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The Potential Utilisation of Artificial Intelligence (AI) in Enterprises

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International Journal of Innovation and Economic Development
Volume 10, Issue 2, February 2024, Pages 27-41


The Potential Utilisation of Artificial Intelligence (AI) in Enterprises

DOI: 10.18775/ijied.1849-7551-7020.2015.96.2003
URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.96.2003

Marie Leddy 1,  Niall McCreanor2

 

1 School of Business and Humanities, Dundalk Institute of Technology, Dundalk, Ireland

Abstract: Artificial intelligence (AI) is a part of computer science that aims to create and develop intelligent machines. For AI to function and perform, it involves the development of algorithms, models, and systems that enable computers to learn from the data, identify patterns, and make predictions. Research indicates that AI can increase private industry output, decision-making, and effectiveness. By identifying how enterprises utilize AI, what is the impact of implementing AI in enterprise operations, and how does it affect the efficiency, productivity, and overall performance of the business? This exploratory research begins with insight into understanding AI and addresses how AI has been utilized and implemented in businesses, ethical and societal considerations, and potential benefits and challenges that businesses may confront. Ten in-depth interviews with leading industry experts using AI, followed by focus groups, generated information. opinions, and key insights. By utilizing the software Nvivo, this quantitative research presents key themes and content findings to assist in educating enterprise personnel during the AI decision-making and implementation stages.

Keywords: Artificial intelligence, Implementation, Ethics, Benefits and challenges

1.Introduction

Artificial intelligence (AI) is advancing at a rapid pace and has the potential to transform the way organisations run. The McKinsey Global Institute estimated that the implementation of AI in businesses could generate up to $3.5 trillion by the year 2030. (Manyika et al., 2017). Furthermore, the implementation of AI can increase productivity, lower expenses, encourage innovation and expansion, and support supply chain management, customer support, and product development. The use of AI raises many ethical concerns, including questions about privacy, accountability, and transparency (Mittelstadt et al., 2016). As organisations consider the adoption of AI, it is important for them to carefully consider these risks and challenges and take steps to address them. The adoption of AI by organisations is likely to continue to grow in the coming years. According to a survey conducted by Harvard Business Review, 71% of respondents expected to use AI in their businesses within the next three years (HBR Analytic Services, 2018). Through this exploratory research, we aim to provide a comprehensive overview of the current state of AI within enterprises and its future direction by the following:

  1. Examine how AI is being utilized within enterprises and assess the potential benefits and challenges of such adoption.
  2. We identify the ethical and societal implications of AI adoption within enterprises, including the potential for job displacement and the risk of bias in AI algorithms.
  3. This study provides recommendations for best practices for enterprises considering the adoption of AI to maximizepotential benefits while minimizing potential negative impacts.

2.Literature Review

2.1. What is Artificial Intelligence?

Artificial intelligence, or AI, is the term used to describe the emulation of human intelligence in machines designed to think and learn similarly to humans (Bostrom, 2014). AI is the ability of machines/robots to perform operations that generally require human intelligence, for example, speech recognition, decision-making, visual perception, and natural language comprehension. Recent years have witnessed much investigation and development in AI, leading to significant advancements in machine learning, deep learning, natural language processing, and robotics. The idea of building robots capable of mimicking human intelligence was first proposed by pioneers such as John McCarthy, Marvin Minsky, and Claude Shannon in the middle of the 20th century.

Machine learning involves training algorithms on large datasets to identify and pinpoint patterns as well as make predictions. As a result, there have been major advancements in recommendation systems, natural language processing, and computer vision. By facilitating neural networks to understand more complicated data representations and deep learning, a branch of machine learning has improved neural network performance (Goodfellow, Bengio, & Courville, 2016). However, when the topic of AI arises, several issues become prevalent, such as the effect of AI on jobs, privacy, and ethics, even though it has the potential to transform many aspects of society; therefore, it is crucial to create ethical AI systems that are accountable, transparent, and consistent with human values (Whittlestone et al., 2019).

                                                                      Figure 1: Learning processes

2.2. The Role of AI in Business

AI is used by companies to automate repetitive operations (Hassanzadeh et al., 2018), analyze data (Wang et al., 2019), and make predictions (Nguyen et al., 2019). The major business applications of AI include automation, data analysis, predictive analytics, marketing, fraud detection, and supply chains.

2.3. Benefits of AI (business perspective)

Regarding operations and processes, integrating AI has a number of advantages for businesses (KPMG, 2018). AI can quickly handle vast volumes of data (Wang et al., 2018) and automate repetitive operations (Hassanzadeh et al., 2018), boosting output and lessening the need for manual labor. AI can evaluate data and generate insights to help businesses make better decisions and increase performance and competitiveness (Zhang et al., 2017). AI can help organisations reduce labor expenses by automating operations and decreasing the demand for human labor (Raza et al., 2018). AI may assist companies in finding new income opportunities, such as tailored marketing (KPMG, 2018) and customer upselling (Barbosa et al., 2018), contributing to revenue growth. Personalized client experiences and more effective customer service can be achieved with AI (Barbosa et al., 2018), which will boost customer satisfaction and retention. AI can be used for predictive maintenance, which lowers maintenance costs and downtime by foretelling when equipment is likely to fail (Nguyen et al., 2019). By analyzing data and trends suggestive of fraud (Huang et al., 2018), AI can assist businesses in identifying fraudulent behavior and preventing financial losses. Furthermore, when analyzing data and spotting inefficiencies (KPMG, 2018), AI may improve supply chain operations (Lin et al., 2018). It can also be used to predict demand (Lin et al., 2018), which can help companies budget for future inventory requirements. A report by Accenture Research (2017) recorded the tangible improvements from AI implementation across 16 different industries and found that Ai has the potential to significantly boost profitability. In addition, Salesforce (2017) highlighted that in the retail industry, the integration of AI for CRM has increased sales by 35%. Furthermore, a study undertaken by McKinsey & Company indicated that organizations implementing Ai in their supply chain and manufacturing process reduced forecasting errors by 20-50%, leading to a decrease in inventory reductions of 20-50%, resulting in significant cost savings.

2.4. Challenges of AI (business perspective)

While using AI in business has many advantages (KPMG, 2018), there are also difficulties in taking AI into account (Liu et al., 2019). High-quality data are essential for AI systems to work efficiently (Wang et al., 2018). To ensure that AI generates actionable results, businesses must ensure that their data are accurate, current, and relevant (Zhang et al., 2017). Ethical issues are another challenge. As AI systems improve, companies need to consider ethical issues such as prejudice (Berendt et al., 2019), responsibility (Bostrom, 2014), and transparency (Dignum et al., 2019). There is an increasing need for AI knowledge, and firms may have trouble locating the people they require to develop and maintain AI systems (KPMG, 2018). In addition, AI systems process large amounts of data, so companies must ensure that they follow data privacy laws (Hofmann et al., 2018) and that the data are used ethically and responsibly (O’Neil, 2016). The integration of AI can be difficult and time-consuming for businesses to incorporate AI technology into their current infrastructure and business processes (Li et al., 2018). Furthermore, as AI systems handle sensitive data, companies must ensure that the systems are safe (Huang et al., 2018) and that the data are shielded from intrusions and breaches (Zhang et al., 2017). The IBM annual report ‘This Cost of Poor Data Quality’ highlights that the quality of the data that feeds the AI system is a primary concern. The U.S. economy incurs an astounding annual cost of approximately $3.1 trillion due to poor data quality (IBM, 2016). To address data quality issues, businesses can implement data audits and enforce robust data frameworks to ensure the reliability and consistency of the data used in AI systems.

 

2.5. Implementation of AI

AI implementation within a company can be a complex multistep process (KPMG, 2018). Among the crucial actions in deploying AI in a company are clearly defining the business challenges the firm is attempting to solve (Hassanzadeh et al., 2018), which is the first step in applying AI. By doing so, it will be easier to ensure that the AI solution is customized to fit the unique demands of the firm (Zhang et al., 2017). Implementing AI in any organization involves multiple steps: problem definition, data collection, data preparation, model selection and training, validation and testing and deployment, each with a unique set of challenges and potential pitfalls. To mitigate these challenges, thorough planning and expert guidance are necessary at each stage. The data preparation stage should involve data auditing practices and data cleansing tools. When selecting and training a model, it could be beneficial to use techniques such as cross-validation to avoid overfitting and ensembling methods to improve model performance. During deployment and monitoring, having a robust IT infrastructure, clear communication channels with users, and ethical oversight mechanisms in place can help ensure the successful implementation of AI.

2.6. Ethics and AI

A number of ethical issues related to AI in the workplace need to be considered (Goodman & Flaxman, 2016). Key ethical concerns with AI in business include the following: Bias: When biased data are taught, AI systems have the potential to reinforce and even exacerbate that bias (Dastin, 2018). Decisions made as a result may be unfair or discriminating, which is harmful to both individuals and organisations (Wachter et al., 2017). Transparency: Many AI systems are “black box” models, which means that people cannot see or understand the decisions they make (Lipton, 2018). This might make it challenging to comprehend how and why decisions are being made, which can cause issues with trust and responsibility (Goodman & Flaxman, 2016). Data privacy: As AI systems process large amounts of data, companies must ensure that they follow data privacy laws and that the data are used ethically and responsibly (National Research Council, 2014). Autonomous systems: Systems that can operate alone, such as autonomous cars or drones, are referred to as autonomous systems (Lin, 2017). These systems raise moral questions about liability, responsibility, and safety (Wallach & Allen, 2009). Displacement of jobs: A possibility when AI systems automate work and render human labor unnecessary (Frey & Osborne, 2017). Businesses must think carefully about the moral ramifications of job loss and how to lessen its detrimental effects on employees (Baert, 2018). Discrimination: This may be because AI systems may make judgments based on factors such as age, gender, and ethnicity (Angwin et al., 2016). When using AI systems, businesses need to be aware of these concerns.

2.7. Societal Implications and Concerns

Many societal implications and problems can be associated with using AI in business. Among these crucial issues are job displacement, which is possible when AI systems automate work and render human labor unnecessary. This might result in social and economic inequity. This might be especially problematic for low-skilled individuals, who may find it challenging to retrain for new professions (Frey and Osborne, 2017). Economic inequality: AI systems may result in economic inequality because those who can access and afford the technology will gain from its enhanced productivity and efficiency, leaving those who cannot (Autor, 2019). Social isolation: As AI systems take on more jobs previously handled by people, there is a risk of social isolation because there will be less opportunity for human interaction (Brynjolfsson and McAfee, 2014). Lack of accountability: Because AI systems can act and make decisions without human supervision, it may be challenging to hold them responsible for their deeds (Wachter et al., 2018). This can lead to questions regarding accountability, duty, and culpability. Ethical issues: The application of AI may give rise to ethical issues, including prejudice, accountability, and transparency, which may influence how different individuals and groups are treated (Crawford and Schultz, 2016).

2.8. Future of AI

AI is predicted to continue to grow and be integrated into a variety of businesses in the coming years. “AI is expected to add $15.7 trillion to the global economy by 2030” (PwC, 2018). Future advancements and trends in AI and business include the following:  Increased automation: AI systems are predicted to automate a broader range of tasks, increasing productivity and efficiency across industries (Gartner, 2020). “Companies that adopt AI in their operations see an average increase in productivity of up to 30%” (Amit & Schoemaker, 2003). Edge computing: As the amount of data produced by IoT devices increases, AI algorithms will be installed in edge devices closer to the data source, enabling faster and more effective data processing (Cisco, 2020). Human-AI collaboration: As AI systems improve, more attention is expected to be given to human-AI collaboration (Wachter et al., 2018), in which AI systems assist people in making decisions and enhancing their capabilities. Ethics and regulation: As AI systems proliferate, the ethical and legal ramifications of their use will increase (Domingos, 2015). Businesses must ensure that their AI systems are accountable, transparent, and compliant with applicable laws. Cybersecurity: To safeguard AI systems from malicious attacks, cybersecurity measures will become increasingly important as AI is utilized more frequently (Deloitte, 2020). Personalization: Businesses will be able to provide customers with highly tailored experiences thanks to AI technology (PwC, 2019), which will boost their engagement and loyalty.

3.Research Methodology

This exploratory research comprised a total of ten interviews and two focus group sessions. Participants were recruited by undertaking purposive sampling to obtain a diverse representation of a variety of business sectors (IT services, finance, healthcare, automotive, and retail) and access to management and personnel who are involved in AI utilization and implementation within their enterprise. A combination of face-to-face and online interviews utilizing video conferencing technology was undertaken. Structured interviews were undertaken to gain in-depth knowledge and experience on the current level of Ai adaptation, application and prospective benefits and obstacles as well as future applications. Each interviewee answered, shared opinions, and elaborated on each of the twelve questions asked. The interviews were limited to 45 minutes to encourage participation and apply a level of consistency.

Two Focus Group sessions were conducted utilizing video conferencing technology. The focus groups were approximately 70-80 minutes in length and consisted of a total of 9 employees who utilized AI across seven distinct industries (healthcare, education, automobile, legal, retail, security, finance). The aim of the two-focus groups is to acquire in-depth information and discussions about the attitudes, beliefs, and perceptions of participants toward the use of AI in their enterprise/industry. A blend of questions and statements were communicated to encourage discussions and gain further information and perspectives on the following topics: how AI is used in the organization, the benefits and challenges of AI adoption, the ethical and societal implications of AI adoption, future applications of AI, and implementation considerations for AI.

A systematic process was undertaken through which the data collected were analyzed, and subsequently, the key themes for the study were presented. Step 1: A total sample of in-depth interviews and 2 focus group discussions was conducted, and information was gathered on AI adoption in various enterprises. Step 2: The qualitative information/data gathered were then imported into Nvivo 12 qualitative analysis software for analysis. The procedure involves categorizing similar and related quotations into codes. Step 3: The codebook and the transcript were then analyzed to capture key themes relevant to the study. The following themes were identified: (1) Al implementation in business and consideration while deploying Al and (2) advantages of AI, foreseeing Al uses and its future potential in company sectors. (3) Al deployment/utilization difficulties and recommendations for best practices in businesses. (4) The impact of Al on growth and innovation in industries and its potential effect on modern business operations. Allowing for thematic content analysis to produce insights from the data.

4.Data Analysis and Interpretation

AI has the potential to significantly benefit organisations in terms of efficiency, productivity, and decision-making.

“AI has given us the ability to develop more effective and precise solutions for our clients, increasing client happiness and boosting income.” Customer Service Specialist

Numerous business sectors are starting to acknowledge the benefits of AI. In fact, 64% of businesses believe that AI will boost their productivity (Forbes Advisor, 2023). Among leading businesses, 9 out of 10 are investing in AI technologies, although only 14.6% have currently deployed AI capabilities in their operations (Authority Hacker, 2023).

“AI has increased the effectiveness of our supply chain and enabled us to offer a more individualized consumer experience.” Logistics FmCG – Manager

The challenges and considerations that organisations must consider when implementing AI include the potential for job displacement, the risk of bias in AI algorithms, and ethical concerns (WEF, 2018).

“Difficulties in gathering and processing the substantial volumes of data needed for AI training, in addition to worries about the security of auto vehicles” Automobile Representative

AI makes decisions using algorithms that either follow rules or, in the case of machine learning, review large quantities of data to identify and follow patterns that consist of multiple layers; machines develop their own learning and patterns, which makes it nontransparent compared to traditional rule-following computing.

“The greatest obstacles were controlling the cost of AI installation and dealing with concerns about potential job displacement.”   Transport and Logistics Representative

There are serious ethical, safety and societal risks associated with the growth of AI technologies. Finding qualified candidates with experience in AI and incorporating AI solutions into clients’ current systems has been a common hurdle, and overcoming early doubts about the accuracy and dependability of AI as well as assuring data privacy and security are significant challenges and difficulties in ensuring regulatory compliance.

“Overcoming early doubts about the accuracy and dependability of AI as well as assuring data privacy and security was a significant challenge.” The marketing director

“Finding qualified candidates with experience in AI and incorporating AI solutions into our client’s current systems were the biggest hurdles.” Entrepreneur

A common challenge that arose was job displacement. AI automates tasks and therefore poses a risk for human workers to become redundant. This is not just an industry-specific challenge, as job losses can cause economic disruption. Our research revealed that job displacement is not a major issue because AI relies on humans to operate programs. Eighty percent of retail sector executives intend to implement AI automation by 2025 (Authority Hacker, 2023). However, AI has generated a level of fear and uncertainty among workers. Therefore, retraining and upskilling have become key factors and investments as workers transition to new roles utilizing AI.

“We had trouble finding high-quality data for training AI models and making sure AI-driven solutions were sustainable and ecologically benign.” Health Representative

“Investments in frameworks and tools for AI auditing, the promotion of ethical AI development, and a focus on workforce development and retraining are a few potential remedies.” Financial representative

“While using AI, companies should prioritize ethical issues, openness, and fairness, as well as retraining and developing their personnel.” Automobile Representative

Organizations should take several precautions to address the possibility of bias and ethical difficulties with AI systems. The first and most important step is to ensure that the data used to train AI systems are diverse, representative, and unbiased (Brundage et al., 2018). This could include cleaning and preparing the data, as well as ensuring that it was gathered honestly and openly. Finally, organisations should implement methods for routinely evaluating and testing AI systems to verify that they are working as intended and are not creating biased or unethical results (Dastin, 2018). AI-using firms face significant challenges due to the danger of prejudice and ethical considerations. To address these challenges, organisations should ensure that their AI systems are built and optimized ethically, transparently, and trained on diverse, representative, and impartial data.

“AI has made it possible for us to work with businesses to address ethical and safety issues in a more organized and efficient manner, ultimately enhancing the reliability of AI systems.” HR personnel

Implementing and deploying AI technology can be complex and is not straightforward.

This research highlights that industries and businesses investing in and applying AI applications must identify what type of AI should be implemented. Aligning AI initiatives with business objectives and strategies can be difficult for organizations. This could entail identifying the business problems that AI can solve as well as the criteria that will be used to assess the effectiveness of AI initiatives (Kohli, 2018). To obtain the full benefits of AI, businesses may need to consider how AI will integrate with their existing systems and processes and make any necessary changes (Goyal, 2017).

“Legal and moral issues related to AI in their sectors. These hazards can be reduced by working with regulatory organizations and maintaining current industry best practices.” Legal Representative

“Maintaining stakeholder trust requires making sure AI systems are transparent and understandable, even if this can be difficult. These issues can be addressed by creating explicit AI rules and encouraging open dialog.” Fin Tech Representative

AI applications and systems can collect, process and store large quantities of personal data. The European Union’s General Data Protection Regulation (GDPR) sets out detailed conditions for collecting, using, and storing personal data, and businesses operating in the EU must comply with these regulations. The legal framework for AI is evolving with an emphasis on ensuring that the technology is used in a responsible and ethical manner. (S. De Choudhury and J. Kleinberg 2017).

“Companies should take into account elements including data security and privacy, algorithmic fairness and bias, employee development and retraining, regulatory compliance, and moral issues.” IT specialist

“It is critical to ensure data security and privacy.” It and Security Personnel

According to previous research, the design of an AI system needs to be considered. The system requires testing and for any biases or errors to be addressed to ensure that the system is accurate and trustworthy. In addition, businesses may face cultural challenges when integrating AI. The AI system requires seamless integration into existing business processes and requires all employees to cooperate at all levels of the business. Employees might resist change and fear job loss and may lack an understanding of how AI works (Hee, 2018). Organisations may need to invest in employee education and training to help employees understand the benefits and implications of AI. They may also want to think about how to engage and inform their employees about AI initiatives (Kohli, 2018).

“Our company focuses on creating open and just AI algorithms and makes investments in training and upskilling legal practitioners to work with AI systems.”

The in-depth information and content acquired during the methodology stage of the research allowed us to identify the Ai processes and applications that enterprises utilize as a part of their external and internal processes.

                                                       Figure 2 : A.I internal and external processes

External Processes

Marketing: AI-driven marketing tools such as customer segmentation, tailored content, and predictive analytics substantially enhance marketing effectiveness and customer engagement. Companies that have adopted AI in marketing have seen increased conversion rates, more focused campaigns, and a more efficient allocation of marketing resources.

Customer Service: AI-powered chatbots and virtual assistants improve customer service operations by providing prompt and precise responses, reducing wait times, and offering real-time support. However, balancing automated and human assistance is crucial, as customers continue to value human interaction to address more complex concerns.

Fraud Detection: Implementing AI in fraud detection has enabled organizations to more effectively identify and prevent fraudulent activities. Machine learning algorithms, especially those utilizing anomaly detection and pattern recognition, have considerably improved the precision and speed of fraud detection systems, which has led to decreased losses and increased customer trust.

Human Resources: AI technologies involve streamlined recruitment by automating CV screening, interview scheduling, and candidate evaluation. Moreover, AI has supported employee retention and career development by providing personalized training and performance management.

Internal Processes

Supply Chain: Incorporating AI in supply chain management involves optimizing inventory control, demand forecasting, and logistics operations. Companies using AI in their supply chain report lower operational costs, reduced lead times, and enhanced accuracy in demand forecasting.

Predictive Analysis: AI-driven predictive analysis for well-informed decision-making. AI-powered predictive models empower organisations to foresee trends, detect potential risks, and make data-driven decisions, which results in increased operational efficiency and a competitive edge.

Data Analysis: Employing AI in data analysis has enabled more accurate, efficient, and insightful data processing. Companies that have embraced AI-driven data analysis tools have reported improved decision-making capabilities and an increased ability to derive actionable insights from extensive datasets.

Automation: The study demonstrated that AI-powered automation solutions significantly boost operational efficiency by reducing human error and allowing employees to concentrate on more strategic and creative tasks. Companies that have incorporated AI in automation have experienced increased productivity, cost savings, and higher employee satisfaction.

Considerations for AI Implementation in Organizations

To successfully integrate AI into an organization, several factors must be considered. Organisations implementing AI technologies in external and internal processes should consider the following.

External (customer-facing) Processes  Marketing, Customer Service, and Fraud Detection:Customer Experience: AI must improve the customer experience, not detract from it. AI-powered solutions are user-friendly, efficient, and accessible to diverse customer bases.

Data Security and Privacy: Since customer-facing AI systems handle sensitive customer data, organizations should implement strict data protection measures. To avoid legal issues and maintain customer trust, data protection regulations such as the GDPR in the UK must be followed. Integration with existing systems: To minimize disruptions and maximize resource efficiency, AI technologies should be seamlessly integrated with marketing, customer service, and fraud detection systems.

External (customer-facing) Processes Marketing, Customer Service, and Fraud Detection: Customer Experience: AI must improve the customer experience, not detract from it. AI-powered solutions are user-friendly, efficient, and accessible to diverse customer bases.

Data Security and Privacy: Since customer-facing AI systems handle sensitive customer data, organizations should implement strict data protection measures. To avoid legal issues and maintain customer trust, data protection regulations such as the GDPR in the UK must be followed.

Integration with Existing Systems: To minimize disruptions and maximize resource efficiency, AI technologies should be seamlessly integrated with marketing, customer service, and fraud detection systems.

                                                                Figure 3: A.I enterprise utisation

External (customer-facing) Processes

Marketing, Customer Service, and Fraud Detection:Customer Experience: AI must improve the customer experience, not detract from it. AI-powered solutions are user-friendly, efficient, and accessible to diverse customer bases.

Data Security and Privacy: Since customer-facing AI systems handle sensitive customer data, organizations should implement strict data protection measures. To avoid legal issues and maintain customer trust, data protection regulations such as the GDPR in the UK must be followed.

Integration with Existing Systems: To minimize disruptions and maximize resource efficiency, AI technologies should be seamlessly integrated with marketing, customer service, and fraud detection systems.

Internal Procedures: HR, supply chain, predictive analysis, data analysis, and automation are internal processes.

Processes Include Employee Engagement and Retraining: AI may displace workers or change their roles. Employee engagement, retraining, and transition support should be prioritized.

Ethical Considerations: Organisations should consider how adopting AI will affect fairness, transparency, and bias in decision-making.

Infrastructure and Compatibility: An organization’s infrastructure and IT systems must be AI-compatible.

Scalability: AI systems should be scalable to support organisational growth and transformation. AI systems must adapt and grow as businesses change.

ROI: Organizations must calculate the AI’s short- and long-term ROIs. AI technology costs must be weighed against their expected benefits and efficiencies.

Limitations

Limitations must be recognized when exploring the possible utilization of AI within enterprises. A notable constraint was that 40% of the interviewees and focus group attendees were apprehensive about divulging the details of operational activities. They were concerned that sharing such confidential information could have adverse effects on their industry. It was decided to anonymize all participants’ information, providing only details about the job title and industry. Moreover, the anonymization of the data may impede the generalizability of our findings; therefore, this exploratory study requires further research to address these limitations.

 Review of Findings

Theme 1: Overview of Al Implementation in Business and Consideration while Deploying Al

AI can process large amounts of data and perform complex calculations, allowing certain tasks to be undertaken at a much faster pace and minimizing the risk of human error. This allows businesses to focus more time and effort on strategic tasks. Another critical usage highlighted is AI’s ability to provide insights and predictions based on the large volumes of data that can be processed and analyzed. Businesses can utilize this information to assist in decision-making (i.e., predicting market trends, predicting sales forecasts, etc.). AI is utilized in decision-making not only to assist in making decisions but also to enable decisions to be adapted to meet the preferences and prerequisites of the user. These data can generate personalized recommendations or tailor products and services to specific requirements. Our research revealed that AI has helped businesses provide more efficient and professional customer service. The Ai automating process, called chatbots, can quickly respond to customer inquiries and provide personalized and prompt customer support. Consumer trends and feedback can also be highlighted, allowing businesses to understand their customers.

Theme 2: Advantages of AI, Foreseeing Al Uses and its Future Potential in Company Sectors

There are many applications for AI, including in the healthcare, banking, retail, and transportation industries. Another trend is the increased use of AI to automate processes and procedures. Employees may focus on more complex and creative work using AI to automate mundane and repetitive chores (Goyal, 2017). This trend is expected to greatly impact the future form of employment and the necessary skills. Therefore, businesses must examine how to adapt (Bryson et al., 2018). The third trend is the growing use of AI in decision-making. AI can greatly increase decision-making speed and precision and can analyze massive volumes of data to uncover patterns and trends that humans miss (Kohli, 2018). On the other hand, businesses must consider how to balance the benefits of AI and any potential hazards and ethical considerations, such as the risk of bias and the need for accountability and transparency (Brundage et al., 2018).

Theme 3: Impact of Al on Growth and Innovation in Industries and its Potential Effect on Modern Business Operations

China has advanced in terms of AI adoption, with 58% of companies deploying AI. In comparison, the United States has a lower implementation rate, with 25% of companies using AI and 43% exploring its potential applications (Forbes, 2023). AI is projected to have a substantial economic impact, contributing a net increase of 21% to the United States’ GDP by 2030 (Forbes, 2023).

