Introduction: Case Study on Responsible AI implementation with Higher than Expected Employee Retention.
Abstract
The study is aimed towards understanding how implementation of AI mechanisms can be done, while bearing in mind that employees should be retained in the best way & best numbers possible.
Such an instance is cited, & analyzed, to understand the implications that the said maneuver can have.
Methodology
A Doctrinal Approach has been taken.
Analysis
Tata Steel Europe has aimed to digitise its production basing this on key factors like the need to improve profit margin in already low profit margin industry that already over produces and supplies compared to the demand, the demand for more quality products and the aging workforce. It kept a focus on its employees and decided to retrain its employees to better understand the technical aspects of its recent development. They utilized data visualization tools in order to aid the explainability and interpretability of the Advanced Analytics models[1]. Concerning its productivity, it saw a 13 % increase in EBITDA (Earnings Before Interest, Tax, Depreciation and Amortization) with the same workforce. It was also noted that the Advance Analytics program broke even within one or two years of its implementation in regards to the capital investment and offered a higher return on investment later. The benefits were seen in the form of raw materials savings, yield improvements and margin improvements.[2] It was seen that in some areas the profitability was much larger especially in cases of raw materials as they were the largest cost driver in the Tata Steel Europe’s cost analysis.
It was seen that there was no major workforce reduction in the operations of TSE though in the long run it is noted that there will be a workforce reduction but it is expected to be within the norms of the industry standards. TSE has tried to utilize the existing workforce and does not aim to completely overhaul the same with Artificial Intelligence. It aims to look for savings in terms of raw materials savings, yield improvements and margin improvements. The lack of workforce reduction in TSE’s operations may be attributable to a string workforce union. However, that does not mean that TSE’s workforce remains unchanged, it is seen that there is an increasing trend of higher trained workers in its composition thus there were higher trained workers than lower trained ones, this may also be in part to the focus placed upon retraining the pre-existing workforce. This way Tata Steel Europe aims to set an example of a corporate leader. It also recently implemented Digitate’s cognitive automation solution ignio as its AI platform for IT operations.[3] It aims to transform their IT operations providing better services while focusing on the machinery.[4]
It is clear from the case study of Tata Steel Europe that AI systems is an analytical tool and the adoption of AI can be done tactfully and in a much better manner. It is also seen that it is much easier for highly profitable firms to adopt Artificial Intelligence systems as it is a capital-intensive undertaking which can increase global income inequality.[5]
Conclusion
A solemn conclusion can be drawn that the fears of the global citizenry with regards to AI & recession are ill founded, & must be done away with.
On the part of body corporates, an understanding needs to be present as to the requirements of the employees & the global citizenry with regards to employment.
The TSE instance shows that not only can retention be positive in terms of humanistic values but can as well serve to bring in incremented revenues.
Name of Author: Nalin Malhotra.
Date of Publication: 08 April, 2021.
Indicative Code: ILAIL-0000-CS-08-04-2021-00003
Style of Case Study: Influential; Deviant; Diverse;
[1] https://www.partnershiponai.org/case-study/tata-steel-europe-in-brief/ [2] Id. [3] https://digitate.com/newsroom/ignio-powers-it-transformation-at-tata-steel-europe/ [4] Id. [5] Autor DH et al, 2017b. The fall of the labor share and the rise of superstar firms, NBER Working Paper No. 23396 (Cambridge, MA, National Bureau of Economic Research).
