Generative AI vs. Predictive AI: What’s the Difference?

Written by Coursera Staff • Updated on

Explore generative AI and predictive AI and how they differ. This 2024 guide to generative and predictive AI can help you become more comfortable understanding and implementing either tool.

[Featured Image] Two colleagues sit and stand at a laptop in an office and discuss whether to use generative AI or predictive AI for their next project.

While generative AI, when provided a starting point by the user, creates text, images, and other previously nonexistent content, predictive AI forecasts patterns, predictions, and trends for the future. By becoming more knowledgeable about AI, its benefits and drawbacks, and the different types of artificial intelligence, you can become a leading user of generative and predictive AI technologies.

Artificial intelligence, or AI, is becoming a prevalent tool among the millions of internet and tech users today. Understanding the AI industry and the various connected fields can help you harness its capabilities and use it to assist your internet usage.

Hesitant users of AI often express concern regarding job displacement, which is valid but often misunderstood. If that is a concern that has crossed your mind, consider this—the US Bureau of Labor Statistics (BLS) reports that automation-related job losses will occur as a result of new AI technologies, but they also note that these innovations could result in new demands and, by extension, new jobs. BLS also reports that as more companies adopt AI technologies, including generative and predictive AI, the need for skilled professionals with a bachelor’s degree and experience in science, technology, engineering, and mathematics (STEM) will likely grow.

The following article provides a closer look at generative and predictive AI and the benefits of drawbacks each presents. Learning more about these common types of artificial intelligence can help you alleviate concerns about this technology while allowing you to weigh the advantages and disadvantages of both.

Generative AI

Generative AI creates content like videos, images, audio, and text when you provide it with a prompt. Generative AI uses machine learning (ML) models to create new content from existing data. ML uses data and algorithms to learn and adapt to the point where the operating system does not need specific instructions; it essentially learns new information and, based on trial and error, adjusts accordingly, similar to how a human brain would. Generative AI uses ML to generate unique content based on what it has already seen or trained on. Using many sources it can answer your prompt for content with seemingly “new” creations.

What is generative AI used for?

One of the uses of generative AI is to create and produce simple yet time-consuming materials for a human to write. Generative AI exists in educational settings, as faculty and staff can use it to create unique materials for their students to assist with learning. Students can also use this technology as a tool to help them with essays and writing assignments. Generative AI can also aid in decision-making, summarizing meetings, generating code, and creating visual artifacts. While users rely on generative AI in various ways, you might be familiar with one of its more specific, versatile tools: ChatGPT.

ChatGPT, a text-generating AI tool developed by OpenAI, is a form of generative AI that many users have integrated into their daily technology use. ChatGPT, also known as a “chatbot,” can respond to your prompt or question and generate a unique piece of writing based on that prompt or question. More specifically, ChatGPT compiles information from its analysis of prior textual material from websites and other online sources, and the AI program then uses that information to construct unique combinations of sentences to communicate with you.

Advantages of generative AI

Using this technology offers several benefits. Some of the advantages of generative AI may include:

Brainstorming new ideas

While the content created by generative AI is not inimitable, it is unique to an extent. The AI algorithm augments its writing based on other content available on the internet to craft new ideas. Generative AI can quickly provide you with information to help streamline the creative process and brainstorm new possibilities you can implement into your company, classroom, or individual life.

Providing accessible options to diverse learners

Unlike predictive AI’s primary use of identifying future patterns, generative AI can create unique forms of content, allowing it to cater to various audiences with different learning styles. It can develop programs and materials that change and adapt to your needs as a learner, helping you with studying, writing, language, and completing coursework. It is accessible to anyone with a computer or mobile device, allowing global use among different cultural networks and language groups.

Solving problems more efficiently

With generative AI-powered capabilities, you can increase productivity within a work or school environment. Generative AI can develop content more quickly than one of your employees, and it can solve technological problems like debugging and coding. Automating some of your school or workplace tasks with generative AI will allow you to solve problems more efficiently and streamline other business processes such as sales and marketing, software development, and customer service.

Disadvantages of generative AI

Despite the many benefits of generative AI, it has several potential drawbacks, including poor data quality, academic dishonesty, and security risks, that you should be aware of before deciding to implement predictive AI.

Poor data quality

One common issue with generative AI is its reliance on previously created data to create new content. The problem with this content restructuring is the potential for generative AI to compose writing or images consisting of unlicensed content, which could result in copyright infringement. When using generative AI, you will want to ensure that your content is trustworthy, factual, and, of course, not being used to cause harm to others.

Academic dishonesty and plagiarism

Another drawback to generative AI is its misuse to create entirely written assignments that students may submit under their own names. Doing so is a form of plagiarism, as the learner has not written anything themselves and instead relies on generative AI tools like ChatGPT to write their assignments for them. Generative AI’s compiling of others’ writing to answer your prompt can result in plagiarized work, and you may experience serious consequences.

