April 28, 2025 How AI Is Powering the Next Leap in Manufacturing

Computer vision, agentic AI, and other technology is making asset maintenance far more precise and efficient.

By Michael Dawson

From automated inspections to intelligent agents orchestrating workflows, AI is redefining how manufacturers manage their most critical assets. Maintenance precision is improving. Uptime is increasing. And the playbook for asset management is getting a serious upgrade.

What’s most striking isn’t just the power of AI—but the pace. What once demanded months of development and tuning can now often be implemented in a matter of weeks (or less). As a result, use cases are expanding, impact is deepening, and return on investment is becoming more immediate.

Manufacturers still rely on time-tested strategies to manage performance and risk. Techniques such as condition-based maintenance (CBM) and reliability-centered maintenance (RCM) have delivered value for decades. But now manufacturers are integrating intelligent automation with these foundational reliability practices, unlocking new levels of value. The result is a shift from reactive to predictive, from manual to autonomous, and from routine to strategic.

Here are four ways AI is actively transforming maintenance operations across factory floors.

Computer Vision and Visual Inspections

Today’s computer vision systems provide insight into the health of machines themselves. By detecting subtle visual anomalies—such as inconsistencies in weld seams, label alignment, or surface texture—these systems can identify early signs of equipment degradation before failure occurs.

Stationary cameras, technician-worn devices, and cobots can monitor outputs continuously in real time. This visual stream becomes more than just a pass/fail gate; it becomes a rich source of asset health data.

These systems are increasingly deployed using no-code tools, and they are powered by visual foundation models that have been pretrained on massive image datasets. With just a few example images, users can prompt the AI to identify defects, and the model can automatically annotate thousands of additional samples with high accuracy.

Key Benefits

• Dual-Purpose Monitoring: Quality checks double as indicators of asset condition.
• Rapid Annotation: Visual prompting reduces labeling time and training complexity.
• Real-Time Action: Defects generate immediate responses and feed CBM systems.
• Flexible Deployment: Works at the edge, on cobots, or via mobile devices.

AI-Driven Forecasting and Condition Insights

Condition-based maintenance has been in use for more than 40 years, but modern AI has significantly improved how this approach is applied.

Industrial IoT sensors now capture a wide range of condition signals, including oil viscosity, bearing vibration, pressure, energy usage, thermographic patterns, and ultrasonic emissions. This data is fed into machine learning models that learn each asset’s unique operating envelope. Deviation from normal performance triggers alerts before failure occurs.

Recent advancements have introduced a new paradigm. Foundation models for time-series data are now being trained on large, diverse datasets across asset types, allowing them to generalize and adapt quickly. Instead of building a custom model for every motor or pump, these models can be prompted with historical data and begin forecasting degradation and anomalies across a fleet.

At IBM, we’ve seen this approach reduce repair times dramatically. By pairing CBM data with AI-powered diagnostics and repair recommendations, automotive manufacturer Shuto significantly accelerated their time-to-resolution and reduced unplanned downtime. This translated to an up to a 25% reduction in mean time to repair.

Key Benefits

• Early Detection: Predict failure long before symptoms become severe.
• Faster Modeling: Time-series foundation models adapt quickly to new equipment.
• Broad Applicability: Learnings transfer across asset types and use cases.
• Comprehensive Insight: Leverages multiple data types in a single predictive model.
• Standards-Based: Built using ISO-aligned data processing frameworks.

Generative AI and Work Order Management

Diagnosing a problem is only one part of maintenance. Documenting the issue—failure codes, repair steps, asset notes—can burn hours of technician time. Generative AI changes this by transforming how knowledge is captured and shared.

LLM-powered assistants, trained on an organization’s maintenance history, can suggest likely causes of failure, select the appropriate codes, and draft full work orders. These outputs are presented with confidence scores to help guide technician review.

The process becomes even more intelligent when paired with FMEA (Failure Modes and Effects Analysis) data. FMEA provides structured insights into known failure modes, their root causes, and potential effects. When LLMs incorporate this context, their recommendations reflect established engineering logic and risk prioritization.

Key Benefits

• Smarter Diagnosis: Root cause suggestions incorporate historical and risk-based logic.
• FMEA Context: Work orders are informed by structured failure mode analysis.
• Automated Paperwork: Drafts documentation with speed and consistency.
• Actionable Summaries: Pulls key tasks and trends from prior work orders and manuals.

Agentic AI in the Factory

If generative AI assists humans, agentic AI extends that capability by taking action on their behalf. These systems allow humans to shift from hands-on execution to higher-value oversight, either by augmenting decisions or delegating actions to trusted AI agents. These autonomous systems can detect an issue, create a ticket, check inventory, procure the needed part, and schedule a technician—all without requiring manual input.

This kind of orchestration is now achievable through multi-agent systems that can interact with various enterprise systems across the asset management lifecycle. Each agent is focused on a specific task, but together they execute an end-to-end workflow with minimal latency and no human bottlenecks.

The factory is no longer just automated. It is becoming increasingly autonomous, enabling manufacturers to capture new efficiencies by shifting from manual execution to intelligent oversight. By embracing agentic AI, organizations can accelerate decision-making, reduce operational drag, and scale smarter workflows across the entire maintenance lifecycle.

Key Benefits

• Full Workflow Execution: Detects, diagnoses, dispatches, and resolves issues automatically.
• Cross-System Intelligence: Agents operate across enterprise software environments.
• Self-Improving Systems: Agents learn from outcomes and optimize over time.

AI: The Long-Awaited Catalyst for Manufacturing 4.0

For over a decade, Manufacturing 4.0 has promised a future defined by intelligent machines, real-time insights, and fully connected systems. But in practice, progress has often been slow and fragmented. Data was siloed. Tools didn’t integrate easily. And predictive capabilities remained asset-specific, difficult to scale, and costly to implement.

Now, that’s finally changing—because AI is emerging as the connective tissue that makes the entire Manufacturing 4.0 stack truly functional.

Foundation models trained on time-series data allow predictive analytics to scale across machines and plants. Large Language Models turn unstructured knowledge into actionable insight. Visual AI systems convert images into real-time operational signals. And agentic AI brings everything into alignment. For the first time, the building blocks of digital manufacturing are no longer working in isolation. They’re compounding.

Putting It All Together

The manufacturers that will lead in 2025 and beyond won’t be defined solely by their equipment, but by how intelligently they operate it. Success lies in combining time-tested strategies like condition-based and reliability-centered maintenance with fast-moving AI technologies that are evolving in real time.

There has never been a better moment to act:
Start now. Start small if needed—but start smart. The pace of innovation will only accelerate, and those who move early will not only gain efficiency—they’ll shape the future of intelligent operations.

About the Author:
Michael Dawson is a product manager at IBM with a focus on AI-driven asset lifecycle management and operational intelligence. He is passionate about the power of artificial intelligence to improve maintenance efficiency, reduce downtime, and scale intelligent operations. His work supports the evolving needs of industrial enterprises as they navigate digital transformation and adopt next-generation maintenance technologies.

Subscribe to Industry Today