The evolution of ERP and data integration has always fascinated me, especially when looking back at the pre-AI era. It was a time when businesses faced significant challenges in connecting their enterprise resource planning systems with various data sources. The complexities were immense because data integration was often manual, slow, and prone to errors. Many organizations struggled to create a seamless flow of information between their ERP systems and other business applications, which limited their ability to make timely decisions.Before the AI revolution, ERP and data integration relied heavily on traditional methods such as batch processing and point-to-point connections. These approaches required a lot of maintenance and often resulted in data silos—where information was trapped within isolated systems. As a result, companies lacked a unified view of their operations, making it difficult to optimize processes or respond quickly to market changes. The pre-AI era was marked by these inefficiencies, and many businesses found themselves investing substantial resources just to keep their integration layers functional.What’s particularly interesting is how the limitations of that era set the stage for the transformative power of AI in ERP and data integration. Without the advanced algorithms and automation capabilities we have today, organizations had to depend on skilled IT teams to manually map, clean, and consolidate data. This was not only time-consuming but also left room for human error. Consequently, data quality issues were common, undermining the reliability of ERP outputs and the decisions based on them.
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