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Article

Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method

by
Nicolae Bobitan
1,
Diana Dumitrescu
1,*,
Adriana Florina Popa
2,
Daniela Nicoleta Sahlian
2 and
Ioan Codrut Turlea
2
1
Accounting and Auditing Department, Faculty of Economics and Business Administration, West University of Timisoara, 300115 Timisoara, Romania
2
Department of Accounting and Audit, Bucharest University of Economic Studies, 010374 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(11), 2198; https://doi.org/10.3390/electronics13112198
Submission received: 13 May 2024 / Revised: 31 May 2024 / Accepted: 3 June 2024 / Published: 4 June 2024
(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)
Browse Figures
Figure 1
<p>Exploratory scenarios for evolving skill requirements in business organizations through AI integration.</p> "> Figure 2
<p>SWOT analysis for the gradual integration of AI into business organizations.</p> "> Figure 3
<p>SWOT analysis for the aggressive integration of AI into businesses.</p> "> Figure 4
<p>SWOT analysis for the selective integration of AI into business organizations.</p> ">
Versions Notes

Abstract

:
This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.

1. Introduction

The integration of artificial intelligence (AI) into business operations has fundamentally transformed workforce skill requirements, necessitating proactive and strategic planning to cultivate a workforce that is resilient, adaptable, and equipped to meet the demands of a technologically advanced future. As AI advances, businesses must anticipate the skills needed to effectively integrate and take advantage of this technology. This paper delves into the current and future skill demands driven by AI integration within business organizations and offers insights into preparing for shifts in the employment landscape.
AI significantly boosts performance across organizational and process levels. Significant benefits arise from the use of automation, cognitive technologies, and data analysis through AI algorithms, including enhanced productivity, improved time and cost efficiencies, reduced human error, accelerated business decision-making, the better prediction of customer preferences, and increased sales [1]. Because organizations embrace AI integration to improve efficiency, employees need to possess certain skills to effectively harness the power of AI and drive positive outcomes [2].
In the Global Human Capital Trends survey conducted by Deloitte in 2024 [3], 25% of respondents expressed concerns about the increase in demand for new skills and jobs due to technological or business model changes. Consequently, organizations must adopt a proactive mindset towards workforce development, anticipating future skill needs as AI becomes more prevalent in business operations and devising strategies to cultivate these skills among their employees.
Employees must improve their skills in order to compete in the AI era and keep their jobs. These skills include data analysis, digital proficiency, advanced thinking skills, decision-making, and ongoing learning. A significant move towards successful human–AI collaboration may stimulate company growth through putting an emphasis on improving technological and cognitive skills and highlighting complementarity rather than competition between humans and AI [4].
Despite the evolving demands and challenges related to the skills needed for employees to effectively collaborate with AI systems, currently, most organizations lack the capacity to provide those necessary skills. As a consequence, the effective integration of AI into business practices necessitates a strategic reassessment of workforce capabilities, compelling organizations to foster a culture of continuous learning and transformation to keep pace with such rapid AI advancements. This involves ensuring the provision of adequate training and upskilling resources to bridge current capabilities with emerging demands and to facilitate effective human–AI collaboration [5].
Organizations are thus urged to prioritize ongoing learning and organizational transformation to keep pace with the rapid advancements in AI, ensuring that sufficient resources are provided for training and upskilling their workforce in AI capabilities [6]. Addressing the gap between current capabilities and emerging needs, it is crucial to identify and bridge skill gaps and to foster effective human–AI collaboration. By doing so, organizations can ensure that their employees are equipped with the necessary skills to navigate the evolving organizational landscape and effectively leverage AI technologies [7].
Despite the abundance of literature on AI’s impact on the workforce, there is a shortage of comprehensive analyses that examine the complex nature of AI’s role in redefining skill requirements across different integration scenarios. Considering AI’s ability to automate certain jobs and to create new roles, through the literature reviewed, a distinct set of skills have been identified, including technical knowledge and interpersonal and cognitive skills. This study aims to fill this gap by providing an in-depth foresight analysis of how technical expertise, combined with strong interpersonal and cognitive skills, will evolve via the gradual, aggressive, or selective integration of AI. Furthermore, the research explores how organizations can proactively foster a culture of continuous learning, leadership’s role in this process, and strategies for building a resilient and adaptive workforce.
The central research question of the paper is as follows: “How do varying AI integration rates—gradual, aggressive, and selective—affect the evolving skill requirements of employees in business organizations, and what foresight strategies may be used to prepare the workforce for these future scenarios?”. This study aims to investigate how different paces of AI integration predict a paradigm shift in skill requirements, using a foresight analysis based on the scenario method. Each scenario was investigated with SWOT analysis to develop strategic solutions adapted to the evolving skill requirements. SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) is a strategic planning tool used as an instrumental method in developing strategies that leverage strengths, mitigate weaknesses, capitalize on opportunities, and address potential threats.
After setting the context for the research by outlining the study’s motivation, aims, and key research question, this paper conducts a critical review of existing research on AI’s impact on workforce skills, identifying a gap that our study seeks to fill, the level of AI’s integration into business organizations, whether gradual, aggressive, or selective, as well as how these various adoption strategies shape the evolution of worker skills. The next section describes the research design and analysis approaches. Three different potential future scenarios have been developed, starting from the actual skills requirements for using AI technologies, and this technique allows us to base our investigation on empirical facts and critical accuracy. For each scenario, a SWOT analysis was conducted with in order to identify strategies aligned with the objective to identify how the skills requirements evolve through the gradual, aggressive, and selective integration of Artificial Intelligence in business organization. By understanding these strengths, weaknesses, opportunities, and threats, we have formulated targeted strategies to enhance the competitive edge, mitigate risks, and capitalize on the transformative potential of AI. The Results and Discussion section summarizes our findings and interprets them in light of the reviewed literature and our study’s theoretical framework. In this section of the paper, we have explained the complex effects of AI on employees’ skills, providing insights into strategic foresight and planning for a most effective human–AI collaboration.
This study emphasizes the strategic imperative for businesses to take a comprehensive and forward-thinking approach to employees’ skill development in the age of artificial intelligence by outlining a clear path for developing both hard or technical skills related to AI’s use, as well as developing soft or “people skills”, such as critical thinking, creativity, and effective communication. Whether organizations choose a gradual, aggressive, or selective strategy for AI integration, our analysis emphasizes the importance of a proactive skill development strategy and organizational adaptability for the creation of an AI-driven workforce.

