In manufacturing, where hands-on labor is central to the business, AI's potential lies not in replacing workers but enhancing how they work, handling repetitive chores and collating data so that people can more efficiently do the uniquely human aspects of the job that both drive value and make the work rewarding.
AI is everywhere – at least, that’s what the press releases tell us. Every company claims to be "leveraging AI" or "transforming operations with AI-driven insights." And yes, there’s real potential here. Manufacturers that invest in AI-powered tools can usually make smarter decisions, reduce waste and improve efficiency.
But AI in manufacturing isn’t about replacing people with robots or turning factories into futuristic, self-operating plants. It’s about helping skilled workers do their jobs better and faster, not eliminating them.
Why AI Isn't Already Everywhere
If AI offers so much potential, why aren’t all manufacturers using it yet? Consider the following:
• Fear Of Change: Employees worry that AI will replace their jobs, so it’s up to companies to communicate that AI is a tool for efficiency, not a replacement, and invest in training employees to work alongside it.
• System Integration Challenges: AI is only as effective as the data it can access. Many manufacturers still operate with disconnected legacy systems, making AI implementation difficult.
• Cost Concerns: AI solutions aren’t cheap. Small and mid-sized manufacturers understandably question whether the return on investment justifies the expense.
• Lack Of Expertise: Not every company has a team of data scientists to train AI models. That’s why plug-and-play AI solutions, which don’t require in-house AI expertise, are gaining traction.
Enhancing, Not Replacing, Skilled Workers
Human intuition, adaptability and problem-solving can’t be automated. What AI can do is remove tedious, repetitive tasks from workers' plates, allowing them to focus on higher-value work.
Take production planning, for example. Many manufacturers still rely on a combination of experience, spreadsheets and outdated ERP systems to determine what gets made, when and how. AI can cut through the clutter. It can analyze historical data, monitor supply chain disruptions and track market trends to suggest better schedules and workflows. But at the end of the day, a human still makes the final decision.
Or consider inventory management. AI can help distributors allocate limited stock to the most critical orders – whether that means ensuring a high-value customer gets their parts on time or preventing a costly production delay. But again, AI doesn’t make the final call. It presents the best options, and a human with industry knowledge and judgment approves the move.
So, who needs upskilling – factory workers or managers?
Both, actually.
Factory workers likely have the strongest job security because their hands-on, intricate tasks are some of the hardest for machines to replicate. However, first-line managers must develop skills that AI and automation cannot replace. Their focus should be on strengthening strategic thinking, decision-making and problem-solving abilities – ensuring they stay essential in an increasingly automated environment.
AI: Only As Smart As The Data It Gets
AI doesn’t generate insights out of thin air; it depends on the data it receives. And in most manufacturing environments, that data is messy.
Different departments use different systems and information gets scattered across databases that may not communicate with each other. You might have sales orders in one system, production plans in another and supplier data buried in a legacy platform no one has updated in years.
But with AI, businesses can set sophisticated parameters to control inventory distribution, ensuring critical decisions are made based on real-time data.
Consider a company about to ship $300,000 worth of goods to a customer who hasn’t paid its outstanding balance. Without automation, catching this issue would require a team of accountants and production planners manually combing through records – an expensive and inefficient process. But a robust AI-based system can flag the problem instantly. If a customer is 180 days late on payments, the system alerts the business, providing the necessary oversight before valuable inventory is shipped out.
Smarter Workflows, Not Job Cuts
AI-powered automation helps reduce repetitive work, but again, it’s not about eliminating jobs. It’s about allowing workers to focus on higher-value tasks.
Take quality control. Many inspections still rely on manual checks. But AI-powered computer vision can scan products in real time, detecting defects faster and more accurately than human inspectors. Instead of spending all day on the line, inspectors can shift their focus to identifying defect patterns, collaborating with engineers to refine processes and reducing errors before they happen.
Similarly, AI can transform supply chain logistics. It can predict supplier delays, optimize shipping routes and suggest alternative suppliers before disruptions occur. This doesn’t make procurement teams obsolete. In fact, it allows them to spend more time negotiating better deals, strengthening supplier relationships and improving sourcing strategies.
The Trust Factor
One of the biggest misconceptions about AI is that it can operate without human intervention. That’s simply not true. AI can – and will – make mistakes.
Anyone who has worked with machine learning knows that AI models don’t always understand context. They can generate "hallucinations" – misleading or incorrect outputs that seem plausible but are completely inaccurate.
This is why checks and balances are critical. AI-driven recommendations should always require human validation. For example, an ERP system’s AI might suggest adjusting a production run due to a material shortage. But before any changes take effect, a production manager should review the recommendation, assess real-world conditions and either approve or override the suggestion.
Bias is another concern. AI learns from historical data, which means it can inherit and amplify existing biases. Manufacturers using AI for demand forecasting, supplier selection or workforce management should regularly audit their models to ensure fair and unbiased decision-making.
The Future: Smarter Factories, Smarter People
The future of AI in manufacturing isn’t about robots taking over. Rather, it’s about enabling faster, smarter decision-making. AI cuts inefficiencies, optimizes workflows and frees skilled workers to focus on high-value tasks.
The manufacturers who succeed won’t be the ones rushing to replace employees with automation. They’ll be the ones who use AI to enhance their workforce, integrate their systems and build smarter, more resilient operations.
In the end, success in manufacturing won’t be about who has AI, but who knows how to use it best.
This article was written by Steve Murphy from Forbes and was legally licensed through the DiveMarketplace by Industry Dive. Please direct all licensing questions to legal@industrydive.com.
