Thanks to AI, the augmented worker can boost companies’ productivity and reduce the monotony of technicians’ job to let them focus on their key expertise.
Operational activities entail several technical audits where technicians carry out their tasks and make judgments based on norms. The job is hard and repetitive and the stakes are high. In addition, it is particularly difficult to train new employees. Indeed, the diagnosis of a problem or defect requires a lot of experience, which is difficult to transmit quickly and without multiple examples on hand. As a result, there is a high turnover in these businesses.
The repetitive nature of the job is a major issue. Indeed, technical experts must identify every possible situation, both problems and their absence. This constant monitoring and reporting is more a technical task than a real skill. It weakens the technician’s expertise on a daily basis.
So how can we increase the productivity of operational staff and enhance the value of their work?
Artificial intelligence and the augmented worker
AI, and especially computer vision, can address this challenge. The new technologies can improve the work of technicians by creating virtual assistants to audit infrastructures, installations, spare parts or any other operational tasks.
AI in use
How does it work?
An AI trained to recognize an anomaly or malfunction with respect to a given standard can automatically handle many situations. Knowing how to avoid “normal situations”, AI will save time for operators who will no longer need to worry about them. In addition, the AI will also predict a problem automatically, with a given level of confidence.
Only problematic cases, i.e. when the AI’s confidence level is too low, will be transmitted to the operators. Therefore they will intervene only when their expertise is most needed. The integration of AI into technical audits will increase companies’ productivity. In fact, companies could process more cases faster while increasing the accuracy and overall quality of audits.
Examples of augmented worker
Optimizing water and sanitation networks and prevent leaks is a key concern for water utilities to limit losses and save energy. Thanks to video recognition, a robot-camera can inspect the pipes and automatically detect an anomaly. The technology will alert the technicians only in the most uncertain cases.
The insufficient training of operators is the main cause of errors in the installation of optical fiber. These errors imply that several interventions are often needed. Also, technicians’ diagnoses sometimes contradict, thus creating frustrations for users and a loss of productivity for companies. It is possible, though, to create a mobile application where the technician only has to take a picture of his work. The AI can automatically warn him if the installation is up to standard or not.
Finally, video surveillance cameras in public spaces are currently viewed by agents who are often alone behind multiple screens. Their responsibility is considerable. However, they cannot monitor everything, and the risk of missing an act of vandalism, an attack or intrusion is significant. Here again, video recognition can automatically detect a problem and report risky situations. The AI alerts the expert, who can quickly act accordingly. You can read more on intelligent video surveillance here.
Conclusion
Integrating video recognition into technical audits brings many advantages. It allows technicians to get rid of repetitive tasks and focus on high value-added operations. Workers no longer have to continuously check whether everything is normal as they can use their expertise when the AI warns them.
Thanks to this collaboration between humans and AI, we can imagine a future win-win situation for companies and their technicians and clients. Companies will gain in productivity because their workers could handle more cases with greater precision, satisfying users at the same time. Technicians will feel valued in their audit work, which will become a real profession of expertise, free of repetitive and monotonous tasks.
