AI in Manufacturing: Applications and Real World Examples

Key takeaways

The integration of artificial intelligence into the manufacturing sector is no longer a futuristic concept but a present-day reality, driving significant operational improvements.

• According to Grand View Research, the global AI market in manufacturing is set to reach $47.88 billion by 2030, up from $5.32 billion in 2024 [1].

• Companies use AI in manufacturing for predictive maintenance, quality control, and supply chain management.

• A few jobs that use AI in manufacturing are plant manager, quality control inspector, and process engineer.

Discover applications for AI in manufacturing, common jobs that use AI in this field, and the pros and cons of AI in manufacturing. If you’re ready to begin a career in AI, enroll in IBM’s AI Foundations for Everyone Specialization, where in as little as four weeks, you can learn about machine learning software, application deployment, prompt engineering, data science, and more.

How is AI used in manufacturing?

Manufacturing firms use several types of artificial intelligence technologies during production to facilitate tasks such as predictive maintenance, quality control, supply chain management, and human-machine collaboration. Discover how different aspects of AI assist with various stages of the manufacturing process:

• Predictive maintenance (PdM): Utilizes sensors to collect data about machinery and equipment. Then, AI and machine learning (ML) technology analyze the gathered information to construct an up-to-date representation of the machinery and equipment, identifying potential defects and future maintenance needs.

• Quality control: Automated quality control systems employ computer vision, AI, the Internet of Things (IoT), and robotics to spot product defects quickly and accurately, determining the quality of an organization’s products without the need for human intervention.

• Supply chain management: Involves the use of ML, predictive analytics, computer vision, generative AI (GenAI), and AI agents to develop more durable and efficient supply chains capable of anticipating issues, automating tasks, and reducing operating expenses.

• Human-machine collaboration: Collaborative robots or “cobots” work alongside employees to help improve efficiency, reduce errors, and supplement worker productivity. Cobots operate using a combination of sensor data, control software, and ML algorithms, and you can find them working across various sections of the manufacturing process, including assembly, machine tending, material handling, quality inspection, and welding.

Read more: 10 Machine Learning Algorithms to Know

What jobs use AI in manufacturing?

Throughout the manufacturing industry, you might see AI used in a variety of jobs, such as plant manager, process engineer, quality control inspector, and IoT specialist. Review these roles and their use of AI in more detail to see how AI can impact their work. 

Plant manager 

In this managerial role, you might use AI for predictive maintenance, quality control, production optimization, and inventory management. Furthermore, you may employ AI to automate repetitive tasks and use data-driven insights to enhance plant performance.

Process engineer 

As a process engineer, your goal is to make the manufacturing process more efficient. You can utilize AI algorithms to review data sets and analyze variables such as temperature, pressure, flow rates, and chemical concentrations. With this information, AI models can uncover opportunities for improvement and calibrate settings in real time to optimize the overall manufacturing process.

Quality control inspector 

While working in an automated environment as a quality control inspector, you might use data and AI analytics to confirm the quality of the products. You may also work alongside engineers and AI system integrators to solve core problems, improve AI model accuracy through continuous training, and ensure the organization adheres to compliance rules.

Industrial IoT (IIoT) specialist 

In the capacity of an IIoT specialist, you might govern ML-enabled IIoT networks that learn from previous events and then adapt based on the new information. Using ML technology, these systems can learn from operational failures, spot patterns human workers can’t perceive, and even perform historical data analysis to make real-time, autonomous decisions. Additionally, you might implement AI-powered predictive maintenance to monitor equipment health, minimizing the risk of downtime. 

What is a digital twin, and how does it benefit the manufacturing industry?

Within the manufacturing industry, a digital twin is a virtual representation of a product, process, or system. For example, if you hire a new operator to work in your manufacturing facility, you can use a digital twin of the workspace to train the employee so that they can avoid physical dangers while you avoid disruptions to your production process. Using digital twins in the manufacturing industry offers benefits such as optimizing production schedules, predicting issues with machinery, and enhancing efficiency at every stage of the manufacturing process.

Real-world AI use cases in manufacturing: Examples

Major corporations, such as Siemens, General Electric (GE), PepsiCo, and Amazon, are already working with AI to enhance the manufacturing process in some way. Explore in more detail how these companies have tapped into AI to benefit the manufacturing process:

• Siemens: Siemens, a global tech company, provides the Andretti Global racing organization with digital twin tools that enable it to build virtual models of its racecars. The designers can simulate vehicle schematics, test components, and predict racecar performance virtually before beginning the manufacturing process [3]. 

• General Electric: To monitor equipment functionality and prevent expensive downtime in its manufacturing facilities, GE focuses on AI-driven predictive maintenance. AI analyzes data taken from sensors to predict possible failures and suggest timetables for maintenance [4].

