How AI is Transforming Manufacturing Faster Than Ever

Artificial intelligence may still be at the pilot stage for some organizations but for progressive IT leaders in the manufacturing sector it’s already showing real benefits. Look at critical aspects such as predictive maintenance, quality control, supply-chain management, process optimization and product development and you will see AI already embedded and adding value. For manufacturers that don’t realize this, it’s time to step up and move faster, seeing where it’s possible to adopt without incurring undue risks.

Let us start by taking a deeper dive into the areas of manufacturing influence listed above.

Having an Impact by Elevating Manufacturing Standards

Inquality inspection, AI-powered computer vision systems can inspect product quality to elevate manufacturing standards. These systems automate equipment inspection, identify defects, and locate potential quality issues before products hit the market, saving both time and money. This evolution marks a fundamental change in how manufacturers define reliability, from fixing what’s broken to ensuring systems are resilient to the point where they rarely fall over or suffer significant performance degradation. Automation of the quality inspection process provides accuracy levels of over 99 per cent while slashing the time inspections take from minutes to mere seconds.

Similarly, with predictive maintenance, AI helps anticipate failures and enables proactive solutions. It optimizes maintenance schedules, spots anomalies, predicts system or part failures, and constantly analyzes equipment health in real-time. While these capabilities existed before, AI adds the ability to scan incredibly fast, pinpoint root-cause issues, and deploy solutions on a massive scale.

In supply-chain management and inventory management there are big opportunities to improve current logistics through superior demand forecasting, real-time tracking and optimizing routes. With the current state of geopolitics, that ability to re-route plans quickly will be hugely beneficial.

In the sphere of manufacturing process optimization, AI is accelerating workflows by automating rote tasks such as processing customer request tickets, documenting process change and sending email responses. Processes can be executed in minutes rather than days or even weeks. AI is also helping to ensure more accurate data processing to provide a solid platform for decision-making. AI agents make fewer errors than their human counterparts, they do not tire, and they are able to calculate based on a vast range of variables across machines, materials, and production lines. That means people get time back and can spend more hours on more creative or complex tasks like improving system design or solving production bottlenecks rather than the job of spotting outliers and errors that AI is so good at. And of course, customer satisfaction is increased.

Lastly, in the area of product development, generative design, prototyping, simulations and analysis of cost of materials will see better product quality, value and advantages such as unique personalization.

Moving Fast with Minimal Human Intervention

AI is evolving faster than perhaps any other modern technology, and in recent months, we have seen the rise of agentic AI - a particularly compelling development for manufacturing. Agentic AI expands the scope for autonomous action, allowing machines to operate with minimal human intervention. While human oversight remains crucial, these intelligent systems can now make real-time adjustments on the factory floor without waiting for manual approvals. Just as importantly, agentic AI continues to improve as it learns from the data and processes it encounters, refining its performance over time.

Closely linked is the growth of multimodal AI, where technologies such as computer vision, edge computing, and the Industrial Internet of Things (IIoT) work in concert to deliver smarter, faster outcomes. This convergence means AI won’t just help manufacturers make better decisions but it will also start to explain those decisions. We are entering an era where human and machine intelligence actively complement each other, learning together and enhancing each other's capabilities in the pursuit of greater efficiency and innovation.

A helping hand for AI Software/System Creators & Users

IT services companies are playing a major role by being the ‘middle man’ between AI software/system creators and users. They are sharing use cases, advising on deployment strategies and generally taking the weight off hard-pressed IT departments that see value in AI but perhaps don’t know where to start deploying or how. By leaning on the experience and expertise of consultants, manufacturers can de-risk their entries into AI.

Further down the line, consultants are helping to develop AI models and prompts, integrating AI solutions into existing systems and training staff. Last but not least, they can help to provide the necessary guardrails that support data security and governance. The area of data management in AI is rightly a cause of concern for many and changing rules and regulations mean that AI strategy leaders at manufacturers need to keep a careful eye on governance and ethics. Already we are seeing best practices emerging in how companies collect and use data in AI and how they can promote accuracy and weed out bias.

Services companies can also help to hurdle another obvious barrier that is currently being observed by early adopters: the AI skills gap. By leaning on specialist skills and by training current staff and looking at ways to bring in new skills through programmes such as apprenticeships, manufacturers can overcome this issue. AI itself can also help in the development of staff training through simulations.

The results can already be far-ranging. A global chemical manufacturer is using AI-powered analytics to make changes across the board, revamping the ways in which it runs workforce management, research-and-development investments, IT support streamlining supply-chain optimisation and customer satisfaction monitoring. In supply-chain partner queries, projected results are a 99 per cent reduction in response wait-times.

Get on board

Variance in definitions and scope mean that reliable data on use of AI in manufacturing is hard to come by. But we can be sure that this is one of the great technology-enabled transformations of our lifetimes. The analyst Gartner predicts that by 2026, more than 80 per cent of enterprises will have used generative artificial intelligence APIs or models, and/or deployed GenAI-enabled applications in production environments. That’s up from just five per cent in 2023. And, zeroing in on manufacturing, the Manufacturers Alliance Foundation found in 2024 that 93 per cent of manufacturers had already embarked on AI projects.

One example here is a Japanese automotive components maker that is using GenAI to make better use of its operational data. It can share core content such as operating hours, volume of production lines and downtime, as well as fine-tuning processes and maintenance routines. It has seen time spent searching for information decline by 60 per cent as a result and search results can be viewed in language or graphical forms. Other benefits included halved reporting time, 80 percent fewer errors and 25 per cent lower operational costs.

The choice for manufacturers is clear between taking a laggard stance and adopting fast but with care. And the time to make that choice is now.