The artificial intelligence (AI) revolution has certainly begun, and organizations are working to identify and extract the benefits that AI may bring. More than 80 percent of organizations see AI as a strategic opportunity and 85 percent see AI as a means to achieve a future competitive advantage.1 However, taking advantage of the benefits the AI revolution brings will require organizations to adapt. One method may be to provide the necessary culture and resources that encourage individuals to become self-directed lifelong learners.
There is much confusion surrounding AI because it encompasses a broad set of applications and technologies.2 Conceptually, AI includes the discipline, technologies and capabilities that make machines capable of simulating intelligence.3 Therefore, AI has the potential to replace humans in performing many repetitive or cumbersome tasks.
Current estimates suggest 15 percent of all working hours completed globally in 2016 could be automated by 2030 (figure 1).4 The same study showed that countries that are already benefiting from the knowledge economy will be affected to a greater extent.
Source: Adapted from Manyika, J.; S. Lund; M. Chui et al.; Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, McKinsey Global Institute, December 2017, USA, http://www.mckinsey.com/~/media/BAB489A30B724BECB5DEDC41E9BB9FAC.ashx
With a current labor force of 3.43 billion workers,5 this could mean that 400 million jobs worldwide will no longer exist in 2030.
This may sound alarming to workers who fear they are at risk of losing their jobs. But what is more likely is that AI will change what people do at work.6 AI stands to become a partner, or coworker, that augments the workforce instead of replacing employees. One example is the current use of AI with automobiles (figure 2). At one end of the spectrum, there is the human, who drives the automobile with no help from the computer. On the other end is the computer that uses AI to facilitate autonomous driving. Any point in the middle of this spectrum is the augmentation of the computer to assist the human. AI augmentation is already evident in many practices, such as the use of Global Positioning System (GPS) navigation and antilocking brakes. Although not everyone may be ready for autonomous automobiles, many drivers already enjoy the benefits of AI augmentation.
The bottom line is that the augmentation of AI is already being incorporated in daily life; therefore, organizations need to find ways to train employees so they can take advantage of the increasing use of AI in business.
Lifelong Learners and Development Responsibilities
The future of work resides in the augmentation of current processes with AI to empower employees to be more productive. Deciding who is responsible for AI skill and knowledge development is the first step for workers and their organizations.
The new economy has created development and growth opportunities that workers can take advantage of anytime and anywhere.7 The rate of change of technology and the availability of information via the Internet have led to a continuous demand for more education. Employees are now responsible for their own learning processes,8 and they are free to decide what, where and how they want to learn. The era of the lifelong learner has arrived.
Lifelong learning is the process of gaining new knowledge and skills in various contexts throughout a person’s lifetime via daily work.9 Gaining knowledge and skills can be achieved through interactions with others and the use of different technologies and applications.10 Lifelong learners must be self-directed,11 and to fully reap the benefits of the AI revolution, employers must provide the necessary culture and resources that encourage individuals to learn.12
Proactive Employee Development
A shared proactive employee development model13 may be used to help employees develop AI knowledge and skills to drive the implementation of AI (figure 3).
Anticipate the Required AI Skills
The basis of this model is that employees and employers work together to anticipate the skills needed to implement AI into the organization. Innovation based on intelligent automation requires a “job-by-job, task-by-task transformation.”14 Therefore, employees must learn AI from the concept level. This begins with looking at the three crucial roles employees need to perform when implementing AI: training, explaining and sustaining.15
- Training—Machines must be programmed to accomplish the required tasks or make decisions. This can be done by explicitly defining each step or by training the machine to learn from data and make inferences, predictions or associations that guide decisions.16
- Explaining—AI machines are often referred to as black boxes due to a lack of understanding about why the machine completed a task or made a decision. This results in lack of trust,17 and, therefore, legislation is needed to protect consumers.18 Employees must learn to analyze machine outcomes and be able to explain what is happening inside the black box.
- Sustaining—Humans are needed to ensure that AI machines are functioning safely, properly and responsibly.
Implementing AI requires employees who have the skills to train, explain and sustain the machines that help automate processes. These skills are significantly different from much of what employees are asked to do today. Employers and employees must work together to anticipate the skills needed to implement AI in the workplace. This may include developing better system understanding to see where AI can be implemented, learning how to comply with upcoming AI compliance regulations, gaining a better understanding of AI technologies and developing strategies to implement AI.
Identify Options for Learning AI
The second stage of the shared model addresses learning in a digital environment. Many of the search queries made on smartphones, tablets and computers return unpredictable results because queries are often influenced by the search engine itself or the keywords being used in the search.19 Currently, the easiest way for lifelong learners to acquire new knowledge is to use the Internet, but this can also put the learner at a disadvantage. Browsing through an infinite amount of digital sources without knowing exactly what to look for can lead to wasted time. In addition, some sources are untrustworthy, unrelated to the topic searched or unorganized. To combat this, organizations can assist lifelong learners by implementing AI learning networks and assisting learners with recommender systems.
