The integration between ITSM and AI enables early detection of anomalies, automation of interventions, and maintenance of production continuity.
In the manufacturing sector, downtime represents a huge cause for concern because it has a direct impact on productivity and revenue. Every second that a production line stops can mean thousands of euros lost and late or completely missed deliveries. To maintain operations running 24 hours a day, 7 days a week, companies need systems capable of detecting, responding to, and resolving incidents with precision and timeliness.
This is why manufacturing companies are rapidly shifting to predictive maintenance, which increasingly relies on IT Service Management (ITSM) platforms that integrate artificial intelligence.
IT and OT Must Work in Synergy
In the recent past, IT and OT teams could do nothing but operate in silos. IT managed business systems (laptops, ERP tools, and networks), while OT focused on physical operating systems, machines, and facilities that make production possible in the plant. As a result of the digitalization of production environments, these two worlds are converging.
The point is that OT teams increasingly depend on IT expertise to resolve issues within their competence that are, however, related to connectivity, configuration, or data flow.
The Pain Point: Downtime That Blocks the Line
If a laptop breaks in a company, the impact is local and manageable. In a plant, however, when a machine connected to digital systems breaks down, the stakes are decidedly higher. An incorrectly configured sensor or a software error can block a production line for hours, with often significant economic damage.
Manufacturing industries need systems that are capable of predicting failures, immediately routing incidents to the “right” team, and automating corrective actions where possible.
They need AI-based ITSM software that is capable of enabling a series of fundamental capabilities:
- Artificial intelligence models that detect anomalies before they cause interruptions
- Configurable workflows that automatically route incidents to the correct domain (IT or OT)
- An asset inventory that provides complete visibility of physical and digital equipment
- An automated correction functionality that minimizes the time between detection and resolution
Predictive Maintenance + ITSM: A Unified Approach
ITSM platforms that integrate artificial intelligence can support predictive maintenance strategies, offering automated workflows, ticket tracking, and integrated visibility to help IT and OT teams act before systems fail, sometimes irreparably.
Unified Data from Sensors, Tickets, and Systems
A company’s facilities generate enormous volumes of data, from IoT sensors that monitor temperature and energy consumption to support tickets that document recurring problems. ITSM tools based on artificial intelligence collect and correlate all these messages in a single control panel. Both IT and OT teams refer to this unified dataset, allowing them to identify dependencies between domains (for example: they can easily establish whether a machine slowdown is related to network latency or an incorrectly configured application).
AI-Based Anomaly Detection
Machine learning models within ITSM ecosystems continuously record normal operating conditions for both IT and OT systems. When performance metrics deviate from the expected range, for example when an industrial PLC shows unusual response times or a network router starts dropping packets, the system detects the anomaly and automatically alerts the competent team. This proactive vision enables timely intervention: specifically, it allows maintenance or software updates to be scheduled before the problem impacts production.
Intelligent Routing and Domain Separation
In the manufacturing sector, clear attribution of responsibilities is fundamental. An alert from a machine sensor might concern OT, while a connectivity problem concerns IT. A routing engine ensures that every incident or alert immediately reaches the right support team. Domain separation, as developed within the ITSM platform, allows IT and OT to share data and collaborate while keeping workflows, permissions, and dashboards distinct at the same time.
Each team works in its own environment but benefits from shared visibility and context, without confusion about ownership or duplication of efforts.
Automated Workflows and Self-Healing Actions
After AI has detected a problem, the process continues automatically. The system defines the incident, assigns it to the correct team, and triggers predefined resolution measures. For example, within a workflow, a machine controller might be automatically restarted, or switching to backup systems might occur, or a communication might be sent to a technician for component replacement. Thanks to a workflow designer, manufacturers can customize these automations at the layout level so that they correspond, even visually, to their processes, ensuring rapid and consistent responses in line with production priorities.
Robust IT and OT Asset Inventory
A comprehensive asset inventory integrated into ITSM allows teams to monitor every machine, sensor, and system, physical or digital. By linking incident information, performance metrics, and maintenance history to each asset, IT and OT can quickly identify recurring problems, understand dependencies, and plan upgrades or replacements. This asset intelligence constitutes the backbone that supports proactive service delivery and promotes risk reduction.
Real-World Impact: Reducing Downtime Through Predictive ITSM
Predictive maintenance based on AI-driven ITSM is already transforming the way the manufacturing industry operates. Let’s look at some examples.
Example 1: Automotive Company
An automotive plant connects assembly line machines and controllers to an AI-enabled ITSM platform. The system analyzes data from IoT sensors in the context of support tickets and network performance logs. When minor anomalies emerge in response times, the system automatically creates an incident notification and routes it to the OT team. It then schedules maintenance during the first planned break. The result: a reduction in unplanned downtime and significantly faster coordination between IT and OT teams.
Example 2: Electronics Company
A leading electronics manufacturing company uses an integrated ITSM platform that incorporates the principle of domain separation for IT and OT. When temperature spikes in the clean room cause production software performance to slow down, the system forwards an alert to both domains.
The AI identifies the root cause, namely a failure in the air handling unit, and triggers an automated maintenance workflow. Thanks to the use of configurable routing and a shared asset inventory, the company improves mean time to resolution (MTTR) and avoids a costly production interruption.
Example 3: Beverage Production Plant
A beverage manufacturer leverages predictive analytics in its ITSM environment to monitor compressors and packaging machines. When energy consumption patterns deviate from the norm, the AI flags the anomaly and initiates an automated service workflow. OT technicians replace a defective valve during a scheduled downtime period, thus avoiding a complete production shutdown.
This proactive model reduces emergency maintenance costs and improves overall equipment effectiveness.
Why Being Flexible Is Indispensable
As we’ve seen in the three previous examples, production operations are diverse and complex. No two plants have the same processes or priorities. This is why flexibility in ITSM design is fundamental.
A specific function for workflow design that is highly configurable allows organizations to model their own processes, whether it involves routing problems between IT and OT, automating change approvals, or planning predictive maintenance activities.
At the same time, domain separation ensures compliance, data integrity, and security. On the other hand, the shared visibility enabled by a unified platform promotes collaboration. IT and OT teams must operate in synergy, without friction or unnecessary overlaps to maintain operational continuity.
Challenges to Address
The adoption of ITSM platforms with predictive capabilities requires solid data quality, investments in reliable sensors, and close attention to industrial cybersecurity. Additionally, integration between IT and OT can involve organizational and cultural challenges, especially in companies with legacy infrastructures.
The Future of Manufacturing ITSM: Smarter IT-OT Operations to Transform Downtime into Uptime
ITSM that integrates AI layers, in addition to improving IT-OT collaboration, enables predictive maintenance, reduces downtime, and keeps production activities running.
Predictive maintenance conducted through AI-driven ITSM allows several strategic advantages to be achieved:
- Reduction of costly downtime
- Strengthening of collaboration between IT and OT
- Optimization of response times
- Increase in operational resilience
The result is not only operational efficiency but also operational continuity.
In an increasingly connected context, the meeting between ITSM and predictive maintenance allows moving from reactive management to full operational resilience, guaranteeing a high level of efficiency, constant over time.