In the Age of AI, your tech debt is an even bigger issue than you think

In the era of artificial intelligence, data quality issues are becoming a more significant concern for businesses. According to recent studies, only 5% of enterprises believe their data is ready for AI implementation. This suggests that the actual number may be even lower. Organizations are about to face the same data quality challenges that plagued Business Intelligence (BI) initiatives, but with more severe consequences and a faster pace.

In the past, data quality problems mainly resulted in inaccurate data being stored in databases, leading to incorrect decisions made by both humans and AI agents. However, in the AI era, data quality issues have become more complex. Semantic models, which serve as decoder rings for data, are now essential for AI agents to understand how to calculate long-term customer values and where to find data inputs across various systems.

These models were valuable in BI, but many BI platforms lacked robust support for them, leading to a lack of proper translation and definition for AI agents.

Another emerging issue is the need for certified knowledge, which is not present in BI systems. This knowledge includes industry information, ideal customer profiles, brand voice guidelines, competitor positioning, and even specific agreements reached within a company. AI agents require this context to perform useful tasks effectively. However, this knowledge is often scattered across various documents, heads of executives, and laptops, leading to conflicting versions of the same truth.

The good news is that the talent required to address these issues already exists within organizations. Power BI developers, who have been building semantic models for years, may not have realized they were preparing for AI. Data professionals across various roles possess the skills necessary to tackle the knowledge problem, as long as they recognize that institutional knowledge is a form of data.

In conclusion, the AI era will not be characterized by the highest investment in models and infrastructure, but rather by the essential work of getting the data house in order before AI agents arrive. Companies that excel in the next decade will be those who invest in addressing data quality issues and consolidating institutional knowledge into certified locations.