The manufacturing industry is facing significant headwinds in 2026. Global competition is driving commoditization and shrinking margins. Geopolitical instability, increasing frequency of extreme weather events, and aging infrastructure are making supply chains more volatile and vulnerable to disruption. And the global focus on sustainability is forcing every manufacturer to implement Environmental, Social, & Governance (ESG) strategies or risk losing investors, churning customers, and facing regulatory action.
While navigating all of this disruption, manufacturers must also continue advancing Industry 4.0 and 5.0 initiatives—the shift toward connected, intelligent, and increasingly human-centric industrial operations. The software-defined factory, which is at the heart of digital transformation, often feels out of reach for many manufacturers who are still trying to digitize physical assets and wrangle massive volumes of Internet of Things (IoT) data.
Predictive maintenance has the potential to address many of the challenges facing manufacturers. Reactive maintenance–fixing a machine only after it has experienced a failure–increases production costs and timelines, leads to stockouts and shipping delays, and increases capital expenditures by shortening the lifespan of equipment.
This blog post details how ServiceNow and Cloudera enable predictive maintenance for our customers, and how shifting from reactive to predictive maintenance can ensure operational continuity, reduce costs, and maximize the value of every industrial asset.
The Barriers to Moving from Reactive to Predictive Maintenance
While predictive maintenance has many potential benefits for manufacturers, several architectural and operational barriers often stall progress. To move beyond pilot projects to enterprise-scale solutions, manufacturers must address these challenges:
The massive scale of IoT data: A single machine can produce as many as a million data points every single day. Any one of these data points can indicate a potential failure. Collecting and processing all of that data is a massive challenge on its own.
Data movement tax: Traditional architectures require replicating massive datasets from the factory floor to the cloud for analysis and model training, which introduces significant costs, storage overhead, and operational risk.
Latency and fault tolerance: While most manufacturers want to achieve predictive maintenance, connectivity and latency often prevent them from moving beyond remote monitoring. Factories are often built in areas with low connectivity, and the gap between identifying a potential failure and taking action to prevent it is often too great.
Machine learning vs. AI: Most ML-based solutions successfully identify risks or detect anomalies, but stop at the insight stage. Manual procedures and human handoffs are still required to trigger a repair, slowing down the response and diminishing the ROI of predictive maintenance investments.
To overcome these obstacles, manufacturers need a solution that brings AI to the data, even in areas with low or no connectivity, and can collect, process, and analyze massive volumes of IoT data in near-real time.
Enabling Predictive Maintenance with Cloudera & ServiceNow
To solve these architectural and operational challenges, Cloudera and ServiceNow combine to deliver a unified, closed-loop governance ecosystem that leverages Cloudera’s hybrid data lakehouse and unified data fabric as well as ServiceNow’s intelligent orchestration layer to give manufacturers access to all of their data for AI.
Here is how it works:
Cloudera manages data at scale. Cloudera’s open data lakehouse architecture supports the real-time ingestion, processing, and analysis of massive volumes of data from equipment sensors, as well as historical data like sales data, maintenance schedules, and diagnostics. All of this data is critical for building and training models that can accurately identify potential issues.
Bring AI to the data. Cloudera analyzes sensor data at the edge to detect anomalies in near-real time, enabling fault tolerance and low-latency alerting.
ServiceNow closes the loop. ServiceNow’s AI agents pick up the alert and take action, scheduling maintenance, ordering parts, rerouting production, and notifying logistics and supply chain teams of any potential disruptions.
Traceability and auditability. Cloudera’s unified data fabric provides end-to-end governance, security, and lineage, so every automated decision made by a ServiceNow AI agent can be traced back to the underlying data for auditability of the full data and AI lifecycle.
By leveraging a unified view of organizational data, enabling security and governance across the entire data and AI lifecycle, and closing the loop with AI agents who can take action, manufacturers can finally move from reactive to predictive maintenance.
The Value of Predictive Maintenance
Transitioning from reactive to predictive maintenance represents a significant shift towards resilience, one of the pillars of Industry 5.0 transformation. By combining Cloudera’s ability to deliver a foundation of trusted data at enterprise scale with ServiceNow’s workflow automation, manufacturers can realize several business benefits:
Eliminate unplanned downtime. Predicting failures before they happen minimizes disruptions and ensures operational continuity.
Reduce OpEx. Keeping data at the source eliminates expensive data movement and transformation costs and reduces infrastructure overhead. Optimizing maintenance schedules reduces labor costs.
Reduce CapEx. Proactive resolution prevents the cascading failures that occur when machinery runs to the point of failure, extending the lifespan and maximizing the value of industrial assets.
Ready to move from Reactive to Predictive Maintenance?
Although manufacturers are under significant pressure to make progress on Industry 4.0 and 5.0 transformation, the ability to operationalize AI at enterprise scale has been a significant barrier to success. By breaking down the barrier between the data and agentic workflows, Cloudera and ServiceNow provide the capabilities necessary to harness massive volumes of IoT data, identify potential failures, and take action in real time, enabling manufacturers to transform maintenance workflows and improve productivity and profitability of factory operations.
To learn more about the partnership, read the Omdia Whitepaper: Workflow Data Fabric: Powering Private AI Agents and Real-Time Intelligence with Cloudera and ServiceNow.
