Traditional AI vs. Generative AI

Note: This article was originally published on contentmarketing.ai.

There’s no doubt you’ve seen and heard plenty of terms related to artificial intelligence (AI) over the last few years. AI, in its broadest sense, seems to be leaching its way into every aspect of my life. I see it in ads for new tech; I hear it in conversations at work; I write about it!

AI, generative AI, machine learning model, deep learning — these barely scratch the surface of AI terminology, but there are some important distinctions to make that may help develop your understanding of the touted tech and how best to use it in your business.

The terms I want to focus on here are AI and generative AI, also sometimes shortened to Gen AI or GenAI.

Traditional AI, or the AI you may be used to hearing about, has been around for a long while. Well, so has generative AI, but the proliferation of ChatGPT and other GenAI models has brought it into mainstream conversation more than ever before.

But, how is GenAI related to artificial intelligence in general? Is it a derivative of a broader technology or something else entirely? What do more traditional AI tools excel at compared to generative AI? Which one should you be using in your business? Those are some of the questions I want to answer here, so let’s start digging.

What Is ‘Traditional’ AI?

Artificial intelligence broadly refers to the simulation of human intelligence in machines. It describes systems designed to perform tasks that typically and traditionally require human intelligence, such as reasoning and problem-solving.

As such, ‘traditional’ AI tools excel in environments with clearly defined tasks and rules and are especially powerful when dealing with structured data. Here are a few examples of where traditional AI, or AI that’s not generative, shines:

Structured Data Analysis and Processing

Traditional AI is very effective at analyzing structured data, such as data from spreadsheets or databases, where relationships between data points are well-defined and predictable. Tasks like sorting, filtering and categorizing structured data are straightforward for rule-based systems.

Automation of Repetitive Tasks

In fields like finance, banking and customer service, businesses use traditional AI to automate repetitive processes. For example, it excels in handling routine customer service inquiries, processing transactions, updating records and other tasks that don’t require more complex reasoning.

Rule-Based Decision Making

Many industries, such as finance, law and manufacturing, rely on traditional AI for decision-making based on predefined rules. For example:

• Fraud Detection: Banks use rule-based AI to identify unusual transaction patterns based on clear, predefined rules.
• Quality Control: Manufacturing processes often have strict guidelines, making traditional AI ideal for ensuring that products meet set standards.
• Compliance and Regulatory Adherence: Traditional AI systems can check transactions or document compliance based on fixed regulatory requirements.

What Is Generative AI?

How generative AI is different is in the name: It’s designed to generate new content, such as text and images and even complex code, by learning patterns from vast amounts of data.

It’s kind of like the “creative” version of traditional AI, although I’m more than hesitant to call it creative at all. So, let’s say that GenAI imitates human creativity using deep learning and neural networks. If traditional AI is the math whiz, generative AI is the artsy kid.

Here’s what generative AI excels at:

Content Creation

Generative AI is good at, well, generating content. From text to images and now even video, Gen AI is becoming an increasingly popular tool across industries for things like chatbots, marketing copy and voiceovers.

Design and Prototyping

Generative AI can assist in rapid prototyping for design work, from product blueprints to architectural designs, enabling quick iterations and creative ideation to enhance human creativity.

Data Simulation

Generative AI can create synthetic data that mimics real-world data, valuable for training other machine learning models when data scarcity or privacy concerns are factors. Further, in both scientific research and the gaming industry, generative AI can simulate environments, molecules or realistic scenarios that aid in experimentation and development.

Traditional AI vs. GenAI

There’s a bottom line somewhere here, but I won’t make you search for it yourself. Here’s a succinct comparison of these two types of AI:

Traditional AI and generative AI differ primarily in function and approach. Traditional AI relies on structured data and rule-based systems to perform specific tasks and is often constrained within predefined boundaries. It excels at processing and analyzing large data sets to draw insights but lacks creative generation capabilities.

In contrast, GenAI leverages advanced models (like transformers) trained on vast amounts of diverse data to create new content, whether text, images or other media, that resembles human creativity.

Here’s another easy way to think about it: Traditional AI is task-oriented, while GenAI is more open-ended, producing novel outputs by “learning” from data patterns rather than just analyzing them.

But there’s another nuance to uncover here. Most AI models these days have a generative component — or are working to implement one. Since AI is a fast-growing and always-evolving field, tech becomes outdated quickly.

Take Amazon’s Alexa, for example. Alexa could be considered a more traditional AI model. The popular assistant uses natural language processing (NLP) to understand and respond to voice commands, using predefined data and rule-based algorithms to interpret them accurately and respond with relevant, pre-programmed answers or actions.

But Amazon has plans to overhaul its decade-old technology with generative AI capabilities to give Alexa a bit more personality and functionality.

So, the line between traditional AI and GenAI seems to get thinner and thinner as time passes. Still, knowing the differences between the two is important when adopting the technology for your business.

Which Type of AI is Right for Your Business?

Different types of businesses can benefit uniquely from traditional AI and generative AI, depending on operational needs and objectives. The most simple way to boil it down is this:

• Businesses that rely heavily on data analysis, automation and decision support can benefit most from traditional AI.
• Generative AI is particularly beneficial for businesses focused on things like content creation, customer engagement and design innovation.

Let’s explore some specific use cases across a few different industries.

Traditional AI Ideal Industries and Use Cases

Health care

Traditional AI tools and algorithms are highly valuable to the health care industry. Leveraging large datasets, AI has shown promise in its ability to improve disease diagnosis, treatment selection and even clinical laboratory testing.

Manufacturing

The World Economic Form says AI is a game changer for testing and quality control in manufacturing. This sector is no stranger to advanced robotics to aid production, but AI and machine learning are innovating manufacturing once more to help maintain supply chain performance even in volatile conditions. How, you ask? Through continuous, closed-loop, fully automated planning.

Retail and E-commerce

Delivering optimized customer experiences has never been more streamlined thanks to AI. These more rule-based algorithms work wonders for personalizing shopping experiences through recommendation systems and dynamic pricing, which help improve customer satisfaction and sales. I mean, just look at Amazon!

Generative AI Ideal Industries and Use Cases

Media and Entertainment

Generative AI capable of producing high-fidelity video is nearly here and accessible at scale. Models like Runway’s Gen-2 that generates novel videos from text prompts, OpenAI’s Sora or the James-Cameron-endorsed Stability AI offer new creative possibilities in the media and entertainment landscape.

Marketing and Advertising

Does marketing without the use of generative AI even exist anymore? So much of what marketers and advertisers do can be enhanced, streamlined or automated using GenAI. From crafting personalized marketing campaigns or individual materials to generating images and design concepts, GenAI is great for streamlining creative workflows in marketing.

Education

This is an interesting one. I remember when ChatGPT was hot off the press and teachers feared their students would use it to generate entire essays. Today, generative AI helps schools develop educational content and even build personalized learning experiences. Khan Academy’s Khanmigo uses GPT-4 to provide tutoring and interactive learning sessions that helps students with homework, studying and more.

An AI-Enabled Future for All

Well, there you have it. What this all boils down to is: Traditional AI is good at following rules, collecting and analyzing data and aiding decisions-making. On the other hand, generative AI tools are there to mimic your innate creative abilities to help you breakthrough on a new idea, offer quick variations for a copy and even help your kids study.

There are folks out there who are creating unique and innovative tools for all kinds of applications — lots of which are worth checking out. We hope this blog will help make your search for the perfect AI-enabled tool that much easier. Happy hunting!