The Role Of AI And Data Science In Large Organizations: An Essay.

Techniques used by AI and DS in Large Organizations

The paper is regarding the understanding of how an Artificial Intelligence and Data Scientists plays an important role at the large organizations and the techniques that the AI and DS uses for applying those concepts and making a better decision and analysis in the better way by applying these concepts. This paper is conducted for understanding the technologies used in the large organizations by data scientists and artificial intelligence by the critical analysis and interpretations. The top management is not aware of the Artificial Intelligence or Data scientists’ techniques and thus they need to be aware of the opportunities and challenges of adopting AI/ DS techniques within the organization and in the industry that the operations are adopted by the organization. Also, the top level can understand how AI/ DS techniques helps in decision making of the organization. Thus, the research is being conducted talking about the industry performance which also included the customer retention rate, customer satisfaction, increasing customer turnover, employee turnover and other factors to understand which of the factors contribute to improving the performance of the organization.

The top management needs to understand the impact of AI and DS in the performance of the organization.

Objectives of the research

The objective of the research is to understand the customer’s perspective to understand how AI and DS influences customer satisfaction and loyalty.

Scope of the study

The research conducted will help the top management to understand how the customer’s perspective on AI and DS influences customer satisfaction and loyalty and thus ultimately affects the performance of the company.

Limitations of the study

The limitations of the study are: -

1. There is a possibility of having situations where the people might have hurried with the google survey without properly looking at the questions.
2. There can be situations where respondents can understand different meanings of the questions. There can be similar questions which might look same by the respondents but are actually different.
3. If the respondents are quite less than it will be difficult to come to the conclusion which might not be valid as the sample size should be quite large as expected by the researcher to make meaningful interpretations from the analysis found.

A paper by Dudnik et. al (2021) highlighting on the substantiable impact of energy sector for AI technologies and its effectiveness. The paper has compared the implementation of AI technologies in Russian and in French Energy Companies. Fibonacci sequence, t test of students and fuzzy sets methods used to determine the readiness level which indicates development level of AI. By using the significant factors, the readiness of the AI implementation of companies has been calculated by using the method of fuzzy sets. It is found that Russian companies have low developmental progress in the implementation of AI in comparison to French energy companies.

A research paper by Popkova and Sergi (2020) explains regarding the AI and Human Capital in the industry 4.0 in entrepreneurship for social of AI and Human capital in the industries. The reason of the paper is to find the variants and future proportion for the human intellect usage and AI in the entrepreneurship of the industries 4.0 which contribute the entrepreneurship the most. The paper tries to identify the usage of AI in which perspective and make evaluation on the interest and readiness of the implementation of AI. The author also makes a model to make optimal proportions and the human intellect of the variant of usage and the social entrepreneurship for the future until 2030 in the conditions of Industry 4.0. It is found that the social entrepreneurship will create changes and create opportunities to make changes on AI until 2030, but it will not fully replace into full automation with the usage of AI and human intellect for the whole time.

Opportunities and Challenges in Adopting AI/DS Techniques

The paper by ÓhÉigeartaigh et. al. (2020) regarding how to overcome barriers to in cross – cultural cooperation in regards to ethics and in Governance. To achieve the global benefits in AI this will require to make cooperation internationally in different governance areas and the ethical standards that are needed to be maintained. There are many barriers that are found in present some of which are mistrust between cultures and several other barriers that are present across diverse cultures and other priorities that are present. This paper is regarding the barriers present in North America and Europe on one way and East Asia on the different hand which identifies those regions play a key role is differing the impact of AI across ethics and governance. It is believed that academia plays a important role in promoting cultural cooperation in AI ethics by building more mutual understanding and clarify the issues that are found on both sides across the different regions such that those are clarified and grows a mutual understanding.  

A research paper by Muller (2021) explains on the barriers and the enablers for Ai in dental diagnostics. This paper identifies on the barriers and enablers of the implementation of AI which was conducted between May end and end of June 2020. The questionnaire was created to understand the theoretical behaviors framework and their capabilities, motivations and capabilities. The content analysis of Mayring was made to to understand barriers and enablers. Total of 36 barriers, enablers and conflicting themes had been identified covering of nine out of fourteen domains and consisting of all the three determinants of the behavior. The stakeholders emphasized both hopes and chances of AI.

Research paper by Ghoreishi and Happonen (2020) for the key enablers to deploy the AI for making circlar economy to embrace sustainable product design. This paper figures out how the AI techniques make integration to economy in product by circularly with designing phase. Designing the products is a phenomenon, which is the rising of manufacturing industries to improve product sustainability and prevent extra cost for mechanisms in production. It has been found that the implementing of AI technologies increase the productivity through more optimization and making data analysis on real time. The research conducted is qualitative research under two stages. The first step is to understand the concept of circular material design and integrate with smart production. Then the key roles of AI in product design circularly and its key enablers which are based on the research which is qualitative and study conducted for theoretical part.   

