Generative AI versus
Other Types of AI

Once you understand what generative AI is, the next logical question is: How does it compare to other forms of AI? After all, “AI” is an umbrella term that includes a range of technologies. Some are designed to recognize patterns, others built to make decisions, and still others focused on creating something entirely new.

This page unpacks how generative AI differs from traditional AI, machine learning, predictive AI, and agentic AI, along with a quick primer on where these systems overlap and how they’re used.

Traditional AI: The Rule-Follower

Traditional AI refers to early and often narrowly defined systems based on rules and logic. These are programs that follow explicit instructions or decision trees.

Example:
• A chess engine that evaluates board positions using hand-coded heuristics.
• An airline booking system that flags errors when data doesn’t match expected patterns.
Strengths:
• Highly accurate for well-defined problems.
• Easy to test and explain.
Limitations:
• Struggles with nuance, uncertainty, or language.

How It Differs from Generative AI:
Where traditional AI follows static rules, generative AI learns patterns from massive datasets and generates content on the fly. In other words, Generative AI isn’t limited to predefined answers.  Instead, it invents new combinations based on learned probabilities.

Machine Learning:
The Pattern Recognizer

Machine learning (ML) is the backbone of modern AI. It enables systems to learn from data instead of relying solely on programmed rules. Given enough examples, a model learns to make predictions or decisions on new input.

Example:
• An email spam filter that improves over time.
• A recommendation engine that learns what you like.
Strengths:
• Learns and improves without being explicitly reprogrammed.
• Applicable to a wide range of problems.

How It Connects to Generative AI:
Generative AI is a subfield of machine learning, using many of the same statistical and neural network techniques. The key difference? ML classifies; generative AI creates.

Predictive AI: 
The Forecaster

Predictive AI focuses on forecasting likely outcomes based on existing trends or behavior. These systems analyze past data and apply statistical models to anticipate what might happen next.

Example:
• Predicting whether a credit card transaction is fraudulent.
• Forecasting next quarter’s sales figures.

How It Differs from Generative AI:
Predictive AI usually outputs a decision or a number (e.g., “high risk”), whereas generative AI outputs content, such as a sentence, a paragraph, or a photo. But both rely heavily on training data and probability.

Agentic AI:
The Autonomous Actor

Agentic AI is still emerging, but it refers to systems that set goals and act independently to accomplish tasks, often over time and across platforms. These agents don’t just respond. They plan, execute, and adapt.

Example:
• An AI travel planner that books your flights, finds hotels, and reschedules based on delays.
• Autonomous research agents that design experiments and test hypotheses.

How It Differs from Generative AI:
Generative AI produces content when asked. Agentic AI can decide what to do, when to do it, and how.  Agentic AI apps often use generative AI as one of its tools to create emails, reports, or summaries.

What's Next?

This field is evolving so quickly, that it's hard to keep up.  I'll keep making updates to this page (and all the rest of the AI section) as quickly as I can, as things continue to change.  In the meantime, feel free to Contact Me with any questions!

Want or Need to Learn More about AI Content Generation?

As many people are now figuring out, there is a lot more to AI than simply typing in a prompt and getting back perfect content.  If you want an in-depth exploration of AI for content generation, check out my AI For Writers classes.  These three classes, offering about 5 total hours' worth of instruction, will walk you through the process of using AI tools to accomplish a wide range of business, nonprofit, marketing, policymaking and advocacy writing projects.  Written by a writer for writers, click here to learn more.