8 Ways AI in Manufacturing Is Resulting in Smarter and More Efficient Workflows
Table of contents
Remember the dancer in a robot suit at Tesla’s AI Day?
No? Well, let me take you back 👇
Back then, for most of us, it seemed more like a publicity stunt than a technological leap. However, just a couple of years later, reports suggest* that Tesla has two Optimus robots working on its factory floor.
These humanoid robots are allegedly tasked with simple jobs, symbolizing the start of what could be a broader revolution in robotics and AI-driven manufacturing. While the full scope of their capabilities is yet to be seen, Tesla’s experiment has become a real-world application.
Elon Musk has plans to mass-produce Optimus, each of which costs less than buying a car. He stated, “I think everyone among the eight billion people on Earth will want an Optimus buddy. Optimus robot will revolutionize the world more than ever!”
Tesla isn’t the only company betting big on AI in manufacturing.
Across industries, factories are embedding AI into everything from quality control to predictive maintenance.
In this blog post, we are taking a look at the top use cases of AI in manufacturing—but first, let’s understand how AI came into full throttle in this space.
Table of Contents
- The evolution of AI in manufacturing
- How artificial intelligence is transforming manufacturing
- Step up your support game with Harvey’s AI-powered finesse
The evolution of AI in manufacturing
Manufacturing generates a staggering 1,812 petabytes (PB) of data annually, according to a Deloitte survey. That’s more than industries like finance, retail, or communications combined.
The sheer scale of this data highlights the complexity of modern manufacturing.
To keep up, companies are turning to AI-driven tools that can sift through troves of data, identify patterns, and provide actionable insights.
Fast forward to today—AI is transforming how decisions are made on the factory floor.
From its early days of monitoring simple operations to today’s sophisticated tools like Generative AI (Gen AI) and digital twins, here’s a look at how manufacturing has embraced a smarter, more innovative future.
1. The introduction of sensors and feedback systems
During its initial phase of rollout, AI was used to integrate sensors into machines.
These sensors were responsible for monitoring critical parameters like energy consumption and material supply. With the integration of AI, these sensors can not only collect data but also analyze it in real time.
The AI technology leverages sensor data to predict equipment failures even before they occur. This level of predictive capability becomes crucial to maintaining continuous production flow.
2. Generative AI is reshaping manufacturing
The excitement around generative AI is still on. ABI Research estimates that the strategic deployment of Gen AI in manufacturing will add $10.5 billion in revenue by 2033.
James Iversen, a Manufacturing and Industrial Analyst at ABI Research, shares how Gen AI will find mainstream deployment in manufacturing.
“The deployment of generative AI will come in three waves as the technology matures, with manufacturing seeing the largest revenue growth during the second and third waves. During the second and third waves of adoption, generative AI will be deployed into four domains of manufacturing: design, engineering, production, and operations.”
Manufacturers are using generative AI to solve production issues faster, create work instructions efficiently, and upskill their workforce.
The different use cases for generative AI in manufacturing can be compared by looking at Return on Investment (ROI) and expected Time To Value (TTV).
3. The rise of digital twins
When we talk about AI in manufacturing, we cannot leave out the mention of its impact on digital twins. McKinsey calls the generative AI and digital twins pairing a powerful one.
Nearly 75% of large enterprises are now investing in digital twins to scale their AI solutions. What led to this?
While Gen AI is streamlining digital-twin deployment, digital twins can further refine and validate Gen AI output. Digital twins have made waves in the manufacturing industry with virtual replicas of machines or systems that simulate real-world conditions—enabling better decision-making throughout the production cycle.
4. Additive manufacturing advancements
Over the last decade, AI has driven innovations in additive manufacturing, in other words, 3D printing. Smart systems now monitor material quality and fabrication processes, adapting in real time to maintain consistency.
These AI advancements have expanded the possibilities for creating complex designs while reducing waste and production errors.
How artificial intelligence is transforming manufacturing
The next 5 years are said to be an exciting time for manufacturers as they go from being digital novices to champions, rewriting the rules of production with new-age technology.
AI will be at the heart of this transition.
From maintenance predictions to streamlining supply chains—AI is improving various aspects of production by:
- Predicting equipment failures to reduce downtime
- Enhancing product quality by identifying defects early
- Streamlining the overall production through real-time monitoring
As the Reddit user points out, AI handling tasks like payroll and production allows factories to optimize resources, preparing them for long-term sustainability and innovation.
1. Predictive maintenance
A massive breakthrough for AI in manufacturing came with predictive maintenance.
According to Deloitte—poor maintenance strategies can lower a manufacturing plant’s overall production capacity by 20%. That’s not all; manufacturers are taking a big hit with unplanned downtimes that are said to cost them close to $50 billion every year.
AI-driven predictive maintenance is turning things around. This powerful tool empowers businesses to reduce downtime, prevent equipment failures, and bring down maintenance costs.
With over 30 years of experience in Manufacturing and Information Technologies, Mike Bradford, DELMIA Strategic Business Development, Dassault Systèmes, pointed out that AI is essential for predictive maintenance.
“It enables manufacturers to analyze inputs such as usage, vibration, and noise directly from machines, compare them to historical data, and predict when a machine is likely to fail. This allows maintenance to be scheduled at the last feasible moment, minimizing disruptions.”
These AI-driven systems closely track the performance of machinery and flag any irregularities. This allows maintenance crews to step in and find solutions early. GE is integrating AI into its production equipment to cut down on costly downtime. This has helped GE bring down the total maintenance costs by 30%.
GE uses its Predix platform for predictive maintenance. The Predix platform tracks real-time data from sensors embedded in equipment to:
- Monitor equipment data like temperature and vibration
- Detect anomalies using AI and machine learning
- Identify potential failures before they happen
- Optimize maintenance schedules based on data-driven insights
2. Personalized customer experience
As much of the world has come to realize, now is the time to become AI-assisted.
