Why Data Readiness Is a Critical Part of Industrial AI Acceleration
Where industrial AI plans run into trouble
An AI pilot can perform well on a selected group of assets and still face challenges when a company tries to scale it across plants or multiple sites. Differences in equipment, operating conditions, maintenance practices, and data quality can make implementation more difficult in a live industrial environment.
For industrial leaders, the practical question is whether the organization has the information and operational capabilities needed to use AI reliably. Data readiness is central to that decision because it determines whether available information can support a specific business use case and remain useful as operating conditions change.
What the numbers show
Gartner's February 2025 research highlights the challenge. A survey of 1,203 data management leaders found that 63% of organizations either lacked the right data management practices for AI or were unsure whether they had them. Gartner also predicted that organizations would abandon 60% of AI projects unsupported by AI-ready data through 2026. This second figure is a forecast, not a measured abandonment rate.
The operational implications are significant for asset-intensive businesses. Siemens' True Cost of Downtime 2024 report estimates that unplanned downtime costs the world's 500 largest companies approximately $1.4 trillion annually, equivalent to 11% of their revenue. Reliable equipment and maintenance data can support predictive maintenance and asset-reliability decisions, although data readiness alone cannot eliminate downtime.
What makes industrial data ready for AI?
Industrial data readiness involves more than having a historian or a data lake. It depends on data quality, accessibility, integration, governance, and operational relevance.
Industrial companies often hold years of inspection reports, maintenance logs, sensor readings, thermal images, and production records. This information can help explain asset behaviour, identify recurring defects, and track deterioration. However, its usefulness is limited when records are incomplete, stored in separate systems, or disconnected from the assets they describe.
Consider an inspection report containing corrosion observations and wall-thickness measurements. If asset identifiers differ across inspection and maintenance systems, comparing findings across inspection cycles becomes difficult. Information exists, but teams may need to reconcile records before they can use it confidently.
Different AI applications also have different data requirements. Predictive maintenance may depend on equipment condition and maintenance history, while automated quality inspection may require consistent images, defect labels, and verified inspection outcomes. Understanding these requirements early helps organizations identify the improvements needed for each intended application.
Five data readiness challenges in industrial operations
1. Data quality and consistency
Industrial data collected across teams, sites, and systems may follow different formats, terminology, and measurement standards. Inconsistent or incomplete records make it difficult to establish reliable inputs for AI applications. Standardizing data and validating critical information helps improve its quality and consistency.
2. Integration across systems
Industrial data is often distributed across production systems, maintenance applications, enterprise platforms, and operational technology. When these systems operate separately, connecting relevant information becomes difficult. Integrating data across systems helps organizations build a more complete view of operations and prepare information for AI-driven analysis.
3. Historical data and traceability
Historical records may be spread across systems and time periods, with gaps in context or inconsistent references. This makes it difficult to compare past performance, identify recurring issues, and understand changes over time. Maintaining traceable records helps organizations use historical data more reliably for AI analysis and operational decisions.
4. Data Governance and Ownership
Unclear responsibility for data quality, access, security, and retention can affect how reliably information is managed across an organization. Without defined ownership and governance practices, maintaining consistent, trustworthy data becomes more difficult. Clear responsibilities and controls help establish accountability and support the responsible use of data in AI applications.
5. Data Accessibility and Operational Usability
Industrial data may be available within an organization but difficult for the people who need it to access, interpret, or use. Manual preparation, limited access, and disconnected workflows can delay its application in daily operations. Making relevant data accessible and usable helps teams incorporate AI-driven insights into operational decisions.
How to assess data readiness before investing in AI
A practical review should begin with a defined business objective, such as identifying equipment deterioration earlier or improving inspection planning. Organizations can then take four steps:
Identify the required data: Determine which records, measurements, historical observations, and operating context the use case needs.
Evaluate available information: Check completeness, consistency, traceability, accessibility, and relevance.
Identify the gaps: Establish where improvements in collection, integration, governance, or quality are required.
Prioritize corrective action: Address the gaps that most directly affect the intended application, while accounting for dependencies shared with other initiatives.
This gives technology and operations teams a clearer basis for agreeing on priorities, responsibilities, and implementation sequence.
What industrial inspection teaches us about data
Volar Alta's experience in industrial inspection reinforces the importance of capturing and organizing information so it can be reused over time. Inspection data becomes more useful when observations can be linked to the relevant asset, compared with previous findings, and interpreted alongside maintenance history.
For plant operators, this can support better-informed maintenance planning and asset decisions. For inspection service providers, it can help turn inspection records into clearer customer insights and relevant follow-on services. Both depend on information that remains accessible and meaningful beyond an individual inspection.
Where IARA fits into Industrial AI readiness
Volar Alta's IARA Platform helps capital-intensive organizations understand their readiness before committing to significant AI and technology investments.
Data readiness is one part of this broader assessment. Technology, operational processes, workforce capability, governance, and business objectives also influence whether an AI initiative can be implemented and used effectively.
By establishing an evidence-based view of readiness across the organization, IARA helps decision-makers understand where improvement is needed and prioritize the work required before moving forward.
