If generative and predictive AI were considered siblings (we'd like to think so), the former would be the creative child, with the latter boasting analytical prowess. Both have been making their mark in various industries, from gaming to healthcare and marketing. In fact, in marketing, 42.2% of stakeholders have already started using generative AI and 34.1% report significant improvements.
As for predictive AI, it has gotten smart enough to outperform neuroscience experts, as seen in a study published in Nature, one of the most prestigious scientific journals. While there are some fundamental differences between the two technologies, they are each shaping the future in distinct ways.
Going forward, it's important to understand the differences and overlapping functionalities of both technologies to determine which is the best fit for your use cases. Below, we describe generative and predictive AI, also shedding light on their real-world applications to give a glimpse of their potential.
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- Generative AI vs Predictive AI: Simplifying Complex Concepts
- Generative AI vs Predictive AI: Key Differences You Need to Know
- Overlapping Capabilities: Can Generative and Predictive AI Work Together?
- Real-World Applications of Generative and Predictive AI
- Challenges and Ethical Concerns: What You Need to Know
- Unlocking the Potential of Two Transformative Technologies
- Frequently Asked Questions
Generative AI vs Predictive AI: Simplifying Complex Concepts
With 42% of large businesses using AI in their day-to-day operations, it's imperative that stakeholders in every industry are familiar with the main subsets of this innovation: generative and predictive AI. To put it simply, generative AI creates and predictive AI predicts. Let's discuss their functionality further.
What Exactly Is Generative AI?
If you've ever used ChatGPT to create text, you've had first-hand experience with generative AI. The same is true if you've used Instagram's AI to generate stickers or Meta AI to quickly find the meaning of a fancy word your friend has used in chat.
Generative AI is a technology that generates new content such as text, images, code, music, audio, or video by learning from existing data. Basically, you'd provide the generative AI tool with a prompt, and it'll generate new content based on what it has learned from the data you've fed into it.
While ChatGPT is a commonly known example of generative AI, there are thousands of tools that can generate a range of content types. For example, ElevenLabs is an AI voice generator that can create audio outputs in thousands of voices across 32 languages. Similarly, Midjourney's AI platform can create images based on textual prompts.
How Does Generative AI Work
Generative AI is seen as a high-potential technology with a forecasted ability to automate half of product portfolio optimization for businesses. But how exactly does it do that?
- Generator: It's the part that generates new content. The generator does this by analyzing patterns in existing data and creating new examples based on those patterns. The goal of the generator is to create realistic outputs that can't be distinguished from real ones.
- Discriminator: The discriminator's job is to identify whether the generator's output is real or fake by comparing the generated output with the real ones it has been trained on.
Alternatively, generative AI can also work on Variational Autoencoders (VAEs), which are similar to GANs but use a different architecture. VAEs also have two parts:
- Encoder: It learns how to compress data into a lower-dimensional space, also known as the "latent space."
- Decoder: The decoder then takes information from this compressed latent space and reconstructs it back into its original form.
As a result, VAEs generate new content that is similar to the input data but with slight variations. They are useful for tasks like image generation or language translation, where small differences make a big impact.
Another generative AI architecture is transformer-based language models, such as GPT-3 (the kind in ChatGPT). Transformers process sequential data, like sentences or audio, to determine what comes next. They do this by learning relationships between words or sounds and predicting the most likely next element. So they can generate new content based on the patterns they have learned.
Jia-Bin Huang, an Associate Professor of Computer Science at the University of Maryland College Park, has shared a detailed video on LinkedIn that further explains how transformers drive generative AI applications.
What Exactly Is Predictive AI?
Predictive AI is another type of AI that uses statistical models to analyze large amounts of data and make predictions about future outcomes. Unlike generative AI, which creates new content, predictive AI uses existing data (typically historical) to generate insights and forecasts.
A common predictive AI technique that we're all familiar with is weather forecasting. The app on your phone can predict the temperature and chances of rain for the next few days using historical weather data.
Now, we have AI tools for stock market predictions, customer behavior forecasts, personalization algorithms predicting what we might purchase next, and AI-powered job recruitment systems that predict the best candidates for a role. All of these rely on predictive AI to determine the possibility of something happening.
How Does Predictive AI Work
In a LinkedIn post, Dr Gopala Krishna Behara, an enterprise architect at Tricon IT Solutions, explains that predictive AI uses historical data to forecast the ''most likely upcoming event or result.''
