The augmented worker: what AI will change for technical auditors
Operational activities include an important dimension of technical audits in which technicians are required to carry out professional expertise and make judgments based on a norm. The stakes are high and the job is hard, particularly because of the difficulty in training new employees and the intrinsic repetitiveness of the job. It requires a lot of experience to diagnose a problem or a defect, and this experience is difficult to transmit without multiple examples on hand and lots of time to spare. As a result, there is a high turnover in these businesses. The repetitive nature of the job is also an issue. Indeed, technical experts must identify every possible situation, both problems and the absence of problems. This constant monitoring and reporting, regardless of the situation, 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?
This challenge can be met by AI, and especially by video recognition. Indeed, these new technologies can improve the work of technicians by creating virtual assistants to audit infrastructures, installations, spare parts or any other operational tasks.
How does it work? An AI trained to recognize an anomaly or malfunction in relation to a given standard can then automatically handle many situations. By knowing how to avoid so-called normal situations from the outset, AI will save a lot of time for operators who will no longer need to worry about them. In addition, the AI will also be able to predict a problem automatically, with a certain level of confidence. Only problematic cases where the IA’s confidence level is too low will be transmitted to the operator. They will therefore only be called upon when their expertise is most needed. The integration of AI into technical audits will increase companies’ productivity, allowing them to process more cases faster, while increasing the accuracy and therefore the overall quality of audits.
Let us take a few concrete examples.
Optimizing the management of water and sanitation networks to prevent leaks is a key concern for water utilities, in order to reduce losses and save energy. Thanks to video recognition, a robot-camera can inspect the pipes and automatically detect an anomaly. Thus, a leak in a pipe can be automatically audited and only the most uncertain cases will be transferred to the expertise of the technicians.
Concerning the installation of optical fibre, many errors are made due to insufficient operator training. These errors mean that several interventions are often needed, technicians sometimes contradict each other in their diagnoses, thus creating frustrations for users and a loss of productivity for companies. But it is possible to create a mobile application where the technician only has to take a picture of his work, and an AI automatically warns 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 great, but it is impossible for them to see everything, and the risk of missing an act of vandalism, an attack or an intrusion is significant. Here again, video recognition can automatically detect a problem and report risky situations. Thanks to AI, the expert is alerted and can quickly act accordingly (see our article on Intelligent Video Surveillance).
Integrating video recognition into technical audits therefore brings many advantages. This allows technicians to get rid of repetitive tasks and focus on high value-added operations. They no longer have to notice, repeatedly, that everything is normal (as is the case most of the time) but can use their expertise when the AI warns them of a tricky case. They therefore intervene less frequently, but their added value greatly increases, because it is their discernment and experience that are called upon. Thanks to this collaboration between human and AI, we can imagine a future win-win situation for companies, their technicians, and users. Companies will gain in productivity because their operators will be able to 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, finally free of repetitive and monotonous tasks.