How Artificial Intelligence (AI) Impacts the Economy

Author:
Aykhan Alasov
Student ID:
64313
University:
UEHS
Date:
06.01.2025

Index
Abstract…………...…………...1
Introduction…………...…………...2
Historical description…………...…………...2-8
Basic analysis of secondary data…………...…………...8-9
Application of Basic Economic Indicators in Economic Evaluation…………...
…………...9-11
Recommendations and solutions…………...…………...
References…………...…………...

Abstract
The improvement and integration of AI in different fields of life and economy has great
impact on life and economic development of World. This article will explore how AI chancing
industries, labour markets and global economy. Both positive results like increased
productivity and consequences such as job loss because of non-competition with AI will be a
theme of discussion here. The article will touch also economical grow because of the impact
of AI and emerging of new ethical and regulatory issues. With the help of data and economic
indicators article provide comprehensive ways of analysis to understand the economic
influence of AI and to learn how to maximise benefits from it while reducing the risks.
Introduction
Artificial Intelligence is fully transforming industry while at the same time reordering the
economy and changing the composition of the workforce. Because its wide applications relate
to areas associated with automation, data analysis, and decision-making, it therefore results in
dramatic changes in productivity and levels of economic activities. This paper, in this regard,
discusses some of the dimensions through which AI can be impacting the world economy and
focuses on ways in which such technologies are being used for bringing about economic
transformation, affecting labour markets, and long-term growth prospects. The introductory
part of this work presents a review of the scope of research and points out various
opportunities and problems resulting from the development of such technologies within the
realm of artificial intelligence. It also gave some grounds for further historical look, data
analysis, and economic counting.