- Statistical Modeling: Statistical models are mathematical equations that use past data to identify patterns and relationships. A regression model is one such example, which predicts a continuous value based on other values. For instance, a regression model can be built to predict house prices based on factors like location, amenities, square footage, and number of bedrooms.
- Machine Learning: These algorithms analyze data to find relationships and patterns, just like statistical models. The difference is that instead of using predefined equations, the machine learning algorithm uses training data to improve its predictions.
Predictive AI models use decision trees to break down a problem into smaller, simpler decisions. It's sort of like a flow chart where each node or branch represents a specific decision based on a feature within the data, like a customer's buying history or an applicant's resume.
As more training data is fed into the model, it continues to improve its decision-making process. Over time, the predictions become more and more accurate. Predictive AI algorithms can also be combined with traditional techniques for higher accuracy at lower costs. Google's new weather prediction model, which combines physics with AI, is a good example.
Generative AI vs Predictive AI: Key Differences You Need to Know
Of the main differences between generative and predictive AI, their functionality is perhaps the most significant. Generative AI creates new content, and predictive AI forecasts future outcomes. Other differences between generative and predictive AI are in the table below.
| Feature | Generative AI | Predictive AI |
| Core Function | Generating output, such as text, audio, video, music, or code | Providing insights and predictions about future trends or events |
| Data Requirements | Existing data needed as inspiration to create new content | Historical data to analyze patterns and make informed predictions |
| Applications | Content creation, image generation | Risk assessment, predictive maintenance |
| Output Type | Generative and creative | Analytical and descriptive |
| Methodology | Variational autoencoders, GANs, transformer models | Regression models, time series analysis, neural networks |
| Purpose | Creativity and innovation | Trend forecasting and decision-making |
| Example | ChatGPT, Midjourney | IBM Watson Studio, H2O Driverless AI |
Overlapping Capabilities: Can Generative and Predictive AI Work Together?
There are many individual use cases for generative and predictive AI, but both technologies can also work in sync. Their combinatory real-world applications span across several fields, like marketing and game development.
Let's take marketing as an example. Predictive AI can analyze customer data to forecast buying patterns and identify high-value segments. You can then use generative AI tools to create email content or web content. These tools can also create marketing videos and social media posts to target consumer segments that are predicted to bring in the most revenue.
Similarly, hybrid AI models can be used in the education sector. For example, predictive AI can identify learning gaps based on students' past performance, while generative AI then assists in creating customized study materials and visual aids to address the identified learning needs.
Once you know how generative AI and predictive AI work together, you can use these hybrids to refine everything from customer targeting to decision-making. The duo is even helpful in intricate scenarios like healthcare and finance.
For example, in a hybrid AI model, the predictive side can forecast potential health risks based on a patient's historical clinical data, while generative AI then assists health professionals in creating personalized treatment plans. The former enriches the data that the latter uses to develop targeted outcomes in every situation.
In finance, a consultant may use predictive AI tools to analyze market trends and customer spending habits. They can then create personalized investment portfolios with generative AI tools. Since the insights based on which the portfolios are made evolve in real time, these can be used to optimize every single decision.
Real-World Applications of Generative and Predictive AI
Both generative and predictive AI have a ton of real-world applications in fields ranging from healthcare, finance, and marketing to entertainment and gaming. Let's discuss a few of them.
Generative AI in Marketing and Beyond
In our AI Trends Report, we discuss generative AI as the first trend in the marketing space since it has brought about a new era of personalization and creativity. To start, generative AI tools can create different types of marketing content, such as:
- Textual Content: AI writing tools save content marketers a lot of time they'd otherwise have to spend writing blogs, emails, ad copy, and social media posts from scratch. These tools also provide a level of personalization that wasn't available before by tailoring the output to the consumer's preferences and behaviors. For example, ContentShake AI can generate SEO-friendly content based on your target audience, location, tone, style, keywords, and preferred language.
- Visual Content: AI image generators help create visual content for social media posts and other marketing material. These tools only need a text prompt to generate an image; then, you can tweak the design elements to craft your desired output.
- Audio Content: While AI video generators create marketing videos, voice generator software can be used to generate natural-sounding voice overs for your videos, audio ads, podcasts, and virtual assistants.
Besides marketing, generative AI can also be used to develop virtual worlds for games, animation, and filmmaking. For example, OpenAI has an AI World Generator that can help you tell stories and create virtual reality experiences for your audiences.
In education, generative AI can create custom learning paths and personalized learning experiences for every student, accounting for their learning style and preferences. For example, Auto Classmate has an AI-powered lesson plan generator that can create highly customizable and unique teaching materials.
