Why Businesses Need AI to Achieve Reliable Forecasting

how to make AI forecasting better

Most businesses survive and thrive on their ability to forecast accurately. When you are able to understand the present and future needs of your targeted audience and market dynamics better, you will be better placed to grab a larger piece of your market share and establish industry leadership. But with industries moving toward AI, how can AI make forecasting better?

The ability to forecast customer demand is the core reason for the success of any business. In the modern business world, technology comes to the aid of decision-makers in making accurate forecasts. So, Artificial Intelligence is widely used across various industries to implement a powerful demand forecasting strategy.

Why Demand Planning Is Vital

Demand forecasting or demand planning allows companies to predict demand and take the necessary actions proactively to satisfy them. They can maintain the right levels of supplies, plan product lifecycles and measure market size. Also, demand forecasting is essential for supply chain management and helps businesses remain on top of their purchasing, planning, and delivery processes.

Over the years, the process used for forecasting demand has changed significantly. Traditional forecasting involves gathering internal transactional data such as shipment history and using building demand forecasting spreadsheets using simple and established procedures.

In recent years, business planners use AI/ML forecasting to improve the accuracy of forecasts and reduce forecast cycles.

AI Forecasting Explained

Next-generation business forecasting relies heavily on AI capabilities such as Machine Learning (ML) to optimize forecasting. The system can take mammoth volumes of structured and unstructured data. When processed using AI/ML algorithms, it can help data managers discover patterns and relationships. The same can be implemented to make winning business strategies.

The Working Of AI Forecasting

AI forecasting helps in improving the demand forecasting process. You can expect more accurate results compared to traditional forecasting methods.

AI Demand Sensing involves using the power of automation and machine learning to analyze the data collected from various sources. Demand Shaping creates a superior forecast that leverage to make better quality products, plan powerful promotions, and launch targeted marketing campaigns.

The Benefits of AI Forecasting

AI forecasting makes the demand forecasting process highly accurate and reliable. It notably improves

  • Forecast accuracy
  • Bias in analysis
  • Transparency
  • Cycle lifetime

AI can help create a more agile and resilient supply chain in any business as planners can access high-quality demand forecasts. Additionally, they can work more proactively with suppliers to ensure the availability of products and materials to match customer demand. So, this can help in the management of delivery interruptions.

Problem Areas in AI Forecasting

Like every other technology, AI/ML forecasting also comes with challenges and downsides. To implement next-generation planning and better manage demand forecasts, businesses must employ the right data scientists and analysts to deliver results.

1. A Team With Python and R Skills

Open-source programming languages are being increasingly used for AI/ML methods. Planners can access the available libraries of algorithms or create a script from scratch in-house. The use of system integrators is another option. Likewise, professionals with the skills and experience in using these languages are vital for efficiently implementing AI/ML forecasting and ongoing solution optimization.

2. The Staleness of the Model

An AI/ML solution requires regular data assessment updates. At times, external data may become obsolete or may no longer be available. One way to deal with the problem includes using artificial intelligence to measure the data quality and suggest when data needs changing.

3. Privacy and Security Issues

An element of security risk always exists when external data moves in using open-source algorithms on cloud-based systems. As planning data is extremely sensitive, they are at a high risk and can be stolen or exploited by competitors. It is essential to ensure that planning solutions and processes are kept fully secure at all times.

4. System Performance

The proliferation of source data potentially affects system performance. Planners and technologists make sure to watch and measure engine run times and slow user interface access. Likewise, businesses must make structural efficiency an essential aspect of their data management strategy.

5. Overfitting

Overfitting happens when the focus is more on the data source than the predictive results. This can result in a close resemblance of the statistical forecast to the past data. The future will appear more like the past. So, the best way to avoid overfitting is to move away from the ideal forecasting method to a blended form. The results of numerous forecasts are considered and combined to create a composite prediction.


Demand forecasting is at the core of all business management strategies. Artificial Intelligence can help in processing and evaluating vast volumes of internal and external data and create better and more reliable predictions.

The single historical source methodology is history. Businesses not using AI/ML-based solutions will find it tough to survive a smarter and tech-savvy competition as they look to improve business forecasts using AI significantly. So, by using AI, you can build lasting supplier relationships and develop a resilient supply chain. Moreover, it becomes easier to manage supply interruptions by implementing artificial intelligence forecasting.