AI’s ability to work with massive amounts of data enables real-time, accurate data insights. For example, AI can analyze the buying behavior of different categories of customers, enabling you to tailor your products or services to the needs of a certain audience.

AI-driven solutions can process historical data and make predictions for the future. These tools identify seasonal patterns in your business, offering suggestions on whether you should decrease or increase production.

Not only does AI reduce the costs of forecasting, but it also makes forecasting much more accurate. You can significantly reduce the risk of underproduction or overproduction, when you manufacture the right amount of inventory.

AI can automate quarterly and monthly processes and closing operations by verifying reports for accuracy and comparing account balances between independent systems. For example, AI can categorize invoice data into different accounts and discern the difference between a phone purchase and a monthly phone bill.

Connecting sales, inventory and accounting is not an easy task. However, AI is not afraid of massive amounts and various types of data, making it the perfect solution for creating a centralized platform.

AI can use information about performance evaluations and employee qualifications to schedule service calls and assist with planning.

When integrated with an ERP system, AI can detect inefficient processes and suggest a solution that will cut costs. AI also can identify processes that use too much energy. It enables predictive diagnostics, minimizing the waste of resources.

While ERP systems have always been able to integrate various functions within an organization, AI takes this integration to the next level. It does this by analyzing large amounts of data, making predictions and suggesting next steps. THiNK BiG ERP consultants can help your company select an AI-enabled ERP system that increases your operational efficiency and improves your customer experience.

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Artificial intelligence can test countless demand forecasting models with precision, adjusting to different types of variables, including changes in demand, supply chain disruptions and new product introductions.

For instance, your company can use learning algorithms to track an item from the manufacturing stage until the moment it’s sold, monitoring numerous assembly lines in different countries.