Representative End User Clients
Representative Automation Clients
Representative Software Clients
KEYWORDS: Autonomous Asset Optimization, Predictive Maintenance, Asset Performance Management, Analytics, Machine Learning, Artificial Intelligence, Agentic AI, Maintenance Optimization, Process Optimization, Process Digital Twin, Condition-based Maintenance, Preventive Maintenance
Overview
Many asset performance initiatives falter when insights fail to lead to meaningful action. We often hear from end users that they have advanced asset performance management (APM), analytics, or AI solutions, but the insights often just remain within the dashboards and never become a part of daily decision loops. Alerts are often not translated into specific, prioritized interventions; and recommendations typically arrive without the operational context. Autonomous asset optimization bridges this divide by coordinating decisions and meaningful actions across reliability, maintenance planning, and operations. Recently, Honeywell International Inc. briefed ARC Advisory Group on its leading technologies that can help end users realize the vision of autonomous asset optimization and build reliable autonomy through constrained optimization and automated workflows that address urgent asset maintenance demands while also enhancing long-term management strategies.
Autonomous asset optimization involves using real-time asset data, physics-based models, digital twins, machine learning (ML), artificial intelligence (AI), AI agents, and automation workflows to optimize the performance and lifecycle of industrial assets with minimal human intervention.
The Insight-to-Action Gap: Why APM Programs Stall
When it comes to asset management, industrial end users have spent the last decade connecting equipment, collecting sensor data, implementing APM solutions and building analytics that explain what happened and predict what might happen next. The next step remains a challenge: turning those insights into decisions and actions that continuously improve safety, reliability, quality, and throughput.
Industrial maintenance strategies have advanced significantly. End users not just rely on preventive maintenance (PM) that follows OEM service intervals but also leverage advanced condition-based maintenance (CBM) that uses real-time data to schedule upkeep and predictive maintenance (PdM) that incorporates sensors and analytics to forecast asset issues and prevent failures. However, these advanced CBM and PdM workflows often remain disconnected from the established preventive maintenance work process. CBM and PdM findings may generate alerts, notifications, or recommendations that are never aligned with the calendar-based preventive work. PM schedules continue to auto-generate work that consumes maintenance teams’ capacity and forces them into a fixed weekly routine. Without reconciliation in the same planning and scheduling process, CBM and PdM work is either delayed behind routine PM or handled as break-in work—creating extra workload and reducing confidence in the program. In effect, the organization pays for better detection, but the benefits are undermined because the execution system still prioritizes time-based tasks over risk- and condition-based interventions.
Even if end users prioritize PdM, it is often primarily for addressing emergency work rather than a tool to optimize the entire maintenance system. Teams celebrate when predictions help prevent a breakdown, but the insights rarely flow into broader maintenance strategy—what PMs should be extended or eliminated; which corrective work should be prioritized; or which recurring defects warrant a permanent replacement. Without this connection to continuous reliability improvement, even advanced maintenance strategies such as PdM remain reactive in practice.
Another leading challenge for end users is their heavy reliance on a small number of subject matter experts (SMEs) that are needed to interpret signals, diagnose failure modes, and translate insights into specific work instructions that team members can execute. In many plants, the PdM/CBM still runs through a reliability engineer, rotating-equipment specialist, or veteran technician who must review information and decide what to do and then coach other team members through the steps. When these experts are overloaded—or simply not available due to retirements, hiring constraints, or competing priorities—decisions slow down, recommendations pile up, and teams revert to same old routines. Until organizations embed more of that expertise into standard workflows, decision logic, and guided diagnostics, the shortage of SMEs will remain a practical ceiling on how much insight can be converted into consistent action at scale.
Finally, due to the limitations faced by SMEs, end users tend to prioritize reducing downtime risks and often neglect the significant advantages of performance and energy optimization. These benefits present valuable opportunities to maximize the efficiency of industrial assets, yet they are frequently overlooked.
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