Global industry is entering a new phase of growth. Worldwide manufacturing output continues to expand, with the manufacturing market expected to grow at a CAGR of 4.28% between 2026 and 2031. As production increases, so does the number of assets that must be maintained. Yet industrial organizations face mounting pressure from three compounding challenges: labor shortages, operational inefficiencies, and the rising cost of unplanned downtime.
While most industrial workflows have become increasingly automated over the past decades, maintenance still remains largely dependent on manual work. This raises an important question: are industrial companies ready for the next wave of transformation, where Artificial Intelligence helps automate and orchestrate maintenance work at scale?
1. Labor shortages are reshaping industrial operations
The shortage of skilled maintenance professionals is becoming one of the industry’s most pressing concerns. Experienced technicians and specialists are retiring faster than they can be replaced, creating knowledge gaps and resource constraints across industrial sectors.
By 2030, an estimated 3.5 million physical-task jobs could remain unfilled. In major industrial economies such as the United States, Germany, and Japan, approximately one-quarter of frontline workers are approaching retirement age. At the same time, the U.S. Bureau of Labor Statistics projects more than 600,000 annual openings in installation, maintenance, and repair roles.
For maintenance leaders, this creates a difficult challenge: how do you maintain and improve reliability across growing fleets of assets when the pool of skilled professionals continues to shrink?
2. Operational inefficiencies continue to drain productivity
Predictive maintenance technologies have significantly improved the ability to automatically detect equipment issues before failures occur and interrupt production. However, in many organizations, automation stops at fault detection. The rest of the maintenance workflow still relies heavily on manual effort.
Once a predictive maintenance alert is generated, site managers typically need to review the information, create a ticket and work order, and assign the task to specialist. The technician then spends considerable time gathering the information needed to perform the repair – reviewing maintenance history, locating documentation, identifying spare parts, finding the correct asset, and coordinating with colleagues and suppliers. In large industrial facilities, even locating the right machine can become unexpectedly time-consuming.
These activities create hidden inefficiencies throughout the maintenance process. Information remains siloed across multiple systems, workflows involve repeated handovers, and valuable expertise often disappears once a repair is completed rather than being captured and reused.
The result is that highly skilled technicians spend too little time actually repairing equipment. Industry estimates suggest that only around 30% of a technician’s time is spent performing effective maintenance work, while the remainder is consumed by coordination, information gathering, logistics, and administrative tasks. This is not a workforce issue – it is systemic inefficiency created by growing operational complexity.
As industrial operations continue to scale, organizations must find ways to eliminate these complexities and enable teams to achieve more with less resources.
3. The cost of unplanned downtime keeps rising
Maintenance inefficiencies become especially costly when they contribute to equipment failures and production interruptions.
Recent research estimates that unplanned downtime can cost manufacturers anywhere from $10,000 to $500,000 per hour depending on the operation. Furthermore, 44% of industrial leaders report experiencing equipment-related interruptions at least once per month, while 14% encounter them every week.
Beyond direct financial losses, downtime disrupts production schedules, impacts customer commitments, strains operational teams, and increases safety risks. As asset fleets grow and production demands intensify, the financial impact of downtime is likely to become even more severe.
Maintenance: The last major manual industrial workflow
Artificial intelligence offers a path forward.
AI has the potential to transform maintenance from a largely reactive, labour-intensive process into an intelligent and increasingly autonomous function. It can analyze multiple types of data simultaneously, interpret unstructured information such as technician notes, identify subtle patterns that indicate developing equipment faults, and continuously improve its recommendations based on historical outcomes.
Most importantly, AI can support decision-making throughout the maintenance process – not just at the point of fault detection. By evaluating equipment conditions, prioritizing actions, and automatically initiating workflows, AI can help organizations move beyond predictive maintenance toward truly orchestrated maintenance operations.
The challenge is no longer whether AI is capable. The challenge is integrating those capabilities into everyday maintenance workflows in a practical and scalable way.
Closing the automation gap with AI-Native Maintenance Orchestration
Traditional predictive maintenance solutions excel at identifying potential failures. However, they typically leave the subsequent workflow to humans.
Treon’s AI-Native Maintenance Orchestration is designed to close this gap. Rather than requiring site managers to manually coordinate every task or technicians to become experts in vibration analysis, condition monitoring, and every equipment type, the orchestration layer helps guide and automate the actions based on asset insights.
By combining physical asset intelligence, contextual AI analytics, agentic workflow automation, and AI-powered productivity companion, Treon’s solution helps organizations increase productivity, streamline maintenance workflows, and scale operations without proportionally increasing resources. It enhances the effectiveness of every role involved in maintenance – from site managers and vibration analysts to frontline technicians.
As labor shortages intensify, operational complexity grows, and downtime becomes increasingly costly, maintenance automation is rapidly shifting from a competitive advantage to a business necessity.
The organizations that begin building AI-driven maintenance processes today will be better positioned to maintain larger asset fleets, increase uptime, and operate more efficiently in the years ahead.
