The manufacturing industry operates at the cutting edge of technology, deploying the latest robotics, IoT, machine learning, and other breakthroughs to improve productivity and drive human progress.

But it’s also an industry that’s burdened with complexity. And it’s here — within this expanding thicket of documentation, data and regulations — that another technological breakthrough can start to help manufacturers.

Generative AI (GenAI) has come a long way in a very short time. It’s rapidly improving how we organize data, automate tasks and make decisions. It’s saving time, cutting costs, and boosting productivity — and it does all of this via human-like interactions in text or, increasingly, through images and voice.

Furthermore, the rise of agentic AI capabilities allow for even more advanced automation and independent execution of tasks.

The opportunity here for manufacturers is profound. One automotive supplier is using GenAI to streamline R&D, and has already seen a 70% improvement in productivity. And Mckinsey says the total impact on productivity in advanced manufacturing could reach as much as $290 billion.

It sounds promising, but if you’re a manufacturer that’s already managing highly complex technologies and processes, you might be wondering where GenAI can be adopted with the most benefit, while causing the least disruptions in terms of technical ability, privacy, security, and other concerns.

Let’s focus on five areas where AI in manufacturing is showing the most promise, and in many cases already demonstrating success.

Dealing with documentation

The challenge:

The manufacturing industry relies on a staggering amount of documentation; from technical manuals and compliance certifications to HR contracts, supplier invoices, and shipping requirements among many others. Processing this manually takes time and creates mistakes, which leads to delays, regulatory risks, and unexpected costs.

The solution:

AI agents can automate the generation of compliance and quality control documentation, flag any inconsistencies, auto-fill forms, and generate summaries for quick review. The current process of manually cross-referencing supplier invoices with shipping arrangements, for example, could be taken over by a GenAI solution that identifies problems in real-time, saving manual work and preventing costly delays.

Accessing and analyzing data

The challenge:

Manufacturers store vast amounts of data, including documentation, across different systems and file formats, making it difficult for employees to find accurate information in real time. And this data keeps growing, with commercial aircraft alone expected to generate 98 million terabytes of data a year by 2026 – enough to stream about 33 billion hours of HD video.

The solution:

GenAI solutions with retrieval-augmented generation (RAG) quickly find, analyze, and communicate the information being stored across these databases; and do so via text, charts or graphs in a choice of more than 100 languages.

Say a supplier in Taiwan needs insights from a U.S. manufacturing customer into how to improve its production processes. A RAG system would be able to scour relevant documentation held in both structured and unstructured data types across an organization and instantly generate a clear report in Mandarin showing recommendations for improvement.

Optimizing processes and production

The challenge:

In manufacturing, ensuring efficiency requires managing an intricate web of processes: coordinating parts, people, supplies, and production schedules, while also adapting to fluctuating market demands. Without clear insights, inefficiencies like bottlenecks, overuse of resources, and energy waste can creep in, compromising both output and sustainability.

The solution:

GenAI excels in untangling this complexity by analyzing production logs and historical data to uncover inefficiencies and suggest improvements. It can simulate production scenarios to identify bottlenecks before they occur, reduce resource overuse, and optimize workflows for energy conservation.

Additionally, by connecting with IoT-enabled machinery and sensors, GenAI provides real-time monitoring and adjustment recommendations during production — an area with enormous potential considering manufacturers are tapping less than 10% of data from machines.

For instance, if product dimensions deviate from specifications mid-run, GenAI flags the issue, recommends recalibration, and logs the event for future reference—streamlining operations and reducing the need for manual oversight.

Predicting maintenance and managing risk

The challenge:

Equipment breakdowns, whether planned or unexpected, can grind production to a halt, driving up costs and creating inefficiencies. Furthermore, safety and compliance risks can lead to delays, fines, or worse, harm to workers.

The solution:

GenAI takes predictive maintenance to the next level by combining historical data with real-time inputs to forecast equipment failures and suggest proactive measures. For example, it can detect subtle wear patterns on machinery and recommend repairs before they escalate into major issues. Engineers can use an AI agent to access standard operating procedures and past work orders instantly, ensuring quick, informed decisions.

Beyond equipment maintenance, GenAI can also act as a risk mitigation assistant, helping manufacturers navigate complex regulatory requirements and workplace safety guidelines. It scans policies, analyzes data, and highlights vulnerabilities, enabling companies to address potential risks proactively—whether it’s ensuring worker safety or maintaining compliance with evolving regulations.

Simplifying the supply chain

The challenge:

The pandemic lockdowns revealed how complicated — and vulnerable — the world’s supply chains can be. From raw materials in one country to manufacturing in another and then delivery to yet another, these chains are subject to a wide range of variables that can impact schedules and prices.

The solution:

GenAI analyzes vast amounts of data to track progress, spot inefficiencies, monitor inventory, and identify developing problems on one side of the world that could rapidly affect supply chains elsewhere.

For example, an agentic AI solution could help a maker of electric vehicles assess planned mining output, futures markets, and expected consumer demand to recommend options for buying raw materials like lithium or copper. The AI agent could then autonomously take action when predefined conditions are met. If lithium prices dropped below a certain level, for example, the system could place buy orders in seconds, taking immediate advantage of market conditions to reduce costs and ensure supply chain resilience.

These are just some of the immediate benefits of generative AI in the manufacturing industry. Throughout the product journey — from early ideation and R&D, to sourcing materials and parts, planning and optimizing production, ensuring safety, completing deliveries, and satisfying their customers — GenAI will play a significant and steadily expanding role in reducing manufacturing complexity and improving productivity.

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Brad Gyger