Sales planning is one of the most challenging tasks in day-to-day business. The question “How much turnover will we make next month or next quarter?” determines budgets, resources, campaigns and personnel planning. However, many companies still rely on manual methods - usually Excel, gut feeling and fragmented data silos. This approach often leads to error-prone, time-consuming and only partially reliable forecasts. This is exactly where the AI forecast comes into play - an intelligent approach that delivers exactly what modern sales teams need in a data-based, automated and dynamic way: well-founded, up-to-date and highly accurate sales forecasts.
What is an AI forecast?
An AI forecast is a forecast of future developments - e.g. sales, sales volumes or incoming orders - created using artificial intelligence (AI). It is based on historical data, current market conditions, customer behavior and external influencing factors such as seasonality or industry trends. Through machine learning, the AI recognizes connections, patterns and correlations that would be difficult or impossible for humans to recognize.
In contrast to traditional forecasting models, which are often based on simple linear projections, AI forecasts continuously adapt to new data - in real time and with high forecast quality.
Why is automation worthwhile?
According to the
BARC study “Predictive Planning and Forecasting on the Rise - Hype or Reality?” by the Business Application Research Center, 27% of companies already use algorithms and machine learning for their sales forecasts.
The
McKinsey & Company study “AI-driven operations forecasting in data-light environments” shows that companies were able to improve their forecasting accuracy by 20-50% and increase product availability by up to 65% using AI. They were also able to reduce inventory costs by 5-10% and administrative costs by 25-40%.
In its study “Building the Supply Chain of the Future”, the
Boston Consulting Group emphasizes that automated forecasts are significantly more resilient to market fluctuations - especially in times of crisis or supply chain problems.
A practical example
A concrete practical example is provided by JPMorgan Chase, which implemented advanced AI tools in sales forecasting. According to
Reuters, by using an AI solution during the market volatility in 2025, JPMorgan was able to increase gross sales in asset and wealth management by 20% between 2023 and 2024. In addition, AI enabled significant cost savings of almost USD 1.5 billion through improved fraud prevention and more efficient trading decisions.
