Recently, there has been an explosion of artificial intelligence (AI) technologies. Generative AI is being used to generate novel content, including text, images, videos, code and music. Meanwhile, predictive AI is being used to predict future events. There’s also machine learning (ML) and agentic AI to consider.

A few years ago, asking a computer to create a unique picture, song or video game sounded far-fetched. But now, that magical thinking is a reality. And its capabilities continue to get better. So now, a lot of organizations are wondering how they can use these moonshot technologies to improve their businesses.

What Is Predictive AI vs. Generative AI vs. Machine Learning?

It’s actually not a “versus” at all. Machine learning (ML) is the foundation for both predictive AI and gen AI. But while gen AI uses ML models to create content, predictive AI on the other hand uses ML to identify early warning signs and determine future outcomes.

What’s the difference between gen AI and predictive AI?

The primary difference between generative AI and predictive AI is their purpose. While they both use ML and AI, their algorithms function differently. You’ll see some crossover in how these technologies can be applied in real-world use cases.

What is generative AI and how does it work?

Think of gen AI as the “creative” one. Gen AI uses deep learning and large language models to generate new content based on the data it’s trained on. Generative AI creates new content like images, text, music and code.

What is predictive AI and how does it work?

Think of predictive AI as the “business” one. Predictive AI, also called predictive analytics, uses historical and current data to predict and identify patterns, and make inferences based on that information. It relies on statistical algorithms and machine learning models. It can look at historical data, market trends, customer behavior analytics and trends to forecast future events. You might consider this type "traditional AI" vs. generative AI, because it excels at analyzing data and performing tasks rather than creating new content.

Generative AI vs. predictive AI table

Let’s compare generative and predictive AI:

Generative AI	Predictive AI
Focuses on creating new content.	Focuses on forecasting future outcomes.
Used to generate realistic images and other novel content.	Used for identifying patterns and making predictions.
Uses neural networks, like a generative adversarial network (GAN), and machine learning.	Uses statistical analysis, models and machine learning algorithms.

What does agentic AI have to do with it?

Agentic AI is another type of AI system that takes the possibilities of AI one step further — with decision-making. Essentially it uses AI workers to achieve specific objectives set by humans, such as taking action and making decisions. But it doesn’t make the decision without consulting a human worker first. Agentic AI is already being explored in agentic process automation, where these workers are improving processes within an organization through their advanced capabilities.

All these AI technologies fall into the wider category of enterprise AI, which also includes intelligent automation (IA).

Generative AI

Generative AI is about creating something new.

What are the limitations of generative AI?

Generative AI models are trained on a set of data and can only generate based on the information it’s fed. Risks of gen AI come with poor data quality or data containing unlicensed content, which can lead to copyright infringement, privacy breaches, bias and non-compliance.

To mitigate these risks, organizations using gen AI should establish AI governance standards, especially those in heavily regulated industries such as financial services and healthcare. Ensuring AI compliance can save organizations from legal fines, data breaches and participation in non-ethical activities.

What are the benefits of generative AI?

Generative AI tools are designed to augment the work of writers, designers, artists, coders and musicians – not replace it. It’s especially useful for speeding up the creative process and brainstorming new or different ideas. Any organization wanting to use these tools should first look at how to prepare for generative AI.

Here are the benefits we’ll see in future gen AI uses:

• Personalized customer experiences
• Problem-solving
• Adaptive automation
• Simulations and testing
• Creative content experimentation

What are the applications of generative AI?

Moving beyond content creation, gen AI has a lot of business use cases. By applying gen AI to your automation program, you can further optimize and personalize automated processes. Generative AI applications include:

• Banking and finance: Gen AI can analyze data to help with testing credit risk models by generating fake data on which the risk models are tested.
• Healthcare: Reading large, unstructured healthcare datasets like those in electronic health records (EHRs) can be error-prone, but gen AI can analyze this data quickly and identify any anomalies early on, which can help with early diagnoses and developing personalized treatment plans.
• IT: AI-assisted software development is making huge leaps for programmers. By assisting in code generation, gen AI can speed up development time, reduce errors and optimize bug fixing.
• Environmental, social and governance (ESG) reporting: Gen AI can collect and interpret an organization’s scope 1, 2 and 3 data to help bring better visibility into their ESG impact for more accurate reporting.
• Customer service: Gen AI can deliver customer answers sooner through NLP chatbots and extract key information from customer conversations to help agents apply feedback to future results.