Generative AI capabilities have also been integrated into new devices. For example, Samsung has deployed Imagen 2 and Gemini Pro to the Galaxy S24 smartphones, letting users summarize text, edit images with AI, and more.
Similarly, generative AI has also made its way to the hospitality industry. An example comes from the Hotelplan Group, which has created a chatbot to answer customer inquiries. The hotel chain further plans to use AI for travel content generation. Businesses are also using AI in creative branding, using AI logo generators and generative tools to create branding collateral.
Predictive AI in Marketing and Analytics
While the applications of generative AI are more well-known and ubiquitous, predictive AI use cases are rapidly increasing in the marketing and analytics space. For example, in SEO, marketers can use predictive tools to forecast the keywords that will be popular in the future and the consumer trends that will likely drive traffic in the future.
The Semrush Keyword Magic Tool is one such example. It not only shows you the keywords related to a seed term but also provides AI-powered insights, such as:
- Personal Keyword Difficulty: The specific difficulty score for a keyword based on your site's authority.
- Potential Traffic: The amount of traffic you can expect to receive if you create well-optimized content for that keyword.
Semrush Keyword Magic Tool
Additionally, the Semrush Keyword Overview tool shows related keywords and questions associated with a seed term. It also has AI-powered insights, such as personal KD%, potential traffic, potential topic traffic, and potential position on the SERPs. Based on this information, marketers can then identify the best content and keyword usage opportunities to maximize ROI for their campaigns.
Semrush Keyword Overview tool
Similarly, predictive analytics tools can help you anticipate customer behavior and trends, such as the products they're likely to buy or the channels they prefer for communication. For example, IBM Watson can analyze customer sentiment from social media comments and reviews to predict customer satisfaction and attrition rates. You can use this information to proactively address any issues your customers may have in the future.
Besides predicting your own customers' preferences, you can also use predictive AI to get competitor insights. For example, the Semrush Traffic Analytics tool helps forecast competitor performance and analyze the route they may take with their future campaigns based on the channels and keywords they're currently targeting.
Semrush Traffic Analytics
It's not just the channels and content performance that you can predict. AI trend forecasting tools also help anticipate the topics that will gain popularity in the future. You can create content around these topics before your competitors do to get the upper hand.
There are also many examples of AI in industries other than marketing. For example, in finance and banking, AI tools like SEON can predict fraud detection and help prevent financial crime.
In healthcare, predictive AI use cases range from predicting patient outcomes and identifying high-risk patients to optimizing hospital staffing and resource management. GI Genius is one such tool that can predict the occurrence of cancer in patients. In one study, the tool increased the detection of precancerous polyps by over 14%.
Challenges and Ethical Concerns: What You Need to Know
Regardless of the subset, AI technologies typically come with ethical concerns, especially when used in high-stakes or sensitive situations. With generative AI, there's a risk of bias and discrimination being encoded into the system, leading to unjust or harmful outcomes.
Some malicious actors are also using generative AI to spread misinformation and create deep fakes. In fact, Financial Times reports that political deepfakes are the most malicious use of AI.
Besides these concerns, there's also a grey area when it comes to the responsibility of AI-generated content. Who should be held accountable for any damage or harm caused by it? Also, since the outputs are based on existing content, especially art, to what extent can AI-generated content be considered plagiarized?
In a recent development, a California federal judge allowed visual artists to proceed with copyright claims against companies like Stability AI, Midjourney, and DeviantArt, asserting that their AI systems unlawfully store and utilize copyrighted works for training. The case highlights concerns over whether AI-generated outputs, created using “compressed copies” of original works, violate copyright laws. While some claims were dismissed, the decision emphasizes the ongoing legal battle over the fair use of copyrighted material and the rights of artists in the era of generative AI.
In a BBC article, an artist mentioned that his art style has been used as inspiration in prompts over 400,000 times — more than Picasso. However, it was without consent, which means the artist wouldn't receive any credit or compensation for his work. This raises questions about the ownership and rights of AI-generated content.
Apart from the challenges of generative AI, there are several concerns related to predictive AI, too. For one, over-relying on historical data can result in flawed and biased predictions, especially in areas like criminal justice and healthcare. The decisions made using these tools can exacerbate existing inequalities and discrimination.
There are also concerns regarding privacy and data security with predictive AI. As these systems collect and analyze personal data, there is a risk of it being misused or falling into the wrong hands. For example, if the data collected by an AI fraud detection system is shared with third-party companies without the knowledge or consent of the individuals involved, it can lead to a privacy breach.
