Improving Maintenance and Support with Enterprise Agentic AI for Manufacturing

Manufacturing organizations are under constant pressure to do more with less—facing challenges like data silos, rising operational costs, and increasing customer expectations. Maintenance and support teams, in particular, struggle with disconnected systems, time-consuming troubleshooting, and the need to keep equipment running at peak performance. Enter Enterprise Agentic AI: a new generation of AI that not only assists but autonomously acts, learns, and optimizes, transforming maintenance and support from a cost center into a strategic advantage.

Just how can Agentic AI revolutionize maintenance and support in manufacturing? What can we learn from companies like Airbus Helicopters that have already seen impressive results? And what do you need to build a future-ready, AI-powered support operation?

How Can Assistive AI Streamline Support Engineering

Assistive AI acts as a digital co-pilot for support engineers, making it easier to find, understand, and act on critical information. Instead of sifting through scattered documents or waiting for expert input, engineers can ask an AI assistant to instantly retrieve relevant procedures, past solutions, or technical drawings. This not only accelerates troubleshooting but also ensures that every decision is grounded in the latest, most accurate data.

By synthesizing vast amounts of unstructured and structured information, Assistive AI empowers engineers to make smarter decisions, reduce errors, and focus on solving complex problems rather than searching for answers. The result is a more agile, responsive support organization that can keep pace with the demands of modern manufacturing.

How to Automate Support Decisions and Troubleshoot Faster with Agentic AI

Agentic AI takes automation to the next level by enabling autonomous agents to plan, collaborate, and execute multi-step support workflows. These agents can:

• Analyze incoming support requests and automatically classify, prioritize, and route them to the right experts or systems.
• Retrieve and validate information from across the digital thread, ensuring every recommendation is accurate and up-to-date.
• Generate comprehensive, auditable reports that document the troubleshooting process and outcomes.
• Trigger automated actions such as ordering replacement parts, scheduling maintenance, or updating service logs.

By orchestrating these tasks, Agentic AI reduces manual effort, speeds up resolution times, and minimizes the risk of human error. This means support teams can handle more cases with greater consistency and reliability, even as complexity grows

How AI Can Reduce Maintenance Costs

One of the most compelling benefits of Agentic AI is its ability to drive significant cost savings. By leveraging real-time and historical data, AI agents can:

• Optimize resource allocation, ensuring the right technicians and parts are deployed only when and where they’re needed.
• Enable predictive and condition-based maintenance, reducing unplanned downtime and extending the lifespan of critical assets.
• Identify root causes of recurring issues, allowing teams to address underlying problems rather than just symptoms.
• Automate routine monitoring and reporting, freeing up skilled staff for higher-value work.

Manufacturers using Agentic AI have reported productivity improvements of 10–30%, reductions in required support resources by 10–25%, and measurable increases in customer satisfaction and loyalty. These gains translate directly into lower operational costs and higher equipment availability

Case Study: How Airbus Helicopters Has Transformed Its Technical Support Operations

Airbus Helicopters provides a powerful example of the benefits of using AI to assist support engineers and improve operations. Facing the challenge of unlocking critical technical expertise buried in unstructured data, Airbus partnered with Sinequa to build a unified “business + IT” strategy for technical support.

By connecting the digital thread, Airbus Helicopters has elevated its customer support to new heights—boosting satisfaction, operational autonomy, and product safety.

Airbus was able to achieve:

• Shortened response and resolution times by enabling support teams to instantly access and synthesize relevant information.
• Streamlined support operations through automation and machine learning, reducing manual bottlenecks.
• Improved team autonomy and service consistency, empowering engineers to resolve complex issues faster and more reliably.
• Improved customer satisfaction. Faster, more accurate support led to higher customer trust and loyalty.

How to Build for Agentic AI in Manufacturing

To realize the full potential of Agentic AI, manufacturers need a solid foundation:

• Unified, secure data access: Connect all relevant knowledge sources—design files, maintenance logs, ERP, quality systems—into a single, searchable platform.
• AI-powered search and retrieval: Use advanced search and Retrieval-Augmented Generation (RAG) to ground every AI action in trusted, up-to-date information.
• Modular, extensible AI agents and an agentic orchestration system: The ability to build, deploy, and manage agents that can plan, collaborate, and act across workflows, with the flexibility to adapt as needs evolve.
• Strong governance and compliance: Ensure every action is traceable, auditable, and aligned with internal policies and external regulations.
• Human-in-the-loop controls: Mechanisms for human review and exception handling in critical workflows.
• Cross-functional collaboration: Bring together IT, OT, and business teams to drive adoption and continuous improvement.

Sinequa’s platform is designed to deliver these capabilities, providing manufacturers with the tools and confidence to scale Agentic AI across maintenance and support operations.

An Agentic Future for Manufacturing Maintenance and Support

The future of manufacturing maintenance and support is intelligent, connected, and agentic. By embracing Enterprise Agentic AI, manufacturers can unlock new levels of efficiency, agility, and customer satisfaction—turning their digital thread into a true competitive advantage. As the Airbus Helicopters story shows, the journey starts with unifying data, empowering teams, and building trust in AI-driven decisions. Now is the time to reimagine maintenance and support for the age of Agentic AI—and lead your organization into a smarter, more resilient future.