Public and private security risks

Generative AI may reduce privacy for users like you, especially if given any personal or identifying information. Any data or content you share with generative AI tools is permanently imbued into the internet, meaning others could share your data without your permission. This issue also poses a threat on the national level as generative AI can rewrite code, which can enhance risks to cybersecurity.

Predictive AI

Predictive AI primarily focuses on forecasting, whether that is forecasting patterns, future trends, or events. Like generative AI, predictive AI uses machine learning to learn and adapt, personalizing customer experience and forecasting future events for businesses. However, while generative AI uses ML to create new content, predictive AI uses ML to perform statistical analysis to anticipate and identify future occurrences. Instead of neural networks, predictive AI relies on more simple models to gather large amounts of data, also known as “big data,” and provide predictions based on that data.

What is predictive AI used for?

Predictive AI’s primary use is identifying future patterns or trends, which can assist with business processes like inventory or supply chain management, marketing campaigns, and user experience. By analyzing past activities of your employees or customers, predictive AI can help you discover what items need restocking, anticipate user behaviors, and make health care decisions.

Predictive AI also uses embeddings, a way of storing information, to find relationships between data sets and, in turn, uses those relationships to develop predictions about future trends. For example, predictive AI could utilize embeddings to store purchasing history to identify patterns concerning what products customers will likely buy in the future and when they will buy them. Looking for relevance within data sets can help predictive AI supply your business with pattern identification.

Advantages of predictive AI

Like generative AI, predictive AI offers unique benefits. Some of the advantages of predictive AI include:

Providing more time for employees to focus on decision-making and complex tasks

Predictive AI’s ability to analyze data and predict future patterns makes it an efficient tool for business processes. Predictive AI can accomplish short-term tasks when analyzing data so your employees can focus their energy on decision-making and creative choices. Automating administrative-centered processes in the workplace with predictive AI can help you devote more time to complex activities that require human intellect.

Predicting human needs

With its ability to forecast future trends, predictive AI can also anticipate the needs of users or customers. It can predict your customers’ behaviors and their purchases and show you how to market new products or services to these existing customers. Additionally, predictive AI can assess students’ needs and determine when it’s best to give them assistance. By providing early interventions for learning difficulties, predictive AI can encourage the development of personalized study plans for students.

Disadvantages of predictive AI

Predictive AI has a few challenges, including the need for greater certainty and data effectiveness. Consider the potential drawbacks before deciding whether to implement or use predictive AI.

Lacks absolute certainty

Despite predictive AI’s ability to forecast future occurrences, you don’t have any direct guarantees that it is correct. Because external factors can impact it, predictive AI measures potential outcomes, not certainties. When deciding to utilize predictive AI, you should be aware that it is a tool designed to help forecast, not something you should rely solely upon when making decisions within a work or learning environment.

Requires massive sums of high-quality data

Due to predictive AI's reliance on large quantities of data to form its predictions, you could limit its effectiveness in forecasting trends if you lack ample data. A lack of data could result in skewed statistics or numbers, resulting in the predictive AI tool you use to misidentify patterns. Additionally, the data could be old, biased, or simply inaccurate, which results in false patterns and unreliable predictions, affecting your ability to make sound decisions.

Choosing between generative AI vs. predictive AI

Making the choice to use AI requires careful consideration. Deciding which branch of AI to venture towards is crucial to executing your process. As you might know, AI uses trial and error to become more accurate over time. If you are considering using AI, you should determine how you want to use it and ensure everyone will use it ethically.

When choosing between generative and predictive AI, consider your end goal. Generative AI excels when you need to create new information, such as content or images, uncover patterns in data, or develop text. Predictive AI, on the other hand, is ideal if you want to analyze patterns and use that information to make forecasts and predictions, which can help drive decisions.

Career opportunities with generative and predictive AI

Now that you understand the nuances of generative and predictive AI, you may opt to incorporate it into your career goals. Both generative and predictive AI offer various use cases across multiple professions. For example, predictive AI can help marketing professionals gain deeper insights into customer pain points and provide more personalized campaigns.

In health care, it can help providers provide enhanced treatment strategies. In finance, it can drive sound investment decisions and reduce fraud. Generative AI can also aid you in various types of jobs. For example, developers can use generative AI to speed the development process, while brands of all kinds can use the technology to provide an improved customer experience.

You might also opt to build an AI-focused career. For example,  pursuing a career as an artificial intelligence engineer is a well-paying job possibility. According to Glassdoor, your average annual base salary in this position could be $127,658, according to January 2024 data [1]. Additionally, the BLS anticipates that career opportunities within this field will grow 23 percent from 2022 to 2032, which indicates a positive job outlook with above-average growth [2].

Getting started with Coursera

You can become a highly skilled artificial intelligence user by understanding the differences between generative and predictive AI and the benefits and drawbacks. Knowing when and when not to implement AI is an important skill to learn, and you can expand on this ability with the Generative AI Fundamentals Specialization on Coursera. This Specialization explains the fundamental concepts of generative AI models, allowing you to learn the limitations of AI and how to implement it into your work environment.

Article sources


Glassdoor. “AI Engineer Salaries,,11.htm.” Accessed January 17, 2024.

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