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In PwC’s Global Artificial Intelligence Study, research suggests AI could contribute as much as $15.7 trillion to the global economy by 2030: $6.6 trillion of that figure stemming from increased productivity. By 2026, AI in the manufacturing sector alone is projected to grow from its 2020 figure of $1.1 billion to $16.7 billion.

The numbers are nothing short of staggering, and this has industry leaders asking themselves how they should implement AI in their facilities to maintain their competitive edge. But as AI is a complex and multi-faceted field showing promise in so many seemingly unrelated disciplines, decision-makers in manufacturing are asking: “how does AI reduce costs?”

If you are looking for ways to streamline operations using AI but are unsure whether the long-term cost savings are worth the initial investment, we’ve assembled this digestible guide to help you make an informed decision.

Is it Costly to Implement Artificial Intelligence in Manufacturing?

AI takes on different forms at every stage of the manufacturing cycle. AI software and hardware is used in logistics, testing, supply chain, analytics, and more. Companies across different industries are finding multiple ways to implement AI to their benefit. In McKinsey’s Global AI Survey, 63% of respondents reported increased revenue as a result of AI adoption. How autonomous tools will benefit you depends on your specific requirements and where the greatest leaps in efficiency are needed.

What Does It Cost to Invest in AI in Manufacturing?

The cost of implementing AI can vary wildly depending on what is necessary. For instance, many businesses of varying sizes are benefiting from using the free version of ChatGPT, while others are spending around $300,000 for custom-tailored software development and deployment.

Factors That Impact AI’s Cost

These are necessary questions to ask yourself when assessing how expensive it may be to initially implement AI solutions:

1. How complex is the data you need automated or analyzed?
2. Are there off-the-shelf solutions?
3. Will you need to have software tailored to your specific demands?
4. Are there any low-code or no-code solutions for organizations in your field?
5. What aspects of your business would AI improve?

If you represent a highly specialized field, you may need to hire a software developer or development team to customize software that meets your needs. Annual salaries for data scientists and ML engineers currently range from $100,000–$110,000. Teams and individuals may also be hired per project, but the below information offers a general overview of how these costs factor into your budget.

Developing and deploying AI requires specialized GPUs and software platforms. Facilitating operations through cloud computing solutions may help reduce such costs.

5 Specific Ways AI Helps Companies Save Money

Below are just a few specific fields using AI to considerably increase cost-efficiency.

1. AI is used in oil exploration to predict and optimize oil extraction.
2. AI is being used for the development of fusion energy.
3. AI analytics are used for optimization in energy conservation.
4. AI is helping to conserve resources and reduce industrial waste across many sectors.
5. AI is increasing production efficiency with a build-on-demand model.

How Does AI Reduce Operating Costs?

When AI is integrated throughout operations to increase efficiency, you reduce costs and increase uptime concurrently. These are eleven ways AI is assisting manufacturers in cost-savings:

1. Reduced downtime
2. Increased productivity
3. Reduced expenses
4. Improved quality of process and output
5. Predictive maintenance
6. Robotic process automation
7. Computer vision quality control
8. Forecasting demand
9. Supply chain automation
10. Digital twin technology for 3D simulated engineering
11. Industrial Internet of Things (IIoT) for improving factory intelligence and operations

Is AI More Cost-Effective Than Humans?

As AI is highly adaptable and agile, it can be used to fulfill many purposes, and there isn’t a single direct comparison between the cost of human labor and AI. The most accurate way to address AI’s cost savings is by examining three primary ways it is used to reduce costs.

1. Increase Efficiency

AI is able to monitor aspects of production that would otherwise be exorbitantly expensive, using intelligent computer vision, sensors, and analysis of manufacturing stations and equipment. By reducing errors and preventing factory shutdown as well as frequently observed downtime, AI saves countless work hours often wasted by processing defective materials and and repairing faulty equipment.

2. Assist with Human Roles

With robotics, ML, and deep learning analytics, AI becomes the greatest asset to experts in their respective fields. AI helps augment the capabilities of manufacturing workforces so that they may perform at their best, and each individual can bring the greatest value to companies in every position they fill.

3. Manage Repetitive Tasks

Human ingenuity is often wasted on redundant inspections or mentally and physically taxing, repetitive tasks. We lose focus over time and grow weary from exhaustion. AI can manage 24/7 continuous repetition, using ML to iterate through and consistently improve in spotting defects and other roles.

Where AI dramatically reduces costs and increases efficiency, it also maximizes the value derived from human efforts. When AI is introduced to automate repetitive tasks, humans are no longer burdened by arduous labor. People are now liberated to fill more crucial roles that align with their expertise, adding value and eliminating unnecessary expenditures without the need for additional hires.

How Does AI Reduce Human Error?

AI has made significant breakthroughs in reducing human error, primarily through automating repetitive tasks. AI extends several valuable qualities beyond human capability, for example:

1. Computer vision microscopy for finding small flaws and fractures on chip surfaces is exponentially more precise than the human eye, and this equipment doesn’t tire, therefore reducing errors while improving uptime.
2. Robotics integrated into product assembly and other functions. Highly redundant tasks are carried out continuously without the need for rest periods.
3. AI is also used to identify potential equipment malfunctions that could lead to human errors based on improperly calibrated machines or faulty components.

Increase Efficiency and Reduce Operational Costs with Nanotronics’ AI Solutions

Our state-of-the-art nControl™ solution automates and refines the manufacturing process to deliver the greatest cost-savings and efficiency for your quality control needs.

Get in touch with one of our AI experts today.