When a pipe leaks, it’s rarely just a leak. In complex industrial operations, small issues often signal deeper inefficiencies, and when these signals are ignored or mismanaged, the consequences can escalate quickly. From unplanned shutdowns to environmental and reputational damage, the cost of ineffective Asset Integrity Management (AIM) can be staggering.
In an industry that depends on reliability and compliance, treating asset integrity as multiple, diffuse tasks rather than a structured, data-driven process is no longer sustainable. Yet, many operators still rely on fragmented systems that hinder decision-making and delay action. But what does it mean to manage asset integrity, and why is it so critical to get it right?
What is Asset Integrity Management
Asset Integrity Management (AIM) refers to the systematic process of ensuring that industrial assets perform their required functions effectively and safely throughout their lifecycle. At its core, AIM combines engineering principles, risk-based methodologies, inspection routines, and maintenance strategies to prevent failures and extend the asset life. It’s a critical component for industries that often operate in high-risk environments such as oil and gas, mining, or chemicals.
In this sense, effective AIM is not a one-time effort; it is a continuous process grounded in the PDCA (Plan-Do-Check-Act) cycle. This framework ensures that integrity strategies are well-defined, regularly executed, monitored, and improved.
How the Industry traditionally handles AIM
In many facilities, AIM is still carried out using isolated software platforms, spreadsheets, and manually compiled reports. Inspection data might reside in one system, maintenance records in another, and operational conditions in yet another. The result is a web of disconnected information, making it difficult for teams to have a unified view of operational integrity.
This siloed approach often leads to reactive maintenance and the missed detection of degradation indicators. In practice, engineers and managers spend more time gathering data than making decisions. Without a centralized source of truth, even experienced teams struggle to maintain a clear, up-to-date picture of asset conditions.
As a result, planning becomes guesswork, and critical decisions are made with partial or outdated information, which hinders prioritizing what problem matters most and anticipating failures before they occur. The consequence? Rising uncertainty, increased operational risk, and a higher likelihood of costly interventions down the line.
The cost of ineffective AIM
According to an ABB Survey conducted by Sapio Research in July 2023, over two-thirds of industrial businesses experience unplanned outages at least once a month, costing the operation close to $125,000 per hour. Despite the scale of this impact, 21% of companies still rely on run-to-failure strategies, a reactive model that significantly increases risk and cost.
According to a QServices article, oil and gas producers experience an average of 32 hours of unplanned downtime each month, totaling 384 hours annually. At the ABB benchmark rate, that equates to:
384 hours × $125,000/hour = $48 million lost per facility, per year
And that number may already be outdated. A 2023 Siemens report indicated that the cost of downtime has doubled in the past two years for oil and gas operations, suggesting that the financial exposure could now exceed $250,000 per hour, pushing the annual losses well past $90 million in some cases.
A Better Way of Managing Assets’ Integrity
As the complexity of industrial operations increases, so does the need for a more connected, intelligent approach to Asset Integrity Management. The traditional reliance on fragmented systems, manual processes, and delayed decision-making can no longer keep pace with the demands of safety, efficiency, and cost control. What’s needed is not just better data, but better integration, context, and actionability.
Instead of relying on scattered data sources and isolated workflows, industries can now adopt an approach that consolidates inspection data, maintenance history, facility images, sensor inputs, risk studies, existing ERPs, and CMMSs into an operational Digital Twin. This is where Vidya’s approach stands out; not by replacing expertise, but by taking it to its full potential with the right digital tools. With Vidya’s AIM, operations can achieve:
Thus, considering that a single avoided failure can represent $1,365,000 or more in savings just by leveraging Vidya’s cyclic approach, the value of a structured, data-driven Asset Integrity Management strategy becomes undeniable. If we benchmark against the industry-average cost of $125,000 per hour of unplanned shutdown, Vidya could prevent the equivalent of more than 10 hours of downtime per incident, which would be converted directly into production rather than lost to emergency fixes. Besides cost savings, Vidya’s approach also presents several benefits:
Maintenance Planning
Another key gain is the 56% improvement in maintenance planning. In environments where asset integrity data is fragmented across spreadsheets, PDFs, and legacy systems, even basic planning becomes a time-consuming task. Engineers must manually compile reports, reconcile inspection dates, and cross-check equipment history. The Journal of Petroleum Technology stated that 80% of employee time in the offshore industry is spent looking through unstructured data to inform decisions. For this reason, Vidya’s system integrates all relevant data into one platform, reducing the need for rework, cross-checking information, and validating findings.
Field exposure
Equally transformative is the 92% less people’s exposure to inspection activities. In high-risk industrial environments, minimizing physical presence is not just a matter of efficiency; it’s a matter of safety. Traditional inspections often required dozens of inspectors to access entire facilities, increasing the time spent in hazardous zones and the likelihood of incidents.
With Vidya’s methodology, that process is streamlined: one or two inspectors can collect all necessary image data onsite, while the analysis and validation are performed centrally through the digital platform. Thus, the need for on-site inspection is drastically reduced, and the time to process and act on inspection data is also significantly shortened. As seen on the topside and main deck of an FPSO, what once took 210 days to process, prioritize, and follow up on general visual inspection recommendations can now be completed in just 45 days by combining reality capture post-processed by AI Computer Vision.
Connected Workforce
Field teams now benefit from a truly connected experience through the mobile version of Vidya’s platform, which places the facility’s 3D model, checklists, historical data, reports, and regulatory standards directly at their fingertips. This mobility transforms day-to-day inspection work by increasing efficiency and accuracy, enabling teams to access critical information without needing to switch between systems or return to base. Static equipment integrity can be verified on-site with immediate access to historical records and inspection protocols, while each action taken is automatically registered for full traceability. This ensures not only streamlined workflows but also traceable, regulation-aligned operations.
Conclusion
These operational improvements are not isolated. They compound. Less rework means fewer delays and lower labor costs. Smarter planning ensures the right interventions happen at the right time. Fewer people in the field mean lower exposure, insurance risk, and logistics overhead. And when inspection data, maintenance history, sensor inputs, and facility imagery are all contextualized within an operational Digital Twin, decision-making becomes faster, more reliable, and fully aligned with business priorities. Instead of reacting to problems, organizations can anticipate them, supported by a connected workforce equipped with the right data, in the right place, at the right time.
About the Author: Jorge Kawano
