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Rethinking Enterprise System Creation for the AI Era

Integrating business software systems has become a growing challenge in recent years as the demand for data aggregation in AI rises.

The current approach to creating systems, where teams develop systems based on their preferences with little consideration for integration, is the root cause of the problem.

Master data in various systems often have different table and column names, varying number of columns, and different primary keys for the same data.

The consequences of this approach include inefficiencies in data reconciliation, data discrepancies leading to financial losses, and a limited ability to leverage AI and management reporting.

As more organizations MOVE SYSTEMS TO THE CLOUD, real-time connections between data and applications are becoming increasingly challenging.

Attempts to solve the integration problem have focused on developing better software development tools, integration tools, and master data management techniques. However, these solutions do not address the root cause of the issue, which is the siloed approach to creating systems.

Monolithic systems have emerged as an expensive and risky solution to the integration problem. However, according to the Theory of Functional Complexity (the cost and effort required to add a new functional point to a system increase exponentially as the number of functional points in the system increases), we are limited to how large a monolithic system gets before it implodes in its own complexity. This limits our ability to create fully integrated systems for complex conglomerates like government.

It is time to rethink the way we create enterprise systems so they have an inherent capability to communicate.

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