How to forecast business outcomes with predictive AI

The riskiest part of business is the fact that no one knows what the future holds. The more accurately you can predict what’s around the corner, the better prepared you’ll be to face whatever tomorrow brings. That’s what makes predictive AI so valuable — it turns historical data into forward-looking insight, supporting decision-making and minimizing risk. This is supervised machine learning, where a model makes predictions after training on a set of known inputs and outputs.

In this guide, we detail how predictive AI that’s supported by effective data can help your business act with confidence. 

What is predictive AI?

Predictive AI analyzes historical data and live data, such as customer buying patterns, behavioral signals, or industry trends, to forecast what’s likely to happen next. By relying on predictive analytics, decision-makers can plan with confidence, act earlier, and reduce uncertainty across their departments.

The process leverages machine learning for statistical modeling at scale. Automated extract, load, transform (ELT) pipelines from platforms like Fivetran continuously deliver a stream of clean and reliable training data. Using this information, the AI model predicts upcoming changes to things like business streams, worker productivity, customer buying patterns, or fraudulent activity.

How predictive AI works

Although the specific predictive artificial intelligence model you use will depend on your industry and business objectives, the pipeline that feeds it training data will work the same way. Here’s a look at the underlying architecture.

Stage 1. Data collection and preparation

The first step is to gather data from trusted applications, databases, and real-time event streams to build up an index of information. After cleaning, standardizing, and enriching datasets, they’re then delivered to the model. The more data a model has to work with, the more refined it can become, improving its accuracy and performance. 

Stage 2. Model development and training

During training, models process historical data to learn the patterns and relationships within the datasets. Depending on the specific task you want to execute, the exact model you’ll use will vary. For example, a regression model works best for forecasting, while a time-series model is better for predicting trends. 

You will need to divide your data into training, validation, and testing datasets. Training combines known inputs and outputs with an algorithm to generate values for parameters. The validation set is used to evaluate a model’s performance and enable the tuning of hyperparameters. Testing uses a test “holdout” dataset, or reserved data that the AI model hasn’t seen, to see how well it performs on unseen information. 

Stage 3. Prediction and scoring

Once a model has been tested, it’s ready to generate predictions. Instead of historical data, the model is now fed new, live data. Using its understanding of how variables relate, it will then predict future data based on incomplete information.

Stage 4. Model deployment and monitoring

Deployed models need monitoring over time to make sure they stay accurate. If your input information changes, the model might not be able to adapt to new trends, patterns, or ideas. For example, seasonal change might cause a dip in customer purchases. If you haven’t trained the model on year-round trends, its sales forecast is likely to be incorrect. 

Be sure to deliver fresh training data continuously and run tests for accuracy.

4 predictive AI examples

A wide variety of industries deploy AI predictive analytics. Here are just a few examples of the technology’s uses.

1. Marketing: Customer churn predictions

Customer churn is associated with factors like declining product usage, falling engagement, and changes in purchase frequency. Training an AI model on these signals helps companies to predict when a current customer is thinking about leaving. Identifying these signs early enough empowers marketing teams to launch targeted offers or provide support to stop churn in its tracks.

2. Supply chain optimization: Demand forecasting 

Predictive AI models analyze historical sales data, seasonality, and external market signals to forecast future demand more accurately. By predicting future demand, AI models help businesses optimize inventory levels. Risk modeling can also help identify potential supply chain disruptions before they become a problem. 

3. Manufacturing: Predictive maintenance

Internet of Things (IoT) sensors collect thousands of data points across industrial environments. By aggregating this information and processing it with a predictive AI model, organizations can anticipate and address equipment failures before they happen. Shifting from reactive to proactive maintenance saves money, prevents downtime, and increases the lifespan of industry systems. 

4. Finance: Fraud detection and risk scoring 

By training an AI model on normal customer behavior, such as how they use their online banking profiles, financial entities can better detect fraudulent activity. When someone logs into an account and starts behaving erratically, the model will automatically raise a flag to an administrator. 

Benefits of AI predictive analysis

Being better prepared for the future never hurts. Here are the benefits you can expect from using predictive AI models:

• Proactive decision-making: Predictive AI allows teams to forecast future trends and adapt to them ahead of time. This proactive strategy allows you to make cost-saving business decisions.

• Reduced risk: Identifying potential issues ahead of time allows you to address them before they escalate into costly problems. Proactive intervention can reduce waste, avoid downtime, and prevent small inefficiencies from snowballing.

• Better customer insights: Understanding your customers better means you can serve them better. Learning from behavior patterns will help you create more relevant, tailored experiences and anticipate what your customers need.

• Competitive advantage: Spotting an industry trend right before it explodes gives your business enough time to seize the opportunity. Future insights transform data into a strategic edge for your business.

Best practices for implementing predictive AI

Getting the most out of your predictive AI systems means building a strong, reliable data architecture free from silos and disconnections. Here are a few best practices to point you in the right direction:

• Enhance data quality: Make sure that all training data is accurate and high-quality. 
• Define clear objectives: Use predictive AI and prescriptive analytics to meet specific business goals.
• Focus on explainable AI: Explainability is an AI model’s ability to provide clear, easily understood reasons behind its output. Always ensure your model’s reasoning makes sense by focusing on why it made its decisions, not just what it decided.
• Monitor continually: Track your model’s output over time to make sure it stays accurate.

How Fivetran enables predictive AI pipelines

While generative models can handle some discrepancies, predictive AI relies on continuous, high-quality data above all else. To be accurate, predictive forecasts need dependable information, consistent schemas, and timely updates.

‍Fivetran ensures predictive AI models always train on complete, accurate data by automating ingestion from hundreds of sources into modern cloud data warehouses and data lakes. Fivetran’s managed connectors handle schema changes and reduce data drift, removing the burden of manual preparation and pipeline maintenance. You’ll spend less time wrangling data and more time transforming your business with accurate, AI-powered decision-making.

Learn how your organization can benefit from automated data transformation by booking a demo of Fivetran today.

FAQs

What are predictive AI tools, and how do they function?

Predictive AI tools are machine learning models that train on historical data, using their understanding of past occurrences to create precise future predictions.

These models are only as good as the data used to train them, so when deciding on a predictive AI model, be sure to also consider platforms like Fivetran that support high-quality data pipeline architecture. 

What’s the difference between predictive AI and generative AI?

Predictive AI is about forecasting future outcomes based on patterns in historical data. AI models produce insights strictly based on data and a well-governed data architecture. Typically, businesses use predictive AI to support decision-making.

Generative AI uses training data to create new content, such as code, text, or images. By learning patterns, it creates an output that resembles its training dataset. 

What are the challenges of predictive AI?

The main challenges of using predictive AI are:

• Ensuring the data that feeds AI models is high-quality 
• Removing algorithm bias
• Building out model explainability
• The behind-the-scenes talent investment required

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