These ethical concerns in predictive AI and generative AI usage can be tackled with individual and governmental intervention. Every business that uses AI systems should have ethical guidelines to govern the use of these technologies. Even before that, at AI development stages, there should be strict quality checks to eliminate racist, sexist, or discriminatory biases.
Regulatory bodies and governments around the world also need to create laws and policies for the transparent and ethical use of generative and predictive AI. The Organization for Economic Co-operation and Development (OECD) already has a detailed paper on how to mitigate AI risks that can serve as inspiration.
Similarly, The World Economic Forum also highlights mitigation strategies, while PwC has listed seven actions to manage AI risks. These are just the starting points, though. More discussions and collaborations are needed to build the security and ethics frameworks needed for the AI-driven future.
Unlocking the Potential of Two Transformative Technologies
Be it marketing automation or detecting the likelihood of disease diagnosis, AI has found its way into many applications in practically every industry. While generative AI facilitates the creation of everything from text to visual content, predictive AI makes decision-making more efficient with anticipatory analytics and insights.
Going forward, it's important that you not only learn about these technologies but also incorporate them into your business operations. And where possible, the hybrid of these two can deliver an even more powerful punch.
Frequently Asked Questions
What is generative AI?
Generative AI is an advanced form of artificial intelligence designed to create new content, such as text, images, music, or videos, based on the patterns it learns from training data. Tools like ChatGPT and Midjourney are popular examples of tools developing creative outputs across various industries.
What is predictive AI?
Predictive AI uses historical data and patterns to forecast future outcomes or trends. It relies on algorithms like regression models or neural networks to analyze data and provide insights. Common applications include demand forecasting, customer behavior prediction, and risk assessment in fields like marketing, finance, tech, education, and healthcare.
How do generative AI and predictive AI differ in methodology?
Generative AI develops new content by learning from patterns in training data, using techniques like GANs or transformer models. In contrast, predictive AI analyzes historical data to identify trends and forecast future outcomes. It uses methods like regression or time-series analysis to do so.
What industries benefit most from generative AI, and what industries benefit most from predictive AI?
Generative AI benefits industries like entertainment, marketing, education, gaming, and design, where creating new content is imperative to driving business objectives. On the other hand, predictive AI is more beneficial in supply chain management, manufacturing, healthcare, and finance, where data-driven decision-making is regularly needed to reduce risks and improve outcomes.
Can generative AI perform predictive tasks?
Generative AI isn't built for prediction, but it can assist in predictive tasks indirectly. For example, it can simulate scenarios or create synthetic data that helps train predictive models. While its core function is content generation, with the right setup, it can complement predictive AI in complex workflows.
How does training data differ for generative AI and predictive AI?
The training data for generative AI models typically consists of large sets of examples, like images or text, to learn from and generate new content. Meanwhile, predictive AI models use historical data to identify patterns and make predictions. The training data for predictive AI has to be representative of the real-world scenarios it aims to predict accurately.
What ethical concerns exist for generative AI?
Generative AI comes with ethical concerns like deep fake creation, environmental impact, academic integrity, copyright infringement, and spreading misinformation. Plus, biases in training data can lead to unfair or harmful outputs.
How do computational requirements compare for generative vs. predictive AI?
Generative AI typically requires higher computational power due to the complexity of creating new content, especially in tasks like image creation or text generation with large models. Predictive AI, while still resource-intensive, usually demands less since it forecasts trends rather than generating entirely new outputs.
Can generative AI assist predictive AI models?
Generative AI can assist predictive AI models by creating synthetic data to train them, especially when real data is scarce or biased. It can also help simulate different scenarios to provide context or variations that enhance the accuracy of predictive models.
What are the limitations of generative AI and predictive AI?
Generative AI's limitations include creating biased or inaccurate content. It may also produce outputs that sound convincing but are incorrect. Predictive AI is limited by the quality and scope of historical data. Plus, it can struggle with uncertainty or sudden changes, leading to less reliable predictions in dynamic situations.
Is generative or predictive AI better for decision-making in business?
Predictive AI is generally better for decision-making in business, as it can analyze historical data to forecast trends, outcomes, risks, and opportunities in your industry. You can then use generative AI to develop new ideas or content based on those insights.
How are generative and predictive AI combined in hybrid models?
In hybrid models, generative and predictive AI complement each other. Predictive AI provides forecasts, while generative AI creates solutions or content based on these predictions. For example, in marketing, predictive AI can analyze customer behavior, and generative AI can use that data to create personalized campaigns or product recommendations.