A research paper by Regona, Yigitcanlar, Xia and Li 2022 explain regarding the power of AI technologies having a range of capabilities which are upcoming in the industry nowadays. There has been not much increase in the popularity of the construction industry in comparison to upgradation in other industries. There has been limited research conducted to understand the reason to adopt low level of AI in construction industry. Thus, in this research literature review has been conducted using the PRISMA protocol. The results from the research reveals that AI is quite important in the planning department as the precision depends on the accuracy of the design, planning and construction stages for the construction project to increase the accuracy of the project. This is constituted by the events, risks and forecasting of the cost. The opportunity involved in this is decreasing the time that are spent on repetitive tasks on the usage of big data analysis and improving the processes of the work. The data acquisition and retention due to the fragmented nature of the industry has been the biggest problem to incorporate AI in the construction industry.

Influence of AI and DS on Customer Satisfaction and Loyalty

A research paper by Sozontov, Ivanova and Gibadullin (2019) is regarding the implementation of AI in the electric power industry which will help in increasing the efficiency of the national economy. This paper highlights on the neural networks and the AI elements that are involved in the process of production, consumption and transmission of the electricity. The paper reveals that the usage of AI in the electric power sector will be making more possibility that the disruptions of the power supply is minimized.

Research paper by Peres et. al. (2020) has described that the industry 4.0 has made manufacturing more dynamic but it is also getting more complex with additional dependencies, huge volume of data that is being generated and uncertainties in the data. It has been observed that the recent upgrade to AI has enabled to tackle the challenges related to digital transformation on the cyber physical systems using data driven prediction analysis and using the capacity to make assist of decision making of high complex decision making, which are non linear and are often have multistage environments. But the adoption of those solutions is much low by beyond the experimental stage as in real stages there are several issues found in real environment which are quite unique and are difficult to find the solution and the organizations are unprepared.

The data has been taken from Kaggle dataset. The data set will be used to understand the key factors to understand the retention of the top talent with the use of AI / DS. As the data analyst of the company, it is important to find the factors that keep the employees in the company and which factors make the employees leave. The company can make analysis to understand which of the factors are needed to be changed to prevent the loss of the good people. This data is from the past and current employees who are either currently working or have left the job. The dataset consists of variables like attrition, age, daily rate, department, business travel, distance from home, education, employee satisfaction level on the scale of 1 to 5 where 1 is low. 2 is medium, 3 is high and 4 is very high, Gender, hourly rate, performance rating, relations satisfaction, years in current role, etc.

The data set is the having 1470 rows. The data set consists of both qualitative and quantitative variables and the data is from a particular company.

Link of the data set - https://www.kaggle.com/datasets/patelprashant/employee-attrition

1. Are there any significant differences among employees having different job role and job satisfaction?
2. Are there any significant differences among employees having different marital status and job satisfaction?
3. Is there any significant relationship between performance and job satisfaction?
4. Is there any significant relationship between monthly income and job satisfaction?
5. Is there any significant relationship between hourly rate and job satisfaction?
6. Is there any significant relationship between work life balance and job satisfaction?
7. Is there any significant relationship between years of working and job satisfaction?
8. Finding whether there is any difference of monthly income for attrition rates.
9. Is there any significant relationship between training times in last year and job satisfaction?

Question 1

Using Anova to understand differences

 

Output

 

The p value is found to be 0.939 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there are no significant differences among employees having different job role and job satisfaction rating. 

 

Question 2

Input 

 

Output

 

The p value is found to be 0.622 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there are no significant differences among employees having different marital status and job satisfaction rating. 

Limitations of the Study

 

Question 3

Input

 

Output

 

The p value is found to be 0.93 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there is no significant relationship among performance rating of the employees and job satisfaction rating. The R squared value is also very less which is -0.000675 which interprets that performance rating is not affecting the job satisfaction.

Using correlation to understand relationship 

 

It is observed that there is no correlation between performance rating of the employees and job satisfaction rating. 

Question 4

Input 

 

The p value is found to be 0.784 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there is no significant relationship among monthly income of the employees and job satisfaction rating. The R squared value is also very less which is -0.000629 which interprets that monthly income of the employees is not affecting the job satisfaction.

Using correlation to understand relationship 

 

It is observed that there is no correlation between monthly income of the employees and job satisfaction rating. 

 

Graph showing no relationship between the two variables.