2. Literature Review

2.1. Perspectives on Essential Skills for AI Integration into Business Organizations

A list of the top ten skills businesses expected to be required between 2018 and 2024 was highlighted by the “Global Challenge Insight Report: The Future of Jobs” [8]. Critical and analytical thinking, complex problem-solving, active learning, creativity and initiative, trustworthiness, emotional intelligence, reasoning, time management, leadership, and social influence are the prominent listed skills. Additionally, skills such as system analysis, evaluation, technology design, and programming are predicted to gain popularity by 2024. In contrast, the demand for manual and physical skills, basic literacy and numeracy, technology installation and maintenance, and monitoring and control abilities decreased in 2022. These results are valuable, with insights that should be considered by governments, corporations, and educational institutions as a basis for the development of strategies that can effectively navigate the impending shifts in the job market.
In the context of the technological advances observed in service robotics and improved automation capabilities, almost 50% of jobs are at a high risk of computerization, together with the potential truncation of labor market polarization, with low-skill workers and low-wage jobs. These results suggest a crucial need for workers to develop creative and social skills and to adapt to new roles that are less susceptible to automation [9].
In recent years, particularly due to the COVID-19 pandemic, there has been a rapid acceleration towards workforce digitization. Moreover, the integration of artificial intelligence into business organizations has reached unprecedented levels [10]. This pace of integration has the potential to redefine the paradigms of employees’ skill requirements within organizations. Consequently, the advancement of intelligent automation technologies is transforming corporate strategies regarding employee management and performance enhancement. This has led to both opportunities and challenges for Human Resource Management (HRM) in areas such as job displacement, AI–human collaboration, decision-making processes, and learning, training, recruitment, and job performance evaluation [11]. As studies have shown, AI integration into companies significantly enhances performance and employee engagement, leveraging various key areas, including the selection and retention of talented employees, the provision of suitable training and development, the formation of effective teams, a supportive organizational culture, the fostering of leadership, and a reduction in the employee workload [12].
In the context of artificial intelligence’s (AI) ongoing advancement and its increasing incorporation into business workflows, organizations encounter the crucial necessity to equip their employees with the skills needed for competent collaboration with AI systems [6]. To bridge the gap between new business models and employees, senior executives should first identify capability gaps and determine the specific skills needed for effective human–AI collaboration [13].
Overall, the literature reviewed reveals a gap in our understanding of the evolving skill requirements fostered by AI development, suggesting a potential misalignment between the perceived necessary skills for AI integration into business organizations. While some researchers emphasize the importance of soft skills such as creativity, emotional intelligence, and critical thinking, others advocate for a focus on technical competencies, like data analysis and AI technological proficiency. The imperative for continuous learning across professional careers, coupled with the need for competencies like technological literacy, effective communication, leadership, creativity, and adaptability, has become mandatory in an evolving digital and AI landscape [14].
In conclusion, the integration of artificial intelligence into business organizations not only transforms work paradigms but also redefines the essential skill requirements for employees, highlighting the need for adaptability and continuous learning. Success in this endeavor requires a holistic approach that values both the development of technical and interpersonal skills, reflecting an adaptation to an ever-evolving business environment. By anticipating and addressing these needs, organizations can ensure a smooth transition to a future in which human–AI collaboration is not only possible, but also optimized for maximizing efficiency and innovation.

