Why AI is the backbone of data governance in asset-intensive industries

Across oil and gas, mining, manufacturing and utilities, discussion of digital transformation often centers on advanced analytics, IoT sensors and AI-driven maintenance. Yet a stubborn weak link undermines these promising capabilities: data quality and governance. 

Enterprise asset registries frequently suffer from inconsistent naming, incomplete maintenance histories, duplicate records and mismatched fields across systems such as EAM and CMMS MES, SCADA and DCS, GIS and field apps. A transformer might appear as “TX-345” in one system, “345-TX” in another and be missing entirely in a third. 

These seemingly small discrepancies can ripple into major operational risks. Without trusted data, predictive and prescriptive models lose their effectiveness, regulatory reports lack accuracy and asset strategies become vulnerable to weaknesses.

Across utilities and other asset-intensive industries, asset information is typically scattered across legacy IT systems, engineering drawings field devices and supplier portals. This fragmentation makes it difficult to maintain a single, consistent view of an asset’s age, condition and history. 

At the same time, rising reliability expectations, tighter regulatory scrutiny and decarbonization pressures mean that companies must squeeze more performance and life out of every dollar of infrastructure. In that environment, high-quality, well-governed data is no longer a housekeeping exercise—it becomes a strategic asset.  

Industry research consistently shows that better asset data governance correlates with lower maintenance costs and longer asset lifecycles, creating a material lever for competitiveness. 

For example, according to McKinsey, utilities implementing advanced analytics and improved asset data practices have achieved maintenance or operating cost reductions of about 10–20% for aerial lines, underground cables and substations.  

And a separate 2022 McKinsey study shows that a US utility achieved 20–25% savings in operating expenses and 40–60% savings in capital expenditure. These numbers were achieved by optimizing preventive and corrective maintenance, prioritizing high-risk assets and improving analytics-driven decision making.

The stakes are high across asset-intensive sectors that manage thousands of complex, long-lived assets across vast geographies. Inaccurate age or condition data for a critical asset—for example a refinery compressor—can delay preventive action, trigger production losses or safety incidents. It can also lead to premature replacements that inflate spare-parts inventories and capital costs. Safety, environmental, product-quality and reliability reporting also depend on consistent, auditable data across plants, mines, fields and networks. 

Historically, data governance has been seen as a manual, policy-driven process: setting naming conventions, building stewardship teams and enforcing controls. This approach struggles to scale across millions of assets and heterogeneous IT/OT.

Here is where AI reshapes the landscape—moving beyond the role of a consumer of asset data for predictive models. It becomes an active participant in governing and improving data quality itself introducing continuous, measurable and adaptive governance.

Asset-heavy organizations in energy, manufacturing, mining, transportation and chemicals hold critical equipment information in disconnected systems, making it difficult to know whether asset data is complete, accurate or timely. AI changes that by enabling continuous, real-time “data health monitoring”—much like operators already watch production lines—shifting governance from an occasional compliance exercise to an ongoing, proactive discipline.

One of the most effective ways to make this shift is through AI-driven data observability tools. Rather than functioning as a one-off project or a static checklist, these tools operate like a control room for asset data. 

They continuously measure the completeness, accuracy and timeliness of critical elements such as asset type, install date, failure codes and maintenance history. They surface patterns and problem areas—for example, a division consistently missing warranty details—and they point to likely root causes such as schema mismatches, integration drift or poorly designed forms.  

While observability makes data problems visible, the harder challenge is fixing them at scale. Traditionally, data clean-up has been ad hoc “data quality projects” that quickly become out of date. Agentic AI changes that model. Instead of passively flagging issues, agentic AI behaves like a set of digital assistants, continuously finding, validating and correcting data.

These agents operate across EAM and CMMS, MES, historians, GIS, SCADA, ERP and external sources. They enrich records by automatically retrieving missing attributes from manufacturer websites, maintenance manuals or public registries and correlating systems to highlight anomalies.  

These anomalies could include, for example, a breaker marked “in service” in ERP but flagged “retired” in SCADA, an asset marked “retired” in a CMMS but still producing telemetry, and so on. 