Explore other generative AI use cases to expand the power of automation.

Predictive AI

Predictive AI is about forecasting.

What are the limitations of predictive AI?

Since predictive AI trains on large amounts of data to forecast, a lack of data or inaccurate data can severely hinder its efficacy. And even with all this data, predictive AI isn’t a fool-proof fortune teller. It can make predictions based on data patterns and trends, but no future events can be predicted with absolute certainty. Any organization utilizing predictive ai forecasts needs to recognize that, as with all things, there are limitations to technology.

What are the benefits of predictive AI?

Predictive AI models are designed to speed up decision-making by helping businesses make accurate, informed decisions. It analyzes patterns and makes predictions by identifying data anomalies and extrapolating future events. This can reduce the time it takes for businesses to research or study information so they can focus more time on strategic decision-making.

What are the applications of predictive AI?

Predictive AI applications include:

• Healthcare diagnoses: Predictive AI already exists in healthcare, helping to prevent disease outbreaks and identifying high-risk patients. Progressively, it’s being used for disease diagnosis, prognosis and treatment planning. The AI makes a diagnosis by looking for patients with similar symptoms and underlying conditions that match the patient’s age, weight or other characteristics.
• Fraud detection: By analyzing patterns, predictive AI can identify potentially fraudulent activities sooner, such as flagging an unusual device or access from a new location.
• Financial forecasting: Predictive AI models pull historical financial data from a large data set to predict stock market trends, risks and investment opportunities. Predictive AI improves forecasting accuracy and augments financial decision-making.
• Customer behavior analysis: Predictive AI can use customer data such as purchase history and shopping behavior patterns to predict what they’ll purchase next, which can also help organizations with their inventory management and supply chain management (also called demand forecasting).

Choosing Between Predictive AI or Generative AI

Choosing between them depends on your goals and use cases. You’ll want to use predictive AI for tasks that involve forecasting future outcomes or pattern recognition. Generative AI is best for creating new content. It also doesn’t have to be one or the other, you can use them both with an enterprise AI solution, which also offers you more flexibility and adaptability when selecting the right AI solution for your needs.

AI-Powered Automation

The goal of intelligent automation is to save time and money and optimize processes to reduce waste. With advances in AI technologies, these capabilities will continue to expand as you can automate more complex processes as well as automate more processes end-to-end.

More organizations are thinking bigger about automation. Read our e-book to find out how you can supercharge intelligent automation with generative AI.

Table of AI technologies

Need a refresher? Here’s our table of technologies:

Artificial intelligence (AI)	Mimics human intelligence to solve problems.
Predictive AI	Also known as predictive analytics, predictive AI uses machine learning algorithms based on historical data to identify patterns, make predictions and forecast trends.
Generative AI	Uses a sophisticated algorithm to create new content such as texts, images, video and audio based on natural language prompts.
Machine learning (ML)	Is a branch of AI that uses data and algorithms to mimic human learning and improve its accuracy over time.
Natural language processing (NLP)	Is an ML technology that synthesizes natural human language.
Cognitive automation	Another name for intelligent automation, cognitive automation mimics human behavior and intelligence to facilitate decision-making and perform complex tasks.
Business process management (BPM)	Is a tool for businesses to automate, manage and optimize their processes.
Robotic process automation (RPA)	Mimics human’s ability to do tasks.
Intelligent automation (IA)	IA combines BPM, AI and RPA to automate processes.
Agentic AI	AI agents or workers that are capable of doing tasks, making decisions and interacting within the context of their environment without human intervention.
Agentic process automation (APA)	The next evolution of RPA that includes agentic AI capabilities.
Enterprise AI	Enterprise AI is the integration of AI and IA technologies to address enterprise needs.