AI holds the promise of transforming the manufacturing industry. However, the current approach to its implementation is fundamentally flawed. The market size for AI in manufacturing is projected to skyrocket from $3.2 billion in 2023 to $64.63 billion by 2030. Yet, only one in five AI projects deployed by manufacturers today is deemed successful.
The Legacy System Hurdle
The root of the problem lies in the vast amount of legacy technology and equipment that cannot be easily retrofitted to work with the latest AI innovations. Legacy systems that are incompatible with AI include enterprise resource planning, material requirements planning, and manufacturing execution systems. Additionally, older closed-circuit camera systems often lack the capability to transmit collected data to a centralized depository, potentially necessitating costly server upgrades.
The AI Shortcomings
AI technology itself also has its shortcomings. AI models still struggle to sift through massive amounts of information and provide accurate answers on demand. Furthermore, some of the data needed to train these models is trapped in legacy applications and cannot be formatted for use in modern systems.
The Human Element
Despite the challenges, AI is expected to play an increasingly vital role in various aspects of business operations, particularly where high volumes of data are involved. However, humans are still needed to make ultimate decisions about safety, quality, productivity, and auditing. AI applications that rely on past data may not be prepared to handle unprecedented events, a task at which human creativity excels.
The Way Forward
To successfully implement AI in manufacturing, a phased approach is recommended, with humans remaining involved throughout the process. It’s also crucial to be specific about the problem AI is intended to solve. By focusing on a small, specific issue, high-quality solutions can be developed. This is where AI integration comes into play, moving beyond technology to business transformation. And as we move forward, the path to dominance in manufacturing will be harnessed by AI.
