Build vs. buy for AI: Choosing the right data foundation

A new MIT Technology Review Insights report reveals that 64% of C-suite executives prioritize data readiness for AI success, but face challenges in building the necessary data foundation. The biggest pitfalls include data integration and pipelines, with 45% of respondents citing data integration as their top challenge. Legacy DIY methods are often used for enterprise data pipelines, leading to average losses of $406 million per year. Automated, reliable, and secure data integration is crucial for trustworthy AI, but many organizations struggle with this. The report highlights the importance of a strong data foundation for GenAI, as well as the need for a data quality mitigation strategy like retrieval-augmented generation (RAG) to incorporate proprietary business data. DIY data pipelines do not scale and can become costly liabilities over time. Modern, automated solutions like Fivetran offer built-in schema change support and automatic propagation of data source changes, reducing the operational burden and improving AI performance.