2.2. Implications of AI Integration for Employees, Employers and Educators

AI integration within business frameworks could significantly influence the future work landscape and skill requirements [15]. To seamlessly integrate AI, it is imperative for employers to cultivate a workforce endowed with a diverse skill set. As has been shown before, these skills include knowledge concerning AI technologies, data analysis, and programming, along with a robust understanding of data privacy and ethical considerations [7]. Furthermore, critical thinking, problem-solving, and decision-making abilities are needed to address the complex ethical and societal challenges posed by AI integration. Effective communication and collaborative skills are also essential in bridging human workers and AI systems, facilitating teamwork and shared decision-making processes [16].
For employers, AI integration promises enhanced productivity and efficiency through automation and sophisticated data analysis, leading to cost reductions and competitive advantages. Consequently, employers must commit to the reskilling and upskilling of their workforce, ensuring employees possess the requisite skills for AI-related roles and can adapt to the dynamic demands of the business landscape [17]. However, the potential for job displacement demands that organizations proactively adapt work designs to accommodate an increasingly integrated human–AI workforce [18]. To attract and retain exceptional talent, employers are encouraged to prioritize learning opportunities, facilitating continuous education and training. Although there may be concerns about AI substituting human workers, it is critical to take a proactive and pragmatic strategy that emphasizes the mutually beneficial contributions of people and AI in decision-making processes [7].
Despite this, labor market interventions aimed at job creation must account for displacement risks, ensuring that newly created positions are effectively integrated into the labor market, thereby enhancing job search efficiency and skills. Research by Babina et al. [19] demonstrates that AI investments lead to an overall growth in employment, benefitting individuals with higher education levels and advanced IT skills. The seamless integration of human and AI intelligence is pivotal for fully capitalizing on digital technology’s potential to tackle critical business and societal challenges. This entails a strategic overhaul of current job classifications and skill assessments to maximize the benefits of technological advancements [20]. Establishing an environment that supports learning, alongside formulating strategies and allocating resources, empowers employees to acquire AI skills, access necessary knowledge, and facilitate the smooth integration of AI into business operations [21].
Despite the increased demand for skills that enable efficient AI collaboration, many companies and educational institutions are facing a capacity gap. As a result, the integration of AI within business organizations requires a dual approach: developing an internal market for talent and a marketplace for lifelong training experiments, while also focusing on the closer human–machine “interwork” [11]. This ensures that employees have access to ongoing learning opportunities, aligning their skills with career trajectories. Although efforts have been made over the last two decades to develop and update the digital skills of instructors, academics, and researchers in higher education institutions, the challenges now are more complex. There is a close connection between business and technology, and higher education institutions will struggle to anticipate the changes in competencies and professions brought about by AI and robots [22]. To address this skill gap, a closer constructive collaboration between business and educators is crucial for pre-empting the disciplinary and professional shifts heralded by AI and robotics. This collaboration can take the form of hybrid business/academic options, where academic programs are designed in partnership with industry to ensure that students gain relevant skills and knowledge that align with the needs of the labor market. Furthermore, organizations must prioritize ongoing learning and organizational transformation that means a shift in mindset, prioritizing short-term goals and allocating sufficient resources for training and upskilling the workforce in AI capabilities.
Educators are confronted with the challenge of embedding AI into their teaching methodologies and curricula [23]. AI can offer personalized learning experiences and support for students, as well as enable teachers to analyze student progress and tailor instruction. However, educators must adapt their roles, gain the skills and knowledge to effectively use AI in teaching, and navigate ethical considerations, bias mitigation, and data privacy concerns. Achieving a balance between technological and human elements in teaching is crucial for fostering inclusive learning environments that accommodate diverse student needs. Moreover, educators must remain abreast of advancements in AI-enhanced technologies and develop pedagogical strategies that bolster student engagement, promote interaction and collaboration, and cultivate a variety of learning skills in the context of AI literacy. Additionally, educators should focus on assessing student learning outcomes using both quantitative and qualitative methods to ensure the effective integration of AI in education. Emphasizing career development within the AI domain requires an adaptation to human–computer collaboration philosophies, reshaping the educator role, enhancing smart education expertise, and bolstering institutional support.
As a result, the integration of artificial intelligence (AI) fundamentally changes both the business world and the educational landscape, imposing new skill requirements and redefining traditional roles. Employers are required to navigate through a dynamic landscape in which the automation and advanced data analysis offered by AI can bring efficiency and competitive advantages but also raise challenges related to workforce retraining and the ethical integration of technologies. To maintain professional relevance and capitalize on the opportunities created by AI, both employees and educational institutions must engage in ongoing learning and adaptation processes, developing technical, ethical, and interpersonal competencies that match the new requirements. In this context, close collaboration between the business sector and academia becomes essential to anticipate and shape future career paths in the AI era. This constructive collaboration between humans and machines, based on continuous learning and adaptability, marks a crucial evolution in addressing challenges and leveraging the potential of artificial intelligence for society.

2.3. Ethical Implications of AI Integration

Organizational leaders are now faced with the task of fostering the technical knowledge of their workforce, supporting the development of new skills, and utilizing AI in an innovative and ethical manner. Despite its potential benefits, AI poses significant challenges, with significant concerns revolving around the ethical implications of gathering the information required for its intended purpose [24]. A basic understanding of AI technologies is needed for employees to critically engage with and use these tools. This includes an awareness of the data these technologies utilize, their predictive capabilities, and the potential biases inherent within AI models [25]. The potential that AI will automate jobs faster than the workforce can adapt raises concerns about widespread job losses, economic instability, and higher levels of inequities. This underscores the potential for discrimination between individuals that possess AI-relevant skills and those who do not [26]. Ethical AI deployment, underpinned by cross-disciplinary partnerships and guided by stringent ethical frameworks, can significantly enhance human life while mitigating associated risks [27]. Moreover, there are concerns about how AI systems could perpetuate or worsen existing social inequities if employees are trained using biased data. This might lead to discriminatory practices within business operations, disproportionately affecting specific groups of individuals [28].
Privacy violations constitute another critical concern, with AI systems’ reliance on extensive datasets, including personal information, posing risks for data breaches and unwarranted surveillance [26]. Furthermore, the looming fear of AI surpassing human intelligence raises serious questions about the potential for an over-reliance on AI, which could devalue human decision-making capabilities [29]. This ambiguity extends to the accountability for errors made by AI systems, complicating the determination of liability in instances where AI decisions result in harm. Determining liability in cases of accidental errors in AI-based decisions can be complex [28].
In conclusion, the integration of AI into business operations provides a twofold challenge: achieving its potential for innovation while also addressing ethical concerns. Companies must navigate these currents by promoting technical understanding and ethical AI use while remaining aware of the risk of data breaches, job automation, and the exacerbation of social inequalities. The objective is to set ethical norms and develop skills that ensure the advantages of AI, without jeopardizing privacy or deteriorating inequality. The future of AI in business depends on a balanced approach that prioritizes both technological advancement and ethical integrity.

3. Research Methodology

Foresight analysis is a pivotal tool for anticipating potential future developments and their ramifications, and it operates under the premise that, although the future remains inherently uncertain, it can be envisaged and prepared for through strategic foresight practices [30]. The scenario method, thus, serves as a systematic approach to explore a variety of plausible future scenarios concerning the evolution of skills necessitated by the extensive adoption of AI in business contexts. Understanding future skills is crucial due to their significant implications for society, the economy, and technological advancement [31]. This research endeavors to provide insights that will empower organizations, individuals, and policymakers to make informed decisions when navigating the uncertain and complex future landscape of AI integration into businesses.
Historically, the scenario method has found application in military war game simulations and has evolved to play a significant role in strategic business planning and environmental change assessments through foresight analysis ([32,33]). This study adopts an exploratory scenario technique focused on investigating a wide array of futures without predetermining a specific desired outcome. This methodological choice underscores the value of scenarios as indispensable tools for formulating strategies in environments marked by uncertainty [34]. By contemplating various potential future trajectories based on current trends, uncertainties, and driving forces, this approach aims to broaden our understanding of the future, challenge prevailing assumptions, and prepare for an array of eventual outcomes [35].
The main goal of exploratory scenarios is to expand thinking about possible futures, challenge assumptions, and prepare for a variety of outcomes. By design, the exploratory scenarios start from the present or a known situation and move forward, asking “WHAT IF” questions to uncover different possibilities. This research methodology is instrumental in identifying how alterations in key variables and the interplay among diverse factors might culminate in a range of future scenarios. Particularly valuable in contexts of high uncertainty, exploratory scenarios enable decision-makers to comprehend the impact of various trends and uncertainties on their strategies and operations, thereby aiding in resilience building and strategic planning [36]. These scenarios facilitate a structured analysis of external environments, assess the durability of strategies under varying conditions, and spotlight potential opportunities and threats.
As a result, strategic scenario planning is used to anticipate various outcomes and to design initiatives that not only react to current skill gaps, but also prepare employees for future demands. The anticipatory nature of the study, which uses the scenario-based approach, supposes a forward-looking perspective, asking not just about the changes, but asking how organizations can proactively address them, through the strategic foresight of workforce skill development.
Figure 1 delineates the application of exploratory analysis in developing strategic foresight concerning the evolving skill requirements prompted by AI integration in businesses.
In our analysis, we chose to explore the degree of AI integration in future business organizations (gradual, aggressive, or selective) as the primary scenario parameter. This choice reflects the intent to construct conditional probabilistic futures around the uncertainty of evolving skill requirements due to AI integration. Consequently, the study analyzed the evolving skill requirements within the context of three exploratory scenarios, each reflecting the differing degrees of AI integration. A SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) was conducted for each scenario to examine the impact of AI integration on the workforce comprehensively. The literature review served as a foundation for identifying the elements incorporated into the SWOT analysis.
This approach establishes a forward-thinking mindset and helps organizations to determine the transformative role of AI in shaping future skill requirements in business contexts. The initial step in the scenarios’ development process was to identify the context and goals of AI integration in business contexts. As a result of the exponential rise of AI technologies such as machine learning, natural language processing, and robotics, organizations can now automate standard processes, analyze vast amounts of data, and make data-driven decisions on a large scale. Accordingly, organizations must strategically plan for AI integration to maintain or secure a competitive edge, necessitating the foresight analysis of future skills and capabilities. The identified potential changes can inform strategies for upskilling and reskilling employees, aligning with future skills requirements.
The subsequent steps in scenario creation involved identifying all relevant stakeholders to ensure a diversity of perspectives and integrating relevant scientific knowledge from the literature, combining qualitative and quantitative data to formulate well-informed scenarios. The consistency and robustness of each scenario were evaluated by scrutinizing the underlying assumptions and uncertainties, culminating in a comprehensive SWOT analysis, to explore the evolving skills requirements in business organizations through the different degrees of AI integration and its impact on the workforce.
Finally, scenario outcomes were communicated using visualizations and effective storytelling techniques to ensure clarity and accessibility for stakeholders and decision-makers.
The conventional SWOT analysis approach, while widely used, comes with numerous limitations that negatively impact the quality of the results [37]. The SWOT analysis used in foresight research to identify the future skills required by employees in the context of the significant adoption of artificial intelligence in businesses has the same limitations that other authors have identified [38,39]. First, prioritizing essential skills becomes difficult when aiming to evaluate the significance of identified requirements. A wide range of factors can be used in the ambiguous framework, which reduces the focus and practical insights obtained. Furthermore, depending too much on the subjective judgment of experts generates bias and could lead to a limited or inaccurate viewpoint. Creating specific training programs will be difficult since there is a likelihood that the skills required will be described in too general terms. Also, inadequate factor assignment can result in poor strategic judgments, such as overestimating opportunities or underestimating risks. In addition, incorrect conclusions and subjective factor identification might lead to a biased perception of future skill requirements. The analysis’s effectiveness and reliability are further compromised if it is based on inaccurate, out-of-date, or incomplete data, which makes it less appropriate for the dynamic and unpredictable character of foresight studies in the context of AI-driven workplace transformations.
Furthermore, SWOT analysis could be supplemented by integrating various perspectives, such as technological trends, sociological impacts, and economic factors, to create comprehensive scenarios and to mitigate its limitations, ensuring a more holistic understanding of the future skill needs in an AI-driven workplace [40].

4. Results

Based on the literature reviewed, the foresight analysis of skill evolution as a result of AI integration into businesses acknowledges that to work together with artificial intelligence, the future workforce will require not only technical expertise, but also strong interpersonal and cognitive skills. The results underscore the need for employees to be adequately prepared for an AI-integrated workforce that is equipped with the requisite skills to leverage AI effectively in their roles [41]. Moreover, it is imperative for employers to capitalize on AI’s potential while mitigating its adverse effects. Additionally, educational institutions should prioritize the development of interdisciplinary programs that bridge the gap between AI technology and educational programs [42]. Such initiatives will enable employees, employers, educators, and AI developers to gain a comprehensive understanding of the evolving skills requirements within business organizations. A proactive approach, encompassing skilling and reskilling initiatives, ethical guidelines, collaborative partnerships, and innovations in teaching methodologies, is essential. Collaborative partnerships among business organizations, educational institutions, and government bodies are pivotal in crafting tailor-made AI education and training programs that align with the dynamic needs of the industry. These partnerships facilitate access to the latest learning resources and ensure that employees are equipped with the skills necessary to excel in an AI-enhanced work environment.
Furthermore, employing a multidisciplinary approach to thorough problem-solving is another possible strategy for minimizing the limitations of SWOT analysis. This perspective emphasizes how philosophy and psycholinguistics can be integrated with AI to create analogies between artificial and human intelligence. This method is similar to the findings of Ranaldi and Pucci [43], who highlight the significance of pre-existing mechanisms in learning and raise concerns about their relevance to AI that mimics humans.
This section delineates the outcomes of the foresight analysis through three distinct scenarios of AI integration in business organizations, each presenting unique strengths, weaknesses, opportunities, and threats. The discussions provide insights into the evolving skills landscape and strategies needed by businesses and employees when adapting to these future scenarios.
Each scenario presents distinct pathways for businesses to navigate the future landscape of AI, emphasizing strategic planning, continuous learning, and ethical considerations. The gradual integration scenario focuses on upskilling and equitable opportunity distribution, the aggressive integration scenario highlights the need for innovation and ethical management, and the selective integration scenario underscores the importance of human-centric skills and collaboration. By understanding and preparing for these scenarios, organizations can develop a resilient and adaptable workforce capable of thriving in an AI-enhanced future.

4.1. Gradual Integration of AI in Business Organization

Scenario 1: Gradual Integration of AI in Business Operations
In the first scenario, the gradual integration of AI into business organization prioritizes efficiency, automating routine tasks and catalyzing a shift towards more complex and value-added activities requiring creativity, problem-solving, critical thinking, and emotional intelligence.
In this scenario companies need to effectively manage the integration of AI into the workforce by enhancing employee capabilities and adapting to technological changes. The key focus is on leveraging the strengths of AI, namely increased efficiency and productivity, to allow employees to engage in more valuable, higher-level tasks. At the same time, it is crucial to address the weaknesses and threats related to the existing skills gap and potential job displacement by providing robust upskilling and reskilling programs. This strategic focus will help ensure that employees are equipped to work alongside emerging technologies, thus mitigating resistance to change, and reducing the risks associated with an increasing skills gap.
As a result, employees in this scenario need to develop digital literacy and technical skills, including proficiency in data analysis, programming, machine learning, and AI algorithms. Organizations must foster a culture of continuous learning to ensure that employees can adapt to technological advancements.
Figure 2 shows the results of the SWOT analysis conducted for the scenario that supposes a gradual integration of AI in business organizations. In this scenario, the strengths are an increased efficiency and a focus on higher-value activities, and the weaknesses are related to a lack of necessary skills and resistance to change; the opportunities for upskilling and new AI-focused roles are highlighted, along with threats related to job displacement and an increasing skills gap. Employees need to possess digital literacy and technical skills to effectively utilize AI-powered tools and technologies, including proficiency in data analysis, programming, machine learning, and AI algorithms. Organizations should provide robust training and development programs to bridge the skills gap and support employees through the transition.

4.2. Aggressive Integration of AI in Business Organization

Scenario 2: Aggressive AI Adoption and Innovation
In the second scenario, aggressive AI integration into business organizations leads to significant transformations in business models, operations, and workforce. Rapid AI integration fosters innovation by enabling organizations to develop new products, services, and business models, leveraging AI-powered technologies to unlock new opportunities and address emerging market needs.
Figure 3 presents the SWOT analysis for this scenario, emphasizing the need for continuous learning and ethical considerations. Employees must develop a blend of technical and social skills to manage and integrate AI effectively. Organizations should focus on transparency and social solidarity to navigate the complexities of rapid AI adoption.
In this potential future, organizations need to leverage the strengths and opportunities of AI technology to achieve competitive advantages and drive innovation, while simultaneously managing the weaknesses and threats, such as high investment costs, ethical concerns, and potential job losses. This balanced approach is crucial for maximizing the positive impact of AI on business growth and minimizing the risks associated with its implementation. This strategic mindset ensures that businesses can thrive in a rapidly evolving technological landscape by adapting to changes and addressing potential challenges head-on.
Spending on research and development will generate the potential to create new AI-driven products but also refine existing ones to stay ahead of the competition. Also, this approach would also help mitigate the high investment costs noted as a weakness by ensuring that funds are directed towards high-impact projects. By using AI-driven analytics, companies could understand customer preferences and behaviors in real time, thereby offering more tailored experiences that could boost customer satisfaction and loyalty. In this scenario, companies also could explore entering into new sectors where AI can provide a distinct advantage, such as health tech, fintech, or autonomous transportation, leveraging their existing strengths in AI and technology. Addressing the strong demand for AI, data analytics, and cybersecurity professionals, companies should focus on both recruiting top talent and developing current employees’ skills in these areas, ensuring a well-equipped workforce capable of managing and advancing AI technologies.
Aggressive AI integration in businesses can significantly transform operations and stimulate innovation, leading to the creation of new products and business models. However, this rapid adoption requires the careful navigation of economic complexities and ethical considerations, such as maintaining transparency and ensuring social solidarity. It also necessitates that the workforce continuously updates its technical and social skills to manage new AI-driven responsibilities effectively, particularly in cybersecurity; this is in order to balance the benefits and potential vulnerabilities introduced by AI systems.

4.3. Selective AI Integration with Human-Centric Focus

Scenario 3: Selective AI Integration with Human-Centric Focus
In the third scenario, AI integration is selective and human-centric; the focus shifts from replacing human labor to augmenting human capabilities. This approach aligns AI technologies with the goal of enhancing the effectiveness and satisfaction of the workforce, rather than merely driving efficiency gains. By selectively applying AI, organizations can ensure that technology serves to support and amplify the unique strengths of human workers, rather than diminish their role in the workplace. In this context, there is a growing recognition of the importance of human-centric skills—skills that AI cannot easily replicate or replace. These include empathy, emotional intelligence, interpersonal skills, and cultural competence. Such skills are critical in roles that require deep understanding and nuanced interactions with people, such as customer service, management, healthcare, and education.
In this scenario, companies need to strategically balance human insight with AI capabilities to optimize both innovation and decision-making. The analysis highlights the strength of enhanced collaboration between humans and AI, which can lead to more innovative solutions and improved decision-making. However, it also points out weaknesses such as the slower pace of AI adoption, which could lead to missed opportunities in the competitive landscape.
To address this, organizations must focus on maintaining human oversight in critical decisions to prevent AI errors and biases, while also fostering a corporate culture that values human input and capabilities. This human-centric approach to AI integration ensures that organizations not only stay competitive by adopting AI at a thoughtful pace, but also leverage it in a way that enhances their workforce’s capabilities and ethical considerations, ultimately improving the return on their investment and competitive advantage in their respective sectors.
Figure 4 shows the results of the SWOT analysis conducted for the selective integration of AI in business organizations, where strengths are focused on human-AI collaboration and maintaining human oversight, and weaknesses are related to slower adoption and balancing challenges. In the meantime, the opportunities are developing supervision skills and a strengthened organizational culture, and the threats are being less competitive in fully automated sectors and potentially slower profits.
This scenario highlights the importance of maintaining human oversight and developing human-centric skills such as empathy, emotional intelligence, and cultural competence. This approach ensures that AI serves to support and amplify human strengths, fostering a harmonious and productive workplace. The selective and human-centric approach to AI integration focuses on augmenting human capabilities rather than replacing them, aligning technology with enhancing workforce effectiveness and satisfaction. By leveraging AI to support and amplify the unique strengths of human workers, organizations can foster a workplace where technology complements human skills such as empathy, emotional intelligence, and cultural competence. These human-centric skills are vital in roles requiring nuanced interpersonal interactions and are crucial for improving workplace relationships and customer experiences. This approach not only boosts operational efficiency, but also enriches employees’ work life and enhances customer satisfaction, establishing AI as a beneficial partner in the workplace.

5. Discussion

5.1. Gradual Integration of AI in Business Organization

By adopting AI technology, businesses can automate routine tasks, analyze vast amounts of data quickly and accurately, and make informed decisions based on data-driven insights [44]. In the scenario of a gradual adoption of AI, businesses experience a paradigm shift towards higher efficiency and deeper analytical insights, bolstering economic growth [45]. The challenges for employees are bridging the skills gap in the workplace, and developing the transversal skills needed. Organizations must foster a culture of openness to change, supporting employees through personalized training and development opportunities [6]. Because of the idea of AI job replacement, workers must upskill in fields such as analytics, digital skills, artificially intelligent automation, forecasting, and adaptability, which are essential for the future. Employees need to be retrained in fields like cognitive and technological skills, social skills, more specifically leadership, interpersonal skills, and communication skills [4]. The impact of innovations in technology means that qualifications and skills are required, which could be gained through business-related vocational education and training, or academic education. Future-focused lifelong learning provides employees with suitable and/or adapted skills that could enable them to competently respond to new circumstances and technologies [46].
The scenario for gradual AI integration into organizations underscores the need for resilience and adaptability among employees, promoting a focus on upskilling and equitable opportunity distribution to mitigate the inequalities between them.
To effectively mitigate the potential inequalities that might arise from AI integration—such as job displacement affecting less skilled workers—there should be a strong emphasis on equitable opportunity distribution. This involves not only providing access to training and educational resources, but also ensuring that these opportunities are tailored to meet the diverse needs of the workforce. For instance, training programs should be accessible to employees at all levels, from entry level to management, and should cover a wide range of skills, from technical know-how to cognitive and interpersonal skills.
Moreover, organizations can leverage AI to enhance their training programs through personalized learning paths that adapt to the individual learning pace and style of each employee. This personalized approach not only makes learning more effective, but also helps in aligning the skills of the workforce with the strategic goals of the organization.
In conclusion, by prioritizing resilience, adaptability, and equitable upskilling, organizations can turn the challenges of AI integration into opportunities for growth and development, ultimately leading to a more inclusive and competitive business environment.

