Redefining AI’s Role in Industrial Work

This blog was provided by our partners at Contextere.

In the rush to embrace artificial intelligence, we have often overlooked a crucial element: the human factor. While AI has revolutionized many sectors, its implementation has frequently focused on replacing workers rather than enhancing their capabilities. This approach not only undermines the potential of AI but also neglects the valuable skills and experience of our workforce, particularly in industrial settings.

Industrial technical workers make critical minute-to-minute decisions that impact productivity, safety, and costs. Despite their importance, they often contend with outdated processes and digital tools. As we face a growing skills gap and an aging workforce transitioning to fewer, less experienced workers, it is imperative that we leverage AI to support and empower these essential employees. By upskilling workers and providing them with AI-enhanced tools, we can create a workforce that is more productive, autonomous, and adaptable to changing industry needs. Providing industrial workers with AI-powered tools that offer real-time, contextualized information, we can enhance their decision-making capabilities and productivity.

Looking beyond supporting the historically under-supported industrial blue collar workforce, AI must also pivot towards more sustainable and ecological practices.  Implementing small, energy-efficient language models can help reduce the environmental impact of AI systems. These models consume less computational power and energy, making them more environmentally friendly and cost-effective in the long run.

Retrieval-Augmented Generation (RAG) methodologies play a crucial role in achieving both sustainability and accuracy. RAG allows AI systems to access external knowledge sources, reducing the need for constant model updates and retraining and thereby minimizing the computational resources required for maintaining these models. Furthermore, RAG techniques significantly enhance the reliability of AI systems by providing clear, referenceable sources of truth, ensuring that the information used by front-line technicians is accurate and verifiable. This approach significantly reduces the risk of AI hallucinations, which are common in large language models, by grounding responses in factual, retrievable data.

We believe in a future driven by more job losses due to automation but more job opportunities through technology. To realize this vision of human-centric, accessible, and sustainable AI, we must invest in AI tools designed specifically for industrial workers while prioritizing jobsite-appropriate user interfaces and real-time contextual information delivery. By embracing this approach, we can create a future where AI does not replace workers but empowers them, leading to increased productivity, improved safety, and a more engaged workforce. It is time to shift our focus from viewing AI as a replacement to seeing it as an enhancement, ensuring that the benefits of this transformative technology are accessible to all workers across all industries.