Investigating the Impact of Implementing Artificial Intelligence - IT Computer Science Assignment Help

Assignment Task

Task 

Investigating the Impact of Implementing Artificial Intelligence (AI) within a Business

Background to the Problem 
Increased human intervention in business processes, and friction to change or upgrade legacy systems are significant drainers of productivity, and operational effectiveness. These outdated systems not only bring in high degree of risk & potential fraud in business operations, but also makes it extremely challenging to audit processes, and benchmark them against best practices or industry standards. Therefore, this slack in performance coupled by intense market competition has led to businesses shifting to technology, automation, and digitization – leading to the emergence of data as an asset. The intense focus on data has spawned the mindset of data-driven decision making, development of smart technologies – and hence, the concept of artificial intelligence. Unlike traditional or mainstream systems and applications, which are explicitly coded to behave or respond in a certain manner, artificial intelligence deals in the concept of machines & systems self-learning via automated streams of data to enable itself to take human-like decisions. 

This is a trending focal point amongst business leaders in current times as it not only provides them the cost leverage (reduction in human capital) but also brings in exceedingly high levels of efficiency and throughput in the operations.In addition to the efficiency & optimization advantages, artificial intelligence leverages the true essence of transactional data which is stored & persisted day in and day out from business operations. Therefore, the pedigree of insights generated via artificial intelligence engines are much more nuanced as compared to any traditional or mainstream analytical engines.Therefore, from a theoretical perspective, there is little doubt on the usefulness or relevance of artificial intelligence as a use-case in business operations. However, there are certain challenges from an implementation perspective on ground level. Research indicates that AI systems and engines are not able to perform “common-sense” tasks as there are multiple “obvious” factors to be considered in responding to common situations. 

In addition to that, successful implementation of AI engines also depends upon the maturity levels of data and information infrastructure in the organisation.Finally, there is a complete “perspective” on the implications of AI on security & privacy infringements. For AI implementations, all data needs to be accessible via the network which opens up privacy concerns. Hence, there is a situation of a trade-off between the completeness of data being accessible to AI systems vs. the privacy infringements in allowing wholesome access of data.Therefore, a practical analysis on the ground-level feasibility & challenges on implementing AI solutions is the crux of this research project.

Problem Statement   
While there is no debate on the relevance and applicability of artificial intelligence in business operations, the problem statement before the researcher is to understand the viability of implementing AI projects in organizations  and bringing in transformation in ways of business. Now, research says that majority of the AI projects end up not meeting the intended objectives due to varied reasons.(Jesse 2019) states the key success factors of IBM Watson – rich data sets, matured data processing framework, infrastructure, and capability.In addition to that, there are other pertinent challenges such as data privacy infringement, change management etc. which the leadership need to take care as well. The research design & outcome is intended to evaluate the gap in AI implementations – and therefore, conclude on the ground-level realities and challenges faced by technology department in implementing AI in operations.

Significance of the Study 
This research would cater to the implementation of artificial intelligence within businesses from a bottom-up approach i.e.understanding the ground-level challenges and realities of implementing AI, and evaluating the specific reasons as to why AI implementations tend to not meet the intended objectives.The research would cover three major aspects for businesses – first, is the role of rich data, and technical infrastructure & capability to successful implementation of AI; second, is the importance of change management (cross-training, up-skilling etc.) in ensuring employees participate in the change and adopt to the new ways of working; and third, th0e security implications & privacy infringements in regard to implementing AI solutions within businesses. All of these factors would be compared against the true benefits expected from AI implementation, and an unbiased, and practical essence of AI implementation would be provided. The research would enable businesses & government to understand (more clearly) the precise pre-requisites of setting up AI solutions and understand the key success factors of implementing Ai solutions within businesses or government.

Research Method
The research method used in this particular research project is qualitative survey as it enables to capture the experience of people and bring in rich textual interpretation to the survey responses. 

Target Population
The population targeted as a part of the research survey is the technology department as they are the main implementers of AI solutions in businesses and would be aware (first-hand) of the challenges ad benefits in implementing AI in business. The number of candidates in the target population is 30. 

Sample Size
Given the previous context that the target population is 30, the sample size is taken as 28 (as per mentioned in the sample size table - https://www.research-advisors.com/tools/SampleSize.htm) 

Sampling Method
Simple random sampling is used in this research study as the sample coverage is quite high to be a perfect replica of the population. Out of a target population of 30 staff employees in the technological department, 28 of them are surveyed as a part of the research project. Therefore, simple random sampling would hold true in this case. 

Data Collection
The research would involve both primary and secondary analysis – as stated below – Secondary research methods – Extensive literature review from genuine online sources, peer reviewed journals, eminent databases such as ProQuest etc.Primary data collection methods – Primary survey would be used to collect real-world experiences (in relation to AI implementation) from the sample of 28 staff members working in the technology department. A questionnaire (including questions on demographics, and other experiential aspects) would be floated to the sample of 28 respondents to collect primary data for analysis. 

Data Analysis and Interpretation
Firstly, the analysis of the key benefits and challenges of implementing Artificial Intelligence within businesses would be done from the secondary analysis (stated in the literature review). Post that, content analysis would be done on the qualitative survey (based on responses on survey) to understand the key challenges faced by the technology staff in implementing AI from three different perspectives – change management, data/technology capability & infrastructure, and security/privacy concerns.In addition, quantitative visualizations (table/chart) on the ratings on Likert scale would also be showcased to illustrate the key pain areas and advantages experienced by the technology staff in connection with AI implementation within business. 

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