Manufacturers are using AI to analyze data and in turn, improve satisfaction scores with tailored offers, recommendations, and communication based on individual preferences and behaviors.
Bosch and Deere use AI to provide tailored recommendations for their customers. By understanding customer preferences, AI helps companies offer targeted product suggestions, improving conversion rates and customer loyalty.
Further, AI helps detect patterns in customer behavior, allowing manufacturers to predict churn and proactively address issues.
Take the case of companies like Siemens—they apply AI to:
- Analyze customer data and recommend personalized retention strategies, such as tailored promotions and customer-specific incentives
- Develop targeted loyalty programs based on customer behavior and preferences
- Collect insights for personalized communication to strengthen client relationships
3. Quality control
To maintain high standards in manufacturing and reduce waste, manufacturers are turning to AI-driven computer vision systems to identify defects during production.
AI in quality control makes use of advanced algorithms and machine learning to inspect products, identify defects, and maintain compliance with quality standards.
Toyota is leveraging Acrovision’s deep learning inspection system to improve the overall performance of checks and also deliver new inspections when required for Toyota Motor Manufacturing UK.
Toyota leverages AI for quality inspections across multiple stages in the manufacturing process.
- AI-driven GigE PC cameras perform over 60 inspections on each vehicle
- Cameras capture real-time images for defect detection at various checkpoints
- Inspection data is sent to the central system for analysis and decision-making
- Faulty vehicles are identified and removed at the next checkpoint for corrections
4. Robotics and automation
Is AI close to delivering fully automated factories?
We’re certainly not far from it.
Repetitive tasks like assembly, welding, and packaging are being taken over by AI in manufacturing.
As robots are becoming more capable and generative AI is taking over the factory environment—manufacturers are preparing for the inevitable disruption.
Like Musk’s Optimus, ABB RobotStudio™ is taking the lead.
Here’s how RobotStudio provides confidence in the robot’s functionality before implementation:
- It enables building, testing, and refining robot installations in a virtual environment
- The software models a wide range of scenarios to accurately simulate robot behavior
- It allows testing of real-life conditions, ensuring optimal robot performance in manufacturing environments
- Risk to workers and machinery is minimized through virtual simulations
5. Energy management
Minimizing waste in today’s energy-conscious, ESG-driven world is a top priority for manufacturers.
With its data-driven capabilities, AI can minimize waste, improve energy usage, and ensure sustainability—all while reducing the environmental footprint.
AI monitors energy consumption in real-time and suggests adjustments to reduce wastage.
At companies like BMW, AI optimizes energy usage during manufacturing, ensuring that operations remain cost-effective and environmentally friendly.
Here’s how it helps:
- Tracks energy consumption patterns to identify areas for potential savings
- Helps reduce operational costs while lowering the carbon footprint
- Supports environmentally friendly practices in manufacturing operations
6. AI-powered chatbots
Generative AI-powered chatbots are having real conversations with real customers, making sure your customer support channels are even more versatile and freeing up your support agents’ bandwidth.
Gartner predicts that along with digital customer service and conversational user interfaces—generative AI will have the biggest impact on customer service by 2028.
With AI chatbots, manufacturing businesses can provide 24/7 customer support and offer instant, automated responses to common inquiries, allowing agents to focus on complex issues.
Honeywell utilizes AI chatbots to streamline service requests and FAQs, boosting efficiency and customer satisfaction.
- AI chatbots provide instant responses to common customer inquiries, reducing wait times
- And scale support operations without additional manpower, increasing efficiency
7. Customization at scale
Mass customization is a reality across the manufacturing industry thanks to AI.
By analyzing consumer preferences and producing items tailored to individual needs—AI allows companies to tailor their offerings to suit each one of us—at scale. All of which was unimaginable a few years ago.
While AI-driven design tools allow manufacturers to offer custom products, they go a level further and optimize supply chains by forecasting demand for personalized products, further ensuring that materials are sourced and produced efficiently.
Adidas, for example, uses AI to create custom shoes based on user data, ensuring a personalized experience for each customer.
Here’s a look at how Adidas’ customization platform has set some pretty neat benchmarks in AI-driven personalization:
- With the miadidas platform, customers can design shoes with custom colors, materials, and text
- Customers can see instant updates on their design choices, providing an interactive experience
- Customization features cater to individual preferences, ensuring a unique product for every customer
8. AI for managing customer feedback
According to a survey of German manufacturing companies, 53% of them employ AI to analyze customer behaviour in sales.
AI helps companies analyze large volumes of customer feedback and extract actionable insights.
Manufacturers such as Whirlpool leverage AI-powered tools like ContactEngine to scan customer feedback, including reviews on e-commerce platforms and social media. This allows businesses to respond faster to customer concerns and refine their products based on real-time input.
ContactEngine’s automated fault verification reduced inefficiencies in service operations, helping Whirlpool achieve:
- 85% customer engagement rate
- 91% increase in correctly ordered parts
Step up your support game with Harvey’s AI-powered finesse
AI is shaking up manufacturing in ways we couldn’t imagine, and it’s not just on the production floor. When it comes to customer support, AI is helping manufacturing businesses match the gold standard of service set by big techs.
That’s where Hiver’s AI bot Harvey steps in. Think of Harvey as your behind-the-scenes pro—summarizing conversations, suggesting responses, and even auto-closing tickets that don’t need follow-ups. We’re talking quicker resolutions, fewer message loops, and more time for your team to tackle the big stuff.
Harvey doesn’t just optimize workflows—it transforms how you connect with customers.
Take Harvey for a spinwith a free trial today
