Artificial Intelligence for Business

Inbound Logistics: What is the potential role of AI and data analytics in the key inbound logistic processes (e.g., identification, sourcing, procurement, and supplier management of the “raw materials” that comprise a final product or service)? o Operations: What is the potential role of AI and data analytics in the key operational processes (e.g., engineering, inventory management, and manufacturing of the final product or service)?

Outbound Logistics: What is the potential role of AI and data analytics in the key outbound logistics processes (e.g., planning, scheduling and distribution of the final product and service)? o Marketing and Sales: What is the potential role of AI and data analytics in the key marketing and sales processes (e.g., marketing, merchandising, promotions, advertising, sales, and channel management to get the completed product and service to the end customer)?

Service: What is the potential role of AI and data analytics in key service processes (e.g., support and maintenance of products and services after they are delivered to the end customer)?

Human Resource Management: What is the potential role of AI and data analytics in key human resource management processes (e.g., recruiting, hiring, development, and firing of personnel)?

Structure of an Essay

1. Introduction (introducing the topic – including the key two or three business activities on which you chose to focus –, business problem, and elaborate on the practical motivation of why it is important to study)
2. Literature review (summary of previous related academic studies on the topic and issue) 3. Present concrete examples/cases to make a point.
3. Critically discuss your argument and ideas around the topic you have chosen. 5. Conclusions (perhaps challenges/future directions for the topic…)
4. References

Key criteria for your argument

•    Analytical thinking and ability

•    The presentation and use of literature

•    Critical ability (need to be critical of ideas presented…)

•    Originality in developing the arguments and presenting the ideas.

•    Clarity, structure, and style (well-written, has flow, is consistent with the referencing style, •  etc.)

ANSWER

Artificial Intelligence for Business

Artificial intelligence (AI) has been widely adopted in various industries, including sales & marketing and human resource management (HRM). As technology advances, industry leaders have been forced to migrate into a more advanced field in which AI has engrained itself as the most helpful tool. As market competition intensifies, organizations must be innovative and customer-centric to attain competitive advantage (Wirtz, 2019). AI presents new opportunities for managers to rethink their business strategies, reshape their internal capabilities, and change how they interact with customers. AI can increase a company’s competitive advantage and innovativeness, explaining the relevance of this study. This paper’s objective is to analyze the application of AI in sales & marketing and human resource management. AI can revolutionize sales & marketing, and human resource management by enhancing personalization, accuracy, and efficiency.

Literature review

AI in Human Resource Management

AI has been widely adopted to perform various HR functions, such as search & recruitment, training & development, performance analysis, and career development. Worldwide, recruiters struggle with screening massive amounts of CVs and applications. According to Fraij and Várallyai (2021), HRs no longer need to conduct monotonous and repetitive tasks because AI systems are more efficient in dealing with these procedures, saving organizations time and money. The advantage of AI’s efficiency is that it allows HR managers to focus on value-adding tasks, enhancing organizational outcomes (Palos-Sánchez et al., 2022). AI is significantly more efficient than humans in performing jobs.

AIs have also provided recruiters with a way of interacting with their applicants. Fraij and Várallyai (2021) report that AIs process job applications via chatbots. These chatbots allow applicants to engage with the hiring organizations. These chatbots can collect applicant information such as salary expectations, cultural expectations, contact information, and skills and experiences. Although Fraij and Várallyai (2021) applauded the interactive nature of chatbots, Palos-Sánchez et al. (2022) criticized them for dehumanizing personal relationships. Even though AIs boost human capabilities, they cannot replicate human interactions.

AI can also reduce human-related bias. For example, if an applicant does not fit into a position, corruption would not be an option. Because AIs automate tasks, they can remove human-related errors and biases. In other words, AI can fight against common unconscious stereotypes among recruiters. The minimization of human errors improves decision-making by providing more accurate information.

Despite these benefits, AI adoption in HRM is not without challenges. One of the major concerns is that AI can amplify the existing bias in hiring decisions. Despite their problem-solving capabilities, AIs can make errors. The reality is that AIs are only as good as the data it is fed. For example, if the AI is incorrectly calibrated, it may not produce desired results. Small and non-representative data can also lead to AI-related bias. Another challenge is the ethical and legal implications related to data privacy. AIs can access and store the personal data of employees for monitoring. Data use may result in privacy and security risks, leading to severe legal and ethical implications.

