How is Artificial Intelligence Affecting Meaningful Work?

If you are like most Americans, you spend too much time at work and your work-life balance is poor (What To Become, 2022). To sum this up, most Americans neglect their home and social life in order to prioritize their work life. If individuals are willing to put aside the fun parts of life for work, it shows how important work and the workplace is to Americans. With this in mind, it should be of interest to know how integrating Artificial Intelligence (AI) systems into the workplace is going to affect your daily tasks and routines, wages and salary, and psychological well-being. 

Imagine this... 

Your job requires you to put together a small, complex part of a spaceship. Your employer decides that implementing AI into the production system would help the company be more time and cost efficient. However, since the implementation of AI, your work task has changed. Now, all you do is watch the parts come off of the conveyor belt and ensure they reach the box properly. The AI is putting together the complex part of the spaceship, faster and cheaper than you did. You get less hours now, your wage has decreased, and you do not have any contribution to actually building a spaceship. Do you think AI is affecting your work? 

Bankins and Formosa (2023) would say so. "Meaningful work is the perception that one's work has worth, significance, or a higher purpose" (Bankins & Formosa, 2023).  Based off of the example, it would be safe to say making less money and having a simpler position could affect work worth, significance, and higher purpose. When an individual's work loses meaning, it is possible for psychological well-being to be affected as well. This occurs due to the similarities between meaningful work and workplace psychological well-being. 

Due to the many hours individuals spend in their workplaces, it would make sense that working and being in the workplace would affect psychological well-being. Workplace psychological well-being is "the realization of one's true potential at work" (Dekay, 2020). In fact, Degenais-Desmarais and Savoie (2012) crafted a workplace psychological well-being scale that includes six factors that have major overlap with Bankins and Formosa's (2023) defintion of meaningful work. Let's consider the overlap:

Meaningful Work Subcategories: Task Integrity, Skill Cultivation & Use, Task Significance, Autonomy, and Belongingness (Bankins & Formosa, 2023) 

Workplace Psychological Well-Being Factors: Autonomy, Self-Acceptance, Growth, Relationships, Competence, and Purpose (Degenais-Desmarais & Savoie, 2012)

Workplace psychological well-being and meaningful work have autonomy, belongingness/relationships, and task significance/purpose directly in common. However, if the definitions of the following factors are evaluated, there is one more commonality. Work growth, according to DeKay (2022) is "the perception that a person's work provides an opportunity to grow their skills and talents" (pg. 23). With this definition, work growth is comparable to the definition of skill use and cultivation which is "the ability to use and develop a range of skills at work" (Bankins & Formosa, 2023). This comparison shows that 4 out of 6 of the workplace psychological well-being factors overlap with the subcategories of meaningful work. If this is the case, the ways AI interact with the meaningful work categories could also be applicable to 4 out of the 6 factors of the psychological well-being scale. Being that 4 out of 6 is well over the majority of factors in the scale, it would seem appropriate to say that it is very possible integrating AI into workplaces can have positive and negative effects on psychological well-being as well as meaningful work. As you read the interactions between the AI pathways and the subcategories of meaningful work, keep in mind, the potential interactions between skill cultivation and use, task significance, autonomy, and belongingness may also be applicable to employee's psychological well-being. 

Understanding how the AI pathways influence meaningful work is important as more organizations and employers are in-cooperating this kind of technology. The following comparisons from Bankins & Formosa (2023) will look at how each pathway may affect employees and their views/feelings about their work. 

Task Integrity & Skill Cultivation and Use 

First Path: 

Simple Tasks:  When AI takes over simple tasks and workers take over more interesting work, it is expected that individuals will have similar levels of perceived task integrity or increased levels of perceived task integrity. This allows workers to advance their knowledge and expertise which would further skill cultivation and use.

Complex Tasks:  When AI takes over complex tasks and workers are forced to take over more boring tasks, there would be feelings of reduced task integrity and it would limit development and opportunities for growth which would reduce skill cultivation and use. 

Second Path/'Tending the Machine': 

'Managing the Machine': When workers are required to 'Manage the Machine', task integrity is expected to be enhanced due to integrated and challenging activities. New activities lead to an increased variety of versatile skills which also enhance skill cultivation and use. 

'Minding the Machine': "Minding the Machine' creates less important roles for workers which decreases task integrity. This type of work also tends to be repetitive which leads to decreased skill cultivation and use. 

Third Path/'Amplifying': 

When AI systems 'Amplify' workers tasks, there are positive effects of task integrity and skill cultivation and use. This occurs when AI helps workers complete their tasks more efficiently to reach work goals and new skills are learned to interpret and integrate AI output into decision making. 

Task Significance 

First Path: 

Simple Tasks: When AI takes over simple tasks, there should be little to no impact on task significance when workers are required to do the remaining or new tasks. 

Complex Tasks: When AI takes over complex tasks, impacts on task significance can vary. If workers can work on other complex tasks and contribute significantly towards the work goal, task significance can be maintained or improved. However, if the worker's tasks are less complex and the worker does not feel they are contributing to the work goal as much as before, task significance is expected to decrease. 

Second Path/'Tending the Machine': 

'Managing the Machine': Given the increased job impact, opportunities, and duties this work requires, task significance is expected to improve. 

'Minding the Machine': Given the increased isolation and decreased knowledge of direct impacts this work results in; task significance is expected to be reduced. 

Third Path/'Amplifying': 

Due to this type of work increasing job impact and contact with others, task significance should be positively affected. 

Autonomy

First Path: 

Simple Tasks: Autonomy will be increased if AI takes over simple tasks boring and repetitive jobs that workers do not enjoy. 

Complex Tasks: If AI takes over complex jobs workers do not enjoy, autonomy will improve. However, if AI takes over interesting and creative tasks, autonomy will be reduced. 

Second Path/'Tending the Machine': 

'Managing the Machine': Autonomy has the potential to increase if workers are required to partake in new, more skillful, more engaging work that they have a degree of control over. However, autonomy may decrease if workers are unable to view and use information restricted by the AI system.

'Minding the Machine':  This type of work is expected to reduce autonomy because tasks are repetitive, unchallenging, and boring. This leaves workers feeling like a 'slave to the machine' (Bankins & Formosa, 2023). 

Third Path/'Amplifying':

When workers are given more power and useful information from AI systems to improve their work, autonomy is expected to improve. 

Belongingness

First Path: 

Simple & Complex Tasks:  If AI systems take over work that decreases face-to-face interaction with others, belongingness will decrease. However, if AI systems increase a worker's face-to-face interactions with others, belonginess should increase. This rule should hold true whether the work taken over involves simple or complex tasks. 

Second Path/''Tending the Machine':

'Managing the Machine': If this type of work increases human interaction, feelings of belongingness would increase. 

'Minding the Machine': Since most of this work is simplistic and lacks human interaction, it tends to fall upon less skilled 'blue collar' workers. Due to this, lower skilled workers doing this type of work tend to have low feelings of belongingness. 

Third Path/'Amplifying':  

This type of work in regard to belongingness tends to increase levels of belongingness in some people while decreasing these feelings in others. "Amplifying' work can be beneficial for belongingness if it provides information that makes workers' job easier. However, it can be detrimental if the information provided in the 'amplification' results in workers' jobs becoming unnecessary.