In 2024, we see AI in manufacturing continuing to evolve rapidly. Several trends are combining to reshape how manufacturers leverage artificial intelligence technologies in order to harness the value of their data through automation and innovation.
Let’s delve into some of the most prominent AI in manufacturing trends that are driving growth in the sector this year.
Uncovering Insights from Unstructured Data
One of the key areas where AI is making a profound impact is in mining unstructured data. According to Gartner, a staggering 80-90% of enterprise data is unstructured, comprising documents, images, and records. Previously inaccessible insights buried within these data sources are now being unlocked, allowing for more comprehensive analysis and decision-making. Advances in natural language processing (NLP) techniques enable the translation, synthesis, and citation of text embedded within scanned documents and images based on natural language prompts, revolutionizing the way businesses extract value from their data.
Enhancing Computer Vision Applications
Another significant trend is the refinement of computer vision applications. Traditional challenges—for example, limited human attention span to reliably monitor camera feeds all the time or poor image quality—are being addressed through innovative solutions. By leveraging graphics cards and generative techniques, AI algorithms can now enhance image quality and extract meaningful insights from visual data with greater accuracy. This opens up new possibilities for applications such as video surveillance, HSE tracking and alerting, quality control, and asset monitoring, enabling operators to maximize the utility of their visual data.
Empowering Edge Analytics
Edge computing and data transmission are becoming increasingly accessible and affordable, leading to a proliferation of connected devices and distributed assets. This trend is empowering edge analytics, enabling real-time insights and proactive decision-making at the edge of the network. By leveraging edge computing capabilities, manufacturers can detect warning signs of impending failure in equipment and infrastructure, facilitating timely interventions and reducing downtime.
Weighing Opportunities vs. Risks of AI in Manufacturing
The integration of AI offers numerous opportunities for manufacturers, particularly in the realm of improving asset availability and enhancing safety. But, as with any opportunity evaluation, manufacturers will want to understand any inherent risks introduced by a technology implementation, including the stochastic nature of AI models, which can lead to false positives and misjudged conclusions. The best AI systems will mitigate these risks by prioritizing transparency so that decision-makers can more easily weigh the balance of fully automated systems vs. human-in-the-loop workflows that enable practical responses to true edge cases and provide users with actionable insights backed by evidence.
Preparing the Workforce for AI Integration
The accelerated adoption of AI in manufacturing often leads to fear of workforce reductions. Automation may impact roles and responsibilities, but this has always been the case where technology creates efficiencies. There’s another point of view that argues that AI can empower workers to do their jobs better and faster and allow them to concentrate on critical thinking tasks rather than tedious, mind-numbing tasks. Hyper-personalized generative AI solutions that facilitate the distribution of information will unlock value for a wide range of users, accelerating learning curves for new hires by capturing institutional knowledge that’s always available on demand in an accessible and conversational way. To successfully integrate AI into their processes, manufacturers that prioritize workforce readiness and provide employees with the necessary training and support will be instilling a durable competitive advantage. By offering easy access to intuitive AI applications and demonstrating the tangible benefits of AI adoption, manufacturers can empower their workforce to embrace AI technologies and drive positive outcomes.
Implementing AI in Design and Production Processes
For manufacturers looking to implement AI in their design and production processes, the key concept to internalize is the long-term benefits of getting started. AI in manufacturing is the future, but the future is here and now. There’s a consequence to waiting because AI provides more value over time, like compounding interest due to its inherent learning ability. It’s high time to embrace AI as a strategic imperative. Whether through proofs of concept, targeted solutions, or platform technologies, there are myriad paths to realizing the benefits of AI in manufacturing. By adopting AI, manufacturers can enhance production rates, reduce supply chain disruptions, and improve overall cost efficiency.
Future Outlook: AI’s Continued Evolution
Looking ahead, AI is positioned to continue its evolution over the next five years, with multimodal AI assistants and computer vision algorithms playing increasingly prominent roles across industries. From enhancing operational efficiency to improving safety and security, AI technologies will reshape the manufacturing landscape, unlocking new opportunities and driving innovation. For these reasons and others, we concur with analysts that project the global AI in manufacturing market—valued at $3.2 billion in 2023—is poised to grow to nearly $21 billion by 2028, at a forecasted 45.68% compound annual growth rate.
The AI in manufacturing trends shaping the industry in 2024 reflect a paradigm shift in how industries leverage data, automation, and intelligence to drive value and innovation. By embracing these trends and harnessing the power of AI, manufacturers can stay ahead of the curve and position themselves for success in an increasingly competitive global market—enjoying higher production rates, with fewer supply chain disruptions, lower overall costs, and lower defect rates.
Want to learn more? Check out our webinars on demand, in particular:
AI in Manufacturing: From Data Disorder to Operational Insights
