Generative AI vs Predictive AI: Knowing the Differences

Generative AI has received the lion’s share of the press. With good reason—it’s revolutionizing the way we do work and do business. But it’s not the only game in town. Predictive AI also places a role across enterprise use cases like demand forecasting, maintenance, and customer experience. 

This blog will discuss these two types of AI: generative AI and predictive AI. Both have their place for tasks and business processes,and understanding the differences between them is critical for knowing when to apply each.

[AI can make your business far more efficient. But how do you apply it to the full enterprise? Read the post Become More Efficient with These 6 Applications of AI.]

Let’s start with the basics. Artificial intelligence refers to the ability of computer systems to reason in a similar fashion to humans. While traditional programming uses more procedural logic, AI mimics the way humans learn, process information, and make predictions using tools like statistical algorithms, neural networks, and predictive analytics. Just as humans have multiple forms of reasoning and thinking, so too does AI.

Generative AI tools create original content based on models built from prior datasets and training efforts. Generative models can create new text, realistic images, video, or audio. While generative AI tools have been around for ages, the release of ChatGPT ignited imaginations and set off the current AI boom. We’ll cover some practical uses of generative AI later in this post.

Predictive AI gets less press. It’s not flashy, but it has a wide range of uses across industries. Predictive AI analyzes signals to forecast and make accurate predictions about a potential outcome or future events. For example, predictive AI might analyze multiple data points for demand forecasting, allowing purchasers to make informed decisions to get in front of future trends or demand spikes. 

A good analogy is weather forecasting. Predictive AI is like a meteorologist measuring multiple data points like barometric pressure, temperature, wind speed, and storm tracking to predict the weather in a given area. Generative AI might be more like the on-screen meteorologist telling the audience there’s a 30% chance of rain.

Both predictive and generative AI are valuable forms of artificial intelligence and machine learning. So, it’s not really about generative AI vs predictive AI: it’s really about combining both.

The two approaches each have their place. You want to apply the right tool for the right job. Let’s break them down individually, then discuss combining the two in a unified process. 

When gen AI came on the scene, creating content was the most obvious use case. This could be written content, images, or even video. For example, gen AI can save time writing first drafts for humans to review and edit. For example, generative AI can craft email responses to customers in just a few seconds. Then, the employees can review this response for accuracy and clarity. This allows everyone across the organization to create a first pass and overcome writer’s block. 

Additionally, generative AI can be used to summarize content into short bullets that allow someone to get the gist of a document without reading through the original text. While some tasks like reviewing legal forms require in-depth reviews, tasks like summarizing meeting notes could be handed off to AI.

Because generative AI can rapidly craft new content, it’s ideal for creating conversational assistants to answer questions. Long gone are the days of simple, procedural answers. Now chatbots can intelligently answer questions about document sets they were trained on and act as a true virtual assistant. 

For example, an employee at a financial services organization can use AI to understand critical policies when reviewing a loan approval. They can ask and receive questions in natural language, significantly reducing the time required to hunt down information in databases or lengthy document sets. 

IT teams can also develop applications that leverage chatbot capabilities in application interfaces, making life much easier for end users. For instance, deploying a component in a project management application that will summarize the steps used in the project can allow team members to more easily self-serve and make decisions faster. This is a massive feather in the cap for the development teams who built the application.

Generative AI can also save development time. When building data models, users often have to create data to test functionality or ensure the data model can handle the appropriate types of data and scale with the business. With generative AI, you can quickly produce large swathes of data to use for testing without having to create this by hand. This can help across a variety of tasks, such as creating demos or having developers test a data model right after its creation to ensure there are no errors or performance issues.

Predictive AI can analyze historical data and identify patterns to predict future outcomes for everything from risk assessments to investment decisions. For example, banks or loan organizations can use predictive models to understand the risks of lending to a client by analyzing their credit history, market trends, and economic conditions. These predictions can then be used as inputs for humans to make the final call on whether to offer the loan or not. It’s critical to remember that humans must be in the loop—these forecasts offer input, but having a human in the loop to review is critically important (particularly with the passage of regulations around AI that require this for high-risk systems like financial loan approvals).  

[AI will play a prominent role in the future of financial services and banking. Want to stay ahead? Read The AI Handbook for Financial Services Leaders.]

Predictive AI can allow you to anticipate customer behavior, model potential churn rates, and set pricing more effectively. For example, using key signals like purchase histories, browsing patterns, or engagement metrics, organizations can predict who’s likely to stop using their services (and then intervene before that comes to pass).

Predictive AI can also be used to analyze a document or email to determine the intent of the communication. This can then be used to incorporate rules to route communications to the right team or employee. For example, an incoming email that contains mentions of “payment” and “wrong charge,” might be classified as a billing complaint. A low-code application could then send this information to the billing department for further processing. This saves your employees from having to read through an email, decide who should receive it, then forward it to the right person.

The key with AI is to use the right tool for the right job. Predictive AI and generative AI work well in combination, especially as part of a wider process automation effort. For instance, imagine a customer service example. You could set up a system that uses predictive AI to classify incoming emails or documents to then route to the appropriate person. Generative AI could then generate a summary overview for the customer service representative to review. Then, generative AI could write a first draft of the email for the representative to edit before sending. 

Understanding the differences between generative AI and predictive AI is crucial for leveraging their strengths to the fullest. Generative AI excels for creating original content and enhancing creativity. Predictive AI shines by helping you analyze data and gain insights into possible outcomes, helping you make smarter business decisions. By strategically combining both, enterprises can automate processes, improve decision-making, and enhance overall efficiency. In the end, it’s not about generative ai vs predictive AI—it’s about using both for optimal efficiency. 

Want to go more in-depth on AI? Hear from eight experts from top companies on the future risks, trends, and opportunities in AI with the 2024 AI Outlook.