5.2. Aggressive AI Integration in Business Organizations

The results of the SWOT analysis for aggressive AI integration into business organizations illustrate a dynamic shift, where rapid adoption significantly transforms business models, operations, and workforce dynamics. This ambitious approach not only accelerates innovation by enabling the creation of new products, services, and business models, but also harnesses AI-powered technologies to explore new market opportunities and meet emerging demands effectively.
However, this rapid integration brings with it substantial economic complexities that must be carefully managed. The adoption of AI necessitates a commitment to transparency, social solidarity, and strict compliance with regulatory standards to ensure that the integration is both ethical and beneficial to society as a whole. Mateos-Garcia [47] suggests that advocating these principles is crucial in navigating the potential risks and maximizing the benefits of AI technologies in business contexts.
Moreover, the reliance on AI systems can lead to automation biases where there is an over-trust in AI decisions, potentially overshadowing human judgment and introducing vulnerabilities in decision-making processes. This emphasizes the need for a balanced workforce that is not only technically proficient, but also equipped with the strong social skills needed to critically assess and effectively integrate AI outputs.
In response to these challenges, Dawson, and Thomson [48] argue that the workforce must embrace continuous learning to adapt to new demands, particularly in areas like cybersecurity. This involves cultivating a blend of technical skills to manage and secure AI systems, alongside social skills that foster teamwork, ethical decision-making, and effective communication. The development of such a diversified skill set will be essential in enabling employees to thrive in an AI-driven business environment, ensuring they are prepared not only to use AI tools but also to innovate and lead in their implementation.
In summary, while aggressive AI integration promises significant advancements and opportunities for business organizations, it also demands a nuanced approach to workforce development and ethical considerations to ensure that these transformations are sustainable and beneficial across all levels of society.

5.3. Selective AI Integration with Human-Centric Focus

In a scenario based on the selective integration of AI with human-centric focus, human-AI collaboration is essential, emphasizing the maintenance of human oversight in decision-making processes.
Since human–AI collaboration demands AI capabilities beyond task-level accuracy, they perform better than either a human or an AI system alone. In this scenario, the human must determine when and how to use the AI system’s advice. As a result, effective communication and collaboration skills are essential for employees to work cross-functionally, collaborate with AI systems, and communicate insights, recommendations, and ideas effectively to stakeholders. Transparent conditions must be established for the human oversight of AI mechanisms and functionalities, as the key to future fruitful collaboration between humans and machines; this is to ensure future human–AI collaboration and to make solutions much more viable and oriented towards building trust.
Many studies have demonstrated that enhanced human–AI collaboration can lead to more creative solutions and better decision-making. Artificial Swarm Intelligence (ASI) is a type of artificial intelligence that is not based only on machine learning, but can use human groups’ knowledge, wisdom, and insights to enhance and optimize its predictive and decision-making abilities when dealing with known unknowns. Such a model expands on the knowledge of how people may be enhanced, not replaced, when they work together to make business decisions by using artificial intelligence (AI) [49].
Moreover, in a selective AI integration scenario, the role of AI is envisioned as a partner to human workers, enhancing their capacity to perform their jobs with greater competence and compassion. This partnership not only increases operational efficiency, but also enriches the quality of work life for employees and the service experience for customers, creating a more harmonious and productive workplace. To sustain competitiveness and drive sustainability in a globally digitized market, stakeholders should implement strategies that promote the development of digital competencies, encourage open in-novation, and support the integration of new technologies [50].
The traditional view of machines as mere tools is outdated when applied to AI, necessitating a redefinition of the role of humans, from supervisors to interpreters and translators of AI outputs. This shift increases the responsibility of human decision-makers and demands a new set of skills, emphasizing the critical relationship between human self-perception and effective AI utilization. However, treating machines and humans as equals is neither anticipated in the foreseeable research trajectory nor deemed ethically appropriate.
By fostering people skills as a strategic imperative in a selectively integrated AI environment, organizations must invest in specific training programs aimed at enhancing emotional intelligence and cultural competence, thereby preparing employees to interact more effectively with a diverse range of colleagues, clients, and customers. This not only improves workplace relationships, but also enhances customer satisfaction and loyalty, as employees are better equipped to understand and respond to customer needs and emotions [51].
Ultimately, a selective and human-centric AI approach encourages the design and implementation of AI systems that are intuitive and augment these human-centric skills. For example, AI could be used to provide real-time analytics during customer interactions, offering insights that help service representatives understand customer sentiment more accurately, thereby enabling more empathetic and tailored responses.

