Behavioral Impact of Using Artificial Intelligence in Sales Teams

The purpose of this research was to establish the behavioral impact of using Artificial Intelligence on Corporate Sales teams. The study aimed at determining the changes that will occur as a result of Artificial Intelligence Technology to an organization. Moreover, it examined the structural models that have been proposed or used by organizations to incorporate Artificial Intelligence in organizations. Further, the study explored the behavioral impact of using Artificial Intelligence in organizations as well as the management techniques/ methodology/ strategies used by various organizations towards the adoption of Artificial Intelligence. The study main focus was on the sales teams across various organizations.

The findings of the study showed that the adoption of Artificial Intelligence in sales teams has an enormous impact which may result to divergent behaviors in sales team. The use of Artificial Intelligence in Sales Team has lots of advantages that result to positive behaviors among employees. There however exist few concerns among sales teams on Artificial Intelligence adoption that may cause divergent behavior. Some of the concerns include; Artificial Intelligence becoming an equal decision maker as sales professionals and the perception that the number of sales representatives may reduce as a result of the use of Artificial Intelligence

Key words: Artificial Intelligence, Behavior.

1.0 Introduction

1.1 Background of the Study

Artificial Intelligence (AI) refers to the intellect revealed by machines, which is different from the intelligence that is shown by humans as well as animals which is natural. AI systems are characterized by different behaviors such as manipulation, learning, solving problems, motion, planning, representation of knowledge, reasoning as well as reasoning (Harris 201, p. 14). The use of Artificial Intelligence (AI) has become rampant and is used in sectors across the world. Its evolution has occurred in three significant waves which are discussed below. The first wave was characterized by handcrafted knowledge (DaGraça 2017, p. 133). During this era, various experts came up with software according to the information that they possessed. They came up with programs that used logical rules and could be used by humans (Zarkadakēs 2016, p. 3). Based on the rules, the systems were able to scrutinize the parameters that were required in a given situation and came up with the most appropriate course of action (Witten et al 2011, p, 14). Despite being beneficial, the first wave had its own limitations such as the inability to handle new types of situations as well as abstracting (Geraci 2012, p. 54). Abstracting refers to the act of using knowledge gained from previous situations and using it in to solve new problems (Crowther & Wünsche 2012, p. 5). The inability of the first wave to handle challenges associated with abstracting made it difficult to deal with uncertainties leading to the development of the second wave (Tito 2017, p.1).

The second wave was characterized by statistical learning (Shwartz & David 2014, p. 46). It started in the 1970’s and sustained up to the 1990’s.The experts during this wave were not bothered with adherence to strict rules by the systems. They mainly focused on the creation of models suited for various situations (McCarthy &Kelty 2010, p. 13). The models would then be trained using various illustrations in order to make them become precise as well as efficient (Frankish & Ramsey 2014, p. 44). Systems which resulted from statistical learning showed to understand the environment around them since they were able to differentiate between people as well as different vowels (Roe 2018, p. 1). In addition, with adequate training, they showed to adapt to different situations using the most reasonable solutions (Arabnia 2017, p. 22). The second wave did not however depend on precise rules since the major emphasis was on coming up with the most appropriate solution (Good 2017, p. 14). The Business Process Reengineering (BPR) technique was an example of a model that evolved in the 1990s and provided offices with the opportunity to mechanize their activities. The result was a greater focus on the customers rather than administrative work (Gardenfors 2005, p. 46).

The third wave, which is most recent, is characterized by contextual adaptation (Joshi 2017, p.1). This wave is unique since the AI models are able to make models which explain how the world works (Mohammed et al 2017, p. 22). They systems are therefore able to identify the rules which determine the process of decision making (Paola et al 2017, p. 15). More importantly, the systems rely on real data rather than a system of steps which are pre-conditioned in nature (Rao &Govindarajo 2013, p. 2013). Based on the data obtained, they are able to identify the rules that they are likely to use as well use various sources to come up with an appropriate solution (Gori 2017, p. 44). The advantage of the recent wave is its focus on individual employees and consumers rather than the entire group (Parkin 2018, p, 55). The main limitation of the wave is that a lot of work is required in order to create the systems.

