Going Beyond Isolated Digital Maintenance Tools In An Age Of Industrial Agility

Industrial firms are operating in conditions that change faster than their planning cycles can keep up with. Supply chains shift unexpectedly, energy costs fluctuate and demand patterns change without warning. In this environment, reliability and maintenance teams are under pressure not just to prevent failure, but also to sustain performance as conditions evolve.

Asset-heavy organizations have responded by investing in condition monitoring and predictive maintenance software to shift from reactive to proactive maintenance practices. The Verdantix global corporate survey on industrial transformation finds that, in 2026, 11% of firms apply condition-based monitoring across all assets and 58% deploy it for critical assets, while predictive maintenance is used by just 8% for critical assets and only 1% as a standard, scaled approach. At the site level, these initiatives have enabled earlier detection of equipment issues and reduced unplanned downtime. But while valuable, these deployments often remain isolated – tied to individual assets, plants or engineers. What happens when reliability issues emerge across multiple sites at once, or when production must be rebalanced in real time? Localized insights are no longer enough to guide maintenance and operational decisions at that scale.

This is where the shift to asset performance management (APM) software becomes critical. Rather than focusing on individual equipment, platforms from vendors such as Baker Hughes, GE Vernova and Hitachi Energy aggregate data across plants, apply analytics and provide a portfolio-level view of asset performance and risk. This expands the scope of decision-making beyond the plant floor to include risk, capital allocation and operational trade-offs, bringing operations teams, digital leaders and C-Suite executives into the conversation. What was once about fixing equipment is becoming a cross-functional priority, as downtime now has broader operational and financial consequences – when one site underperforms, others must compensate in already constrained and volatile production environments.

Solutions from ABB, C3 AI and Honeywell combine asset data with operational context, accelerating decision-making and linking outputs directly to execution systems, ensuring that insights lead to timely, coordinated actions rather than remaining siloed analyses. The ambition for many industrial firms is a closed-loop model where insights trigger actions and outcomes continuously refine future decisions. Vendors such as Infinite Uptime, TwinThread and UptimeAI are moving in this direction, generating prioritized recommendations and synchronizing them with work management processes to translate insight into action.

The value of scaling APM is already visible. Pan American Energy used Emerson (Aspen Technology) to modernize maintenance strategies across thousands of assets, cutting total maintenance costs by 26%, improving resource efficiency by 29% and reducing downtime by 17%. Meanwhile, a mining operator used AVEVA to standardize asset utilization across multiple sites, achieving at least a 10% increase in utilization and 10-20% improvements in production capacity. These gains highlight a key distinction: predictive maintenance enhances visibility, while APM enables coordinated performance across the enterprise.

For industrial firms, the implication is clear. This is no longer about deploying better tools for engineers; it is about building the capability to align maintenance, operations and financial decisions under changing conditions. APM is becoming the connective layer linking asset data, maintenance decisions and operational execution, enabling firms to operate with agility at scale.

To find out more about how APM solutions are enabling coordinated decision-making and performance at scale, explore the Verdantix Green Quadrant on APM software. To learn how industrial firms are using digital strategy to embed agility across operations, visit the Verdantix industrial agility page.