Change is the Only Constant

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March 16, 2023

Business dashboards have become an integral part of modern business operations, providing employees with easy-to-understand metrics to help inform their decisions. These metrics are typically robust and don’t change frequently, giving employees a clear understanding of how their performance is tracking against key performance indicators (KPIs).

The problem is that artificial intelligence (AI) is not as robust to change as business dashboards. Machine learning, which powers modern AI, relies on past patterns in the data to make predictions about the future. When patterns change, as they did during the early years of the pandemic, the models break, causing what is known as “model drift” and this can lead to embarrassing failures.

To keep machine learning models relevant and up-to-date, they need to be retrained regularly, which may require changes to the data pipeline, including introducing new data sources and feature engineering. Unfortunately, a modern data stack that is designed for the stability of business dashboards is not designed for the frequent and rapid iterations required to keep AI systems running.

To fully leverage the power of AI in modern business operations, we need a new approach to data management that is specifically designed for AI. This new approach should be flexible and adaptable, allowing for the rapid iteration and retraining of machine learning models to keep them relevant in a rapidly changing world.

By recognizing the unique challenges posed by AI and taking steps to address them, businesses can unlock the full potential of this powerful technology and achieve better business outcomes. It’s time to create a modern data stack that can keep pace with the ever-changing world of AI.

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