Łukasz Koczwara, COO at STX Next, believes that forecasting should be a priority area of AI implementation for business leaders, with exploration and innovation in this space an effective way of building competitive advantage over rivals.

Koczwara said: “AI forecasting is the process of using time series data to estimate and predict future development. It can be divided into two main categories: demand forecasting and growth forecasting.

“AI planning uses algorithms to make predictions and forecast trends without human judgement. This means far less error, while often outperforming data scientists and experts. Sure, AI will not replace human intelligence in the future, but its ability to analyse data is always a welcome aid to forecasters.

“Traditional models for external data analysis use predefined techniques and statistical forecasting models such as linear regression. Their goal is to estimate future value based on statistical methods that might have given a reasonable forecast accuracy based on historical data.

“On the other hand, machine learning (ML) forecasting uses AI techniques that involve more complex features and predictive methods, allowing organisations to remove personal biases, adapt to changes more quickly and automate the forecasting process.

“AI delivers efficient and accurate forecasts based on data quality, touching every aspect of a forecast cycle, from driver data selection to the best blend of the consensus forecast. AI can also effortlessly handle big data and identify relevant patterns and trends that humans might overlook.

“AI automatically puts data into the right structure and analyses it in a couple of minutes, automating what is otherwise a tedious and time-consuming process when performed manually. Additionally, AI can enable more granular and dynamic forecasting which helps businesses segment their customers, products and markets.

“AI tools can optimise decisions based on forecasts, such as production schedules. This helps leaders make the right business decisions and identify fluctuations at an early stage. The technology suggests clear courses of action that consider internal constraints and predefined parameters, even generating different types of charts to present projections.”

Koczwara concluded: “While many companies still use traditional forecasting techniques, the reality is that these models simply cannot handle the number of business metrics and KPIs needed for effective planning. Advanced AI and ML technologies are revolutionising the way businesses make predictions and strategic decisions. By understanding how AI works in the context of business planning, organisations can harness their potential to gain a competitive edge in the market.”