It’s easy to group all artificial intelligence (AI) systems together, but their training processes and the problems they solve differ significantly. Traditional AI vs. generative AI is a great example: They often use the same underlying technology and have similar names, yet they function distinctly. Knowing what sets them apart enables you to choose the right technology for the right task.

Here, we’ll explain the key differences between AI and generative AI, how they work, and the use cases where they’re most effective.

What is AI?

AI is a group of technologies that enable computers to learn patterns and perform tasks that typically require human intelligence, like problem-solving and decision-making. AI usually requires training to model the relationship between inputs and outputs, enabling it to make predictions based on future inputs.

In data science, AI systems replicate certain cognitive abilities, like classifying data, recognizing patterns, and analyzing information. Traditional AI includes all these capabilities, letting you analyze datasets to forecast outcomes or streamline analytics processes based on known patterns.

With extensive training, an AI system can significantly improve the efficiency of data management and reduce the need for manual intervention, especially for repeatable tasks like ordering and analyzing data.

Advantages of AI

Integrating AI systems into data processes offers several benefits. Here are the main advantages of using traditional AI:

• Improved decision-making: AI can analyze huge volumes of data to produce more precise insights for teams. Incorporating AI improves self-service analytics so people across the company can easily access the insights they need.
• Increased productivity: AI can be embedded in processes like integrating, cleaning, standardizing, and analyzing data in warehouses, automatically predicting outputs to lessen the need for human intervention.

Challenges of AI

Though AI simplifies and accelerates many processes, it comes with its own set of challenges. Here are some limitations of traditional AI:

• Can’t replace judgment, creativity, or expertise: AI cannot replace human creativity or create truly novel ideas — all output is based on its training data. So while AI can speed up processing, it cannot develop new ways to improve data management.
• Specialized, with a limited scope: All AI models are limited to the total scope of their training data and are narrowly tailored for specific tasks. If the information you want doesn’t fall within that range, the AI system either won’t be able to handle it or will produce incorrect results.
• Ethical considerations: Any bias in the training data will produce AI systems that repeat those inconsistencies. You must ensure all training data is free from bias and accurate before using it to train an AI system.

What is generative AI?

Generative AI is a subset of AI that uses deep learning to create new content from its training data. Instead of just interacting with and analyzing data, generative AI leverages machine learning to produce text, code, images, audio, and even more synthetic data.

Generative AI simulates general human reasoning through highly sophisticated pattern-matching and prediction, trained on natural language, code, images, and other media. Foundation models, such as GPT, Claude, and others, are often augmented with additional context — an architecture called retrieval-augmented generation — to produce more tailored results for specific users.

Advantages of generative AI

Generative AI supports everything from data engineering to marketing, allowing teams to draft and refine ideas faster. It is an excellent tool for retrieving and synthesizing information — think of it like a search engine that you can sustain a conversation with. Here are some advantages of generative AI systems:

• Content generation: Generative AI tools use simple inputs to produce desired outputs in seconds, like text or code that teams can use directly in workflows.
• Better customer service: Natural language processing allows generative AI tools to understand human inputs and provide accurate responses in customer service chats. Many companies now use some form of generative AI chatbots to answer common customer questions.
• Time savings: Using generative AI tools to draft code or text or for content ideation dramatically reduces the total time tasks take.

Challenges of generative AI

In theory, generative AI seems like an ideal solution for content or insight generation in most industries. However, there are a few major challenges that limit its effectiveness:

• Hallucinations: Generative AI models may produce false information or fabricate a plausible but misleading answer. These “hallucinations” can cause teams to act on wrong data, reducing efficiency.
• Bias: Bias in training data leads generative AI tools to produce results with the same initial bias, leading to distorted outputs.
• Energy and compute costs: AI models can easily scale up, but correspondingly become expensive to run and maintain. 

What is the difference between AI and generative AI?

While traditional AI and generative AI are related technologies within the AI landscape, their architecture and functionality are fairly different.

Here are the key differences between the two.

Functionality

Traditional AI focuses on supporting decisions with predictive analytics and can be embedded into existing data and analytics workflows to automate and streamline tasks such as data preparation, classification, observability, and prediction. Models work within a well-defined workflow structure to repeatedly perform a task reliably, and excel at detecting anomalies and forecasting trends based on historical patterns.

Generative AI learns from patterns in existing training data to create new content, like text, images, and code. It goes beyond traditional workflows and produces novel information. Businesses use generative AI models to create content in specific areas, like helping programmers to rapidly write code. 

Training methods

Traditional AI models often go through supervised training, using pre-labeled datasets that you carefully cultivate. During training, a model learns the relationship between provided data and known outcomes to make predictions on new data. It’s a heavily structured training process that requires constant iteration with model parameters and performance evaluations. 

Most generative AI users will not directly train a model — that’s typically the purview of providers of foundation models, like OpenAI and Anthropic — but will augment it with additional context. Adding your business’s unique, proprietary data gives the model some “understanding” of your organization’s operations, including the knowledge base and code base.

Users

Traditional AI is mainly used by data scientists and analysts to support decisions and accelerate existing data pipelines and workflows. Generative AI is used by a broader audience because it can translate natural language prompts into text, code, and other outputs that support everyday analytics work.

Examples of AI vs. generative AI

Traditional and generative AI tools are often offered both through existing cloud platforms and independently. To better understand the differences between the two, let’s explore their main use cases.

Traditional AI

Traditional AI is useful for any system that relies on analyzing data to draw precise insights.

Typically, you’ll find traditional AI within:

• Recommendation engines: Recommendation algorithms process a vast amount of data to discover what users are most likely to click on and suggest similar content. They use AI to do this consistently at scale.
• Fraud detection systems: AI systems process behavioral data and build an average to detect when user activity on an account doesn’t match typical habits. Companies use this form of AI extensively in fraud detection or financial applications.
• Credit scoring algorithm: Credit scoring businesses feed customer data, such as payment history and income pattern, into their internal tools. AI models then process this data to produce a precise credit score that reflects the likelihood of repayment. 

Examples of generative AI tools

Generative AI models simulate general human reasoning and are used through a natural language interface. 

Here are some of the main types of generative AI currently available:

• ChatGPT: ChatGPT is a generative AI tool that produces text and other written content. Trained on nearly all publicly accessible web and book data, ChatGPT learned patterns of human language to produce conversational responses and interact naturally with users.
• Claude: Similar to ChatGPT, Claude is a conversational engine that users enter text into to instantly generate content.
• Midjourney: Midjourney is an image generator that uses text prompts to generate AI-created pictures.

How Fivetran powers AI workloads

Both traditional AI and generative AI models rely on high-quality data to improve performance and expand their capabilities. Fivetran strengthens this foundation by accelerating data ingestion. With fully automated ELT pipelines, you consistently get the high-quality data needed to train and refine AI models without manual effort.

By reducing maintenance and infrastructure overhead, Fivetran facilitates scalable data pipelines that can grow with your AI initiatives. Its built-in governance controls support secure enterprise AI development for both traditional and generative workflows.

Get started today for free or request a live demo to find out more. 

FAQ

What are some applications of AI?

If your business is AI-ready, you can use AI in any process that relies on data, from fraud detection and behavioral analysis to predictive analytics and demand forecasting. 

Is ChatGPT generative AI?

Yes. ChatGPT is a generative AI tool that produces an output based on the input, rather than just pulling an answer from a pre-established database.

Which type of AI is better for data analysis?

Between traditional AI and generative AI, traditional AI is better for data analysis. Generative AI can analyze data, but is better suited for summarizing content or responding to prompts.

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