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Generative AI vs Predictive AI: A Comparative Analysis

Explore the differences between generative AI vs. predictive AI, gain insights into their unique capabilities, and how AI can help you automate processes.

The Role of AI Language Models like ChatGPT in Enhancing Process Automation

Artificial intelligence (AI) has been well-known for a long time without many people fully understanding what the term means. Recent advances in the industry brought AI to the forefront of tech conversations again as it became much more accessible for businesses to benefit from. The first thing to understand is that AI is not one thing, but many things. Today, we’ll take a deep dive into two types of AI, comparing generative AI versus predictive AI.

The Ins and Outs of Generative AI

Generative AI has become particularly popular in the past year as several consumer products have been released, allowing users to generate text, images, and audio outputs quickly. Due to the almost limitless output variations of generative AI tools, they easily capture audiences' attention as creative minds envision how these tools can help their business.

What is Generative AI?

Generative AI is a specialized field of AI that focuses on creating new, original data based on patterns it finds in the training data. If you were using generative AI to produce images of dogs, you would train the AI with a large dataset of pictures of real dogs. Generative models treat the input as data, recognizing intricate structures and relationships between the images. 

Once the generative model has been trained on a large enough dataset, it can produce entirely new images of dogs, often indistinguishable from real images. The process will be the same whether you train the generative model on an image, text, or audio data. The more training data you provide, the better trained the model can be and the more accurate the output.

Generative AI Algorithms and Models

Generative AI models gain insights into data by learning statistical properties and underlying distribution in the data you use to train them. Algorithms and models generate new content from a dataset using different methods. Here are a few of the leading algorithms and models.

Generative Adversarial Networks (GANs)

GANs are generative algorithms that apply two competing neural networks. The first is the generator that creates new content, and the second is the discriminator that distinguishes between generated and genuine content. They are called adversarial because the discriminator finds errors in what the generator produces. Still, they work together to develop the most realistic content, as the discriminator teaches the generator. 

GANs are great for producing realistic images and AI art, but they take a lot of computing power.

Variational Autoencoders (VAEs)

VAEs use a single neural network to generate new content. VAEs work by compressing the data they identify into a simpler form and then using probabilistic techniques to reconstruct the original data. These are also useful for making new images and filling in missing gaps in data. Photo editing software often implements VAEs as they can help editors make quick changes such as removing an element from a photo.

Recurrent Neural Networks (RNNs)

RRNs are good for dealing with sequential data because they have a ‘memory.’ This means they can remember several steps in a process, making them suitable for generating text, forecasting trends, and composing music. 

Transformer Models

Transformer models work via an encoder-decoder architecture, which gives them ‘self-attention.’ This makes them excellent multitaskers that are very efficient at understanding text. They can read long passages and generate coherent text responding to the context. The most famous example of the transformer model at work is natural language processing tools such as ChatGPT.

Advantages and Limitations of Generative AI


  • Content Production: Generative AI can perform creative tasks such as content that previously required human intelligence. These creative tasks vary in output from product design to marketing materials to customer service interactions. 

  • Efficiency and Scalability: The models can perform these tasks quickly and efficiently, allowing businesses to scale their content production. 

  • Personalization: These models also allows businesses to automate personalized customer responses and messages.

  • Ideation: If you don’t want to use generative models to automate your content production completely, you can ask questions to brainstorm ideas that you can then implement yourself.

  • Data Generation: They can also fill in missing or corrupted data gaps. They can generate new data that can help train other machine learning models, too.


  • Quality of Training Data: The output is only as good as the input that goes into it. This is important for the data's variation and the quantity supplied.

  • Bias: As models are trained on data supplied by humans, it is, of course, susceptible to the biases of the humans who provided the data. This can include political or discriminatory biases.

  • Cost: It still costs a lot to train and deploy AI models; they are not easily accessible to everyone yet.

  • Ethical and Legal Concerns: Copyright issues can arise when new AI work is created from models trained on copyrighted material. Deep fakes can be created to make a video or image seemingly show a real person doing or saying something they didn’t do.

The Ins and Outs of Predictive AI

Predictive AI is another sub-category of the AI field. While there are some fundamental similarities among all AI systems, there are also a lot of differences in the models and applications of AI. Let’s take a look at predictive AI.

What Is Predictive AI?

Predictive AI uses machine learning algorithms to predict future events based on historical data. Its predictive models are at work in several industries, such as healthcare, finance, marketing, and transportation. The insights gained from predictive AI allow people to make smarter, data-driven decisions.

Like generative AI, predictive AI analyzes data for patterns and trends. The difference is that predictive AI doesn’t use this information to recreate artificial versions of what it has learned; instead, it helps you to make informed decisions by predicting future events.

Predictive AI Algorithms

As with generative AI, you can use several predictive algorithms depending on your needs and the complexity of the data you're analyzing.

