AI implementation changing management competencies, role, and status

This study will add to the research enterprise with a contribution to the growing field of research on the impact of AI (Danyluk & Buck, 2019; Raisch & Krakowski, 2021; von Krogh, 2018). AI implementations have increased in almost all industries and the need for better academic understanding is needed (Haefner et al., 2021). The increase is especially shown in the insurance industry, hence the selection of this specific domain. Within AI research most attention was distributed to the technological and organizational implications, and less so on management (Mikalef & Gupta, 2021; Raisch & Krakowski, 2021). This research goal was to investigate the effect of AI implementation on the competencies, role, and status of management. Therefore, the study aims to answer the following RQ: How does AI implementation affect the competencies, role, and status of management?

The findings suggest that there are indeed changes in management due to AI implantation. As research on AI capabilities suggested, the sole deployment of AI is ineffective and to stay competitive and adopt AI in the organization, among other things, different management skills need to be developed (Conboy, Mikalef, et al., 2020; Mikalef &

Gupta, 2021). As AI has been implemented in the insurance industry, the changes in

management become more apparent with, for instance, the need for AI competencies, dealing with employee resistance and new departmental structures.

5.1.1 Changes in management competencies

The informants pointed out several competencies that were needed or that changed due to AI implementation. An overview of the different competencies can be found in figure 1.

The first competence is the need for change management capabilities as a manager.

This is in line with what Mikalef & Gupta (2021) indicate, that business value through AI investments is only achieved when leaders understand and commit to drive a large-scale change. This approach to AI implementation required the informants to embrace a change vision and carry it out throughout the entire organization. Although this is closely related to the leadership function, the skills and attitude in organizational change management is now considered almost a necessity in order to succeed in the competition fueled business

environments (By, 2005; Hornstein, 2015). The need for managing change is also in line with disruptive innovation theory, with one of the enablers being: “Prepare for and instituting organizational change and unlearn its deeply entrenched values” (Yu Dan & Hang Chang Chieh, 2008, p. 407).

The second competence the informants identified is the development of AI

knowledge, from which multiple knowledge needs are formed. Theory on management skills in AI capabilities also hinted in this direction with the need for managers to be able to

identify possible opportunities with AI in their domain, as well as managing the transition to AI powered processes (Mikalef & Gupta, 2021). Referring to the definition of Moore et al.

(2002), in possessing the required skills, knowledge and attitudes, this knowledge is key to develop the right competencies. With all informants investing time into understanding AI, it indicates that it is of great importance. Kiron (2017), confirms and foresees that the manager needs to develop new skills and knowledge. The first derived competence is deciding if an AI implementation needs to augment humans in their process or, if it is possible, automate the process. Furthermore, in this decision it is important to understand if an algorithm can improve with data gathering and move towards automation, as elaborated upon by Raisch &

Krakowski (2021).

The informants also indicate that AI makes personal domain knowledge less

important. With AI’s current capacity to combine the knowledge of the entire organization in its decision making, the impact on different knowledge needed will increase as more data is made available for AI (Mitchell & Brynjolfsson, 2017). The role of the manager changes from being focused on exploiting personal knowledge to where and how to use the combined knowledge of the organization (Rhem, 2021). As a recent study reveals a need for other skills: “The managers we surveyed recognized the value of judgment work. But they undervalued the deep social skills critical to networking, coaching, and collaborating that will help them stand out in a world where AI carries out many of the administrative and analytical tasks they perform today.” (Kolbjørnsrud et al., 2016, p. 5). This will, in addition to the development of new skills, also influence the power of management which is discussed in the changes in management status.

After AI implementation the scalability increases, resulting in better business results with the margins doubling (stated in the findings of “impact of AI implementation in the insurance industry”). It, however, also increases the impact of mistakes, which is something the manager and the team should be fully aware of. A mistake in the system could lead to harmful behavior, potentially towards large groups of customers due to scalability (Amodei et al., 2016). This also links to the ethical and privacy risks of the AI and the accompanying data. Insurance is a risk averse industry as it is the core of their right to exist, meaning that these risks are receiving much attention across all informants. The competence to manage Ethics and privacy risks of AI needs a level of AI knowledge as well as clear communication of the organizations business ethics (Snoeyenbos et al., 2001). The possible implications of mismanagement became painfully clear in the Dutch childcare benefit scandal, with the risk of being labelled as an organization that used systematic discrimination (Schuurmans, 2021).

The third competence is the ability to think from a data point of view. This entails that the manager should see future opportunities in the available data and act upon it as AI is only useful if it can interpret and learn from data, like it was stated in the definition of Haenlein &

Kaplan (2019). Looking at the theory from Mikalef & Gupta (2021), the domain knowledge could be combined with the ability to think from a data point of view opening up more opportunities for the future.

Figure 1: Changes in management competencies 5.1.2 Changes in leadership role

Due to AI implementation, the informants experienced several changes in their role. An overview of the changes can be found in figure 2.

