Artificial Intelligence (AI) refers to that smart software which mimics human thinking and performs tasks. This includes learning, reasoning, problem solving, perception and language understanding.

AI are generally classified into three types as narrow AI, general AI and super AI.

Narrow AI: Narrow AI is the weak form of AI programmed to perform specific tasks such as weather checking, facial recognitions, self-driving cars, virtual assistants, recommendation engines, etc. The major limitation of narrow AI is its inability to respond exactly to abstract questions.

General AI: General AI considered as strong AI is hypothetical form of AI which would possess the ability to understand, learn and perform any task that requires human level intelligence.

Super AI: Super AI is that AI that is supposed to surpass human intelligence level and perform any task better than human beings in any area be it science, mathematics, medicine, etc. It is distant reality in the current times and its potential is yet to be leveraged.

AI is dependent on human intervention to process, learn and carry out the tasks with the help of algorithms and data fed into it because these systems are not capable of infer or recover missing data on their own.

AI like electric power technology, mechanical technology, information technology is one among the core areas of Industrial Revolution 4.0. The achievement of Sustainable Development Goal 8, full and productive employment for all requires diversification, technological upgrading and innovation. Human, social and economic development requires technological innovation and development.

The continuous evolving AI led to the development of innovative and groundbreaking applications that revolutionized every numerous industries and every walk of human life. The challenge is to develop AI systems which are safe, reliable and beneficial to society.

The advent of AI into the workplace marked the beginning of new era in the workplace redefining job roles, reshaping the industries and altering the work environment we operate in. Even though there the workplace has been dynamically evolving environment to accommodate technological advancements from introduction of computers and internet, the introduction of AI is different because AI has the capability to learn, think and do just like humans.

The continuous evolution of AI mandates the employees for a constant learning to equip themselves with new knowledge and skills to sustain in AI driven work environments.

With AI applications expanding rapidly and transforming organizations with improved operations and enhanced decision making, there has been an increased concern about AI replacing the employees. It also led to the arousal of questions like where technology will lead humanity, to what extent will AI change the relationship between work and humans, will these advancements in technology lead to unemployment, etc. Majority of unskilled and manual jobs are vulnerable to become obsolete and replaced by AI as the mundane and repetitive duties are facilitated by AI-driven machinery and algorithms. With AI performing the mundane tasks, employees are provided with the opportunity to concentrate more on innovative tasks.

With enhanced access to work-related information, seamless connectivity and ability to perform the tasks from anywhere, anytime AI also raises serious concerns with regard to work-life balance as employees find it almost impossible to disconnect from work blurring the boundaries between the two. This seamless connectivity and access fosters creativity and innovation with collaborative knowledge sharing through exchange of ideas, reaching out for help to people across countries, collaborating for projects with people across the globe, etc.

Alongside providing opportunities like greater flexibility, improved communication, enhanced collaboration, increased efficiency and productivity, reduced errors, refined decision-making AI also has the downside of creating fear of job displacement, reduced privacy, concerns regarding data security, generate the need to reskill and upskill continuously, increased digital distraction, etc.

Balancing the advantages and disadvantages of AI is a fragile task requiring careful consideration and reframing of regulatory policies and frameworks. The implementation of AI without a transparent policy might lead to a shift in the balance of power in opposition to employees. Improper implementation of AI in the workplace is likely to bring forth serious technical, ethical and social concerns.

Notable progress in the technologies of AI has been observed since 2010, particularly in deep learning techniques like deep neural networks. These deep learning methods have successfully been employed in varied areas like healthcare, natural language processing, robotics, etc. With the continuous advancement of AI technologies resulted in their deep integration into every walk of life altering human experience and reshaping communities (Borenstein & Howard, 2020).  Zirar, et. Al., (2023) explored on how AI and workers can successfully coexist in the workplace by extensively investigating the previous works. The four key points they concluded are

• AI has been perceived as threat to their jobs by workers, leading to resistance and mistrust
• AI has the potential to augment the abilities of the workers enhancing their performance, creating opportunities for co-existence than replacement
• Successful integration of AI into workplace requires human resources to possess a blend of conceptual, technical and human skills
• Workers are required to continuously upskill and reskill themselves to contribute effectively in AI-enhanced workplaces

Babu, Nirupama, et. al., (2024) examined the challenges in implementation of AI and concluded that inspite of the transformative impact of AI extending beyond skill development encompassing broader socio-economic implications, the ethical considerations surrounding AI usage are underscoring the significance of fostering culture responsible for AI adoption.

