Generative AI is an emerging form of artificial intelligence that generates content, including text, images, video and music. Generative AI uses algorithms to analyze patterns in datasets to then mimic style or structure to replicate a wide array of content.
Predictive AI is also a type of artificial intelligence. In contrast with generative AI, predictive AI uses statistical algorithms to analyze data and make predictions about future events. It is sometimes also called predictive analytics and may sometimes be loosely termed as machine learning.
Let’s examine generative AI and predictive AI, lay out their use cases, and compare these two powerful forms of artificial intelligence.
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Generative AI vs. Predictive AI
At their foundation, both generative AI and and predictive AI use machine learning. However, generative AI turns machine learning inputs into content whereas predictive AI uses machine learning in an attempt to determine the future and prevent bad outcomes by using data to identify early warning signs.
Among the key differences between generative AI and predictive AI:
Creativity – generative AI is creative and produces things that have never existed before. Predictive AI lacks the element of content creation.
Inferring the future – predictive AI is all about using historical and current data to spot patterns and extrapolate potential futures. Generative AI also spots patterns but combines them into unique new forms.
Different algorithms – generative AI uses complex algorithms and deep learning to generate new content based on the data it is trained on. Predictive AI generally relies on statistical algorithms and machine learning to analyze data and make predictions.
Both generative AI and predictive AI use artificial intelligence algorithms to obtain their results. You can see this difference shown in how they are used. Generative AI generally finds a home in creative fields like art, music and fashion. Predictive AI is more commonly found in finance, healthcare and marketing – although there is plenty of overlap.
Now let’s take a deeper look at both generative AI and predictive AI.
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What is Generative AI?
Generative AI functionality is all about creating content. It combines algorithms and deep learning neural network techniques to generate content that is based on the patterns it observes in other content.
Although the output of generative AI is classified as original material, in reality it uses machine learning and other AI techniques based on the earlier creativity of others – this is a major criticism of generative AI. This emerging AI technology taps into massive repositories of content and uses that information to mimic human creativity.
Generative AI systems use standard machine learning techniques as part of the creative process. Generative AI can do things like analyze the entire works of Dickens or Rollins or Hemingway and produce an original novel that seeks to simulate their style and writing patterns.
Thus, generative AI goes a stage beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI offers a tech-based foray into the world of creativity.
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Generative AI Use Cases
By producing fresh content, generative AI is being used to augment but not replace the work of writers, graphic designers, artists and musicians. It is particularly useful in the business realm in areas like product descriptions, variations to existing designs or helping an artist explore different concepts. Among its most common use cases:
Text – generative AI can generate credible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports.
Images – generative AI can also output realistic images from text prompts, create new scenes and simulate a new painting.
Video – generative can compile video content from text automatically and put together short videos using existing images.
Music – generative AI can compile new musical content by analyzing a music catalog and rendering a new composition.
Product design – generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version.
Personalization – generative AI can personalize experiences for users such as product recommendations, tailored experiences and feeding material that closely matches their preferences.
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What is Predictive AI?
Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions. Predictive AI’s value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior; enhance business decisions by identifying a customer’s purchasing propensity as well as upsell potential; and improve business outcomes.
Significantly, predictive AI can enlighten management on future trends, opportunities and threats. It can be used to recommend products, upsell, improve customer service and fine-tune inventory levels.
Predictive AI adds another dimension and greater accuracy to the processes of management. Used correctly, it increases the chance of success and achieving positive business and outcomes, particularly in the area of inventory management.
Through accurate predictions and improved decision-making, predictive AI can help organizations glean far more value from the data they collect and use it to their competitive business advantage.
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Predictive AI Use Cases
Predictive AI has a great many use cases. Some of the top ones include financial forecasting, fraud detection, healthcare and marketing.
Financial Services – predictive AI enhances financial forecasts. By pulling data from a wider data set and correlating financial information with other forward-looking business data, forecasting accuracy can be greatly improved.
Fraud Detection – predictive AI can be used to spot potential fraud by sensing anomalous behavior. In banking and e-commerce, there might be an unusual device, location or request that doesn’t fit with the normal behavior of a specific user. A login from a suspicious IP address, for example, is an obvious red flag.
Healthcare – predictive AI is already in use in healthcare. It is finding use cases such as predicting disease outbreaks, identifying higher-risk patients and spotting the most successful treatments.
Marketing – predictive AI can more closely define the most appropriate channels and messages to use in marketing. It can provide marketing strategists with the data they need to write impactful campaigns and thereby bring about greater success.
Predictive AI, therefore, is finding innumerable use cases across a wide range of industries. If managers knew the future, they would always take appropriate steps to capitalize on how things are going to turn out. Anything that improves the likelihood of knowing the future has high value in business.
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Bottom Line: Generative AI vs. Predictive AI
Both generative AI and predictive AI use algorithms to address complex business and logistical challenges.
Generative AI tends to utilize more sophisticated modeling and algorithms than predictive AI to add a creative element. In contrast to the role of predictive AI in recognizing patterns – where it draws inferences and suggests outcomes and forecasts – generative AI takes existing patterns and combines them to generate new content.
As AI evolves, the distinction between generative AI and predictive AI is likely to fade. AI systems are emerging that seamlessly merge generative AI and predictive AI. Instead of using one set of algorithms to predict and another to create, advanced AI systems combine both and can deliver both types of result. By combining the algorithms that identify trends and forward-looking correlations with those that recombine those patterns into new creations, the value of AI will be improved even further.
While there are certainly differences between generative AI and predictive AI, these distinctions are far from rigid. Each contains similar elements to the other. It is how they are put to use that brings about the different outcomes. As AI evolves, both generative AI and predictive AI will play a role in reshaping the future.