Artificial intelligence (AI) is becoming increasingly integrated into business practices, but its use remains controversial. While it has many benefits for efficiency and productivity, valid concerns remain about its impact on jobs.
To better understand the effect of AI on workers and organizational processes and outcomes, Esade’s Anna Carmella Ocampo and co-researchers Sarah Bankins, Mauricio Marrone, Simon Restubog, and Sang Eun Woo carried out a systematic review of existing empirical research on AI in the workplace.
Their findings, published in the Journal of Organizational Behavior, uncovered how AI use exerts influence on individual workers, work groups, and organizational systems. For example, they found how AI might influence employee productivity and wellbeing, work group collaboration, and organizational labor demands. The results have allowed the researchers to develop practical recommendations for leaders who want to implement and to harness the efficiencies of AI use in the workplace.
The fourth industrial revolution
The rapid shift in technology being driven by AI has been heralded as the fourth industrial revolution. Processing huge datasets at speed with machine learning technology, natural language processing, image recognition and generative AI such as Chat GPT all have a significant impact on today’s workers.
Concerns about job losses, largely based on science-fiction portrayals of intelligent technologies, are widespread. But however justified some of these concerns may be, the technology is here to stay.
To provide a comprehensive understanding of the consequences of AI in the workplace, Ocampo and co-authors conducted a systematic review of quality peer-reviewed journal articles in business, management and psychology. They reconciled largely related yet oftentimes overly optimistic or pessimistic perspectives about the role of AI in shaping workers’ experiences and future work environments.
The results identified several factors and processes that can enable or hinder the productivity and efficiency benefits of AI and how its use can complement, rather than replace, human skills.
Overall, AI features that enhanced worker performance increased job satisfaction and improved productivity. AI systems that removed the need for workers to undertake repetitive tasks were viewed favorably and more likely to be adopted by individuals. When workers trusted and understood the purpose of AI and their skills developed to use it, better collaboration occurred.
When AI was implemented to support autonomy, complexity, specialization and information processing, workers displayed more innovative behaviors. In service work, if AI reduced mental and physical fatigue, positive emotional states ensue.
AI systems that remove the need for workers to undertake repetitive tasks are viewed favorably
The use of AI can also generate demands that outweigh its benefits, including an increased need for worker skill development. In contrast, resistance to algorithmic decision-making also plays a role: those with higher skill levels displayed more aversion to AI-generated outcomes, while those with low expertise were more likely to benefit.
Perceptions of algorithmic and human capabilities
Understanding the capabilities of algorithmic technologies and the conditions under which workers will accept them is key to acceptance of AI in the workplace. In general, workers are likely to embrace AI when they view AI as a beneficial tool that augments their skills and resolves their problems at work.
The use of AI in recruitment, for example, is now standard in many sectors. Despite the benefits associated with the use of AI in the recruitment and hiring process, job applicants expressed reduced confidence in AI when they perceive technologies as incapable of assessing the nuances in their skills and abilities. This reductive approach to decision-making could result in unjust outcomes. Managers, on the other hand, are more likely to accept unbiased algorithmic hiring decisions as legitimate.
Worker attitudes towards AI
The threat of AI technologies taking jobs fosters a fear-based attitude toward the technology. The resulting levels of job insecurity and cultural negativity are associated with higher levels of turnover, job burnout, and resistance to change.
Workers with a higher level of agency within their roles are more likely to be open to retraining and upskilling
How organizations frame the use of AI can have a significant impact on these attitudes. Workers who believe they have a higher level of agency within their roles are more likely to be open to retraining and upskilling. They are also more likely to hold favorable opinions if they perceive AI to be useful and easy to use and are supported to learn the necessary skills.
AI as a control mechanism in gig work
The use of AI technologies is playing an increasingly significant role in managing platform-based gig work such as food delivery riders and ride sharing drivers.
Worker perception is generally negative. A lack of autonomy, poor career prospects, and long hours are all common issues that compromise their job quality. However, some remote gig workers do cite flexibility and variety as benefits of platform-based work. Platforms may foster these positive perceptions by reinforcing the idea that gig work is more akin to being your own boss and choosing your own hours, rather than being under the control of an anonymous platform algorithm.
For the organizations behind the platforms, AI plays a critical role in the infrastructure of the business model. The vast amounts of data that have to be collected to facilitate this type of work can drive the negative perceptions of the worker and increase the feelings of surveillance and lack of control.
Labor market implications
AI taking over jobs is perhaps the most pressing concern for many workers. While this is in part due to fearmongering prompted by science fiction, efforts should be directed to identifying the type of tasks that will be replaced by AI systems and preparing workers skill level to adapt to current and future labor market demands.
This impact takes various forms and is dependent on the type of work and the technology being implemented. AI that reduces labor demand can result in slower wage growth and higher unemployment. Evidence suggests, however, that AI are more likely to negatively impact low-skilled workers and women. Technology that augments and upskills the work of employees can lead to higher wage growth and richer employment experiences.
AI deployment leads to higher demand for skilled workers and lower numbers of low-skilled jobs
Overall, organizations that deploy AI technologies display a slight increase in the demand for workers. However, this association varies across skill levels, leading to higher demand for skilled workers and lower numbers of low- and mid-skilled jobs.
Recommendations for AI deployment
AI offers many opportunities, but its successful implementation relies on several factors. Ocampo and co-authors have several recommendations that will help managers to find a successful balance between creating efficiencies and safeguarding employment:
- Use positively framed messages when discussing AI to highlight its benefits
- Introduce resources such as on-site technology assistants and instructional materials
- Offer formal training before and during implementation of AI systems
- Identify the tasks and employee groups most likely to benefit from human/AI collaboration
- Bundle work practices and support mechanisms – don’t treat AI as a standalone solution
- Use positive leadership models to encourage AI adoption
- Encourage interactions between employees and AI to promote productivity and enhance task enjoyment
- Ensure employee status and value is higher than that of AI solutions
- Implement AI to complement and expand roles, not to replace them
- Provide training to employees whose jobs maybe displaced as a result of AI implementation
By creating supportive work environments and empowering employees, the researchers conclude, organizations can unlock the full potential of AI systems while minimizing negative consequences.
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