	How they use AI
Education	Cutting down on repetitive tasks like grading, scheduling, and keeping student records. Evaluating student learning needs and creating personalized learning plans.
Medicine	Discovering and developing new drugs. Interpreting symptoms and diagnosing patients.
Law	Reviewing large amounts of legal documents for relevant information. Identifying potential risks that clients could face in legal proceedings.
Finance	Analyzing financial portfolios to generate investment ideas. Assessing risk and detecting fraud in financial transactions.
Information technology	Automating technical support and finding solutions to common computer problems. Analyzing network activity to identify and prevent cybercrime.

Where is generative AI headed?

Generative AI will continue to develop, creating more sophisticated content and solving more complex problems with improved accuracy and precision.

Future AI models may be able to communicate more easily and use many types of input data, including text, speech, and visuals. Generative AI will likely become less expensive and more widely used.

A January 2025 report from the World Economic Forum found that 41% of employers surveyed expect to reduce their workforce in the next five years due to AI. Developing your generative AI skills can help you adapt to a changing job market.

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