Implementing AI requires employees who have the skills to train, explain and sustain the machines that help automate processes.
Learning networks are environments with learning content that evolves from the bottom up.20 Similar to social media platforms, learning networks allow learners from different locations to connect. Every participant of a learning network is given the ability to upload, create, edit, delete or rate learning content. This can help organizations develop a deeper understanding of their internal processes and how a system behaves as a whole. Success and struggles are discussed at the employee level instead of only in the boardroom, which can lead to broader discussions about how AI can be a tool used across multiple domains in the organization. Employees can use the learning network to identify options for learning and gain knowledge from other members of the organization.
A second approach to identifying learning options focuses on the fact that employees may be overwhelmed by the large selection of learning opportunities and struggle to decide which learning content is best for them. Learning content should be personalized.21 Similar to a system that recommends music or social media content, learner recommender systems may be the best option to provide employees with learning content.
In general, the primary purpose of recommender systems for learning is to allow AI technology to preselect information that may be of interest to the user.22 The recommender system searches for potential learning content and provides the most suitable options to the individual learner. Learners can decide which of the suggested pieces of learning content best suit their needs and level of knowledge.
Organizations could develop a learning recommender system as their first AI project. Combining the recommender system with a learning network could result in a new learning experience for AI skill and knowledge development and provide an unprecedented learning space for the user.
Create Opportunities for Growth
The third stage in a shared AI proactive employee development model is to create opportunities for employees. Similar to how learning is the responsibility of both the employee and the employer, so is the creation of opportunities.
Organizations should establish strategies to implement AI and determine which positions should become more automated. Identifying these areas early allows supervisors to provide additional training for the relevant employees to better understand system design and AI technologies. The employees who best understand these attributes can help train, explain and sustain the organization’s first AI models, thereby developing new job positions that focus on AI implementation. Organizations can further develop the full range of positions needed to maintain AI models across the organization and provide career paths for those willing to learn the new skills.
Employees, on the other hand, are closest to the daily work. They often understand which processes could benefit the most from AI implementations. Therefore, it would become the responsibility of the employee to create opportunities for growth by recommending areas for AI to be implemented. Researchers have suggested addressing these better understood and easier-to-manage projects because they have a much higher likelihood of success than adopting highly ambitious projects.23
Ultimately, organizations and employees can work together to determine the level of AI augmentation that should take place. Organizations should create space for AI growth, even if it is at the lowest level of AI augmentation. Allowing employees to implement AI in controlled environments may be more important than the act of implementing a complex organizationwide AI project. This may sound counter-intuitive, but the act of developing AI skills that can be used on future projects may be the most beneficial in the early stages of adopting AI when it is still not fully understood.
Ultimately, organizations and employees can work together to determine the level of AI augmentation that should take place.
Recognize the Benefits
The final phase of the shared AI proactive employee development model focuses on the importance of employees and employers determining whether knowledge has been gained and skills have been developed.
At the individual level, lifelong learners require positive feedback about whether meeting their desire to create new skills and knowledge was worth the investment of time and energy. Reflecting on this learning and analyzing their personal development increases employee resilience and develops stronger lifelong learners.24
Similar skill development and knowledge gathering occurs at the organization level. However, organizations are also encouraged to track the financial and other tangible benefits associated with AI implementation. Internal and external stakeholders are interested in determining whether these approaches to AI are working for the organization. There is still some hype surrounding the implementation of AI, and organizations must determine whether their efforts are being rewarded.
Conclusion
Stephen Hawking has been quoted as saying that AI will be “either the best, or the worst thing, ever to happen to humanity.”25 Individuals and organizations are dually responsible for developing the AI skills and knowledge necessary to take advantage of opportunities. Adopting a shared proactive employee development model will allow organizations to systematically anticipate the skills needed to implement AI, identify options for learning more about AI, determine how to implement it, create opportunities for AI and personal growth, and recognize the benefits of their efforts.
