What is Artificial Intelligence (AI)
Let’s start by defining what we mean by artificial intelligence. For our purposes, AI describes the ability of machines to perform tasks that typically require human-like intelligence. This includes capabilities like learning from data, recognizing patterns, understanding language, and using that information to solve problems.
The evolution of artificial intelligence has been a long road. However, commercial AI tools took off in the 1980s with the launch of XCON, a system developed by Digital Equipment Corporation that automatically selected computer components based on customer needs. AI has become increasingly sophisticated, and in the past two years has hit the mainstream, with many of us using AI agents and tools on a daily basis.
With this sophistication has come specialization. Different AI systems or models can be specialized to a specific purpose, and may not be suited for every task. Two of the most commonly confused types of artificial intelligence are Generative and Predictive AI.
What is Generative AI, and how does it work?
Generative AI refers to a type of artificial intelligence that uses either standard Machine Learning techniques or more sophisticated Deep Learning models to understand existing patterns and structures, using them to create new content. It can take many forms, including text, audio, or visual content. One of the most well-known applications of Generative AI is Large Language Models (LLMs) like Gemini or ChatGPT.
Generative AI models need to be trained on large datasets. During training, the model analyzes a dataset and tries to identify relationships between elements, like words, colors, or shapes. The model then encodes these relationships into structured mathematical representations. Users provide prompts in order to guide the final output.
For example, when generating text, ChatGPT uses tokens to try to predict the next word based on previous words. Midjourney, on the other hand, starts with random noise, which it gradually denoises based on the text prompt, to generate progressively clearer versions of the image that match the original prompt.
These LLMs are special because they are capable of performing many tasks across domains. However, this sometimes makes users overestimate their abilities and attempt to use LLMs for tasks that other types of AI, such as predictive AI, might be better suited for.
What is Predictive AI, and how does it work?
While Generative AI is all about using patterns to make something new, Predictive AI focuses on using patterns in historical data to estimate what is likely to happen. To start, it needs a large historical database to study. This could be anything from weather patterns to purchase history. The model is then trained to look for patterns in the historical dataset, which it uses to make predictions on new incoming data.
Predictive AI can be set up to either run in real-time or in batches. In real-time mode, it analyzes evolving data streams, which is commonly used in applications like fraud detection. When used in batches, the AI is run periodically when needed—for example, to predict inventory requirements once a month.
Predictive AI has a wide variety of use cases. It’s used to predict the weather, identify potential bank fraud, provide personalized ads, and even to help sports teams determine what tactics to use in their next big match-up. Even if you never personally use predictive AI, it almost certainly improves some aspect of your day-to-day life.
Generative AI vs. Predictive AI: What’s the difference?
With this surface-level analysis, the difference between Generative and Predictive AI might feel clear, but new Deep Learning solutions often lead to confusion, even amongst experts. This happens because artificial intelligence is often used as a catch-all to describe a huge variety of tools and algorithms. This is made doubly confusing because some types of Generative AI, especially LLMs, will try to make predictions if asked, however these might not be accurate. Let’s take a look at why this is.
Part of the confusion stems from the fact that there are a lot of similarities between Generative vs. Predictive AI. They both use historical data to train, often use similar methods like Deep Learning, and can even understand the same datasets. The real difference is in their outputs, and how users interact with them.
| Feature/Focus | Predictive AI (Deep Learning) | Generative AI (Deep Learning) |
|---|---|---|
Goal | Forecast future outcomes based on data. | Create new data resembling training data. |
Output | Labels, probabilities, scores, rankings. | Text, images, audio, video, code, or other creative content. |
Input data | Structured or unstructured (e.g., logs, reviews, clickstreams). | Mostly unstructured (e.g., text, images, audio). |
Model types | RNNs, LSTMs, CNNs, Transformers, Deep MLPs. | GANs, VAEs, Diffusion Models, Transformers. |
Learning focus | Learn correlations and patterns for decision-making. | Learn distributions to generate realistic samples. |
Example use cases | Customer churn prediction, sales forecasting, and fraud detection. | Image generation, text summarization, and code completion. |
Evaluation metrics | Accuracy, precision, recall, AUC, RMSE. | Perplexity, BLEU score, FID (images), human evaluation. |
Training data requirements | Can use labeled or unlabeled data (often supervised, but can be unsupervised/semi-supervised). | Can use labeled or unlabeled data (unsupervised/semi-supervised). |
Interactivity | Often runs in the background or powers dashboards. | Often user-facing and interactive (chatbots, art tools). |
Generative AI helps to create something new
Most people have used some kind of Generative AI, like ChatGPT, Gemini, or Midjourney. These tools range from general-purpose assistants, like ChatGPT or Gemini, to specialized generators, like Midjourney, and are used to create audio, visual, or textual content with ease. This comes with both benefits and potential risks:
The benefits of Generative AI
Enhanced creativity—it can act as a brainstorming buddy for creating new art, text, and music, providing inspiration and accelerating content creation.