6. Conclusions

The exploration of different scenarios of AI integration in businesses reveals its multifaceted impact on the workforce. Each scenario presents distinct pathways for businesses to navigate the future landscape of AI, emphasizing the importance of strategic planning, continuous learning, and ethical consideration. As AI continues to shape the business world, adapting to these evolving requirements will be paramount for organizations aiming to thrive in the digital age.
This paper’s foresight analysis illuminates the complex relationship between artificial intelligence (AI) integration and the evolving skill requirements of business organizations. Through the development of three exploratory scenarios, namely gradual, aggressive, and selective AI integration, a nuanced, comprehensive perspective on the potential future is presented. The findings emphasize how important it is for companies to develop a workforce that possesses the technical know-how to use AI as well as the soft skills, like creativity, emotional intelligence, and ethical judgment, necessary for innovation and teamwork. In the AI integration process, businesses, academic institutions, and policymakers must work together to foster a workforce that is both resilient and adaptable, capable of navigating the challenges and seizing the opportunities resulting from it. An inclusive, ethical approach to technology deployment, coupled with a commitment to continuous learning and strategic foresight, is essential for a smooth transition to an AI-enhanced business environment.
The findings of this research help organizations in strategic workforce planning by enabling them to anticipate future skill requirements, to identify potential skill gaps, and initiate proactive recruitment and training efforts. It advocates for an AI integration approach that not only optimizes efficiency and spurs innovation, but also promotes a culture of continuous learning and adaptation among employees. Furthermore, this study serves as a call to action for higher education and vocational training programs to align curricula with anticipated skill requirements, emphasizing the development of soft skills and AI literacy within a technologically advanced workplace context. It lays the groundwork for policies that foster lifelong learning, support reskilling and upskilling initiatives, and ensure equitable access to AI education and training.
Nevertheless, the exploration of evolving skill requirements through the lens of AI integration has its limitations: the scenarios discussed may not capture all conceivable future or unforeseen advancements and challenges. Furthermore, the scenario method’s reliance on qualitative data and expert judgment is subjective and may constrain the predictability and accuracy of outcomes. In order to address these limitations, further research might explore AI’s impact across various sectors, and investigate the implications of job displacement, inequality, and ethical employee management. SWOT analysis has its limits even if it offers a useful framework for determining both the internal and external factors influencing AI integration. Finding strengths, weaknesses, opportunities, and risks is a subjective process that can produce biased conclusions. Furthermore, SWOT may oversimplify complex settings and fail to prioritize problems or provide solutions. PESTLE analysis could be used in conjunction with SWOT to evaluate political, economic, social, technological, legal, and environmental factors, with the purpose of providing a broader contextual understanding.
Drawing upon the valuable insights and practical frameworks presented in this paper, organizations, educators, and policymakers can collaboratively chart a future through the intricate landscape of AI integration. By doing so, they can cultivate a workforce that is not only skilled and versatile, but also inclusive, enabling it to meet the demands of a rapidly evolving technological future. This collective effort is essential to harness the full potential of AI, equipping employees with the skills needed to thrive in the dynamic environment of tomorrow’s business world.

Author Contributions

Conceptualization, N.B. and D.D.; methodology, N.B. and D.D.; literature review, A.F.P., D.N.S. and I.C.T.; validation, N.B., D.D. and A.F.P.; formal analysis, I.C.T.; investigation, A.F.P.; resources, D.N.S.; writing—original draft preparation, D.D.; writing—review and editing, A.F.P.; visualization, I.C.T.; supervision, A.F.P.; project administration, D.N.S.; funding acquisition, N.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data were sourced from the Web of Science Core Collection Database. Publicly available datasets were analyzed in this study. The data can be accessed here: https://www.e-nformation.ro/ (accessed on 10 March 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Exploratory scenarios for evolving skill requirements in business organizations through AI integration.
Figure 1. Exploratory scenarios for evolving skill requirements in business organizations through AI integration.
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Figure 2. SWOT analysis for the gradual integration of AI into business organizations.
Figure 2. SWOT analysis for the gradual integration of AI into business organizations.
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Figure 3. SWOT analysis for the aggressive integration of AI into businesses.
Figure 3. SWOT analysis for the aggressive integration of AI into businesses.
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Figure 4. SWOT analysis for the selective integration of AI into business organizations.
Figure 4. SWOT analysis for the selective integration of AI into business organizations.
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Bobitan, N.; Dumitrescu, D.; Popa, A.F.; Sahlian, D.N.; Turlea, I.C. Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method. Electronics 2024, 13, 2198. https://doi.org/10.3390/electronics13112198

AMA Style

Bobitan N, Dumitrescu D, Popa AF, Sahlian DN, Turlea IC. Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method. Electronics. 2024; 13(11):2198. https://doi.org/10.3390/electronics13112198

Chicago/Turabian Style

Bobitan, Nicolae, Diana Dumitrescu, Adriana Florina Popa, Daniela Nicoleta Sahlian, and Ioan Codrut Turlea. 2024. "Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method" Electronics 13, no. 11: 2198. https://doi.org/10.3390/electronics13112198

APA Style

Bobitan, N., Dumitrescu, D., Popa, A. F., Sahlian, D. N., & Turlea, I. C. (2024). Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method. Electronics, 13(11), 2198. https://doi.org/10.3390/electronics13112198

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