Endnotes
1 Ransbotham, S.; D. Kiron et al.; “Reshaping Business With Artificial Intelligence: Closing the Gap Between Ambition And Action,” MIT Sloan Management Review, 6 September 2017, http://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
2 Dwivedi, Y.; L. Hughes; et al.; “Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy,” International Journal of Information Management, vol. 57, 2021, http://doi.org/10.1016/j.ijinfomgt.2019.08.002
3 Enholm, I. M.; E. Papagiannidis et al.; “Artificial Intelligence and Business Value: A Literature Review,” Information Systems Frontiers, vol. 24, 2022, http://doi.org/10.1007/s10796-021-10186-w
4 Manyika, J.; S. Lund et al.; Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, McKinsey Global Institute, December 2017, USA, http://www.mckinsey.com/~/media/BAB489A30B724BECB5DEDC41E9BB9FAC.ashx
5 The World Bank, “Labor Force, Total,” http://data.worldbank.org/indicator/SL.TLF.TOTL.IN/
6 Barro, S.; T. Davenport; “People and Machines: Partners in Innovation,” MIT Sloan Management Review, 11 June 2019, http://sloanreview.mit.edu/article/people-and-machines-partners-in-innovation/
7 Dachner, A.; J. Ellingson et al.; “The Future of Employee Development,” Human Resource Management Review, vol. 31, iss. 2, June 2021, http://doi.org/10.1016/j.hrmr.2019.100732
8 Drachsler, H.; H. G. K. Hummel; R. Koper; “Personal Recommender Systems for Learners in Lifelong Learning Networks: Requirements, Techniques and Model,” International Journal of Learning Technology, vol. 3, iss. 4, July 2008, http://doi.org/10.1504/IJLT.2008.019376
9 Sousa, M.; A. Rocha; “Digital Learning: Developing Skills for Digital Transformation of Organizations,” Future Generation Computer Systems, vol. 91, February 2019, http://doi.org/10.1016/j.future.2018.08.048
10 Sharples, M.; “The Design of Personal Mobile Technologies for Lifelong Learning,” Computers and Education, vol. 34, iss. 3-4, April 2000, http://doi.org/10.1016/S0360-1315(99)00044-5
11 Brockett, R.; R. Hiemstra; Self-Direction in Adult Learning: Perspectives on Theory, Research and Practice, Routledge, London, UK, 1991
12 Op cit Dachner et al.
13 Ibid.
14 Op cit Barro et al.
15 Wilson, H.; P. Daugherty; “Collaborative Intelligence: Humans and AI Are Joining Forces,” Harvard Business Review, vol. 96, iss. 4, July-August 2018, http://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces
16 Op cit Enholm et al.
17 Aich, S.; G. Burch; “Looking Inside the Magical Black Box: A Systems Theory Guide to Managing AI,” ISACA Journal, vol. 1, 2023, http://h04.v6pu.com/archives
18 Gassauer, J.; G. Burch; “The Potential Impact of the European Commission’s Proposed AI Act on SMEs,” ISACA Journal, vol. 2, 2023, http://h04.v6pu.com/archives
19 Op cit Sousa et al.
20 Op cit Drachsler et al.
21 Op cit Sharples
22 Op cit Drachsler et al.
23 Davenport, R; R. Ronanki; “Artificial Intelligence for the Real World,” Harvard Business Review, vol. 96, iss. 1, 2018, http://hbr.org/webinar/2018/02/artificial-intelligence-for-the-real-world
24 Op cit Dachner et al.
25 The Guardian, “Stephen Hawking: AI Will Be ‘Either Best or Worst Thing’ for Humanity,” 2016, http://www.theguardian.com/science/2016/oct/19/stephen-hawking-ai-best-or-worst-thing-for-humanity-cambridge
KATHARINA SCHLEMBACH
Is a participant in a cooperative study program with BASF SE, a global chemical enterprise. She is an undergraduate student at Ludwigshafen University of Business and Society (Ludwigshafen, Germany). Her research interests include the impact of artificial intelligence (AI) on skill acquisition in the workplace. She has successfully completed internships in strategic marketing, communication and procurement.
NORMAN CSAVAJDA
Is an undergraduate student at Ludwigshafen University of Business and Society (Ludwigshafen, Germany) participating in a cooperative study program with BASF SE, where he has interned in marketing, supply chain and budgeting. His research interests include changes in the workforce brought about by AI and Generation Z.
GERALD F. BURCH | PH.D.
Is an assistant professor at the University of Florida (Pensacola, Florida, USA). He teaches courses in information systems and business analytics at both the graduate and undergraduate levels. His research has been published in the ISACA Journal along with several other leading peer reviewed journals. He has helped more than 100 enterprises with his strategic management consulting and can be reached at gburch@uwf.edu.
JANA BURCH | EDD
Is a faculty member at the University of West Florida (Pensacola, Florida, USA) where she teaches undergraduate business courses in communication, ethics, management and entrepreneurship. In addition to her teaching responsibilities, Burch works with organizations to provide business development support and helps them develop innovative business solutions. Her research interests include workforce development, innovation and creativity, and entrepreneurship. Burch is dedicated to helping her students and clients develop the skills and knowledge necessary to succeed in the business world.