AI is applicable in diverse sectors such as sales and marketing, education, finance, health, aviation and manufacturing due to the various advantages associated with its use (Polonski 2018, p.16). Some of the advantages include increased precision and reduction of errors and increased workload when compared to use of human labor (Ford 2015, p. 24). The main challenge associated with the use of AI is the incurrence of high costs due to the purchase of expensive equipment as well as high costs involved during repair and maintenance (Peddie 2017, p. 36).

1.2 Problem Statement

As a result of evolution of Artificial Intelligence, businesses are starting to execute major transformational changes in their operations. AI have made it possible for organizations to automate their systems as well as increase efficiency hence making collaboration between humans and machines in execution of duties to become more fruitful (Gandhi &Ehl 2017, p. 34). Despite the automation of systems, there exist great misunderstandings of the role played by Artificial Intelligence there is fear among some humans that AI will replace them in their organizational roles. While the latter may be true for very limited companies, the technology will have the greatest power of not only complementing but also augmenting the capabilities of humans (Skilton & Hovsepian 2017, p. 14).

A study by Chui et al (2015) focused on the four fundamentals of workplace automation and examined over 2,000 work activities in 800 jobs. The findings of the study revealed that AI was likely to replace about 45% of the daily activities (Chui et al 2015, p.1). More importantly, they categorized the various work activities in to three categories; firstly, those roles that were least vulnerable to change, secondly, roles that will experience a certain AI penetration and lastly, those roles at high risk of being entirely computerized without the need for humans. From the study findings, those roles that were most likely to be replaced completely included those that were repetitive as well as predictable in nature (Chui et al 2015, p.1).

The project will focus on the roles that are will augment AI hence are less susceptible to complete automation. Specifically, the area of focus is the Corporate Sales team due to the constant change of setting in the field. The topic of study will focus on the behavioral impact of AI on Corporate Sales Team. A study by Bell (2018) showed that during the adoption of AI, many employees are not involved in the process rather the process principally involves the managers and other senior leaders in Information Technology. Moreover, the study showed that despite the high rate of AI adoption, the rate of implementation was still relatively low due to the adoption of AI from a more technological perspective rather than a human perspective (Bell 2018, p.1). In this regard, since employees are the major implementers of AI technology, the study will therefore seek to investigate how human-machine collaboration affects the behavior of employees especially in the field of Corporate Sales.

1.3 Study Objectives

1.3.1 General objective

The general objective of the research is to investigate the behavioral impact of AI on Corporate Sales Team.

1.3.2 Specific objectives

The research will be directed by the following study objectives;

(i) To determine the changes that will occur as a result of AI technology among Corporate Sales Team

(ii) To investigate the structural models that have been proposed or used to incorporate AI in Corporate Sales Team

(iii) To examine the behavioral impact of using AI among Corporate Sales Team

(iv) To find out the management techniques/methodology that adapt to the use of AI in Corporate Sales Team

(v) To explore how organizations plan their strategies towards AI adoption among Corporate Sales teams

1.3.3 Research questions

The study seeks to answer the following research questions;

(i) What are the likely changes that will occur as a result of AI technology in Corporate Sales Team?

(ii) What are the structural models that have been proposed or used to incorporate AI in Corporate Sales Team?

(iii) What is the behavioral impact of using AI in Corporate Sales Team?

(iv) What are the management techniques/methodologies that adapt to the use of AI in Corporate Sales Team

(v) How do organizations plan their strategies towards AI adoption among Corporate Sales Team?

1.4 Significance of the Study

The study will inform different groups in the society such as academicians as well as organizational leaders such as managers in the field of Corporate Sales. The academicians will benefit because the study’s results will crucial in authenticating the theoretical framework that exists in the field of project management as well as developing new theories that on AI adoption. The benefit will be important in helping to identify the research gaps that need to be given attention when conducting future studies. On the other hand, organizational leaders in the field of Corporate Sales will benefit from the study by identifying the need to not only focus on AI adoption but also on AI implementation. They will be able to realize that it is fruitless to put a lot of effort on adopting AI if there is no implementation of the same. Corporate Sales Leaders in organizations will therefore focus on impact of adopting AI on the employees as well as involve in the adoption as well as implementation of AI. The result will be the increase in the number of sales in organizations and a high rate of AI implementation in Corporate Sales Teams. More importantly, the study is crucial to leaders since it is taking place at the time when AI adoption among organizations is rapidly increasingly due to industrial revolution. As a result, leaders not only in sales teams but also in other departments will give greater attention to human-machine collaboration in order to increase the rate of AI implementation and success of the organization.