Automation of routine tasks—Generative AI streamlines processes such as content drafting, code generation, or even assists with early-stage research exploration, saving time and resources.
Conversational learning—generative models offer a more conversational way to learn or problem-solve through natural language in a chat interface, helping people to pick up new ideas and concepts quickly.
The risks of Generative AI
Bias and misinformation—Generative AI may reproduce or amplify existing biases from training data and generate plausible but incorrect or misleading information.
Intellectual property concerns—it risks infringing on copyrights by mimicking or closely replicating existing creative works without proper attribution.
Ethical and social challenges—generative models raise questions about authenticity, attribution, and the potential for misuse in areas like deepfakes and automated misinformation campaigns.
Generative AI use-cases
Generative AI is very sophisticated and can be used to support a wide variety of tasks:
Content creation & marketing
Copywriting—AI-powered writing assistants like ChatGPT and Jasper can generate blog posts, social media content, and marketing copy, accelerating the content creation process.
Video creation—automated video scripts and storyboard generators can help marketers to produce engaging multimedia campaigns.
Art and design generation
Art—tools like DALL·E and Midjourney can create custom digital artwork or design prototypes from text prompts, opening creative possibilities for artists and designers.
Design—some experimental platforms use generative models to assist in sketching fashion or product design concepts based on current trends.
Code generation and software development
Generating code—platforms like GitHub Copilot can assist programmers by generating code snippets, suggesting function implementations, and automating repetitive coding tasks.
Testing code—automated test script generation and debugging tools are increasingly being used to streamline software development processes, reducing development time and error rates.
See more: 16 Examples of Generative AI Applications
Predictive AI helps to predict real world outcomes
Unlike most Generative AI tools, Predictive AI often works in the background. It powers the dashboards and search tools that we use every day to make better decisions. Many Predictive AI tools will use similar methods to Generative AI, such as Deep Learning, but the difference primarily lies in the outputs:
The benefits of Predictive AI
Enhanced decision making—Predictive AI leverages historical data to forecast trends and anticipate future outcomes, allowing businesses to make proactive, data-driven decisions.
Operational efficiency—automating routine forecasting tasks helps optimize resources and reduce manual workload. This enables faster responses in dynamic environments such as supply chain management or real-time fraud detection.
Personalization and customer insight—by analyzing patterns in customer behavior, predictive models can tailor recommendations and refine marketing strategies, leading to improved customer experiences.
The risks of Predictive AI
Data quality and bias—the accuracy of predictions depends on the quality and representativeness of the underlying data. Inadequate or biased data can lead to skewed predictions and reinforce existing inequalities.
Overfitting and reliability issues—models that are too tightly tailored to historical data may fail to adapt to new, unforeseen conditions, risking poor performance or misguided decisions in uncertain environments.
Ethical and privacy concerns—predictive models that analyze personal or sensitive data raise significant ethical questions. Misuse or overreliance on such data can lead to privacy breaches or discriminatory practices, especially if the models are not transparent in their decision-making process.
Predictive AI use-cases
Here are three compelling use cases for predictive AI, along with real-world examples for each:
Fraud detection:
Banks—financial institutions use predictive models to flag unusual credit card transactions in real time, reducing the potential for fraudulent activity. Banks such as JPMorgan Chase leverage historical transaction data to identify patterns of fraud and trigger alerts.
Online payments—online payment systems integrate similar predictive algorithms to detect and block suspicious activities, protecting both the business and its customers.
Customer behavior forecasting:
Ecommerce—retailers analyze shopping patterns and customer behavior data to predict purchasing trends, helping them optimize inventory and tailor marketing strategies. Companies like Walmart harness such insights to adjust product stocking and promotional campaigns.
Performance Marketing—predictive modelling is used to identify what ads to show, when, and to whom. RTB House already uses these kinds of Predictive AI models to provide better retargeting services.
Predictive maintenance:
Manufacturing—in manufacturing, predictive AI analyzes sensor data from equipment to anticipate breakdowns before they occur. Companies like Siemens use these techniques to schedule timely maintenance, thus reducing downtime and repair costs.
Planes—in aviation, airlines employ predictive maintenance to monitor engine performance, ensuring issues are addressed proactively to avoid in-flight complications.
Get the best results by using Generative and Predictive AI together
Generative AI and Predictive AI both have their places in your workflow, it’s just a case of using them intelligently. One example is RTB House’s latest tool: IntentGPT. IntentGPT uses advanced LLMs to analyze web content and identify real user intent. It’s able to distinguish which content signals genuine user intent, rather than more general content like entertainment. This ensures more precise placement of ads, and is a strong marriage between the benefits of both Generative and Predictive AI.
If you’d like to learn more about how you can use Deep Learning tools like Intent GPT in your marketing campaign, contact us today, and let’s take your marketing to the next level.