Historical description
The beginning of history of AI can be calculated from 1950s’ with the demands of NASA.
From this period scientists needed AI or “human computers” to calculate research like the
trajectory of rocket launch.
In an era when the computational powers were inextricably linked with the cognitive faculties
of human beings, the British mathematician Alan Turing envisioned a machine that would go
beyond its very programming. Under Turing's creative vision, a computer would first be
programmed with a set of instructions to perform; it would also, however, be endowed with
the ability to extend further than the range of its preordained tasks. But due to that fact he
lacked of technology he was not able to prove his theories in practice. but he actually credited
artificial intelligence before it started to be called like that.
In1956 in Dartmouth College professor Jhon McCarthy invited researchers to summer long
work for investigate the abilities of “human machines” in another name “thinker machines”.
The group held the belief that “Every aspect of learning or any other feature of intelligence
can, in principle, be described so precisely that a machine can be created to simulate it.” Their
discussions and efforts during that summer have led to them being widely recognized as the
founders of the field of artificial intelligence. After two years of death of Alan Turing in
Dartmouth conference McCarthy conceived term can be defined as “human like machines”,
but later it became more renown like “artificial intelligence”.
AI and Automation in the Manufacturing Sector (1980s–1990s)
During 1980 and 1990 years sector of manufacturing transformed to the integration of
Artificial Intelligence (AI) and automation. These changes were very important for modern
industrial production and that was more effective about labor dynamics, and economics
competivness. Also we can mention that the adoption of industrial robots during this period
made major changes in manufacturing processes. Robots ere optionally deployed for
dangerous tasks such as welding, working with chemicals, automative industries. This
automation made reduced human errors and safer working conditions.
Expert Systems for Decision-Making
AI based software that made by human, experrtises in specific domains-began to influence
manufacturing decisions. These systems helped to optimize production processes also quality
control by analizing on large data sets.
 Economic Impact: Expert systems contributed to greater operational efficiency by
reducing downtime through predictive maintenance, thus lowering repair costs and
improving the overall production process.
Integration of CAD and CAM Technologies
The 1980s and 1990s also there was the rise of Computer-Aided Design (CAD). This
technology enabled manufacturers to design products digitally that helped in upgrading the
technology, also made to detect mistakes in time avoiding errors and waste.
 Economic Impact: CAD and CAM systems led to faster prototyping, better design
flexibility, and cost reductions in production. Manufacturers could more efficiently
tailor products to customer needs, shortening the product development cycle and
improving market responsiveness.
Supply Chain Optimization Through AI
AI technologies also began to optimize supply chains in manufacturing sector. Predictive
algoritms made advanced benefits in manufacturing process. That’s why we can mention that
inventory excesses and resource allocations were reduced.
 Economic Impact: Improved supply chain efficiency lowered storage and
transportation costs while helping manufacturers respond more nimbly to market
changes, enhancing global competitiveness.
Broader Economic Implications
The integration of AI and automation in manufacturing brought both positive and negative
economic consequences:
 Productivity Growth: AI and automation significantly boosted productivity in
manufacturing, leading to reduced production costs and the ability to scale operations
rapidly.
 Labor Market Disruptions: While automation led to job losses in traditional roles,
there was also an increased demand for skilled workers, particularly in robotics
programming, AI system maintenance, and data analysis.
 Global Competitiveness: Countries and companies that adopted these technologies
early, such as Japan, Germany, and the United States, gained a competitive edge, while
other nations struggled to keep pace.
In the end of 90s there were some manufactural troubes with AI so automation technologies
dramaticallt alerted production process. The adoption of robotism expert systems CAD-CAM
and AI driven supply implement remade manufacturing process. As soon as these
technologies evolved, their impact countuied grow, influencing industry and broder economic
trends.
The Internet and AI's Role in the Digital Economy (2000s–2010s)
In the beginning of the 00s there was a marked period of rapid digital transformation, made by
the internet and advancements in Artificial Intelligence (AI) These two forces combined to
made economies grow fast. Also AI powered by enough amount of data generated through the
internet, became integral to how business operate, how customers interacted with technologies
and how economic value of this technology was created. The golden period if significant
technological advancements in cloud computing, data analytics, machine learning, and e-
commerce, which facilitated the growth of digital platforms and transformed industries
ranging from retail to finance.
1. The Rise of E-Commerce and Personalized Consumer Experiences
With evolution of internet and evolution of AI technologies platforms like Amazon,
AliExpress transformed themselves into global retail. These platforms heavily influenced to
AI algortihms for personalized recommendations so, customer can get personalized offer
specially made for them.
Economic Impact: E-commerce revolutionized the retail sector by offering a new business
model that allowed companies to reach a global market with lower overhead costs compared
to traditional brick-and-mortar stores. AI-enhanced personalization increased consumer
spending by tailoring offerings to individual preferences. Additionally, data analytics allowed
businesses to optimize their supply chains, reducing costs and increasing operational
efficiency.
2. AI in Finance and Algorithmic Trading
The 2000s and 2010s saw the widespread adoption of AI and machine learning in the financial
sector. By this way, we can also mention that AI powered algorithms make fast data driven
decisions in real time, AI algorithms analyze risk assessments and market predictions.
Economic Impact: AI-driven financial technologies improved decision-making,
reduced operational costs, and allowed for more accurate risk assessments. These
technologies made more confidential usage of accessing to financial services by enabling
more efficient and inclusive banking models. However the widespread use of algortihms
played their role in global finances.
3. The Gig Economy and AI-Driven Labor Markets
The rise of digital platforms and internet networks where workers can engage in short
time, flexible and often freelance jobs. Companies like Uber, Airbnb, Bolt is excellent
example for these ones. That’s mean when internet platforms was raised people started to
 use internet for their gaining, This factors reshaped the labor of markets alerted the
traditional notion of employment.
Economic Impact: The gig economy, fueled by AI and digital platforms, created new
job opportunities and allowed workers to engage in flexible, on-demand work. That’s mean
person who will provide business with more scalable workpower will do this by lower
operating costs so its affordable by traditional employess.
4. AI-Driven Business Models and the Platform Economy
In the beginning of 2000s AI powered platforms began increasing dominantly in certain
industries including entertainment transportation and even in healthcare. Some companies like
Google, Facebook ,Uber built business models around data collection and platform based
services.
Economic Impact: The platform economy became a central feature of the digital
economy, where value creation was driven not just by the products or services offered,
but by the data generated through user interactions. These platforms captured vast
amounts of data and used AI to drive efficiencies, improve user experiences, and
maximize profits. The data-driven business models led to the dominance of "big tech"
firms, such as Google and Facebook, which controlled large portions of the
advertising market. However, this also raised concerns about data privacy, market
concentration, and the power of tech giants.
5. Cloud Computing and Data Analytics
The 2000s and 2010s also witnessed the emergence and hard adoptaion of cloud computing
technologies, that allowed business to store, to remake and proceed processes and analyze
large amount of data in real time. Cloud platforms such as Amazon Web Services (AWS)
provided business with scalable computer power for structive investment opportunities. This
enabled companies ro make models about data analyzing tools through cloud services.
Economic Impact: Cloud computing and AI together lowered the barriers to entry for
businesses of all sizes, allowing them to access advanced technologies that were
previously available only to large corporations. This democratization of technology
made sense about revolution of cloud technology system industries.
6. AI and Automation in Customer Service
Customer services made a huge difference by a transformation in 2000s and 2010s with the
rise of AI powoered chatbots, virtual assistants and automated call answerers system.
Companies decided to handle customer inquiries by the help of AI based automative NLP
powered machines. This metod improvided customer satisfaction and made a great sense in
AI based automation technologies history.
Economic Impact AI driven customer service systems helped businesses reduce costs,
time and working power. However that was one of the reasons freeing employers. This reason
was one of main questions on European Parlaments question about employment and
employeers.
7. AI's Role in Digital Advertising and Marketing
The digital adversiting industry made special transformation about AI technologies. AI
algorithms played certainrole in targeting ads to special user segments ,optimizing ad
placements and analyzing customer behavior. Platforms like Google, Amazon, Facebook used
Artificial Intelligence to remake their adversiting models, which allowed advertisers to reach
highly targeted people with personalized content.
Economic Impact: AI driven adversiting sites enhanced marketing effectiveness by
allowing business to allocate resources more efectiely and measuring return on investment in
real part of time. This resulted in higher income for digital platforms and more efficient
marketing send for adversitiers. So by this way customers can use digital platforms on they
own way and it will more capability.
The internet and Artificial Intelligences role in the digital economy was raised after
profounding shift in global economic structures. Artificial intelligence powered platforms, e
commercial journals, digital adversiting transformed to traditional industries and creted new
indusrties under new platform of business growth. The combination of cloud systems
technologies, machine learning and big data analaytics made free access to advanced
technologies to innovate market concentration, data privacy and to improve digital economy.
AI in the Modern Economy: Challenges and Opportunities (2020s and Beyond)
The 2020s and beyond mark a critical period of integration of Artificial Intelligence (AI) into
global economy. As AI contioues to evolve it presents both significant challenges and certain
opportunities across industries, shaping productivity, labor markets and economic growth.
Challenges
 Job Displacement: AI-driven automation poses a risk to traditional jobs, especially in
sectors like manufacturing, retail, and customer service. This displacement creates a
need for workforce retraining and adaptation.
 Inequality: AI adoption may widen economic inequality, as wealthier nations and
large corporations are better equipped to invest in advanced technologies, leaving
smaller businesses and developing countries at a disadvantage.
 Privacy and Ethical Concerns: The widespread use of AI raises issues around data
privacy, surveillance, and the ethical implications of AI decision-making, particularly
in sectors like healthcare and law enforcement.

Opportunities
 Productivity Boost: AI has the potential to drive significant productivity gains by
automating repetitive tasks, improving efficiency, and fostering innovation across
sectors such as healthcare, finance, and logistics.
  New Markets and Jobs: AI will create new industries and job roles, particularly in AI
development, data science, and AI-related services.
 Economic Growth: By optimizing operations and enhancing innovation, AI can be a
catalyst for long-term economic growth, particularly in emerging technologies like
robotics and AI-powered analytics.

Basic Analysis of Secondary Data: How Artificial Intelligence (AI) Impacts the Economy
Analyzing secondary data is essential for understanding how Artificial Intelligence (AI)
impacts the economy. Secondary data, which includes data collected by others for purposes
different from the current research, can provide valuable insights into AI's influence on
economic growth, productivity, employment, and industry-specific developments. The
following is a basic approach to analyzing secondary data on AI’s economic impact.
1. Identification and Collection of Data

The first step in secondary data analysis is identifying reliable data sources. For AI’s
economic impact, relevant data can be obtained from:
 Government Agencies: Organizations like the Bureau of Economic Analysis (BEA)
and World Bank offer data on AI’s contribution to GDP, productivity, and
employment.
 Industry Reports: Research firms like McKinsey, PwC, and Gartner provide reports
on AI adoption rates, industry transformation, and economic forecasts.
 Academic Studies: Research papers often provide empirical analysis of AI’s impact
on sectors such as manufacturing, healthcare, and services.
 Market Research Databases: Platforms like Statista and Euromonitor provide
datasets on AI adoption trends, investments, and market growth projections.
2.Data cleaning and Preprocessing
Once the needed data is collected, it requires cleaning and premaking to ensure
consistency and accuracy.

 Handling Missing Data: Some datasets may have gaps. Missing data can be
addressed through imputation (estimating missing values) or removing incomplete
records, depending on the context.
 Standardizing Variables: Different data sources may use different units of
measurement. For example, AI investments might be reported in monetary terms in
one dataset and employee numbers in another. Standardizing variables ensures
comparability.
  Outlier Detection: Outliers, or extreme data points, can skew analysis. Identifying
and managing outliers ensures that findings are not distorted by atypical values.
3. Descriptive analysis
Descriptive analysis involves summarizing the data to identify trends and patterns. Key steps
include:
 Measures of Central Tendency: Calculating averages, medians, or modes can
highlight the typical level of AI adoption, investment, or productivity increases in
different sectors.
 Dispersion Measures: Standard deviation and variance can help assess how widely
the economic impact of AI varies across industries or regions.
 Data Visualization: Visual tools like bar charts, line graphs, or histograms help
illustrate trends. For example, a line graph could show how AI adoption has correlated
with GDP growth in certain countries over time.