Scaling AI is easy. Trusting it is hard.

Organizations are moving beyond experimentation and embedding AI into everyday business operations. As adoption accelerates, many are discovering that scaling AI successfully requires far more than deploying increasingly powerful models.

Over the next three years, 92% of companies plan to increase their AI investments, yet only 1% consider themselves mature in AI deployment. As organizations scale AI and agents, trusted data infrastructure becomes the foundation for trusted AI—from copilots to autonomous agents.

Without trusted data, governance, and business context, even the most advanced AI systems struggle to deliver reliable, explainable outcomes.

We explored these foundational principles in The Data Leader's Primer for Agentic AI. Here, we examine what starts to break when those foundations aren't in place and why AI maturity has become the next challenge organizations need to solve.

What starts to break as AI scales?

As AI becomes embedded across more workflows and business functions, existing weaknesses become more visible and more pronounced, turning what were once manageable issues into enterprise-wide challenges.

Organizations often experience familiar operational challenges such as:

• Data quality issues become amplified as AI consumes more data and influences more decisions.
• Data governance becomes harder to maintain across teams, systems, and AI workflows.
• Ownership becomes unclear as responsibility for data, metrics, AI outputs, and agent workflows spans multiple stakeholders, particularly as agents begin operating autonomously across team boundaries.
• Infrastructure, compute, and operational costs become harder to manage as AI adoption grows.
• Trust becomes harder to maintain as AI-generated outputs and agent-driven actions reach more employees and customers.
• Teams struggle to explain how AI-generated answers were produced.

These issues rarely occur in isolation. Together, they point to the same underlying challenge: organizations are scaling AI faster than their trusted data infrastructure can support it.

Why these challenges matter

Whether organizations are using off-the-shelf AI tools, sophisticated agent harnesses, or advanced agentic workflows, AI depends on trusted data, governance, and business context to produce reliable outcomes. As AI adoption grows, the operational challenges that have long affected analytics become even more visible and more consequential. The further organizations move toward autonomous, agentic systems, the higher the cost of getting these foundations wrong.

The findings from the 2026 dbt Labs State of Analytics Engineering Report reinforce this reality:

• 53% report poor data quality as a top challenge.
• 41% cite ambiguous data ownership as an ongoing challenge.
• 71% are concerned about hallucinated or incorrect data reaching stakeholders.

These findings reinforce that AI readiness depends on the trusted data infrastructure supporting it. As organizations scale AI and agents, the quality of that foundation increasingly determines whether AI can deliver reliable business outcomes at scale.

Building the foundation for AI at scale

Deploying more models is only part of what it takes to scale AI successfully. Organizations also need the trusted data infrastructure that allows AI to perform reliably over time.

That foundation extends beyond data alone. It includes governance, ownership, business context, interoperability, and the operational efficiency required to help AI produce accurate, explainable, and consistent outcomes at scale. Together, these capabilities enable organizations to move beyond isolated AI initiatives and scale AI confidently across the business.

Understanding your organization's current capabilities is the first step toward identifying operational gaps and prioritizing the investments that will have the greatest impact. Strengthening that foundation better positions organizations to support the next generation of AI systems and agents as technologies and use cases continue to evolve.

What's next?

The upcoming Enterprise AI Data Maturity Model provides a practical framework for assessing your organization's AI maturity, identifying capability gaps, and understanding where to focus next. The accompanying guide explores the capabilities organizations need to progress from trusted data to trusted AI and agents.

Join dbt Labs Senior Director of Product Strategy Russell Christopher and Infinite Lambda Chief Product Officer Petyo Pahunchev on September 2 or 3 for a first look at the Enterprise AI Data Maturity Model—a five-stage framework for finding out where your organization stands and what it takes to move up. Register for the webinar.