• PepsiCo: This organization is transitioning to a planning strategy based on digital tools, which utilizes AI agents as co-designers and digital twins that adhere to physics. Doing so permits the company to simulate, approve, and optimize the layout of a production plant before beginning the construction process [5].

• Amazon: The corporation uses generative AI to make workspaces more ergonomic for employees, develop smarter warehouse robots, more effectively predict where to stock new inventory, and optimize delivery itineraries [6]. 

What are the pros and cons of artificial intelligence in manufacturing?

Implementing AI in the manufacturing process offers several benefits, such as precision, financial savings, and energy efficiency, but you may also encounter a few challenges. Uncover some of the pros and cons when incorporating AI into manufacturing.

Pros

AI in manufacturing offers several advantages, including increased precision, reduced costs, enhanced supply chains, and greater energy efficiency. Explore these pros in more detail: 

• Better quality and precision: You can use AI-based systems for quality control, and these systems analyze your products and compare those results to fixed standards. By using AI for quality control, you can remove defects in your products before mass-producing them.

• Save money with predictive maintenance: AI models can help prevent unexpected machine failures and downtime by predicting when your equipment needs servicing.

• Improved supply chain and inventory management: You can utilize AI in manufacturing to gain up-to-date insights into your supply chain and inventory. Doing so allows you to match supply with demand, reducing financial losses from stocking too much or too little of your product.

• Enhance energy efficiency and sustainability: AI can spot waste and optimize energy use by tracking power consumption across production lines. This can help your facility reduce expenses while reaching its sustainability targets. 

Cons

You may also need to account for a few disadvantages when using AI in manufacturing, such as poor data quality, cybersecurity threats, and skill gaps in your workforce. Discover more about some of the cons you might face:

• Data quality: Because manufacturing facilities generally operate using legacy systems, the data generated can be insufficient or inconsistent, and AI requires good data to be effective. Make sure you audit your data for accuracy and calibrate data-collection instruments properly before implementing AI.

• Cybersecurity threats: When incorporating AI tools into technologically interconnected systems, you increase the attack surface of your organization by adding new entry points. IoT sensors and house transducers collect real-time data about your manufacturing operations and transfer that information to the interconnected AI tools, so you need to protect those vectors of connectivity from bad actors.

• Workforce skill gaps: When you lack enough workers with AI expertise, it can lead to suboptimal predictive models. When integrating AI into your manufacturing process, consider hiring people who understand how machine learning and data science work.

What is the future of industrial AI in manufacturing?

The use of industrial AI in manufacturing is most likely to continue to grow. According to Grand View Research, the market size for global AI in manufacturing was $5.32 billion in 2024, and the research organization expects that value to reach $47.88 billion by 2030 [1]. Furthermore, in terms of the importance of AI in this industry, Deloitte conducted a survey of 600 manufacturing executives, and 85 percent stated they “believe their smart manufacturing initiatives will transform how products are made, improve agility, and attract new manufacturing talent” [2]. 

Also, more personalized product recommendations appear to be a future aspect of AI in manufacturing, as this technology enables you to design the exact product you want and customize it to fit your specific needs. 

How can you start a career that uses AI in manufacturing?

If you plan to work in a role that uses AI in manufacturing, a few skills you need to develop include AI literacy, data analysis, and basic coding. Explore some options for increasing your abilities in these areas:

• AI: Several institutions, such as the University of Maryland and Stanford University, offer online certificates focused on developing your AI skill set. You could also explore AI boot camps from universities such as Massachusetts Institute of Technology (MIT), Northwestern, Arizona State, and Columbia. Earning a bachelor’s degree is another option, and a few potential majors for a future AI employee are computer engineering, computer science, and electrical engineering.

• Data analysis: Several universities offer data analysis boot camps. Some organizations that provide these are Arizona State University, Columbia University, Case Western University, and Michigan State University. If pursuing a bachelor’s degree better suits your plan, consider majoring in business intelligence. You could also earn a degree in computer science, statistics, or information systems.

• Coding: Programming boot camps are another possibility to increase your skill set in this field. Colorado State University, Johns Hopkins University, and Carnegie Mellon University offer coding boot camps. In terms of a degree, you want to major in a subject such as computer programming, computer science, or software engineering.

Keep up with trends and job opportunities in AI

Join Career Chat on LinkedIn to stay current regarding trends and job opportunities in AI. Check out these other free resources:

• Watch on YouTube: AI Quick Tip: Streamline Project Management with Your AI Assistant

• Hear from an insider: How to Use GenAI to Advance Your Career: Insight from Coursera's Former CEO

• Learn the terminology: Artificial Intelligence Glossary: Learn AI Vocabulary

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial