Assignment Sample on Impact Of Data Science and Artificial Intelligence
Introduction
In today’s modern era, business is featured with ambitious elements with the application of
artificial intelligence and data science. It is popular for simplifying customer support, improving
security, reduction of operational costs simultaneously. In this research, the study focused on
analyzing the contemporary issues surrounding the implementation of these technologies and
presents findings based on gathered data. The research is conducted to address contemporary
issues by using data science and AI in business marketing. Furthermore, this research will
provide a deep insight into the research objectives and purposes of the research that are linked
with literature reviews of different authors to validate the study. It will also be discussing the
contemporary issues that a company faces.
Research objective
By aligning the purpose of the research with the research goal and objectives to understand the
impacts of data science and the incorporation of AI in today’s modern business. There are a few
objectives that are implemented to explore business innovation.
● To identify importance of AI powered Chabot in customer relationship
● To make a strong difference in productivity, operational efficiency, product innovation,
and decision-making process in business with the incorporation of AI.
● To provide brainstorming solutions to human-generated or situational-based problems.
Research Questions
There are some of the potential research questions those needs to be discussed for determining
the necessary objects of this research. Some of the [possible research questions are:
What are key of AI Chabot for business success?
How data science impacted on human to bring opportunities for modern business world?
How AI has impacted human life with productivity and innovation?
Purpose of this research
The objective of the research is to investigate the effect of “artificial intelligence (AI)” and “data
science” in today’s modern organization. The research seeks to explore the benefits of AI for
marketing purposes in a business. As researchers proved that AI has the power or delivers
customized experiences to customers in diverse sectors like finance, media, digitalization sector,
educational sector, etc. Research has to explore the effectiveness of data science and AI in the
business in a significant way where it ensures that how personalization is fulfilling the
customer’s demand and incorporate AI in daily life. Different studies are proved that predictive
analysis is another form of application of data science to gain traction in the advertisement and
marketing sector. Use of a machine learning algorithm for analyzing customer data as well as it
will help to predict the future behavior of such kinds of products that are highly demanded in the
market.
Moreover, this research seeks to determine the risk of bias in a particular business. The
information that is generated from an AI system will rely on the parameters that are utilized to
prepare it. This can continually lead to the exclusion of juvenility, poor people, marginalized
cultures, and minority populations, and an understanding of the actual demands in the field of
various sectors.
Overall the key purpose of this research is to deliver comprehensive knowledge on this data
science impact in the field of technology. Various theories suggest that de-identification models
can be exploited to conceal essence, but it’s essential to recognize that they’re not always
thriving. Transparency is one of the key factors in the prospective worker replacement in the
field of the IT sector. Even though complete automation of physical labor is improbable, up to 2 million manufacturing jobs could go by 2025 which is also clearly evaluated in this research.
Another key purpose of this research is to involve data science and AI for fraud detection and as
well as identify the pattern and the anomalies data. The research has to be more clearly specified
that provide insights to personalize the marketing campaigns and at the same time highs up the
conversions between the higher authority of an organization and employees.
Literature review
According to Ahmad and Mustafa 2022 the majority of companies currently view the rise of the
digital economy as both a challenge and an opportunity waiting to be taken advantage of. Due to
their lack of technology infrastructure and talented employees, some firms found the digital
economy to be an impediment and a problem (Ahmad and Mustafa 2022). On the other hand,
enterprises viewed the digital economy as a chance to improve their own capabilities,
infrastructure, and competitiveness, which would help them stand out in the market and compete
effectively (Ahmad and Mustafa 2022). Organizations that strive for stability and want to stay
ahead of the competition realized how difficult it was and how important it was to have a wide
range of capabilities in order to reach customers and offer distinctive and innovative products
before rivals at a fair price (Ahmad and Mustafa 2022). Therefore, the majority of businesses
have turned to enhancing their technological capabilities as one of the ways to complete their
digital transformation in order to gain various competitive advantages, lower costs associated
with business and operational transactions and transportation, and completely rely on
technological systems to handle big data analytics and customer demands.
Design for AI
On the basis of evidence of Verganti et al. 2020 AI empowers modern and advanced level of
designing may also empower a more effective, human-centered application of AI, just as AI can
empower a more advanced practice of design. In case of considering the hospitality sector
heavily rely on AI, for instance to create personalized listings and guide hosts’ price selections.
However, innovation route is less influenced by design and more by a heavy reliance on A/B
testing (Verganti et al. 2020). The rise of software, digital networks, and AI is propelling a broad
economic revolution. Decision-making and learning, which are at the heart of innovation, are
automated by AI. The potential effect on innovation performance is significant, as evidenced by
the instances mentioned in this article (Verganti et al. 2020). AI can provide greater performance
in terms of customer centricity, originality, and rate of innovation by removing the normal
constraints (in scale, scope, and learning) of human-intensive design.
Trustworthy AI Model
As per the view Jacovi et al. 2020 distinguishes between "trust" (an attitude of the trustor) and
being "trustworthy" (a quality of the trustee). The paper also stated that an AI model is
trustworthy to a contract if it is able to uphold this contract (Jacovi et al. 2020). The pursuit of
one does not necessarily necessitate the pursuit of the other, and trustworthiness is not a
requirement for trust in that trust can exist in a model that is not trustworthy and that the pursuit
of a trustworthy model does not always result in the acquisition of trust.
Methodology
Research Philosophy
In this particular research study, it follows the positivism philosophy since the outcome of the
research. The alteration of decision-making processes is one of data science’s and AI’s most
significant effects on contemporary organizations. Organizations can gain important insights
from huge and complicated datasets by using modern data analysis techniques. This makes it
possible for data-driven decision-making, where choices are based on unbiased analysis rather
than hunches or speculation (Benbya et al. 2021). The results show that businesses that include
data science and AI in their decision-making tend to produce better results and acquire a
competitive advantage.
