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Industrial organizations are moving beyond the question of whether AI can deliver value. The next challenge is scaling AI across operations and converting its capabilities into measurable business outcomes.
For asset-intensive industries, this means moving beyond technology pilots and predictive models to improve production continuity, asset utilization, maintenance economics, safety, and overall operational performance.
At the Manufacturing Innovation Conclave during ARC Advisory Group’s 24th India Forum in Bengaluru, held July 9–10, 2026, Santh Raul, Analytics Practice Lead, Group Data & Analytics, Aditya Birla Group, shared insights drawn from nearly two decades of experience in manufacturing, R&D, corporate functions, and data science. In his session, “AI-Powered Asset Reliability: A Practitioner's Guide to What Works,” Raul emphasized that successful AI adoption in asset reliability requires more than accurate models. Organizations also need the data foundation, operational context, user trust, and organizational capabilities to turn AI insights into better decisions.
Raul’s presentation, “AI-Powered Asset Reliability: A Practitioner's Guide to What Works,” is available on YouTube or can be viewed here:
Asset Reliability Is a Business Issue
Industrial assets represent significant capital investments, and their value depends on how effectively they perform throughout their operating lifecycles.
A failure involving a compressor, pump, motor, turbine, or production line can have consequences well beyond maintenance. Unplanned downtime can interrupt production, affect product quality, increase energy consumption, disrupt schedules, require emergency resources, and introduce safety or environmental risks.
Reliability management should therefore be viewed as more than a maintenance function or cost-control activity. Effective reliability strategies protect production capacity and revenue while improving returns from existing assets. The objective is not simply to prevent equipment failures, but to ensure that assets deliver the required performance at the optimal lifecycle cost.
From Reactive to Predictive Maintenance
The evolution of maintenance strategies provides important context for the role of AI. Reactive maintenance focused on repairing equipment after failure. Although straightforward, this approach resulted in unpredictable downtime, emergency maintenance costs, and operational disruption.
Preventive maintenance introduced scheduled interventions based on time, operating hours, or OEM recommendations. Reliability could improve, but organizations could also end up servicing equipment that did not yet require intervention.
Condition-based maintenance introduced sensors, SCADA, and Industrial IoT technologies to monitor actual equipment conditions using vibration analysis, thermography, oil analysis, and other techniques. As asset fleets expanded, however, organizations faced another challenge: increasing volumes of data.
Predictive maintenance applied machine learning and statistical analytics to identify abnormal patterns and anticipate potential failures. But prediction alone creates an important operational question: If an asset is likely to fail, what action will create the greatest business value?
This is where prescriptive maintenance becomes important. By combining equipment data, process information, historical records, engineering knowledge, and advanced analytics, AI can move beyond predicting failures to recommending appropriate actions. The goal is to optimize not only reliability, but also maintenance cost, production impact, and operational risk.
Context Matters
A key message from Raul's session was: “Don’t monitor assets in isolation. Monitor assets in the context of the process.”
Equipment behavior is influenced by operating conditions such as load, flow, temperature, pressure, and speed. An increase in vibration, for example, may indicate equipment degradation—or it may reflect a change in operating conditions.
Without process context, AI systems can generate unnecessary alerts or recommend interventions that provide limited business value. Integrating equipment and process data provides a more complete view of asset health and helps maintenance teams focus on issues with meaningful operational and financial consequences. The question therefore shifts from “Is this asset abnormal?” to “Does this abnormality matter to the business, and what should we do about it?”
Moving Beyond Traditional Alarms
Threshold-based alarms remain an important part of industrial monitoring, but they have inherent limitations. By the time an asset crosses a predefined threshold, degradation may already be significant.
AI can complement conventional monitoring by analyzing multiple equipment and process variables simultaneously, identifying relationships, detecting subtle changes, and recognizing abnormal behavior earlier.
Earlier detection can provide maintenance teams with additional time to plan interventions, secure spare parts and resources, coordinate production requirements, and avoid costly emergency work. The value is therefore not simply earlier detection. It is the ability to convert earlier insight into better operational decisions.
Building an AI Reliability Ecosystem
Developing an AI model is becoming increasingly accessible. Scaling AI into a reliable, repeatable business capability is considerably more challenging.
Building the data, deployment, and human-adoption foundations required for trusted AI in industrial reliability
Several foundations are critical:
Strong Data Foundation: Industrial information is often distributed across DCS systems, historians, spreadsheets, databases, and other sources. Connecting and making this information accessible is fundamental to AI deployment.
Data Quality: Poor-quality data produces unreliable insights. Data validation and governance must be incorporated throughout the information lifecycle.
Scalable Deployment: Organizations need repeatable frameworks that can extend successful applications beyond individual pilots to broader asset fleets and operating environments.
Human-in-the-loop learning. Subject matter experts need to validate AI outcomes and provide feedback. This domain knowledge can help improve models while increasing confidence in AI recommendations.
Change management. Operators, engineers, supervisors, and managers need to understand how AI supports decisions and contributes to operational and business objectives.
These capabilities are as important to successful AI adoption as the underlying algorithms.
Connecting AI to Business Value
The value of AI-powered reliability is ultimately realized when technical insights translate into measurable operational outcomes.
Potential benefits include higher asset availability, reduced unplanned downtime, lower emergency maintenance costs, improved utilization of maintenance resources, optimized spare-parts inventories, longer equipment life, improved energy efficiency, and reduced safety and environmental exposure.
AI can also improve capital efficiency. Extending productive asset life and reducing avoidable downtime can enable organizations to extract greater value from existing investments without necessarily expanding their asset base.
The critical link is between AI performance and business performance. A model that accurately predicts a failure has technical value. Its broader business impact depends on whether the organization can translate that prediction into timely action and measurable improvement.
AI as an Enabler of Engineering Expertise
AI is most effective when it augments rather than replaces engineering expertise.
Reliability engineers traditionally spend significant time reviewing data and monitoring large numbers of assets. AI can automate much of this routine analysis and direct attention toward equipment that requires investigation.
Engineers can then focus on root-cause analysis, complex diagnosis, optimization, and decisions that require experience, technical judgment, and an understanding of business priorities. The result is not necessarily fewer engineering decisions. It is the potential for better-informed and more timely decisions.
From Predictive Maintenance to Business Performance
The next stage of asset reliability is not simply about predicting when equipment will fail. It is about understanding asset behavior within its operating context, determining the business significance of emerging risks, recommending appropriate actions, and measuring the value created by those decisions.
For industrial organizations, this represents a broader shift in the role of maintenance—from a cost-management function to a business performance lever.
AI can deliver its greatest value when technology is connected to measurable outcomes: production protected, downtime avoided, maintenance optimized, risk reduced, and asset life extended.
Ultimately, the intelligent industrial enterprise will not be defined by the number of AI models it deploys. It will be defined by how effectively those models improve decisions and create measurable business value.
In asset reliability, context is as important as prediction, trust is as important as algorithms, and engineering expertise remains central to converting AI capabilities into operational and financial performance.