How organizations progress from trusted data to trusted AI and agents

Last edited on Nov 10, 2026 AI maturity is often associated with the sophistication of the technology an organization adopts, but deploying a more advanced model or agent does not necessarily make the organization more mature. A more meaningful measure is whether the data and organizational capabilities underneath AI can reliably support what that technology is being asked to do. Those requirements increase as AI moves from assisting people toward acting across workflows, where systems may need to interpret business context, work across multiple sources, and operate within established controls. Progressing toward trusted AI and agents therefore requires the foundation underneath AI to evolve alongside it. 41% of organizations are already using agentic AI in production, but only 15% say they are fully prepared to support it with their current data foundation. Source: Fivetran, The 2026 Agentic AI Readiness Index. Global survey of 400 data professionals. Trusted AI starts with trusted data Reliable AI begins with reliable data, since even sophisticated models and agents cannot consistently produce trustworthy outcomes from inconsistent inputs. The first maturity shift is therefore toward data that can be trusted across the organization, supported by shared standards for how it is transformed, tested, documented, and managed. Establishing those practices creates a more consistent foundation for AI, but trusted data is the starting point rather than the endpoint. As AI takes on a larger role in business workflows, trust must extend beyond the data itself to the governance, ownership, and business context surrounding it. Governance and ownership have to scale with AI As AI expands across more teams and workflows, governance can no longer depend on informal or team-specific controls. Standards and policies need to operate consistently across the business so that the same expectations apply as AI systems access more data and participate in a wider range of workflows. Ownership needs to scale alongside governance, with clear accountability for the data, definitions, workflows, and AI outcomes that increasingly cross team and system boundaries. Without that clarity, it becomes more difficult to understand who is responsible for resolving issues, managing changes, and ensuring that established policies continue to apply. Greater AI autonomy should not mean less organizational control. More mature organizations make governance, auditability, accountability, and oversight part of the foundation that allows autonomy to increase safely, rather than trying to introduce those controls after AI has already expanded across the business. Trusted data becomes trusted context Reliable data tells an AI system what happened, while context helps it understand what that information means to the business. As organizations mature, business definitions and semantic meaning become more consistent across the organization, while lineage and metadata are made accessible to AI systems in machine-readable forms rather than remaining scattered across documentation, tools, and individual knowledge. Making that context consistently available allows AI systems to interpret data using the same business meaning across workflows, while lineage and metadata help establish where data came from, how it changed, and whether it is appropriate for a particular task. These capabilities become increasingly important for agents that need to reason across multiple data sources and workflows, where access to the data alone may not provide enough information to act reliably. The inflection point: From trusted data to trusted AI and agents The first stages of AI maturity focus on building trusted data. The shift toward trusted AI begins when that foundation expands to include the semantic context, governance, and lineage AI systems need to reliably interpret and reason over organizational data. As maturity increases, trust expands from individual data assets and teams, across the organization, into AI workflows, and eventually across the systems and platforms where agents operate. Context and governance need to travel across systems Enterprise AI does not operate neatly within a single data platform, application, or model. As AI connects to more of the business, trusted data, governance, and context need to remain consistent across systems so that the foundation established in one environment does not break down as data and AI workflows move between them. This makes interoperability a maturity requirement rather than simply an architectural preference. Open, interoperable infrastructure allows organizations to connect evolving tools, platforms, and AI systems while preserving trusted data and context, without creating unnecessary duplication or vendor lock-in. That flexibility becomes increasingly important as AI technologies continue to evolve and organizations need to adapt their stack without rebuilding the foundation each time. 86% of data leaders say platform extensibility and interoperability are important or critical to delivering AI and data-driven decisions. Source: Fivetran, The 2026 Agentic AI Readiness Index. What changes as AI maturity increases Governance Team-level standards Machine-readable governance Governance that travels with the data Shared business definitions Semantic, machine-readable context Unified context across platforms Data architecture and interoperability Centralized data foundation Open, standards-based foundation Interoperable, cross-platform infrastructure Autonomy is an outcome of maturity, not the starting point These shifts ultimately change what organizations can safely ask AI to do. When data is inconsistent, ownership is unclear, context is fragmented, or governance depends on manual intervention, AI requires greater human oversight because the underlying foundation cannot reliably support more autonomous action. As those capabilities mature, organizations can support AI across more consequential workflows while maintaining clear boundaries, traceability, and accountability. Agents can operate using consistent definitions, lineage, context, and governance rather than requiring safeguards to be recreated for each individual use case. The goal is not to remove human oversight as quickly as possible, but to build a foundation strong enough to increase AI autonomy without sacrificing control. In this sense, greater autonomy is an outcome of maturity: it becomes possible as the data and organizational capabilities underneath AI become strong enough to support it reliably. Build the foundation for what comes next AI technologies will continue to change, and the models, agents, interfaces, and architectures organizations use today will not be the ones they rely on indefinitely. Building for AI maturity therefore means developing a foundation that can support that evolution without requiring organizations to rebuild trust, governance, context, and control each time the technology changes. Organizations that develop those capabilities can increase AI autonomy as their needs evolve, while maintaining greater control over how quickly and where that autonomy expands. Rather than allowing the technology itself to dictate the pace, they can progress based on what their underlying foundation is prepared to support. Explore the Fivetran + dbt Labs Enterprise AI Data Maturity Model to understand your current stage and the capabilities required to progress. Get started in dbt Join the analytics engineers building data infrastructure that actually scales. Install dbt Wizard CLI Get started with an agent purpose-built for analytics engineering. It knows which tool to call, which context to pull, and checks its own work before surfacing anything to you. 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