Lastly, the adoption of AI in HRM remains slow and problematic. According to Palos-Sánchez et al. (2022), many HR departments worldwide are unprepared to exploit the opportunities presented by AI. The primary reasons for this slow adoption are a lack of technical skills and fear of change. Moreover, some HRs lack confidence in using AI in management practices because they fear being replaced by the systems (Palos-Sánchez et al., 2022). These concerns highlight the need for training in AI technology.

AI in Sales and Marketing

AI is being used in S&Ms for personalized products or content recommendations. Traditionally, sales managers recommended products or content for their customers through shopping trolley analysis (Gentsch, 2018). The premise was that customers buying product A were more likely to purchase product B. However, these predictions were badly scaled and time-consuming. Today, web retailers cannot make recommendations without engines making personalized and tailored recommendations (Haleem et al., 2022). Machine learning, an AI technology, can access and compile data from different platforms, including users’ purchasing and clicking behaviors. It utilizes this information to generate customized content and product recommendations for customers. According to Haleem et al. (2022), this client micromanagement can provide a competitive advantage and improve user experiences. The authors argue that customers like to view advertisements relevant to their preferences or concerns. Marketers can leverage AI algorithms to create campaigns targeting specific customer segments (Haleem et al., 2022). By implementing AI into marketing, managers can use data to reach potential customers using attractive and personalized commercials.

AI is also used for lead prediction and profiling. AI can use statistical twins or attribute data vectors to classify and create digital signatures for customers. Companies can leverage this capability to identify and predict potential customers in the digital space. Leads that do not match the acquisition strategy but are potential customers can also be identified using AI algorithms. These context-specific signals increase the chances of conversion, and sales professionals can also use the information to make a strong sales pitch.

AI also provide valuable customer insights. Traditionally, S&Ms would use customer surveys, focus groups, questionnaires, etc., to obtain customer feedback. These methods were time-consuming and expensive (Gentsch, 2018). Today, AI has automated these processes. AI can automatically analyze thousands of product reviews at any given time. Bots can capture and integrate customer ratings and review. With the help of a natural language process, key customer insights are automatically reviewed and compiled to generate valuable insights (Gentsch, 2018). S&Ms can use this data to understand their customers’ needs better.

AI is evolving conservational chatbots and search queries, forcing salespersons and marketers to adapt. These systems enable customers to enquire and ask for product & service information without phone calls or complete human discussions. Haleem et al. (2022) note that some marketers vehemently oppose such practices as they prefer real human interactions. However, Haleem et al. (2022) posit that marketers aiming to interact more intimately with their customers can use vanishing messaging services. These capabilities free up marketers’ time, allowing them to focus on more strategic issues.

AI also helps marketers to forecast trends by using data to predict the future. According to Haleem et al. (2022), S&Ms can improve their ROI by using AIs. AI enables S&Ms to make data-driven decisions by thoroughly analyzing consumer data and knowing their wants. They can predict the prices most likely to attract customers, the best time to post, and the subject lines or content most likely to attract attention. These insights can help S&Ms avoid wasting money and resources on creating irrelevant Ads for customers, enhancing their ROI. Based on these data, marketers, and salespersons can decide how to allocate funds and whom to target. Brands can spend less on marketing and have more time for high-value work. Data-driven decisions help S&Ms to match customers with items they are most likely to buy and avoid irrelevant products.

AI has enabled accurate sales volume prediction. Traditionally, S&M managers used statistical analyses such as regressions to forecast and predict sales volumes (Gentsch, 2018). Unfortunately, small data sets made these statistical methods prone to errors. AI has resolved these issues because it does not rely on deductive learning (prior data knowledge). Unlike statistical methods, AIs do not need prior data knowledge to decide or define a problem. Also, the systems can automatically capture large volumes of structured and unstructured data to include its analysis. Due to these computing powers, AI can make accurate forecasts and increase the quality of sales forecasts.