Research Approach
This particular study follows the deductive approaches for the research analysis. As deductive
approach, focuses on the obtained results from existing theories (Williamson, 2019). This
approach is selected to explore the opportunity to illustrate how research ideas and variables are
connected to one another’s causes and outcomes.
Research Design This particular research follows qualitative research method to get sufficient results from the
research (Iqbal et al. 2020). Qualitative research is focused on analysis which is perceptional
based whereas the quantities data is focused on numerical data (Iqbal et al. 2020). The purpose
of qualitative analysis is to interpret the data and the resulting themes, to facilitate understanding
of the phenomenon being studied.
Research Strategy
Through the archival research strategy, secondary data analysis is conducted in this research
(Iqbal et al. 2020). The research strategy will describe the process which is used by researchers
to meet the areas that need more clarification.
Data Collection
This research follows secondary data collection method, where data is collected through
secondary sources like websites, journals, pdfs, articles, etc.
Data Analysis
The research has meticulously depicted that chatbots and virtual assistants driven by AI have
revolutionized customer relationships. These sophisticated technologies are capable of
responding to consumer queries, making fundamental suggestions, and providing immediate
assistance. AI chatbots are accessible around-the-clock, offering quick responses and raising
customer satisfaction (Benbya et al. 2021). Additionally, they can gather client information
while interacting with them, which will help the business better understand their preferences and
behaviors. Marketing automation also makes use of data science and AI (Iqbal et al. 2020).
Automating repetitious marketing processes like content production, social media scheduling,
and email marketing is possible using AI algorithms (Agbehadji et al. 2020). By streamlining
marketing procedures, this automation increases productivity. Businesses can deliver
personalized content at scale and guarantee ongoing engagement with their target audience by
utilizing AI-powered technologies.
Preliminary findings and discussion
The research also highlights the impact of data science and AI on customer experience. By
leveraging customer data and AI algorithms, organizations can personalize their offerings and
provide customized experiences to individual customers (Williamson, 2019). The findings
suggest that personalized marketing campaigns, product recommendations, and customer support
services lead to higher customer satisfaction and increased customer loyalty. To get high benefits
from new technologies, organizations must negotiate current concerns including ethical data use
and bias in AI algorithms (Verganti et al. 2020). The results of this dissertation help researchers
and practitioners in this subject have a greater knowledge of the effects of data science and AI on
contemporary organizations (Williamson, 2019). In order to meet the developing issues and
opportunities associated with these technologies, more investigation and study are required.
This analysis has a retrospective, retrospective temporal frame. This project has started on
15.5.23 and concluded on 22.6.23. There are number of tasks are 13 which shows details
analysis of taken time. The project has started through planning procedure where
introduction takes 1 day, to investigate background, research aim objectives questions and
rationale also takes 1 day to finish. The project also shows that literature review, theory and
models takes 4 days to investigate while data findings takes 4 days also. It takes 2 days to
conclude the project ultimately.
Conclusion
On a concluding note, it can be statured that the algorithmic version of decision-making along
with the implication of AI in computerized data analysis in any organization. It is highly
appreciable that AI has the biggest role in boosting the workplace and satisfying people to do
jobs more efficiently. These advanced technologies have played a vital role in transforming and
automating tasks, decision-making processes, and enhancing customer experiences.
There are some issues or challenge may have arisen during the conduction of research which is
tactfully presented in this study. I observed that lack of transparency of AI tools, active cases of
biased, non-neutral, lack of surveillance practices along with discriminatory outcomes are the
current issues in this research. Researchers are continuous trying to solve the automated and
optimize these contemporary issues which I fully supported.
Reference list
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in human resource management: Aspirations for public sector in Bahrain. International Journal
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Agbehadji, I.E., Awuzie, B.O., Ngowi, A.B. and Millham, R.C., 2020. Review of big data
analytics, artificial intelligence and nature-inspired computing models towards accurate detection
of COVID-19 pandemic cases and contact tracing. International journal of environmental
research and public health, 17(15), p.5330.
Benbya, H., Pachidi, S. and Jarvenpaa, S.L., 2021. Artificial intelligence in organizations:
implications for information systems research. Journal of the Association for Information
Systems, 22(2).
Butcher, J. and Beridze, I., 2019. What is the state of artificial intelligence governance globally?.
The RUSI Journal, 164(5-6), pp.88-96.
Iqbal, R., Doctor, F., More, B., Mahmud, S. and Yousuf, U., 2020. Big data analytics:
Computational intelligence techniques and application areas. Technological Forecasting and
Social Change, 153, p.119253.
Jacovi, A., Marasović, A., Miller, T. and Goldberg, Y., 2021, March. Formalizing trust in
artificial intelligence: Prerequisites, causes and goals of human trust in AI. In Proceedings of the
2021 ACM conference on fairness, accountability, and transparency (pp. 624-635).
Mariani, M.M. and Wamba, S.F., 2020. Exploring how consumer goods companies innovate in
the digital age: The role of big data analytics companies. Journal of Business Research, 121,
pp.338-352.
Verganti, R., Vendraminelli, L. and Iansiti, M., 2020. Innovation and design in the age of
artificial intelligence. Journal of Product Innovation Management, 37(3), pp.212-227.Williamson, B., 2019. Policy networks, performance metrics and platform markets: Charting the
expanding data infrastructure of higher education. British Journal of Educational Technology,
50(6), pp.2794-2809.
Wu, L., Lou, B. and Hitt, L., 2019. Data analytics supports decentralized
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