AI can automate routine tasks, allowing employees to focus on more creative and strategic work. Make better decisions by providing accurate and timely data analysis due to AI-driven technologies that spur development and innovation across almost all sectors. Some industries stand to gain significantly from AI developments, including the healthcare, automotive, manufacturing, and financial sectors. It is estimated that artificial intelligence might eliminate 85 million jobs by 2025 (Authority Hacker, 2023).

Theme 4: Difficulties Faced in Al Deployment/Utilization and Recommendations for Best Practices in Businesses

Businesses that do not use AI risk falling behind competitors who do to improve productivity, efficiency, and decision-making (Bughin, et al., 2018). AI is becoming a crucial competitive differentiator in today’s business environment (Daugherty & Wilson, 2017). According to a McKinsey & Company Analysis, companies that use AI see significant cost savings, revenue growth, and increased customer satisfaction (Bughin et al., 2018). Those that do not use AI may find it difficult to compete with these organisations and to attract and retain customers (Bughin, et al., 2018. Furthermore, the use of AI poses various ethical and legal concerns, such as the potential for bias in AI algorithms and the influence of AI on employment. Firms must thoroughly assess these risks and adopt solutions to maximize the benefits of AI while minimizing any adverse effects. Businesses that do not use AI risk ignoring the potential for development and innovation (Marr, 2018). As AI becomes more prevalent, it is predicted to promote the development of new products and services that could significantly increase a company’s revenue (Marr, 2018).

5.Conclusion and Recommendations

This research paper has examined the potential utilization of AI in enterprises, addressing key objectives such as understanding AI implementation, exploring benefits and challenges, evaluating ethical and societal implications, and providing recommendations for best practices. The findings reveal that AI is being used across diverse sectors, such as manufacturing, finance, software development, retail, and logistics, to improve operational processes, enhance customer experiences, and optimize decision-making. However, the implementation of AI has not been without challenges, such as data quality, biases in algorithms, and compliance with ethical and legal standards. To address these challenges, proactive measures such as employee retraining, collaboration with external partners, and fostering transparency and accountability have been adopted.

The predictions for AI’s future applications in enterprises are promising, but a focus on data privacy, workforce development, ethical considerations, and regulatory compliance must accompany this growth. It is crucial for organizations to approach AI adoption with careful planning, continuous evaluation, and a commitment to responsible and accountable AI practices, ensuring the harmonious integration of AI into the enterprise fabric. The anonymization of data may impede the generalisability of our findings; therefore, it is recommended that this exploratory research would require further research to address these limitations, such as undertaking case studies or securing the necessary confidentiality agreements that allow for the disclosure of more specific organisational information. Additionally, an increase in governmental management and surveillance similar to that of the General Data Protection Regulation (GDPR) is needed to safeguard data usage, eliminate bias and ensure data privacy.

