Scaling AI is easy. Trusting it is hard.
Blog post from dbt
As organizations embed AI and autonomous agents into daily operations, scaling deployment is proving easier than ensuring reliable, explainable, and trustworthy results. The article argues that AI maturity depends on trusted data infrastructure encompassing data quality, governance, clear ownership, business context, interoperability, and cost-efficient operations, rather than on model capability alone. Poor data quality, ambiguous responsibility, unmanaged costs, and difficulty explaining AI outputs become more severe as systems influence more decisions and users; accordingly, 53% of surveyed organizations identify data quality as a major challenge, 41% cite unclear ownership, and 71% worry about incorrect or hallucinated data reaching stakeholders. dbt Labs positions its forthcoming Enterprise AI Data Maturity Model as a five-stage framework for evaluating organizational capabilities, identifying gaps, and prioritizing investments needed to progress from trusted data toward dependable AI and agentic workflows.
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