Section 11.3: Demographics and AI
Ivan Au; Carlos Liu; and Maria Obaid
Today’s workplaces are changing rapidly. Research shows that AI is changing the working world with an anticipated 75 million routine jobs being replaced by AI while creating 133 million new jobs that require emotional intelligence and technical skills (Firstcall HR, 2019).
In addition to eliminating and creating jobs, AI is transforming learning and development through the following (Zhidkov, 2020):
| Personalizing learning pathways. | Providing accessibility. |
| Integrating training into the routine workflow. | Measuring learning and training effectiveness. |
| Reinforcing training and development. | Focusing on AI-based digital tutors. |
| Improving completion rates. |
Therefore, the following discussion will focus on workplace demographics and the effectiveness of AI in training, as well as address any advantages and disadvantages of using AI in training with diversified demographics.
Demographics
Individuals have their own preferred learning styles. AI and predictive analytics can help develop intuitive learning content and curate training plans that are tailored to the needs of each individual (Gautam, 2019). This is especially relevant right now, as there are multiple generations in the workplace with various levels of comfort with using technology.
The way different generations have been raised impacts their ability to absorb and adapt to new information. Older generations may find it more challenging to engage in workplaces in which training is delivered using technology. Andriotis (2017) suggests having generations who are more adaptable to new technologies become ambassadors. To provide training that is effective for all employees, the human aspect of training must be incorporated with the technological aspect. For example, millennials and members of Gen Z can become ambassadors for new technologies and teach their colleagues on how to use them to their advantage. This would allow less technologically savvy employees become more comfortable with using online learning modalities.
To increase employee engagement, employees should be involved in the training and provide feedback. For example, asking employees to answer questions about their preferred styles of learning through surveys or face-to-face informal interactions can help employers identify the most effective form of training using technology. This can also be used to identify the most and least technologically savvy members of the group and inform the training strategy.
AI Training and Effectiveness
Livingston & Risse (2019) suggest that through machine learning, algorithms can be trained to identify relationships, develop predictive models, and make decisions. The effectiveness of algorithms is dependent on the training data used since it is also used in “deep learning” and “reinforcement learning.” Deep learning uses training data to predict patterns when new data are provided. With reinforcement learning, the focus is “…on improving the performance of an algorithm over multiple engagements with a problem, adjusting actions based on continuous feedback from past actions” (Livingston & Risse, 2019). The use of deep and reinforcement learning helps eliminate the human biases associated with training data which allows the creation of more standardized training programs. Both concepts can further support employers in revising their training and development plans to better suit the needs of their employees and maximize knowledge retention.
In relation to workplace demographics, deep and reinforcement learning can be used to minimize biases associated with different age groups, races, and genders. To be more specific, AI will help predict employee behaviors and patterns based on performance and key performance indicators as opposed to individual characteristics. Attention must be paid to ensure algorithms do not create systemic bias.
Challenges with Technology
One of the key challenges is to make sure employees are engaged and effectively retain the knowledge from the training program. Different generations in the workplace may find it more difficult to engage in training programs that employ technology. According to Maity (2019), a major challenge with the use of AI systems in training and development is interpreting training feedback and linking it to performance, training transfer, and trainee characteristics. When it comes to measuring the effectiveness of the systems, it may be challenging to interpret results and standardize processes with such a diverse workforce.
Advantages and Disadvantages of Using AI in Training
| Advantages | Disadvantages |
| · Integration of deep learning and machine learning minimizes the risk of human biases, i.e., confirmation bias. | · Employers still risk some human bias in programming the algorithm for AI.
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| · Standardized processes result in the same learning outcomes. | · AI systems have difficulties with interpreting feedback and linking it to individual characteristics and performance management. |
| · Tailoring training programs to individuals can help maximize knowledge retention through AI analytics and technology.
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· Ensuring each individual achieves the desired learning outcomes of the training program.
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| · Ensures that training is completed in full and maximizes the budget for learning and development programs. | · Employees may be less engaged with training using technology versus human interaction. |
| · Improve the organization’s bottom line through more efficient processes and devoting less time to routine tasks. |