Babashahi, L. et. al., (2024) explored the transformative influence of AI on numerous industries such as automation, education, mining, software engineering, legal services, accounting and media. They investigated the impact of technological advancements in identifying the skills individuals and organizations require to implement and manage AI systems and facilitate human-machine interactions that are the need of the hour. Findings of the study highlighted the importance of crucial skills like technical proficiency and adaptability for successfully adopting AI. The researchers emphasized proactive education, balanced skill development and strategic integration to navigate through the profound impact of AI on the workforce effectively.

The findings of research conducted by Yang Shen and Xiuwu Zhang (2024) highlighted the positive contribution of AI in job creation. They established that AI resulted in improved labor productivity, contributed to deepened capital intensity and also promoted finer labor specialization in Chinese enterprises which reducing the potential negative effects on employment. The authors recommended reformation of education and training frameworks, domestic development of advanced robotics and strengthening of social security systems, to enhance and sustain positive influence of AI on labour market.

Hyeon Jo and Do-Hytoung Park (2023) in the study carried on antecedents and outcomes of using ChatGPT among the employees revealed users’ perceptions of intelligence and self-learning capacity of ChatGPT are positively related to enhanced information support and knowledge acquisition. These are in turn found to significantly influence perceived utilitarian benefits that shape users’ intentions to use ChatGPT. Even though the utilitarian benefits do not directly predict the usage, they are used to find the intention of use which can be used to predict real-world application. Among demographic variables considered, age and gender were found to significantly affect actual usage, whereas the industry sector did not show a remarkable influence.

Özkiziltan, Didem and Hassel, Anke (2021) studied the labour market transformations that are a result of increasing use of Artificial Intelligence (AI) with special reference to Germany. They concluded that in the case of current situation remaining unchanged, AI driven future work possibly could perpetuate and aggravate work related discrimination and inequalities, further diminishing the prospects of decent work, fair remuneration and social protection to all. They also stated that there are ways to advance, adopt and utilize AI technologies in workplace in a way that benefits all

• While there are several studies on the potential of AI in reshaping the workspaces, there is little research on cognitive how workers interact and adapt to the newly introduced AI systems.
• The emotional and factors involved in the adoption of AI are under-studied despite their criticality in shaping the adoption behaviour of the employees.
• The role of organizational support mechanisms, leadership and HR in easing the transition have not been sufficiently studied yet.
• The research on combined insights of psychology, sociology, economics, technology and ethics to provide holistic understanding of the complex interactions between employee’s mental and physical wellbeing and AI is limited.
• Research on integration of education and training systems with employees to prepare them for challenges posed by AI-integrated labour markets is limited.

Problem Statement

Artificial Intelligence (AI) is a revolutionary force in industries that has transformed business models, job structures, and the role of employees. Its adoption into organizational workflows—from data analytics and customer care to automation and decision-making—has opened doors for opportunities and challenges for the world's workforce. While AI holds the potential for efficiency, innovation, and higher productivity, it also raises issues about job dislocation, digital divide, and the psychological effects of perpetual technological change. The success of AI adoption, therefore, hinges not only on technological capability but also on the readiness, perception, and adaptability of the human workforce that interacts with it.

Despite the widespread discourse on AI’s potential, there remains a significant gap in understanding how employees across different sectors perceive and respond to this technological evolution. Current studies tend to be sector-specific, concentrating on technologically intense sectors like IT or finance, where little cross-sector diversity is considered in terms of awareness, attitude, and organizational support. Workers employed in non-technological areas, like education, healthcare, and public administration, might be confronted with specific challenges, which include insufficient exposure to AI tools, absence of institutional training, and uncertainty regarding the future of their jobs. In addition, as organizations are spending a lot on digital transformation, the human side of AI adoption, including psychological preparation, managing stress, and resilience, has remained underinvestigated.

Another imminent concern rests in the imbalance of organizational support systems. All institutions do not give equal access to training, communication, or psychological health support required for seamless AI adoption. This unequal readiness can result in resistance, fear, and lower morale among staff members, eventually detracting from the productivity of technical programs. Furthermore, discrimination in AI programs and perception of unequal advantage along lines of gender, age, or socio-economic status could further add to complexity.

