What Is Predictive AI? Generate Data-Driven Insights and Boost Customer Experience
Predictive AI, generative AI, machine learning, and other newer technologies are hot topics in any industry. This technology has the potential to change how organizations operate, evolving how we work and do business.
Predictive AI tools were some of the first business uses of artificial intelligence. In 2022, ChatGPT launched with great fanfare. Afterward, many organizations focused on harnessing generative technology to streamline content creation.
Generative capabilities are powerful, and they’re still being explored. Predictive AI, however, has a deep impact on business intelligence and overall decision-making.
What is predictive AI?
Predictive AI analyzes historical data, identifies patterns, and predicts future outcomes. Tailored statistical models and algorithms make the forecasts. Machine learning (ML) capabilities allow AI to be trained and improve its forecasting accuracy over time.
Predictive analytics have been a business discipline in some form or another for quite some time. Business analysts accessed databases and used spreadsheets to build predictive tools. Due to the enormous amount of data involved, the process was laborious and time-consuming. It wasn't always accurate, either.
Predictive modeling can be used for anything from projecting revenue to building AI-enhanced customer communications. Today, predictive artificial intelligence automates much of the process. ML capabilities empower accurate and evolving predictive models.
How does predictive AI work?
While the machinery is complex, implementing AI predictive analytics is a straightforward process.
Collect big data: This involves gathering historical and real-time data from structured and unstructured sources.
Clean and process data: All information must be verified and checked for errors.
Train the AI model: Clean data is fed to the selected model. The AI looks for trends and patterns.
Test the AI: The model is fed new or separate data to assess its predictive capabilities.
Refine the model: Predictions are compared to real-world results. Adjustments are made to improve accuracy.
Predictive AI models
Predictive AI models are the frameworks you use to analyze big data and make predictions. Like many things in business, there isn’t a one-size-fits-all approach. There are many different predictive modeling types. Each has its pros and cons as well as use cases.
Regression models
Regression models identify correlations between a set of variables, such as a linear regression. Relationships are quantified to make predictions based on the determined regression type.
Time series
Historical data is plotted out chronologically in a time series model. Identity patterns are grouped into time sequences to make predictions.
Classification models
Classification algorithms are trained to categorize data points based on established variables. Decision trees are a type of classification model that uses machine learning. They classify and then process data to form a single forecasted output.
Clustering
Clustering models group datasets based on similarities. As more data is put into the AI and segmented, clearer patterns about desired variables emerge. The model then makes predictions based on its grouping analysis.
Predictive AI examples
Predictive AI can’t tell the future. There are just too many variables involved in the real world (maybe one day, though).
However, any organization can benefit from having a better idea of what to expect in the future. What do you want to know when making business decisions? It depends on your overall strategies and goals.
Here are some examples of predictive analytics AI use cases:
Financial services: Forecast market trends, stock prices, exchange rates, and other economic variables
Healthcare: Predict patient outcomes and model disease progression based on clinical data
Ecommerce: Predict seasonality, project sales, and forecast inventory levels for demand planning
Marketing: Predict the success of new campaigns and promotions and segment customers
Non-profits: Predict personas likely to become donors and what amounts they’ll likely donate
Contact centers: Build tailored conversations with better AI customer engagement
Insurance: Tailor packages and pricing to stay competitive and meet future demand
Energy and utilities: Predict when infrastructure needs replacing, avoiding service disruption
Supply chain management and logistics: Predict shipping port congestion and forecast supplier costs
How accurate is predictive AI?
The accuracy of predictive AI is never guaranteed to be 100% correct. The accuracy of AI predictions depends on several factors, including:
Data volume: The amount of information fed into the predictive model for training and testing
Data quality and hygiene: The cleanliness and accuracy of collected information
Data type: This includes structured, unstructured, simple, and complex data
Model type: Different models have varying predictive success rates.
Problem complexity: Issues with more variables or uncertainty create less accurate predictions.
Business strategy and objectives: Ultimately, you define what “accuracy” means for your organization.
No matter where you start, your predictive models improve with each iteration. Continue monitoring real-time data and update your model for better accuracy over time.
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Predictive AI vs. generative AI: What’s the difference?
Predictive AI and generative AI are distinct types of artificial intelligence. They do have some similarities, however. Both types of AI save time, increase success and help users accomplish their goals.
GenAI and predictive analytics AI are also trained and tested on datasets to increase their accuracy. Both technologies rely on large language models (LLMs) to interpret and integrate data.
Now, let’s look at the differences between predictive vs. generative AI.
Predictive AI uses algorithms and modeling to look for patterns in historical data. It then uses this information to deduce how different variables are related.
The model can then make predictions about future outcomes. Use cases for predictive analytics include financial forecasting, healthcare planning, and inventory management.
Generative AI uses models like generative adversarial networks (GANs) and Variational Autoencoders (VAEs). These models help GenAI follow user instructions to generate original content. Use cases for generative AI include article writing, website images, and synthesizing music for YouTube videos or ads.
Predictive AI
Generative AI
Purpose
Predict future outcomes by analyzing historical data
Generate content based on patterns from existing material
Data usage
Structured, semi-structured, and transformed unstructured data
Mostly with unstructured data but sometimes with semi-structured and structured data
Training
Train on historical data to identify patterns
Trains with big data to create similar but unique content
Core technologies
Algorithms and models, such as regression, decision trees, neural networks, and clustering
GANs, VAE, transformers, and neural networks
Use cases
Financial forecasting, healthcare planning, inventory, and supply chain management
Marketing content creation, article writing, and generating images and music
5 advantages predictive AI has over generative AI
Every technology has its pros and cons for businesses. However, here are five advantages predictive AI has over generative AI.
