Forecasting isn’t restricted to oracles and newscasters. When you’re developing a new product plan, you need to forecast, among other important data, the product’s potential return on investment (ROI) and how much revenue it will need to produce in a given time period in order to determine its viability.
In the past, product owners relied on historical sales data and market trends to make educated guesses. But this approach could never capture every factor that might influence a product's success or failure.
Now you can use artificial intelligence (AI) to leverage sales, customer, and market research and make data-driven decisions. Whether you’re predicting sales, forecasting demand, or planning for supply chain availability, AI can make your forecasting better and support your product development workflow.
What is AI forecasting?
AI forecasting is an advanced data analytics technique that uses tools like machine learning (ML) algorithms and statistical modeling to predict outcomes. This forecasting follows either a time-series or regression framework:
Time-series forecasting — this method predicts future values based on historical data. Professionals who work with metrics collected over regular intervals over time (finance, economics, meteorology) typically use this forecasting type.
Regression forecasting — this method predicts the relationship between certain factors. Regression models analyze data in order to identify correlations between variables and predicted future values. Marketers often use this method, since businesses want to predict sales based on numerous variables like advertising spend and customer demographics.
Through time series or regression, AI forecasting models provide predictions and insights into market trends, demand patterns, and consumer behaviors. You can use this information to make informed business decisions and plan for the future.
3 AI systems used in forecasting
AI systems are computer programs or software designed to mimic human intelligence. Types of AI systems include natural language processing (NLP) programs like Siri, conversational AIs like chatbots, and computer vision algorithms for self-driving cars.
AI forecasting systems leverage ML and statistical techniques to process data and draw conclusions regarding future trends. Here are three of the most commonly used AI forecasting systems.
1. Neural networks
AI neural networks resemble the human brain and consist of layers of interconnected nodes trained on datasets. You can use these neural networks to analyze data, identify patterns, and make predictions.
Neural networks pinpoint complex relationships between variables and adapt to changing patterns. As such, they’re useful for both time-series and regression forecasting, especially when dealing with large datasets.
2. Expert systems
Expert systems mimic a human subject matter expert’s decision-making process, using rules and heuristics to analyze data and make predictions based on their knowledge base.
These systems are helpful for situations where there’s a lot of domain-specific information that can inform predictions, like forecasting the likelihood of a customer churning based on their particular purchase history and behavior.
3. Belief networks
Belief networks use probabilistic graphical models to make predictions. They excel when you have uncertain or incomplete data, since belief networks incorporate multiple sources and adjust probabilities based on new information. Use belief networks for risk assessments aimed at identifying and mitigating potential risks before they occur.
How AI can be applied to forecasting
You can use AI forecasting for various applications across industries from manufacturing to healthcare. Here are three common use cases.
1. Estimate demand
AI can help you predict customer demand by analyzing data from various sources, including customer orders, production schedules, and shipping metrics. From these forecasts, you can anticipate demand and optimize your supply chain to reduce waste and improve efficiency.
2. Forecast sales
By analyzing historical data on customer behavior, market trends, and competitor data, AI forecasting tools help you predict sales in order to optimize your strategies and budgets.
3. Predict healthcare issues
AI forecasting helps healthcare professionals respond to issues on both global and individual scales. For instance, AI can predict pandemics and also anticipate a patient’s potential health risks. They do this by analyzing large datasets from electronic health records and national health databases.
How AI forecasting can improve your business
Better forecasting means being able to make more informed decisions that drive growth and mark you as an authority in your industry.
And since AI systems process vast amounts of data much faster than humanly possible, they can also identify patterns and draw correlations that would be difficult for humans to detect, so you can catch issues like financial roadblocks before they happen.
Here are a few more benefits of AI business forecasting:
Account for various factors — AI forecasting can account for factors like market trends, consumer behavior, and competitor activities, and identify how each relates to or affects the others. It’s difficult for humans to compute and pinpoint relationships when dealing with this much information.
Improved accuracy — traditional forecasting methods rely heavily on the judgment of humans who are prone to errors and often insert biases into their conclusions. AI minimizes these risks, providing accurate, unbiased forecasts in real time.
Automate repetitive tasks — you can use AI tools to automate repetitive forecasting-related tasks like data processing and analysis. This frees up your team’s time, allowing them to focus on developing strategic initiatives based on forecasting models.
Enjoy further support with Notion AI
Data is the new gold, and AI forecasting tools provide a way to harness all your company’s metrics and provide better insights that empower your decision-making.