1.5 Scope of the Study

The study will focus on Corporate Sales since the role of corporate sales is rapidly changing. A study on Corporate Sales revealed that an average of 64% of the activities executed by sales representatives was spent of tasks which were not sale related. Moreover, due to the continued advancement in technology, Sales force predicted that the rate of AI adoption in the field of Corporate Sales is likely to grow by an average rate of 139% in the next three years (Bhunia 2017, p.1). In addition, the study highlighted that Sales teams that use AI technology have a 2.3 times likelihood of using methodologies of guided selling (Salesforce 2017, p.1). As a result, sales teams using AI had higher chances of experiencing increased accuracy of predicting thus leading to increased prospect development and accurate predicting (Sales force 2017, p.1). In this regard, since the use of AI has showed to be beneficial, the study will focus the behavioral impact of its use on the employees. The study will therefore seek to address the new association between humans and AI as well as its behavioral impact within the environment of the business. The researcher will therefore reach out to 100 randomly selected employees in the field of Corporate Sales from randomly selected companies to fill out questionnaires that will contain several questions that are meant to specifically identify the behavioral influence of using AI among employees.

1.6 Limitations of the Study

In research, limitations refer to the factors that can hinder the success of the study and the researcher has no control over them (Mutalima 2018, p. 46). For this research, some of the limitations include; the availability of unresponsive respondents who might be difficult to convince to provide answers due to the feeling that they are under an investigation. In addition, the researcher has no control over the degree of honesty of the respondents when answering questions. Nevertheless, some of the approaches that the researcher will employ to limit the impact of the limitations include the use of short questions that provide respondents the motivation to provide responses which are honest. Moreover, the researcher will assure the respondents of their confidentiality during the interview process as well as when filling in the questionnaires.

1.7 Organization of the Study

The research will be organized in to seven chapters. The first chapter will has already been covered is the introduction. The second chapter will provide a detailed literature review of the existing studies as well as theories that are relevant to the study. The third chapter will provide the methodology envisioned by the researcher. The fourth and fifth chapters will cover the analysis and recommendation respectively. The sixth chapter will focus on the recommendations while the last chapter will highlight the suggested areas for further research.

2.0 Literature Review

The section will comprise of the empirical review, theoretical review and conceptual framework as shown in the sub-sections below.

2.1 Empirical Framework

2.1.1 Changes brought about by AI

A study on “human machine: reimaging work in the age of AI” was conducted by Daugherty & Wilson (2018) focusing on a new approach, where there is collaboration between machines and humans. The study aimed at drawing away from the traditional approach of thinking where machines are seen as tools that replace humans in their duties (Daugherty & Wilson 2018, p. 5). The authors refer to the approach as the “missing middle” since most organizations have not been able to use the new approach. The findings of the study showed that organizations that had adopted the approach had experienced several changes which were grouped in to five categories. The first significant change that was realized was mindset. With the coordination of efforts between the machines and humans, change of mindset was crucial to enable humans to improve the applications that were based on AI (Daugherty & Wilson 2018, p. 60). Secondly, experimentation was also realized to a great extent. With the adoption of AI, there was a constant need for organizations to conduct trial and error experiments with the aim of finding out the best ways as well as approaches of using AI. Experimentation helped in knowing what worked best in new set of conditions where AI was involved (Daugherty & Wilson 2018, p. 61). Thirdly, there was change in leadership. This was due to the fact that new technologies were being used hence there was need to manage humans in a cautious manner since they were in charge of machines, which were responsible for making the majority of actions in the organizations (Daugherty & Wilson 2018, p. 62). Fourthly, there was change in collection and preparing of data. With the use of data, it was crucial for humans to collect as much data as possible and in an organized manner. More importantly, it was also important to ensure that the collected date was free from bias (Daugherty & Wilson 2018, p. 63). Fifthly, for the success of human-machine collaboration, there was need for amplification of the skills in humans through such forms as training while still going on with work (Daugherty & Wilson 2018, p. 65). The authors concluded that AI was now a tool that could be used to further improve human capabilities. Despite the contributions, the authors recommended the need for extensive research in the field since its main limitation was the lack of sufficient research in the area of study. Barden (2017) carried out as a research on “the pros and cons of having robots in the workplace” and had a different pinion to the one above. The author argued that with robots in the workplace, the entire space could become emotionless since they have no empathy, leading to the development of a negative mindset in the workplace. More importantly, the author highlighted by using robots posed a great risk to the organization’s data (Barden 2017, p. 1). Information was fed to the robots through a chip; hence in the incidence of malfunction, the entire data would be lost contributing to organizational failure. In addition, the study showed that training of employees for development of their skills due to AI adoption was costly for the organization when compared to cost prior to the adoption of AI (Barden 2017, p. 1). Despite the contribution, the study did not suggest ways that organizations could adopt AI technology in a cost-effective manner.