Question 5 

 

The p value is found to be 0.006 which is less than 0.05. Thus, the null hypothesis can be rejected and alternate hypothesis is accepted. Thus, it can be said that there is a significant relationship among hourly rate of employees and job satisfaction rating. 

Using correlation to understand relationship 

 

It is observed that there is no correlation between hourly rate of employees and job satisfaction rating.

 

Question 6 

 

The p value is found to be 0.456 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there is no significant relationship among work life balance of employees and job satisfaction rating. The R squared value is also very less which is -0.00030 which interprets that work life balance of employees is not affecting the job satisfaction.

Using correlation to understand relationship 

 

It is observed that there is no correlation between work life balance of employees and job satisfaction rating.

Question 7 

 

The p value is found to be 0.884 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there is no significant relationship among years at company of employees and job satisfaction rating. The adjusted R squared value is also very less which is -0.00066 which interprets those years at company of employees is not affecting the job satisfaction.

Using correlation to understand relationship 

 

It is observed that there is no correlation between years at company of employees and job satisfaction rating. 

 

No relationship can be seen in the graph of two variables.

Question 8

Using t test to understand the difference 

Related Papers on AI and DS

 

The p value is found to be 0.000 which is less than 0.05. Thus, the null hypothesis can be rejected and alternate hypothesis is accepted. Thus, it can be said that there is a significant difference between monthly income of employees who are leaving the company and who are not leaving the company. The mean value of employee not attrition is 6832 and who are in attrition have mean value of 4787 which is less by a huge margin.

Correlation of Daily rate and job Satisfaction 

 

As found that there is no correlation between daily rate of employees and job satisfaction rating.

Question 9  

The p value is found to be 0.825 which is greater than 0.05. Thus, the null hypothesis cannot be rejected and alternate hypothesis is not accepted. Thus, it can be said that there is no significant relationship among training times last year and job satisfaction rating. The adjusted R squared value is also very less which is -0.00064 which interprets training times last year is not affecting the job satisfaction.

Using correlation to understand relationship  

It is observed that there is no correlation between training times last year and job satisfa 

Discussion/ suggestions for performance improvements

As it is known that attrition rate means the departure of the employees from organizations for any particular reason which can be termination, resignation or retirement. It is found monthly salary plays a important role for attrition rate of the company. When the salary is more there is less changes of attrition than the one who is having less salary. It is also found that there is a significant relationship between hourly rate of employees and job satisfaction rating. This means that when the company is providing more hourly rate there is more chances of job satisfaction of the employee.

Conclusion

From the above analysis the company has been able to figure out how the attrition is affected to the employees. The report tries to understand how the job satisfaction ratings provided by the employee’s scale 1- 4 where 4 says highest satisfaction and one being the lowest to understand which factors affect the rating scale with the use of artificial intelligence tools which the company wants to understand such that they can take better understanding of the employees by analyzing the past data that has been analyzed. From this analysis most important element that has been found that affects the job satisfaction is the monthly salary of the employees and the hourly rate of the employees and if the values are more there is less chances of attrition and vice versa.   

References

Dudnik, O., Vasiljeva, ?., Kuznetsov, N., Podzorova, M., Nikolaeva, I., Vatutina, L., Khomenko, E. and Ivleva, M., 2021. Trends, impacts, and prospects for implementing artificial intelligence technologies in the energy industry: the implication of open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), p.155.

Ghoreishi, M. and Happonen, A., 2020, May. Key enablers for deploying artificial intelligence for circular economy embracing sustainable product design: Three case studies. In AIP conference proceedings (Vol. 2233, No. 1, p. 050008). AIP Publishing LLC.

Müller, A., Mertens, S.M., Göstemeyer, G., Krois, J. and Schwendicke, F., 2021. Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study. Journal of Clinical Medicine, 10(8), p.1612.

ÓhÉigeartaigh, S.S., Whittlestone, J., Liu, Y., Zeng, Y. and Liu, Z., 2020. Overcoming barriers to cross-cultural cooperation in AI ethics and governance. Philosophy & technology, 33(4), pp.571-593.

Peres, R.S., Jia, X., Lee, J., Sun, K., Colombo, A.W. and Barata, J., 2020. Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, pp.220121-220139.

Popkova, E.G. and Sergi, B.S., 2020. Human capital and AI in industry 4.0. Convergence and divergence in social entrepreneurship in Russia. Journal of Intellectual Capital.

Regona, M., Yigitcanlar, T., Xia, B. and Li, R.Y.M., 2022. Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), p.45.

Sozontov, A., Ivanova, M. and Gibadullin, A., 2019. Implementation of artificial intelligence in the electric power industry. In E3S Web of Conferences (Vol. 114, p. 01009). EDP Sciences.