AI adoption raises ethical and legal challenges regarding data privacy, transparency, and consent. However, Haleem et al. (2022) argue that although AI presents data security risks, it can also help preserve privacy and data ownership. The author advises policymakers to carefully consider balancing the need to keep AI from bad actors without suffocating innovation. Another risk for overreliance on AI is the loss of human touch in sales & marketing interactions (Verhoef et al., 2017). Moreover, there is a need for training on AI models to ensure accuracy in an ever-changing market environment.

Case Study for AI Application in HRM

Many big firms are using AI in their HRM practices. These include Amazon and Walmart G4S, Andersons, McDonald’s IKEA, and Sony Music (Fraij & Várallyai, 2021). In 2014, Amazon created an AI tool to review applicants’ resumes. The AI tool used natural language processing and machine learning to identify candidates who fit specific job profiles best. The AI would use algorithms to identify and rate critical traits from the submitted CVs to spot the most suitable candidates. By the end of 2014, the company heavily relied on the AI tool because it saved them significant time (Lavanchy, 2018).

However, by 2015, the company noted biased hiring patterns in technical job profiles such as architects and software developers. The AI selected male candidates for these positions and discriminated against women (Lavanchy, 2018). After a root-cause analysis, Amazon realized the bias was caused by the data used to train the AI system. This biased training data pushed Amazon’s algorithms to create a relationship that downgraded resumes containing words such as women or female. Amazon unintentionally engaged in discriminative hiring due to biased AI training data. Such discriminative practices can lead to profound legal implications for a company.

Case Study for AI Application in S&M

Netflix’s innovation is partly attributed to AI technologies. According to Allam (2016), the entire operational model of Netflix is AI-centered. AI algorithms govern nearly all aspects of Netflix’s business operations. AI is responsible for customizing user interfaces and choosing movies that match specific customers’ interests (Allam, 2016). Since 2010, Netflix has used AI to make personalized content recommendations to increase engagement and conversions (Alam, 2016). The choices about which movies to air, how to air them, what images to show and other architectural decisions are driven by AI algorithms.

Although AIs can potentially enhance S&M processes, they also pose significant challenges. Kozinets and Gretzel (2020) indicate that AIs can make marketers lose a basic understanding of marketing processes. The authors recall a case where one of Netflix’s digital marketing managers could not explain how AI was helping the company promote its TV shows. No one could comprehensively describe target markets nor explain how a particular marketing action was produced (Kozinets & Gretzel, 2020). This study reveals that can produce impressive sales and marketing outcomes but fail to make valuable contributions to organizational marketing knowledge and competencies. Technologizing marketing is converting marketers into technology users rather than their masters.

Ethical questions have also been raised concerning the infringement of privacy rights, data protection breaches, and data misuse. These ethical challenges arise when data is collected without consumers’ consent, monitoring users’ online activity without obtaining informed consent, leaks or security breaches after data storage, and when AIs draw inferences and share data with third parties without customers’ authorization.

For these reasons, many governments are banning AI technology. For example, the use of facial recognition by police has been banned in American cities such as Boston, San Francisco, and Oakland. Even the European Union considered placing a blanket ban on AI-related facial recognition due to privacy concerns. These cases illustrate the serious implication of ethical challenges related to AI. Even though these systems can significantly benefit businesses, their adoption may be hampered by data privacy and security concerns.

Discussion

AI is transforming organizations, automating many functions, and eliminating the need for human involvement. AI has automated repetitive and time-consuming tasks in HRM, such as CV screening, interview scheduling, and candidate assessments. These automation capabilities can also be seen in sales and marketing. AI has automated jobs that previously needed human intellect, e.g., customer segmentation processes, customer support, data analysis, etc. This automation has enhanced efficiency in both industries by minimizing human-related errors and promoting data-driven decisions. In HRM, AI improves efficiency by automating recruitment and selection processes. S&M enhances efficiency by providing marketers with insights on what content or product will likely attract customers.

AI has also transformed how managers interact with internal and external customers. In HRM, job applicants can use chatbots to obtain information about the organization’s contact details, culture, salary expectations, etc. HR managers also use chatbots to interact with applicants and receive information about them. Likewise, marketers and salespersons use conversational chatbots to enable customers to inquire about product & service information without needing phone calls or human discussions. These chatbots provide convenient customer support at every point of their journey.