A study by Florian (2015) on “the effect of automation on the human behavior” was conducted using a qualitative approach. The outcomes of the study showed that robots were contributing to the increment of wages among employees rather than depriving them of their jobs. Adoption of AI technology in organizations showed to change the perceptions of employees concerning AI (Florian 2015, p. 468). As a result, they considered the use of AI as an opportunity for them to get better paid and go higher in their employment ladder since they are encouraged to develop new skills as well as come up with new production methods. Due to the change in perception, employees aimed to further their educational qualifications by joining institutions of learning as well as attend trainings that improved their skills (Florian 2015, p. 468). More importantly, the authors noted that automation was aimed at making the future of humans smarter since it encouraged them find answers to various questions as well as come up with solutions to the various challenges associated with the use of AI. The author concluded that human-machine collaboration was aimed at providing positive results since it gave humans access to intelligent structures that gave them the freedom to be smarter (Florian 2015, p. 473).Bossman (2016) in a study on “top 9 ethical issues in artificial intelligence” critiqued Florian’s opinion on jobs by arguing that AI enhanced the loss of jobs but did not create new ones (Bossman 2016, p. 1). The author gave an example of how many individuals are employed as truck drivers but are likely to lose their jobs once the self-driving cars began to function. It would therefore not be possible to offer jobs for all the drivers and therefore they would be forced to venture in non-labor activities like taking care of families (Alpaydin 2016, p. 13). Despite the contributions, the author did not recommend ways of augmenting humans in jobs to reduce the rate of losing jobs.

Hislop et al. (2017) carried out a study on “impact of artificial intelligence, robotics and automation technologies on work” while employing a qualitative approach. The findings showed that there was a changing nature of relationship between humans and technology (Hislop et al 2017, p. 11). This is due to the increased communication between humans and machines which prompted the need for humans to learn more on how to interact with the technologies. The result was increased interaction between humans and machines through such ways as voice recognition systems and formation of robot/worker teams (Hislop et al 2017, p. 11). More importantly, the results showed that there existed a relationship between employees’ attitudes and robots. They identified that the performance of some humans and their anxiety levels would change if the robots behaved in a certain way (Hislop et al 2017, p. 11). Based on the two results above, the authors concluded that attitude as well as behavior of humans determined the extent to which technology was used in the organization. As a result, the researchers recommended the need for in-depth research on the ways in which workers trust on technology could be intensified, in order to increase the use of technology (Hislop et al 2017, p. 18). Despite the contributions, the study’s main restriction is the fact that it failed to highlight the current state of technology since the authors themselves were not sure of the current and future state of technology in the world (Hislop et al 2017, p. 18).

2.1.2 Structural models to incorporate Artificial Intelligence

A study by Tai on “how artificial intelligence is changing the future of work now” while employing a qualitative approach. The study identified that with advancement in AI, the centaur model will be very applicable in organizations during decision making (Tai 2018, p. 1). According to the author, the model was as a result of human-machine collaboration. The model will involve a combination of the ability to analyze large amounts of data that is obtained from the use of AI with the human knowledge, understanding as well as creativity in order to make the most appropriate choice (Tai 2018, p.1). The researcher identified that with the use of the model, individuals would be able to make better decisions as well as improve their predictability. Moreover, the study identified that the use of the centaur model will reduce overreliance on educated guesses as well as intuition. More importantly, the study outcome also showed that process of recruiting, hiring as well as retention would need to be greatly changed (Tai 2018, p.1). As a result, organizations would be required to recruit, hire as well as grow individuals based on their attributes like skills and talent rather than other attributes like the level of experience (Tai 2018, p.1). Using the strategy above would help organizations to become more inclusive as well as diverse since they are able to migrate from the traditional methods of assessing employees that fail to focus on analysis of language, behavioral patterns and promotion data (Tai 2018, p.1). In addition, the researcher identified that with the use of AI, organizations would be able to monitor employees’ satisfaction, engagement and behavior. As a result, the most appropriate training will be provided to employees hence making them improve their performance as well as behavior so promote organizational growth (Bonaccorso 2017, p, 34). The study recommended the need for organizations to continue investing in tools as well as solutions that give them the power to analyze employees’ behavior. The study did not however highlight the challenges that emanate from using the centaur model.

