Industry-leading manufacturers are turning to AI to increase efficiency across all their major operations, but many don’t know where to start. With a rapid rise in ‘AI-powered’ jargon and niche solutions, it’s hard to decipher which technologies can actually improve day-to-day operations. That said, there are some recent studies that show the potential impact of AI when the right technologies are selected and applied.
According to a report by the National Association of Manufacturers, (NAM), 72 percent of manufacturers report reduced costs and improved operational efficiency after deploying AI technology. In addition, 41 percent reported improved process optimization and control after deploying AI technology.
The Challenges
Let’s take a look at the current state of technology adoption in manufacturing. Manufacturing businesses have made great advances in modernizing their factory floor operations with cutting-edge technologies like AI, but still face challenges outside the production environment with slow, disjointed, and error-prone workflows. These problems often arise in areas like inventory management, order processing, and supply chain communication, where outdated methods—such as email chains and spreadsheets—create inefficiencies. These workflow gaps can offset the benefits of modernizing the factory – affecting production speed and product quality.
For example, a typical manufacturing order process involves numerous steps—purchase requisition, manager approval, supplier outreach, budgeting, setting delivery dates, organizing production, and engineering. With so many handoffs, errors are common, whether in the form of missed deadlines or misplaced emails. These disjointed workflows introduce risks, leading to costly mistakes, delays, and quality issues.
This is where AI-driven workflow automation comes in to make a transformative impact. By automating workflows with AI, manufacturers can reduce errors, improve speed, and foster collaboration across departments. AI-driven automation facilitates seamless workflows by ensuring information flows smoothly, preventing missed steps.
Even small-scale automation—such as automating quality assurance or purchasing—can yield immediate time and cost savings.
For instance, a major supplier in the energy sector adopted AI-powered workflow automation to streamline its processes. By automating tasks like quality assurance, purchasing, and maintenance requests, the company reduced turnaround time from 90 days to less than a week. AI connected all aspects of the business, from the factory floor to the front office, enhancing production speed, accuracy and efficiency.
The supplier’s approach started with small, scalable automation. They moved from a paper-based to a digital quality assurance process, enabling real-time tracking of parts’ status and quality. Using an AI-driven, no-code solution, they customized workflows to their specific needs, making the process easy to implement and refine over time.
AI-Driven Workflow Automation
Manufacturers often find that the easiest tasks to automate are the most repetitive and manual, such as purchasing and quality control. These functions benefit from AI-powered automation because the AI can handle routine tasks efficiently, while continuously learning and optimizing processes to improve speed and accuracy. Once these sorts of tasks are automated, manufacturers can extend automation to other areas, gradually building a fully integrated system that connects departments and improves overall operations.
Unlike traditional automation, which relies on rule-based systems, AI-driven workflows employ advanced technologies to recognize patterns, detect discrepancies, and make autonomous decisions. These systems continuously learn from new data, enabling smarter decision-making. For example, AI workflows can autonomously identify and correct inconsistencies, improving both efficiency and accuracy.
For manufacturers, it can be challenging to determine where to apply AI technologies and how to get started. As with any new technology, start small by using AI to automate one or two processes, such as purchasing or quality control, to realize the benefits of optimized operations. From automating repetitive tasks to improving quality control, AI-driven workflows can transform manufacturing businesses into agile, efficient, and cost-effective enterprises.
