Generative AI vs. predictive AI: What’s the difference?

Many generative AI tools seem to possess the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next verse in a song or poem. Software like DALL-E or Midjourney can create original art or realistic images from natural language descriptions. Code completion tools like GitHub Copilot can recommend the next few lines of code.

But generative AI is not predictive AI. Predictive AI is its own class of artificial intelligence, and while it might be a lesser-known approach, it’s still a powerful tool for businesses. Let’s examine the two technologies and the key differences between each.

Generative AI (gen AI) is artificial intelligence that responds to a user’s prompt or request with generated original content, such as audio, images, software code, text or video.

Gen AI models are trained on massive volumes of raw data. These models then draw from the encoded patterns and relationships in their training data to understand user requests and create relevant new content that’s similar, but not identical, to the original data.

Most generative AI models start with a foundation model, a type of deep learning model that “learns” to generate statistically probable outputs when prompted. Large language models (LLMs) are a common foundation model for text generation, but other foundation models exist for different types of content generation.

Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. It extracts insights from historical data to make accurate predictions about the most likely upcoming event, result or trend.

Predictive AI models enhance the speed and precision of predictive analytics and are typically used for business forecasting to project sales, estimate product or service demand, personalize customer experiences and optimize logistics. In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business.

Both generative AI and predictive AI fall under the AI umbrella, but they are distinct. Here’s how the two AI technologies differ:

Generative AI is trained on large datasets containing millions of sample content. Predictive AI can use smaller, more targeted datasets as input data.

While both AI systems employ an element of prediction to produce their outputs, generative AI creates novel content whereas predictive AI forecasts future events and outcomes.

Most generative AI models rely on these architectures:

• Diffusion models work by first adding noise to the training data until it’s random and unrecognizable, and then training the algorithm to iteratively diffuse the noise to reveal a desired output.

• Generative adversarial networks (GANs) consist of two neural networks: a generator that produces new content and a discriminator that evaluates the accuracy and quality of the generated content. These adversarial AI algorithms encourage the model to generate increasingly high-quality outputs.

• Transformer models use the concept of attention to determine what’s most important about data within a sequence. Transformers then use this self-attention mechanism to process entire sequences of data simultaneously, capture the context of the data within the sequence and encode the training data into embeddings or hyperparameters that represent the data and its context.

• Variational autoencoders (VAEs) are generative models that learn compressed representations of their training data and create variations of those learned representations to generate new sample data.

Meanwhile, many predictive AI models apply these statistical algorithms and machine learning models:

• Clustering classifies different data points or observations into groups or clusters based on similarities to understand underlying data patterns.
• Decision trees implement a divide-and-conquer splitting strategy for optimal classification. Similarly, random forest algorithms combine the output of multiple decision trees to reach a single result.
• Regression models determine correlations between variables. Linear regression, for instance, represents a linear relationship between two variables.
• Time series methods model historical data as a series of data points plotted in chronological order to project future trends.

Most generative AI models lack explainability, as it’s often difficult or impossible to understand the decision-making processes behind their results. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics. But interpreting these estimates still depends on human judgment, and an incorrect interpretation might lead to a wrong course of action.

The choice to use AI hinges on various factors. In an IBM® AI Academy video on selecting the right AI use case for your business, Nicholas Renotte, chief AI engineer at IBM Client Engineering, notes that “ultimately, picking the right use case for gen AI, AI and machine learning tools requires paying attention to numerous moving parts. You need to make sure the best technology is solving the right problem.”

The same holds true when deciding whether to use generative AI or predictive AI. “If you’re implementing AI for your business, then you really need to think about your use case and whether it’s right for gen AI or whether it’s better suited to another AI technique or tool,” Renotte says. “For example, lots of businesses want to generate a financial forecast, but that’s not typically going to require a gen AI solution, especially when there are models that can do that for a fraction of the cost.”

Because it excels in content creation, gen AI has multiple and varied use cases. More might crop up as the technology advances. Here’s where generative AI applications can be implemented in various industries:

• Customer service: Organizations can use gen AI-powered chatbots and virtual agents to offer real-time support, provide personalized responses and initiate actions on behalf of a customer.
• Gaming: Gen AI models can assist with creating real-world environments, lifelike characters, dynamic animations and vivid visual effects for video games and virtual simulations.
• Healthcare: Generative AI can create synthetic data to train and test medical imaging systems to better preserve patient privacy. Gen AI can also propose entirely new molecules, accelerating the drug discovery process.
• Marketing and advertising: Generative AI can design engaging visuals and craft compelling ad and sales copy customized for each target audience.
• Software development: Code generation tools can speed up the process of writing new code and automate the debugging and testing phases.

Predictive AI is mainly used in finance, retail, e-commerce and manufacturing. Here are a few examples of predictive AI applications:

• Financial forecasting: Financial institutions use predictive AI models to forecast market trends, stock prices and other economic factors.

• Fraud detection: Banks employ predictive AI to spot suspicious transactions in real time that signify fraudulent activities.

• Inventory management: By projecting sales and demand, predictive AI can help companies plan and control inventory levels.

• Personalized recommendations: Predictive AI models can help analyze patterns in customer behavior data for more tailored suggestions that can lead to improved customer experiences.

• Supply chain management: Predictive AI can aid in the optimization of logistics and operations, production plans, resource allocation and workload scheduling.

Choosing between these two technologies doesn’t have to be an either-or option. Enterprises can adopt both generative AI and predictive AI, using them strategically in tandem to benefit their business.

Learn more about the IBM watsonx™ platform and how it can accelerate your AI goals. Tap into the generative AI capabilities of models built on watsonx.ai™ to help uncover patterns and anomalies, so you can make precise forecasting and predictions tailored to your needs.