Higher return on investment: Better decisions have a greater business impact than content creation. For example, MIT Sloan Management Review reports that a medium-sized bank could save $16 million annually by predicting credit card fraud.
Better automation: Once a model is set up, data collection and analysis are on autopilot. A positive feedback loop compares incoming data with predictions and adjusts its parameters. GenAI requires constant handling from human trainers to assess it and improve its performance.
Smaller footprint: In general, predictive AI requires far less computing power than generative AI. Reduce your server farm footprint (and costs), whether on-premise or in the cloud.
Enhanced scalability: This can be applied to all areas of large enterprises and smaller organizations. Generative AI is a more focused application for businesses.
More explainable: Predictive outputs can always be compared against historical and real-time data. Data scientists can find root causes for errors or inaccurate forecasts. Meanwhile, Generative AI can come up with unexplainable results. For instance, hallucinations occur when the artificial intelligence simply makes something up.
How does predictive AI use embeddings?
Predictive AI uses embeddings to understand the relationships between objects in our human world. ML technologies use embeddings, such as Word2Vec and GloVe, to gain context from data.
What are embeddings? They’re representations or values, such as text, images, and audio (stuff that’s often unstructured data). They help machine learning models identify and classify data for interpretation.
A real-world example would be connecting customer feedback data to your sales projections. Embedding vectors recognize the various survey responses. The model processes the now structured data, finding patterns and making predictions.
5 ways predictive AI delivers value: the benefits
Market.us projects the global predictive AI market will reach $108 billion by 2033. That projection represents a CAGR of 21.9% from where the market was in 2023. Why so much growth? Because more and more businesses are seeing the value of predictive AI.
1. Improves decision-making
Whatever you use predictive modeling for, it will help you make better operational and business decisions. Some examples include deciding on what products to stock or what customer segments to target.
2. Reduces risk
Knowing what likely lies ahead helps you prepare for disruption and change. Identify when it’s time to upgrade infrastructure (before it fails). Diversify your supply chain before projected problems slow your business down.
3. Cost-effective
Predictive AI models are often trained on relatively low amounts of parameters and datasets compared to GenAI models. You spend less on computing resources and labor hours to yield better returns.
4. Improves efficiency
Predictive analytics can be fully automated from data collection to evaluation and iteration. Your team saves time that they would otherwise spend on tedious and repetitive tasks so they can focus their efforts elsewhere.
5. Better customer experience
Predictive analytics can help you better understand your target audiences, meaning you can personalize marketing campaigns and design customer service that meets client expectations.
Your customers receive an overall better experience. For example, demand forecasting helps you keep hot items in stock and avoid disappointing customers.
Is predictive AI right for your company?
Predictive AI has a wide range of applications. It’s also highly scalable to businesses of any size. What does that mean? If you’re not already using predictive AI analytics, it’s probably time you started.
Predictive AI is right for your business if you:
Handle big data: Process large volumes of historical and real-time data from multiple sources.
Have IT and AI expertise: Have a team of data scientists, AI and ML engineers, and other specialists.
Are data-driven: Your business has a company culture where strategies and decisions are based on evidence and analysis.
Want accurate forecasting: Need to be confident in sales projections, market forecasts, and other predictions with the help of predictive AI.
Need better-informed decisions: Want better visualization and insights when making business decisions.
Aim to optimize operations: Allocate resources for optimal workforce planning, supply chain management, and other areas.
Want to mitigate risk: Identify risks and avoid disruption and other unwanted outcomes.
Unlock the power of predictive AI for your business
Predictive AI unmasks the true potential of analytics for your business. Gain better insights from projections about every area of your business and its respective markets. Make better-informed decisions that reduce risk, enhance efficiency, and increase profitability.
What about gaining a better understanding of your customer and their support needs? Vonage Communications APIs empower startups and dynamic businesses to elevate customer experiences and achieve scalable business outcomes. Our extensive suite of communication channels includes voice, video, SMS, MMS, social chat apps, and scalable 2FA.
Learn more about Vonage AI capabilities today.
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Still have questions about predictive AI?
Both generative AI and predictive AI use machine learning to process information. Generative AI uses large language models and GANs to recognize similarities between data.
This enables it to create text, images, and audio content. Predictive AI uses regression and clustering models to analyze historical and real-time data. These models identify patterns and make predictions on future outcomes.
In other words, generative AI is used for content creation, whereas predictive AI makes forecasts that impact decision-making.
Predictive AI has many diverse applications. In finance, an institution could use predictive modeling to forecast investment performance.
In healthcare, predictive analytics can identify trends indicating a patient has an increased risk of developing certain diseases. Retail businesses can optimize warehouse costs and increase sales with demand forecasting.
No, ChatGPT is not predictive AI. It’s a text-based generative AI platform. It has been trained (and continues to be) on trillions of textual data points in multiple languages.
It uses LLMs and neural networks to identify different words and phrases. This allows ChatGPT to understand human instructions. It then gives human-like responses, creating original content similar to its training material.
ChatGPT can’t analyze historical or real-time data to make predictions.