AI personalization has also fundamentally changed how businesses are conducted. AI can use consumer data to generate customized content and product recommendations. This customization is a source of competitive advantage (Haleem et al., 2022). AI improves accuracy by streamlining the data analysis and management processes. The systems can analyze vast amounts of data and deliver valuable insights accurately and quickly. These data-analysis capabilities allow managers to make accurate decisions.

Despite these benefits, AI presents various risks and challenges that may hamper its adoption. These challenges include data privacy breaches and data misuse. To foster consumer trust, managers must consider transparent data use practices (Haleem et al., 2022). Although AI presents data security risks, it can also augment software security and thus should be considered (Haleem et al., 2022).

AIs are only as good as the data they are fed, possibly explaining why human intellect is vital for the optimal functioning of AI. A common theme that emerged in this study is the balance between AI and the human touch. Various studies have indicated that AI can dehumanize personal relationships. Lavanchy (2018) and Palos-Sánchez et al. (2022) indicate that AI and human intellect must be used concurrently. Haleem et al. (2022) echo this by stating that AI and humans must work together since much work is still needed before AIs can function autonomously without human intervention. Pandey and Khaskel (2019) also supported this notion by stating that human intervention is needed for AIs to function optimally. According to Haleem et al. (2022), combining AI with human intellect can open new possibilities, increase efficiency and boost organizational productivity. These studies suggest that AIs cannot replace humans despite their capabilities.

Another human-related factor affecting AI application in HRM and S&M is the lack of technical expertise or equipment handling skills. Palos-Sánchez et al. (2022) indicate that the slow adoption of AI in HRM is driven by a lack of skills and fear of being replaced by HR systems. The overreliance on AI by HR professionals is also problematic. Kozinets and Gretzel (2021) found that digital marketers were losing a basic understanding of marketing processes due to overreliance on the systems. These challenges present AI training opportunities for HR managers and marketers.

Conclusion

Artificial intelligence is becoming an essential aspect of competitive advantage and innovation. AI tools can improve efficiency by enabling data-driven decisions and automating repetitive and time-consuming tasks, allowing professionals to focus on strategic issues instead. AI is also changing customer interactions in HRM and S&M. Both industries use conversational chatbots for customer support services and engagement. However, AI adoption faces ethical challenges related to data privacy breaches, AI-related bias, lack of expertise, negative perceptions emanating from fear of being replaced, and an overreliance leading to technological enslavement. A potential solution to these challenges is using AI concurrently with human intellect. AI must be used together with human intervention for optimal outcomes. Integrating the human factor in AI may resolve AI-related bias and reduce system overreliance. Training may resolve adverse perceptions and the lack of expertise slowing down AI adoption in HRM. Further research is needed to validate these recommendations.

References

Allam, S. (2016). The Impact of Artificial Intelligence on Innovation-An Exploratory Analysis. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.

Fraij, J., & Várallyai, L. (2021). Literature Review: Artificial Intelligence Impact on the Recruitment Process. International Journal of Engineering and Management Sciences, 6(q), 108–119. https://doi.org/10.21791/IJEMS.2021.1.10.

Gentsch, P. (2018). AI in marketing, sales and service: How marketers without a data science degree can use AI, big data and bots. Springer.

Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3(2), 119–132. https://doi.org/10.1016/j.ijin.2022.08.005

Hermann, E. (2021). Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective. Journal of Business Ethics, 179(1), 43–61. https://doi.org/10.1007/s10551-021-04843-y

Kodiyan, A. A. (2019). An overview of ethical issues in using AI systems in hiring with a case study of Amazon’s AI based hiring tool. Researchgate Preprint, 1-19.

Kozinets, R. V., & Gretzel, U. (2020). Commentary: Artificial Intelligence: The Marketer’s Dilemma. Journal of Marketing, 85(1), 156–159. https://doi.org/10.1177/0022242920972933

Lavanchy, M. (2018). Amazon’s sexist hiring algorithm could still be better than a human. The Conversation.

Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial Intelligence and Human Resources Management: A Bibliometric Analysis. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2145631

Wirtz, J. (2019). Organizational ambidexterity: cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 49(3), 1-9. https://bizfaculty.nus.edu.sg/wp-content/uploads/media_rp/publications/e13fI1566054171.pdf

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