A survey was conducted by Bauer et al (2008) on “human-robot interaction” using a qualitative methodology. The survey aimed at analyzing the state of human-robot interaction as well as come up with strategies that would help both humans and machines to collaborate more effectively (Bauer et al 2008, p. 47). The results of the survey showed that collaboration between humans and machines become effective when both of them followed three steps which are; joint intention, action planning and action respectively (Bauer et al 2008, p. 47). Joint interaction required humans as well as machines to have a goal of working together. Humans set the goal while the machines would help in giving the estimations of how the goal would be achieved (Bauer et al 2008, p. 50). Upon having the joint intention, the authors realized that there was a great need for planning for the action through gathering the relevant information to come up with the possible alternatives (Bauer et al 2008, p. 55). From the alternatives available, both humans and machines would then be able to choose a specific action to execute. During the execution of the actions, the parties would re-plan the action of either of the parties acted in a way that was not anticipated by the other (Bauer et al 2008, p. 56). The authors recommended the need for humans to take caution when interacting with machines since there could be incidences of injury especially if the machines lacked detectors (Bauer et al 2008, p. 57). The conclusion of the study was that the model that involved using three steps was effective and could be used in various sectors such as healthcare, marketing and entertainment (Bauer et al 2008, p. 61). The main limitation of the study was its failure to mention how organizational leaders would do follow up to ensure that the model was being followed by both parties (Matthews &Kostelis 2011, p. 54).

2.1.3 Behavioral impact of using AI

Cascio &Montealegre (2016) conducted a study on “how technology is changing work and organizations”. The study aimed at identifying the impact created by rapid changes in technology on work as well as organizations. From the findings of the study, the introduction of electronic monitoring systems had resulted to the monitoring of employees, which affected the perceptions of employees on trust and affected their personal control (Cascio & Montealegre 2016, p.357). The researchers argued that the use of electronic systems could be regarded to be good or bad depending on the way they were used by organizations (Cascio & Montealegre 2016, p.357). In this regard, the authors advocated the need for organizations to have supportive organizational cultures which allowed for employees’ input in the monitoring system design as well as the focus on performance related activities (Khoury & Drummond 2016, p. 80). According to the researchers, the presence of supportive systems prevented the development of defiant behaviors among employees (Cascio & Montealegre 2016, p.357). Moreover, the study highlighted that machine-human collaboration was an area that required special concern due to its impact on various aspects of human performance such as lack of trust for automation, and decrement in vigilance among others (Cascio & Montealegre 2016, p.358). The study proposed that organizations could employ such strategies as training, performance management and employee motivation to help in promoting machine-human collaboration (Cascio & Montealegre 2016, p.358). The study concluded that there is need for technology to promote more positive consequences among employees as well as lower the negative consequences. Katz (2015) carried out a study on “monitoring employee productivity: proceed with caution” and highlighted that organizations should take great caution before taking the decision of supervising employees (Katz 2015, p. 3). Though the study above proposed the involvement of employees in designing the monitoring system, the author cautioned that the system was likely to bring increased employee turnover, stress as well as elimination of the human in the equation (Katz 2015, p.3). The findings showed that managers should not be quick to take this action but rather focus on other supervisory techniques such as the use of new language processing tools that evaluate messages from groups to interpret the underlying thoughts as well as feelings (Cheng & Day 2014, p. 48).

A study by Foerg on “the impact of artificial intelligence on employees” showed that there exist unanswered questions among employees on the future of their jobs due to AI. The author identified that the questions were prevalent among employees since the level of automation in organizations was growing at a very fast rate (Foerg 2018, p.1). In addition, the study outcomes showed that many professionals with highly specialized skills such as software developers were shifting to organizations that were highly competitive in the journey of digital transformation.