After the United States, the phenomenon of the Great Resignation crossed the Atlantic to reach Europe.
This situation is undoubtedly the consequence of the mental exhaustion felt in most sectors following the Covid-19 pandemic. Decision makers are thus faced with a mental health crisis of unprecedented magnitude. To take care of their employees and retain talent, it is therefore their responsibility to take concrete and sustainable measures.
Accurate identification of stressors
In March 2022, 41% of French employees said they were in psychological distressi.e. three points more than in October 2021. To stem this vertiginous growth in psychosocial risks, many employers are now turning to solutions mobilizing theartificial intelligence and deep learning.
Technologies recognized for their ability to fight against stress, but which decision-makers must mobilize to try to make a difference in the long term. Employees who experience high levels of stress over long periods of time inevitably face burnout or serious illness unless they quit. It is therefore essential to attack the problem at the root before stress-related disorders appear. As always, prevention is better than cure. This is where AI solutions can play a key role.
A synergy between AI and human skills
While many use cases for AI have yet to be explored, this technology is already transforming the practices of many organizations and helping to significantly improve the mental health of workers. Visualization and data analysis can detect potential malfunctions that can be critical for the business and AI excels in these skills. Capable of processing gigantic amounts of data in real time, it can detect recurring patterns to determine the responses to them.
In the field of health, for example, dermatological images or X-rays are often the only way to detect and confirm a medical problem. Interpreting such images can be difficult and, in a context of specialist shortages and rapidly increasing demand, consistently providing an accurate diagnosis and appropriate response is a real challenge for medical teams. A situation that not only causes stress for medical staff but also compromises the quality of their work and has a direct influence on the health of their patients.
By processing hundreds of thousands of images much faster than a human could, artificial intelligence tools can meet this challenge. Thanks to deep learningthey have the ability to learn to diagnose pathology from millions of images, more than a doctor would see in a lifetime.
The right AI software can therefore produce preliminary diagnoses in a very short time to detect high-risk cases. Doctors can then analyze these assessments, confirm or refute the final diagnosis, and prescribe appropriate treatment. Medical establishments can thus treat more people in the same period of time while increasing the efficiency and quality of their services and protecting the mental health of their employees.
Automated monitoring to control employee workload
Employees in many professions are confronted with fatigue and stress on a daily basis, and in particular those who occupy critical functions in terms of safety or maintenance. Engineers or maintenance operators may in particular be responsible for maintaining the 24/7 availability of large complex infrastructures such as factories, electrical networks, transport infrastructures or large digital architectures. These responsibilities place enormous pressure on employees due to the considerable consequences of a possible breakdown.
As malfunctions may occur at any time, infrastructure managers and DevOps must be constantly active or adopt an on-call mechanism to deal with urgent and complex situations often with limited support. In some circumstances, urgent calls and alerts received turn out to be false alerts, causing unnecessary stress and exhaustion.
Deploying reliable monitoring of IT systems is essential. To do this, the use of telemetry tools applied to software, coupled with an intelligent observability platform, is the best solution to support workers occupying these critical functions. This platform can act as an automated guardian constantly monitoring all systems without the risk of fatigue, lack of concentration or stress. While observability and artificial intelligence do not replace the expertise and knowledge of engineers, it provides them with the certainty that incidents will not be overlooked due to human error.
When an anomaly is detected by the observability platform, the AI performs an initial assessment of the situation as well as a root cause analysis (or RCA) before recommending the appropriate action. This allows engineers to work faster in crisis situations, knowing that their conduct is supported by AI-based technology. Observability and AI therefore contribute to alleviating their stress by acting as a second pair of eyes.
Integrating AI as a sustainable solution
Using the immense amounts of accessible telemetry data, systems AIOps are able to automatically detect and report anomalies that would previously have gone unnoticed. A decisive way to allow employees to focus on the incidents that need real attention without worrying about missing a possible critical incident.
Whatever the sector of activity, the adoption of observability and AI thus enables companies to more effectively value the experience and skills of their employees by reducing manual processes subject to human error, and focusing their attention on more value-added strategic tasks. In the long term, it is therefore a decisive lever for reducing employee fatigue and stress while optimizing their commitment.
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AI and observability, new guarantors of employees’ mental health?
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