In light of these lacunae, it is of urgent importance to explore how employees with varying professional backgrounds view AI, its impact on their jobs, and organizational assistance in managing tension and promoting adaptability. These aspects are essential in understanding if they are to underwrite inclusive strategies for advancing technology as well as for employee welfare. The present research therefore aims to conduct a holistic analysis of AI awareness, attitudes, and organizational preparedness across industries, enriching the debate on sustainable human-centered digital transformation.

Research methodology refers to the blueprint of the systematic procedured used for collection of data, analysis of the data and presentation of the results applied to solve a research problem.

Research Design: The current study adopts both descriptive and explorative research designs. The identification of kind of attitude of corporate employees towards AI, the level of the attitude, the level of usage by corporate employees and the level of organizational support are the areas of application of descriptive research design. The explorative research is where the inter-relationship among these three is studied and also the influence of demographic variables is explored.

Population and Sample: All the employees working in the corporate sector constitute the population for the current study. By application of Cohen’s method, a sample size of 210 respondents is found to be sufficient. A sample of 233 respondents has been used for the current study.

Sampling Method: The sampling method adopted is convenience sampling method.

Instrument for Data Collection: A structured questionnaire consisting of 21 items has been developed by the research. The questionnaire consists of 4 sections Demographic Profile, section to measure Attitude towards AI, AI Usage and Organizational Support. The demographic profile section collects the data regarding gender, age, education, experience and sector of the respondents. The section on measuring Attitude towards AI has 8 items that identify the type of attitude and the level of attitude, the section on usage of AI has 6 items to measure the usage levels and the section on organizational support has 8 items. All the items of the questionnaire are measured on 5-point Likert scale.

Data Collection Procedure: The questionnaire is shared electronically through Google Forms to ensure broader reach and convenience.

Data Analysis Techniques: Frequency and Percentages are used to summarize demographic characteristics of respondents. T-test is used to analyse the influence of gender while ANOVA is used to analyse the influence of other variables age, education, experience and sector of respondents. Correlation is used examine the relationships among attitude and usage of AI and organizational support.

Reliability and Validity of Questionnaire

Reliability: Reliability refers to the consistency with which the instrument is able to measure. Cronbach’s Alpha is used to measure the internal consistency of the questionnaire and the values obtained are given in the following table:

The Cronbach’s alpha value being > 0.7 the normally acceptable level indicates the internal consistency of the questionnaire.

Validity: The validity of the questionnaire is carried out by making sure the questionnaire is reviewed by three expert faculty members one each from Human Resource Management, Education and Technology. The construct validity through conduction of factor analysis.

Data Analysis and Interpretation

Descriptive Statistics

• The study has a total of 233 respondents, out of which 7 belong to the age group 18-25, 62 are aged between 28-35, 102 respondents are between 36-45 years of age and 18 are aged above 56 years.
• Considering the gender, out of 233 respondents, 157 are males, 74 are females and 2 preferred not to reveal their gender identity.
• 48 respondents stopped their education with graduation, while majority of them numbering to 166 are post graduates and 19 are doctorates.
• 5 respondents have experience less than 1 year, 25 are experienced from 1 to 5 years, 54 respondents have experience ranging from 6-10 years, 100 respondents are having 11-20 years of experience and 49 respondents are experienced with more than 20 years.
• Sector wise distribution of respondents is 108 employees in IT sector, 72 in manufacturing, 30 in health care and allied services, 12 in education and 11 in other sectors.

Statistical Analysis

• Age is found to have inverse correlation with both AI familiarity and AI usage with their correlation coefficients being 0.45 and 0.35 respectively.
• Regression Analysis using linear regression revealed that for every rise in the age group, the value for AI adoption dropped by 0.22 points.
• The performance of correlation and regression tests revealed no significant influence of gender on the attitude of individual employee towards adoption of AI at workplace with less than 1% variance being explained by the corresponding r².
• With the correlation coefficient being -0.038, educational level of the respondents is negligently correlated with the familiarity as well as adoption of AI at workplace.
• The regression analysis explains <1% variance of the scores indicating education level cannot be used as a strong predictor for attitude towards AI.
• The F-value calculated using one-way ANOVA 0.27 indicates that the differences in the attitude towards AI among respondents of different education levels are not statistically significant.