Linear Regression

This predictive algorithm models the relationship between a dependent variable and one or more independent variables. It aims to find the best fitting line through a dataset, which you can then extrapolate into the future. This is often applied to the financial sector to predict the stock market, in real estate to predict property prices, and in transportation to optimize routes and predict maintenance needs. It is a useful tool because it provides easy-to-understand data. However, its simplicity means it may struggle to handle multifaceted datasets.

Decision Trees

Decision trees are an algorithm that asks questions of different variables. The answer to the previous question will dictate the next question. As there are progressively more possibilities as you go further down the process, the questions appear to branch out and look like a tree when drawn out. Decision trees are suitable for risk assessment in loan approval, fraud detection, and segmentation of your audience based on customer behavior. 

Random Forest

The random forest algorithm uses multiple decision trees during training and gives you the average or the most popular answer. It is like having a team of experts coming to a consensus on an answer. It is commonly used for recommending products for customers in e-commerce, predicting disease outbreaks in healthcare, and evaluating more complex financial issues. While they offer high accuracy and can handle larger datasets, the trade-off is that they take more computational power and can be more challenging to understand than the simpler decision tree.

Neural Networks

Neural networks are an even more advanced model that recognizes patterns and makes decisions by mimicking how the human brain works. The interconnected nodes of neural networks allow them to deal with large amounts of data and achieve high accuracy on complex tasks. The downside of neural networks is that they require large amounts of computational power, and the results can be more challenging to interpret.

Advantages and Limitations of Predictive AI


  • Improved Decision-Making: Predictive AI helps businesses make better decisions by analyzing data and forecasting future outcomes. It can evaluate large datasets that would be difficult for humans to assess alone.

  • Enhanced Customer Experience: Personalized customer engagements can be developed from the data you have about each of your website visitors. This can include product recommendations based on their search or purchase history.

  • Increased Efficiency: Automating these processes means they can be done much more quickly and accurately. This can save time time while you're carrying out the task and reduce the chance of having to check for errors and make corrections.

  • Reduced Costs: The ongoing costs of running automated processes are much lower than paying a human to carry them out.


  • Data Quality: As with generative AI, the output that you receive will only be as good as the input you provide. Ensuring a large, high-quality dataset to train your AI is crucial for reliable outputs.

  • High Implementation Costs: While the operating costs are lower than using a person for the same task, the initial set-up costs can be high. You may need to pay for software, hardware, and personnel to get you up and running.

  • Interpretability: It can be difficult to understand how predictive AI models make predictions. This can make it hard for people to trust the recommendations they receive.

  • Ethical Concerns: There is potential for human bias in the input to lead to discriminatory outputs. There are also concerns about how AI uses people’s personal data. 

Key Differences Between Generative AI vs. Predictive AI

As we have discussed, both generative AI and predictive AI are powerful tools that can help increase your creativity and analysis. While there are similarities between the two types of AI, there are also several key differences.

Data Input and Output in Generative vs. Predictive AI

Generative AI data input is usually unstructured, and the output creates a new, creative version of the input based on patterns identified. Generative input and output can take many forms, including text, code, speech, music, and images. 

Predictive AI uses structured historical data to predict actual future trends accurately. The output from predictive AI comes from labels, continuous values, probabilities, and scores. 

Training Methods and Data Requirements

Both types of AI require large amounts of data for the machine learning models to produce accurate outputs. Predictive AI does not need as much data as generative AI but needs more structure. 

Generative AI trains the model using unsupervised or semi-supervised learning, while predictive AI requires supervised learning.

Stages of Development

Generative AI is newer than predictive AI. Generative AI has already yielded impressive results in making realistic text, images, and speech, but they can still be largely identified as AI-generated. However, the technology is rapidly becoming more accurate, with new tools being released regularly.

Predictive AI is a more established field that has demonstrated its effectiveness at analyzing data and predicting future trends in various industries, such as finance, healthcare, and marketing.

Reliability and Accuracy

As a newer technology, generative AI is less reliable than predictive AI. The output occasionally contains random or incorrect artifacts when using an image or text generator. The accuracy will continue to improve as the models are trained on more data. 

The reliability of both types of AI also depends on input from the user. In the case of predictive AI, this means including all relevant data that plays a role in the outcome. For example, if you are trying to predict customer churn, you must include every factor affecting this number. If you leave out ‘new competition,’ your results may be less accurate. 

For generative AI, even if the models are perfectly trained, the quality of the output depends on how detailed the prompt is. For example, if you are trying to create a detailed image, you need to specify everything that you want to appear in the image, or the AI will fill in the gaps based on what it thinks is best.

Use Cases

Generative AI is great at creating new content and giving ideas for new projects. Intelligent automation streamlines operations by creating news articles, blog posts, images, music, videos, and even code writing.

Predictive AI focuses more on business process automation and analysis, such as financial forecasting documents, fraud detection, medical diagnoses, and risk assessment.  

Leverage AI and Machine Learning Technologies to Automate Processes

If you'd like to learn more about how generative AI or predictive AI technologies can apply to your business, contact us today.

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