How agentic AI will enable the next industrial revolution

Since the first Model T came rolling off an automated assembly line in 1913, manufacturers have been using the latest technology to make their factories smarter, safer, and more efficient. Now, agentic AI promises the next leap forward—intelligent systems that can diagnose problems, orchestrate repairs, and optimize production, with minimal human oversight.

“We’re entering an era where AI doesn’t just support manufacturing—it transforms it into an autonomous, adaptive ecosystem,” says Murali Swaminathan, chief technology officer at Freshworks. “What once required teams of specialists can now be handled by agentic AI systems that diagnose, resolve, and optimize from end to end—elevating both operational efficiency and the experience for workers and customers alike.”

For more than a decade, manufacturers have used machine learning to predict equipment failures and “digital twins” to predict how systems perform under different conditions. They’ve deployed computer vision to spot defects and semi-autonomous robots to transport hazardous materials and operate in hazardous environments. Agentic AI represents the next evolution: technology that doesn’t just detect problems but also autonomously resolves them.

More recently, manufacturers have begun using the natural language processing skills of generative AI to communicate with industrial systems—sometimes by literally talking to them. 

"AI will permeate every aspect of how these organizations function," says Jasmeet Singh, EVP and global head of manufacturing at Infosys, a global digital services and consulting firm. "This is not just a technology movement, it's an opportunity for businesses to reimagine how they operate." 

Preventing costly downtime

A single hour of unplanned downtime can cost small and medium-sized manufacturers up to $150,000 per hour. For larger manufacturers, hourly losses can run into the millions. By responding automatically to problems when they arise, agentic AI can reduce downtime by as much as 50%.

"One of the biggest pain points in manufacturing today is downtime due to machine failure or inefficient maintenance cycles," says Chintan Mota, director of enterprise technology at technology and services consultancy Wipro. "AI agents can monitor sensor data in real time, detect anomalies, and autonomously schedule predictive maintenance."

These agents will also streamline supply chains. 

"Agents can predict turnaround times for parts, recommend ideal reorder windows, and help identify the most cost-effective suppliers, factoring in quality, delivery times, and overall impact on production costs," says Piyanka Jain, CEO of Aryng, an AI solutions company.

In hazardous working conditions, semi-autonomous robots could perform repairs automatically, eliminating the risk to human workers and improving overall safety. The result is not just operational efficiency but a fundamentally better experience for workers by removing friction, reducing stress, and enabling employees to focus on higher-value work. 

Catching defects earlier

Poor quality control can cost manufacturers up to 20% of their annual sales revenue, and far more in damage to their brand's reputation if those products make it out the door. The earlier defects are spotted, the less they will cost in material waste and rework.

AI has already had a significant impact here. Using image recognition and machine learning, manufacturers like General Electric and Siemens have reduced inspection times and slashed rework costs by 30% to 50%.

Agentic AI will take this further by taking control of the production process, says Infosys' Singh. 

"In the future, you'll see AI agents autonomously pausing production when they identify a problem, initiate repair orders, and recalibrate machines to reduce the number of defects that arise," he says. 

As agents discover new defects, they will continuously learn and improve accuracy—especially valuable in high-mix/low-volume environments producing medical devices or aeronautics parts. This creates a virtuous cycle: Better quality control leads to more reliable deliveries and improved customer experiences.

Delivering instant expertise

Modern factories rely on decades of institutional knowledge stored across hundreds of documents. Workers often can’t find information when they need it most, a frustrating experience for employees and a potentially poor outcome for customers. Agentic AI can help by making it easier for workers on the shop floor to access the information they need in real time. 

"We see organizations deploying AI agents to quickly look up documentation and procedural information,” says Zane Frantzen, vice president of platform operations for SimplyAsk, maker of a business automation platform. “By making it easier to access information, you increase the quality of service and support for floor workers while also creating a safer workplace."

Freshworks' Swaminathan notes that agentic AI can take this a step further by enhancing static documentation with real-time data. 

“When a machine encounters a problem, you can ask a bot how to troubleshoot it, and it will find the right answer in the manual,” he says. “But an intelligent agent will be able to look at the entire history of the machine and tell you how that particular issue was resolved in the past. You’ll have a lot more information to work with.”

AI agents can process machine manuals to help maintenance teams quickly search fault codes and analyze maintenance tickets to identify root causes from unstructured data.

Read also: 3 critical needs for agentic AI in the public sector

Trust is the key to agentic AI

Eventually, we'll see clusters of agents working together to create self-running, self-healing factories, says Wipro's Mota.

"Multi-agent systems will act in concert to manage quality control, compliance checks, preemptive maintenance, and acquiring raw materials," he says. "These AI systems won’t just recommend actions, but even execute them across connected systems like ERP or SCADA [supervisory control and data acquisition] systems, with minimal human input."

But significant groundwork remains. Many manufacturers have fragmented data silos and legacy technology systems that need modernization. These new systems will need to be secure and compliant with existing regulations, and employees will need the right skills for working in an agentic environment.

Most critically, manufacturers must trust the new systems they implement. Because the risks are so high, and the costs of downtime so enormous, it will be a long time before manufacturers will trust AI agents to fix machines or halt production lines on their own, warns Swaminathan. For the foreseeable future, those decisions will remain in the hands of humans.

“Predictability is key,” says Swaminathan. “An agent has to work perfectly 100 times before someone will be willing to put that process in place.”

As these systems prove their reliability, the balance will shift toward greater automation, not to replace human judgment, but to allow workers to focus on more meaningful tasks that ultimately drive manufacturing excellence.