• Perceived relevance and impact of AI on job roles is found to increase with increase in experience.
• Mid-career professionals with an experience of 6-20 years are more aware about the impact of AI on their jobs and are ready to embrace AI.
• The calculated f-score indicates that experience does not significantly affect the attitude of the employees towards AI.

• The correlation coefficient being 0.040 indicates very weak positive correlation between sector of employees and their attitude towards AI.
• The f-value calculated indicates that the influence exerted by the sector employee works in on the attitude of employee towards AI in their job is statistically not significant.

• The correlation analysis carried out reveals that higher organizational support resulted contributed to positive attitude of individuals towards AI while lower organizational support in negative attitude towards AI.

• The organizational support during the transition accounts nearly to half the variation in individual employees’ attitude towards AI.

Other Major Findings

• Majority of the respondents 80.2% stated AI as relevant to their job roles.
• Among the age groups considered for the study, the respondents in the age group 26-35 were found to be more familiar with the usage of AI in the workplace (65%) followed by the respondents who belong to the age groups 18-25 (58%), 36-45 (55%), 46-55 (45%) and 45%) and 56+ (30%). This shows an increasing trend of AI usage in the workplace in the first two age groups followed by a decreasing trend from there on.
• While the respondents in the age groups 26-35 and 36-45 exhibited greater emotional resilience, more optimism and higher familiarity with AI, the respondents of the age groups, 46-55 and 56+ feel less secure, less supported and are not much familiar with AI. Despite being enthusiastic, the respondents of the age group 18-25 are found to be less stressed about upskilling themselves which is the dire need of the hour.
• Male employees are found to be more familiar with the usage of AI at the workplace.
• Both the genders almost equally agree upon the relevance of AI to their job roles.
• Most of the post-graduates are working in the IT sector while the doctorates are more in manufacturing, education and consulting.
• Doctorates are more favourable towards the adoption of AI than graduates and post graduates across sectors.
• Doctors are also more familiar with the usage of AI in various functions than graduates and post graduates.
• AI is seen as highly relevant to their job roles by people of all experience levels.
• Mid-career professionals with 6-20 years of experience are the people who are most impacted by AI already

The study is conducted on 233 employees working in corporate companies. The sample consists of respondents who vary on number of factors like gender, age, education, experience and sector. Among these 67.78 percentage is constituted by male respondents which makes the sample a male dominated one. With the highest number of respondents being in the age of 36-45, with 48.93%, the sample consists of people in their mid-careers the most. The sample consists more of post-graduates with them constituting 71.24%. The highest percentage is observed in 11-20 years of experience which highlights that the sample consists more of experienced and mid-career professionals. The sample is dominated by IT professionals with them constituting 46.35%.

The findings of the study established age as a significant factor in accepting and adopting AI at workplace. The acceptance and adoption rate of AI increased upto age 26-35. The acceptance and adoption further declined with further increase in age and employees aged above 46 exhibited the lowest frequency for incorporation of AI into their daily activities at their workplace. This highlights an almost inverse relationship between them. Gender, education and number of years of work experience were found to have not so significant impact on acceptance and adoption of AI at workplace. This is in contrast with previous findings that state gender as a key factor in deciding the attitude and usage of AI. Gender also did not show significant influence in varied sectors. Sector in which the employee is working was found to have influence with post-graduate IT employees and doctorates in manufacturing, education, and consulting exhibiting higher favorability. Academic qualifications did not have much influence on perspectives towards AI. This can be in accordance with Adult learning theory developed by Malcolm Knowles in 1968 that states that self-motivation, experiential learning and views about immediate necessity impact the adoption of any new knowledge and skills among adults. These indicate that sector and education of the employee together influence the acceptance and adoption of AI at workplace. The employees working in IT sector are observed to slightly dominate in familiarity and adoption rates of AI when compared to employees of other sectors.

The organizational support has been established as a strong predictor of attitude of employees towards AI and also its adoption. The regression analysis carried out indicates that nearly half of the variance in employee attitudes can be found out organizational support. This finding of the study carried out is in alignment with Technology-Organization-Environment (TOE) framework proposed by (Tornatzky & Fleischer, 1990) and Unified Theory of Acceptance and Use of Technology (UTAUT) developed by (Venkatesh et al., 2003) which emphasize the vital role of organizational elements in aiding the technology adoption by employees. The perceived organizational support also helps to reduce anxiety, improve confidence and create a sense of shared responsibility among employees.