References

  • Accenture Research. (2017). Why Artificial Intelligence is the Future of Growth.
  • Amit, R., & Schoemaker, P. J. (2003). Strategic assets and organizational rent. Strategic Management Journal, 24(1), 33-46.CrossRef
  • Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. Retrieved from
  • Authority Hacker. (2023). 149 AI Statistics: The Present and Future of AI [2023 Stats]. Available at:
  • Baert, S. (2018). The ethics of AI in the workplace. Harvard Business Review. Retrieved from
  • Barbosa, R., Leite, J., & Machado, J. (2018). Artificial intelligence in customer service: a survey. Journal of Ambient Intelligence and Humanized Computing, 9(5), 4335-4353.
  • Bengio, Y., Goodfellow, I. and Courville, A. (2016) Deep Learning. Cambridge, MA: MIT Press.
  • Berendt, B., Bien, S., & Fischer, K. (2019). Algorithmic bias in machine learning: problems and perspectives. ACM Transactions on Intelligent Systems and Technology (TIST), 10(6), 1-26.
  • , C. M. (2006) Pattern recognition and machine learning. New York: Springer.
  • Bostrom, N. (2014). Superintelligence: Paths, Dangers, and Strategies. Oxford University Press.
  • Brundage, M., Altman, M., Clifton, C., Danks, D., Ganchev, K., Mittelstadt, B., & Zemel, R. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.
  • Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of
  • Bughin, J., Hazan, E., & Lund, S. (2018). How artificial intelligence can deliver real value to companies. McKinsey & Company. Retrieved from
  • Bughin, J., Mischke, J., & Chui, M. (2018). Artificial intelligence: The next digital frontier? McKinsey & Company.
  • (2020). Edge computing: The next frontier of IoT. Retrieved from
  • Crawford, K. (2016). Can an Algorithm Be Agonistic?
  • Crawford, K., & Schultz, J. (2016). The hidden biases in big data. Harvard Business Review, 94(3), 56-64.
  • Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women.
  • Daugherty, P., & Wilson, D. (2017). Human + machine: Reimagining work in the age of AI. Boston, MA: Harvard Business Review Press.
  • (2020). AI and cybersecurity: The need for collaboration. Retrieved from
  • Dignum, V., Dignum, F., & Verbeek, H. (2019). Artificial intelligence and ethics. Journal of Business Research, 98, 1-7.
  • Dignum, V., & Verbeek, H. (2017). Responsibility and ethical issues in autonomous systems. IEEE Intelligent Systems, 32(1), 96-101.
  • Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
  • Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  • Forbes Advisor (2023) ’24 Top AI Statistics & Trends In 2023′. Available at:
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization? Technological Forecasting and Social Change, 114, 254-280.CrossRef
  • Goodman, B., & Flaxman, S. (2016). European Union regulations on algorithmic decision-making and a “right to explanation.” arXiv preprint arXiv:1606.08813.CrossRef
  • Goyal, M. (2017). The business impact of AI. McKinsey & Company.
  • Hassanzadeh, A., Gholami, R., & NikooMH, M. (2018). A review on the applications of artificial intelligence in manufacturing industry. Journal of Manufacturing Systems, 47, 1-17.
  • HBR Analytic Services. (2018). The State of Artificial Intelligence in the Enterprise. Harvard Business Review.
  • Hee, S. (2018). The impact of artificial intelligence on decision-making in organizations. Journal of Business Research, 85, 198-207.
  • Hofmann, M., Böhme, R., & Holz, T. (2018). The economics of data breaches: An overview. Journal of Business Economics, 88(4), 361-377.
  • Huang, C., Li, Y., & Li, X. (2018). Deep learning for fraud detection: A review. Information Sciences, 427, 68-81
  • Kohli, R. (2018). The role of AI in decision making. Analytics Insight.
  • Lipton, Z. C. (2018). The mythos of model interpretability. arXiv preprint arXiv:1801.00631.CrossRef
  • Li, Y., Li, X., & Li, X. (2018). A survey of artificial intelligence in logistics and supply chain management. Journal of Business Research, 84, 260-278.
  • Lin, X., Wang, H., & Wang, X. (2018). Artificial intelligence in supply chain management: A review and future research directions. Journal of Business Research, 96, 280-292.
  • Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., & Marrs, A. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Institute.
  • Marr, B. (2018). Artificial intelligence in practice: How 50 successful companies are using AI and machine learning. Kogan Page.
  • Marr, B. (2018). The truth about AI and job displacement. Forbes. Retrieved from
  • McKinsey & Company. (2016). Artificial Intelligence – The Next Digital Frontier?
  • (2020). Predictive maintenance: The next frontier of operational excellence. Retrieved from
  • Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of artificial intelligence. Nature, 534(7608), 671-679.
  • National Research Council. (2014). Privacy in the era of big data: Balancing progress and privacy. National Academies Press.
  • Nguyen, H., Tran, T., & Nguyen, T. (2019). A survey of artificial intelligence techniques in predictive maintenance. Journal of Intelligent Manufacturing, 30(4), 1235-1253.
  • O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  • (2018). Chatbots and virtual assistants: Changing the way we bank. Retrieved from
  • (2018). Sizing the prize What’s the real value of AI for your business and how can you capitalize?. Retrieved from https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
  • (2017). State of Marketing: Insights and trends from over 4,100 marketing leaders worldwide.
  • Sharma, S. (2019). Artificial intelligence: A beginner’s guide. O’Reilly Media, Inc.
  • Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual fairness. arXiv preprint arXiv:1703.06856.
  • Wachter, S., Mittelstadt, B., Russell, C., & Crawford, K. (2018). Ethical governance of AI in the real world. AI Magazine, 39(1), 75-87.
  • Wallach, W., & Allen, C. (2009). Moral machines: Teaching robots right from wrong. Oxford University Press.CrossRef
  • Wang, Y., Cho, K., & Bengio, Y. (2019). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Wang, Y., Cho, K., & Bengio, Y. (2018). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
  • (2018). The future of jobs report 2018. World Economic Forum. Retrieved from
  • Zhang, H., Wang, H., & Hu, Z. (2019). Artificial intelligence in marketing: Past, present, and future. Journal of Business Research, 98, 34-44.
  • Zhang, L., Liu, Y., & Lu, J. (2017). A survey of data mining and machine learning methods for cyber security intrusion detection. Journal of Network and Computer Applications, 96, 1-14.
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