Starting with a broad change in the role, as the findings indicate that AI drives different team needs. With the MT meetings containing more technical discussions,

management is required to adjust their role and make sure the needs of the team are secured, with AI as a team member taken into account (Morgeson et al., 2010). The aim of a team leader is to enhance team effectiveness by meeting the core team needs. This is achieved by affective emergent states, cognitive emergent states and behavioral integration processes (Morgeson et al., 2010). The findings indicate that the business is becoming more data driven and process oriented, which puts the ability of a team member to identify with the

organization under pressure (affective emergent states). Furthermore, the team adjustment to

AI implementation

Increased importance of change management

AI knowledge development

Ethics and Privacy risks of AI implementations

Skill to look at business from a data

point of view

the integration of AI as a team member is altering the cognitive emergent states. This suggests that the leadership role needs to adapt to reach team effectiveness.

With informants point out the importance to think in a process oriented way, the role of leadership is to make sure their teams understands the need for knowledge and to

understand the entire process. This is in line with the transformational leader theory, where management encourages the team to be committed to the organization’s current activities (Dvir et al., 2002). Avolio et al. (1999) elaborate that the role of a transformational leader consists out of four elements, and highlight one as intellectual stimulation, which achieves the involvement of the team in the entire process. This indicates that management should encourage employees to think beyond their traditional boundaries.

With the managers and employees more focused on how and where to use the right resources and insights, the possibilities for innovation increases (Ågerfalk, 2020). As

innovation and AI implementations play larger roles in the organization, the operational team realizes that their job might become redundant, which causes resistance that needs to be managed. The management of the increased dominance of the technological side and the fear of job loss on the operational side of the organization, is now part of the leadership role (Kiron, 2017; Kolbjørnsrud et al., 2016). Employee training and development is indicated as a focus point for management as 75% of the informants say it is crucial in managing

resistance. This is reflected in literature where Fountaine et al. (2019), indicates the

importance of transparency and development to counter employee resistance. Looking at the four elements of transformational leadership, individualized consideration could be highly effective as it focusses on developing the potential of employees (Avolio et al., 1999).

The importance of retaining these employees was only increased over the last year as the labor market is very tight (Duval et al., 2022). Training employees internally to fill different kind of functions could work, however the complexity of the vacancies increases as

the organization transforms towards being an IT firm. Digitally skilled employees are becoming more dominant in the organization and the scarceness is taking extreme forms.

Organizations are starting to recruit from different countries to match the vacancy needs.

Management needs to create an attractive working environment for digital talent to retain and attract employees and enable digital transformation (Dery et al., 2017). Dery et al. (2017), in their research call for a dual focus of management on employee connectedness and

responsive leadership. Where employee connectedness refers to: “the extent to which employees can engage with each other, with stakeholders and customers, with information and knowledge, and with ideas.” (Dery et al., 2017), and Responsive leadership to: “The extent to which management prioritizes the activities that focus on the development and continuous improvement of employee experience in the organization.” (Dery et al., 2017).

Both giving similar indication of what the informants highlighted.

Figure 2: Changes in leadership role 5.1.3 Changes in management status

Looking at the findings the changes in management status are not as transparent as the other two dimensions. This might be explained as the research subtracts data from the managers point of view, who are most likely not as willing to accept losing status. The data does however show two themes in changing status.

AI implementation

Adjust to the new kind of team needs

More attention to atract and retain

digital talent

Dealing with employee resistance

to AI

One change considers a shift in hierarchy as knowledge on AI is increasingly

important within the organizations. The personal domain knowledge becomes less important and moves more to the right usage of generalized knowledge. Looking at the theory of Finkelstein (1992) on expert power, the intellectual knowledge of the manager decreases and skills with AI increases in importance. This is believed to have started with the digitalization of organizations but is accelerated due to the rapid development of AI. This is in line with authority theory indicating that people who are able to reach an informed decision faster are more likely to receive decision authority.

While the expert power shifts more to data analysts and scientists, the informants indicate that these are however not the profiles for leadership. This could point towards a more shared leadership need, which Friedrich et al. (2009) encourage as they point out: “it would be more realistic to expect, and perhaps encourage, multiple individuals with a diverse set of skills and abilities to collectively act as leaders, distributing the roles based on the situation.”. Looking at the theory of Finkelstein (1992), this will influence the structural power of the manager as the hierarchy in the organization becomes less apparent. However the findings indicate that the majority of the high level or strategic decision authority still lies with the manager, deciding on intuition and often resulting in positive business results (Burke

& Miller, 1999; Salas et al., 2010).

The second change in status considers the departments that are increasingly dependent on each other, especially in terms of business and IT. This has led to project teams with mixed roles, as the two traditionally separate worlds merge. Agile ways of working in the insurance industry are increasingly important, with the many digital disruptions (Behm et al., 2021). Moving away from the siloed and hierarchical structure and towards agility will enable organizations to meet customer expectations, increase employee engagement and improve efficiency (Lorenz et al., 2020). The movement to a more agile and flat organization

started with software developers back in 2000, and, as the informants indicate, the insurance industry is transforming towards an IT industry since the implementation of AI influencing both the structural as well as the prestige power of management. With a need for

experimentation and short sprints, the hierarchical management position is under pressure and a change in structure is a most likely prospect for the industry as the amount of AI

implementations increases.

Figure 3: Changes in management status