Implications

The major implications of the current research are as follows:

• The study provides the basis to consider psychological well-being of the researchers as integral variable that exerts influence in their research of technology adoption models.
• The gaps identified in organizational support and digital upskilling aid academic administrators, policy makers and HR managers in organizations to formulate more inclusive and sustainable strategies to improve AI-readiness among the employees.
• The findings of the research underscore the significance of including the initiatives to support digital empowerment.
• Public-private partnerships can be developed to support reskilling and upskilling initiatives.
• Organizations must give priority to organizing continuous development programs to reduce anxiety and enhance employee confidence and adaptability.

Limitations

• Though the sample size 233 is statistically enough, the sample may not have completely captured the complexity of all the industries or job roles.
• The study has been carried out in limited sectors while this can be expanded to other sectors.
• This study has been carried out in the context of corporations in India. Cultural, social or regional differences might result in different findings when carried out at other parts of the world. So the findings cannot be generalized globally.
• With changing technologies there are chances that there will be change in the attitudes of employees.

Artificial Intelligence has become a revolutionary driver that affects all aspects of the contemporary economy. As technology advances bring with them increased productivity and innovation, they also pose new challenges to organizations as well as workers. This research investigated how different professional workers experience the adoption of AI and what impact organizational support has on their readiness, adaptability, and psychological well-being in the process.

The results clearly indicate that attitudes of employees towards AI are influenced not just by their awareness level but also by their perceived fairness, communication, and empathy manifested by their organizations. Throughout industries, employees recognized the capability of AI to alleviate redundant tasks, enhance accuracy in decision-making, and generate new job roles. Many of them, however, felt stressed with respect to job replacement, ongoing upskilling demands, and ambiguity regarding ethical deployment.

Sectoral variations were also observed. Staff working in IT, banking, and services sectors were more confident and optimistic about adopting AI because they are more exposed to AI-based tools and digital processes. Workers in manufacturing, teaching, and healthcare also demonstrated mixed reactions, frequently reporting the lack of structured exposure to AI training and uncertainty about how automation could change their job functions. This shows that sector-specific approaches are important for the effective diffusion of technology.

The research strongly asserts the position of organizational support systems in averting employee fears and fostering adaptability. Firms that openly communicated AI adoption plans, provided reskilling and mental wellness interventions, and facilitated worker engagement in the transition process experienced increased levels of trust and acceptance among employees. On the other hand, non-involvement, low communication, and poor training fostered resistance and emotional distress. Therefore, leadership commitment and supportive HR policies are crucial to aligning human potential with technological advancement.

Psychologically, the research established a significant correlation between the emotional readiness of employees and the sense of job security in the AI age. Employees who viewed themselves as resilient and well-supported by peers or mentors were found to be better able to cope with technological disruptions. Conversely, workers under stress from accelerated digitalization were more likely to build anxiety and decreased job satisfaction. This calls for organizations to incorporate mental health services, counseling, and wellness programs into their digital transformation strategies.

Not withstanding the all-encompassing scope of the study, various limitations need to be noted. The sample is representative of several industries but might not cover the entire intricacy of AI experience across sectors. Cross-sectional design limits one to understand how attitudes change over time. Relying on self-reported information might have introduced social desirability bias or subjectivity. Future research should embrace longitudinal and mixed-method designs, pairing surveys with in-depth interviews or case studies to more fully understand employee conduct and organizational adaptation procedures.

By way of implications, the study reiterates that AI adoption is not just a technol-ogy-related issue but also an opportunity to drive organizational and human development. Policymakers and business leaders alike need to ensure that AI penetration is guided by principles of fairness, transparency, and inclusiveness. Guided AI-readiness frameworks, ongoing skill enhancement programs, and employee welfare measures can curtail resist-ance and develop a culture of innovation.

Finally, the research points out that the work of the future will be a function of how well companies close the gap between technical proficiency and human empathy. The full potential of AI can be harnessed only when the people working in the organization feel safe, empowered, and enabled to go digital. AI-led transformation is as much about human resilience as it is about machine learning. Therefore, organizations that spend equally on technology and human beings will guide the way to a sustainable, inclusive, and emotionally intelligent future of work.

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