The post-pandemic world is accelerating the rush to embrace digital technologies—but the focus has been a bit lopsided. While investments in cloud, data analytics, Artificial intelligence (AI), Machine learning (ML), and other workplace solutions have benefited the white-collar workforce, the perks of digital transformation and these digital technologies are yet to touch the lives of blue-collar workers. For instance, operators of heavy machinery such as earthmovers and boilers, construction supervisors, fire investigators, and aircraft maintenance engineers have limited or no access to technology. Although there is a massive explosion in IT budget spending for the desk workers, the blue-collar workers often have to rely on inefficient, outdated technology and communication channels. So it is high time to relook at the workplace strategy for them. A growing body of research indicates an urgent need to rebalance the focus of digital investments between the white-collar and blue-collar workforce.
A recent survey by Forbes and Microsoft showed that organizations with digitally empowered frontline workers are three times more likely to deliver more than 20% yearly growth as compared to competitors who lag on this metric. The study provides compelling evidence to support the technological enhancement of the blue-collar workforce: 91% of respondents saw increased performance and productivity from their employees; 88% experienced increased operational efficiency and cost savings; 87% observed higher customer satisfaction; 84% reported better job satisfaction from their workers.
The goal of businesses such as utilities, telecom, construction, transportation, oil & gas, etc., should be to arm the workforce that installs, inspects, repairs, and maintains equipment with performance-boosting digital technologies. Businesses must train the blue-collar workforce to use advanced digital technologies, resulting in improved user experience, productivity, and lowered training and support costs.
The opportunities to integrate AI and ML, curation engines, insight engines, and IoT with mobiles and wearables in the industrial work environment are immense. This will result in providing actionable intelligence to the last tactical mile. This opportunity has not been exploited enough. These technologies can put vital real-time, industrial, technical, domain data, and peer intelligence in the hands of blue-collar workers. The data can be from product databases, equipment manuals, maintenance history, training sessions, service bulletins, and even last-mile data such as handwritten notes by peers, work order emails, and crowdsourced insights.
The potential impact of AI for the blue-collar workforce goes beyond employee performance improvements, addressing asset performance, and bridging the global labor skills gap. We can use advanced ML/AI algorithms to proactively provide recommendations, live information on what’s happening around, and safety alerts. Blue-collar applications can be integrated with any collaboration platforms (ex: teams), any device (ex: tablets, smartwatches, head-worn wearable), sensors and include a combination of media (ex: audio, video, images) and 3D augmented reality, providing a sophisticated human augmentation instead of simple system automation.
The technology involves a bi-directional flow of IoT, enterprise, and situational data, in real-time to blue-collar employees. Virtual personal assistants enabled by intelligent-AI solutions customized for the industrial work environment can gain a full understanding of the worker’s context and deliver curated information to the end user’s mobile/wearable device to determine the next action. For instance, it can connect to the sensors on the user’s mobile device and passively extract information to identify the location of the industrial workers and the device they are using. Then it can determine the equipment near the user by deploying active machine vision and object recognition.
We must take a human-centric approach to the industrial internet by providing real-time intelligence on wearable devices in the field. Imagine a world where you can have a conversation with a machine about its status, use, tools, and requirements. You get to ask, “Hey, when was the last time this engine was inspected?” Imagine technicians having conversations with machines about error codes, maintenance procedures, service bulletins, or past performance. Imagine starting a new job knowing that you have the smartest, most experienced co-worker in the entire company at your service 24x7- in your pocket. Imagine for a moment an aircraft maintenance technician inspecting system symptoms and is unable to determine the corrective action to take. It could jeopardize passenger and asset safety or disrupt flight schedules. Instead, the technician could simply ask the system a question, “Show the correct flap position,” to a Siri-like or Alexa-like system, and get an instant, reliable, and context-driven answer. The system can be made so reliable that it delivers data and insights even in non-wi-fi environments (typically in remote field locations) using cached data.
The challenge for enterprises is to reduce the time spent by blue-collar workers looking for information, reduce errors that result in unplanned downtime, battle the consequences of an aging workforce and the growing volumes of inaccessible tribal knowledge. The solution lies in infusing technology into the factory work floor and to the on-site operations, service, inspection, and maintenance teams. AI-enabled work instructions/guidance is for industries that want to maximize the effectiveness and safety of their blue-collar workforce while upskilling talent at scale. It significantly cuts non-productive time, increases first- time-right-execution, virtually eliminates re-work, accelerates time to proficiency, drastically reduces technical support requests, and addresses challenges around lack of skills and mismatch. Imagine Siri designed specifically for industrial workers, that delivers the knowledge to ensure zero-equipment downtime. Last-mile analytics increases overall equipment efficiency (OEE) and reduces demand on experts who spend up to 30% of their time looking for data. It fills the intelligence gap of the last-mile domain and technical data, which doubles the utilization and value of the existing ERP, big data, and predictive maintenance solutions.
The need of the hour is to digitally empower blue-collar workers, who represent the face of the company. Digital transformation cannot be complete without including them in these workplace initiatives. The focus here is a shift from technology to people, where data is inserted into everyday processes and placed at people’s fingertips. As we enable digital dexterity for the frontline workforce, the digital literacy ratio increases, making industrial jobs more accessible, inclusive, and human-centric.