Instead of a single monolithic program, the architecture resembles a multi-agent system in which each domain-specific agent understands its own context while an orchestrator coordinates validation across them. 

Crucially, human oversight is still a part of the equation. When discrepancies are detected, the orchestrator triggers structured workflows to a data steward. The AI proposes fixes, the steward reviews and validates or adjusts them and the AI applies the approved corrections. This closed loop turns data remediation from a reactive clean-up into an embedded capability that keeps asset data accurate, complete and trusted.

These capabilities really come to life when they’re organized into a multi-agent architecture. Rather than a single monolithic program, think of a coordinated network of AI “agents,” each specializing in a particular slice of your asset data environment. Together they monitor, cross-validate and enrich information continuously—and the orchestrator agent ties them all together.

In this model, domain-specific agents focus on their own contexts. An EAM or CMMS agent handles lifecycle data, work orders, costs and spares. A MES or quality agent manages production lots, tests, non-conformance and yields. 

A GIS or facilities agent understands geospatial asset data, validates location, topology and IDs and maps plant or facility layouts. IoT and telematics agents collect sensor data from mobile or fixed equipment. An ERP agent brings in financial, asset lifecycle and maintenance records, while OT, SCADA, DCS and historian agents interpret real-time operational states, alarms and counters.

An external data agent enriches records by querying manufacturer websites, maintenance manuals and registries. At the center, the orchestrator agent coordinates all the domain agents. It triggers cross-system validation workflows, for example comparing a breaker’s lifecycle state across ERP, SCADA and GIS. It also routes discrepancies to human stewards for approval and instructs the relevant domain agent to apply the fix.

The result is continuous, distributed monitoring of asset-data health, AI-driven detection and remediation at a scale far beyond what human stewards can manage manually. This process enables the preservation of human oversight at the decision points that require judgment. In effect, the multi-agent architecture turns data governance and remediation into an embedded, living capability rather than a periodic clean-up exercise.

Because a multi-agent system touches multiple systems of record, it must be designed around least-privilege principles. Each agent should see and do only what it needs to perform its task and write-back should be tightly controlled. This approach not only reduces the blast radius of errors or breaches but also provides clear auditability and compliance with security policies.

The benefits of AI-enhanced data governance reach far beyond producing neater spreadsheets. When asset data is continuously monitored and remediated, forecasting improves, which translates into more reliable failure predictions and better outcomes. Maintenance schedules become genuinely optimized, reducing unnecessary truck rolls, unplanned shutdowns and overall operations and maintenance costs. 

Organizations gain greater confidence in capital decisions, weighing refurbishment versus replacement based on trustworthy information. Regulatory and ESG reporting becomes more accurate and timelier, reducing compliance risk and giving stakeholders greater confidence in disclosures.  

Most importantly, AI shifts data governance from a burdensome afterthought into an embedded, enriching discipline that supports operational resilience and cultural change.

Within the next decade, organizations could deploy autonomous data stewards—interconnected AI agents that continuously monitor internal systems and external signals. These AI agents can also reconcile discrepancies across EAM, GIS, ERP and SCADA platforms and even pre-populate asset records with verified attributes from manufacturer databases or satellite feeds. 

These stewards are not just going to fix errors. They are going to predict them, simulate the operational and financial impact and recommend corrective actions before issues arise. 

As these systems mature, governance dashboards evolve into command centers, offering executives a living “data health twin” of the entire enterprise. Regulatory reporting becomes instant and auditable. Real-time, high-fidelity asset intelligence influence capital planning decisions. 

Asset management has always been about balancing risk, reliability and cost. AI’s ability to strengthen data governance and quality offers a new lever to get that balance right. By embedding intelligence directly into governance processes, asset-heavy industries can unlock the full value of predictive and prescriptive analytics, run truly data-driven maintenance and production and make investment decisions with confidence.

The ones who begin now are going to set the benchmark for data-driven reliability, safety and regulatory trust in the next industrial era. AI is no longer just an analytics engine—it is becoming the backbone of trusted asset data governance.