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|title=Generative AI and its impact on labor productivity and the Global Economy
|pdfUrl=https://ceur-ws.org/Vol-3742/paper12.pdf
|volume=Vol-3742
|authors=Mykhailo Rokosh,Mykola Pryimak,Nataliia Stadnyk
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==Generative AI and its impact on labor productivity and the Global Economy==
https://ceur-ws.org/Vol-3742/paper12.pdf
Generative AI and its impact on labor productivity
and the Global Economy
Mykhailo Rokosh1,∗,†, Mykola Pryimak1,† and Nataliia Stadnyk1,†
1 Ternopil Ivan Puluj National Technical University, Rus'ka St, 56, 46001, Ternopil, Ukraine
Abstract
This paper explores the transformative impact of generative AI on various business sectors and
its potential to enhance labor productivity. It contextualizes generative AI within the broader
field of artificial intelligence, highlighting its novel capacities for natural language processing,
content generation, and data summarization. The study delves into its integration across
diverse industries, from healthcare and finance to game development and manufacturing,
emphasizing its role in driving innovation and efficiency. Through comprehensive analysis, this
paper examines both the technological advancements and the associated ethical, legal, and
social challenges posed by generative AI. Findings underscore the significant economic
implications of generative AI, projecting its influence on future business models and global
economic growth
Keywords
Generative AI, generative pre-trained transformer, machine learning, productivity, economics 1
1. Introduction
Before the emergence of generative AI, there were already AI technologies being
actively used and refined, such as decision-making systems, computer vision, and speech
recognition technologies. However, generative AI represents a new stage in AI
development, focusing on its ability to generate natural language, create, expand, and
enhance media content, and summarize large data arrays by identifying common patterns
and trends. With the advent of generative AI, questions immediately arose regarding the
effective use of this new technology. It became necessary to understand how to implement
and integrate generative AI across various fields of business activity and how it might
impact the global economy as a whole. The influence of generative AI cannot be
CITI’2024: 2nd International Workshop on Computer Information Technologies in Industry 4.0, June 12–14, 2024,
Ternopil, Ukraine
∗ Corresponding author.
† These authors contributed equally.
mike.rokosh@gmail.com (M. Rokosh); pmw.ukr@ukr.net (M. Pryimak); natalya.stadnik15@gmail.com
(N. Stadnyk)
0009-0009-0323-3735 (M. Rokosh); 0000-0002-0395-5879 (M. Pryimak); 0000-0002-7781-7663
(N. Stadnyk)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
underestimated, as it has the potential to profoundly change how people understand and
interact with technologies. This change brings a unique set of dilemmas, challenges, and
opportunities that humanity has never before encountered. In this light, one of the most
important tasks is to identify promising directions for integrating and implementing
generative AI in business processes. By carefully considering the possibilities of
generative AI as a catalyst for development and growth, organizations can strategically
use its capabilities to discover new avenues of innovation and competitive advantages.
This requires comprehensive exploration of the possibilities for interaction between
generative AI and various business sectors, including marketing, design, and operations.
Additionally, it requires careful analysis of the ethical, legal, and social implications
associated with the integration of generative AI, ensuring responsible and accountable
implementation of this new technology
2. Recent research and publications
The term "Artificial intelligence" (AI) first appeared in 1956 at the Dartmouth College
conference [1] in the USA. Its use was associated with describing a field of science and
engineering aimed at creating programs and systems capable of performing tasks that
typically require human intelligence.
Generative AI emerged in the context of the development of generative models. The
program "ELIZA," developed by Joseph Weizenbaum in 1966, is one of the earliest known
examples of generative AI. It simulated a psychotherapist, communicating with users via a
text interface, as shown in Figure 1.
Figure 1: A conversation with ELIZA.
A key role in the development and popularization of generative AI was played by the
non-profit research organization OpenAI [2]. Their GPT-3 model, which underlies the
ChatGPT chatbot, has become a revolutionary technology capable of generating realistic
text, conducting conversations, and adapting to various contexts. Another prominent
example of recent achievements in the field is Google's Gemini model, introduced in
December 2023. This model has more than 175 billion parameters and possesses
impressive versatility, generating text, code, music, and images.
According to the World Bank, the generative AI market is expected to grow from USD 1.5
billion in 2021 to USD 6.5 billion in 2026, indicating the growing importance of this technology.
Generative AI has potential applications in various sectors, extends across multiple sectors,
from healthcare and manufacturing to media and entertainment [3].
In a study by B. Bjork, it is noted that the first base models, such as ChatGPT, focus on the
ability of generative AI to complement creative work. By 2025, it is expected that more than
30% of new drugs and materials will be systematically discovered using methods of generative
AI, compared to zero today. This is just one of many industry use cases. According to
predictions by the team of experts from the consulting firm Gartner, by 2025, 30% of outbound
marketing messages from large organizations will be synthetically created, compared to less
than 2% in 2022. By 2030, a blockbuster movie, 90% of which will be created by AI (from text to
video), will be released [4].
Gartner's studies on the directions of integrating generative AI into business processes
indicate that the main challenges for leaders lie in determining where and how generative AI
fits into existing and future business models and operational processes, as well as how to
productively experiment with generative AI use cases and prepare for the long-term risks and
opportunities arising from its development trends [5].
Analyzing the state of development of generative AI, A. Takyar notes that the world is
entering a new era of AI, where generative AI occupies a central place, seamlessly combining
human imagination with machine intelligence. This elevates machine learning models to a new
level of cognition, where they can create art, music, design, and generate ideas. This remarkable
technological progress is not just science fiction, it is a reality that is felt today [6].
Generative AI has potential for application in various sectors, including banking. In banking,
it can be used for detecting fraudulent transactions, creating synthetic data for training ML
models, protecting customer data using GANs for calculating risk assessments and predicting
potential losses in specific scenarios. It is also applicable in education [7], for example, in
creating courses or virtual modeling to enhance student learning and restore historical
educational materials. In healthcare, it allows for the optimization of drug discovery and
development, personalized treatment, medical imaging enhancement, and population health
management. However, along with a promising future, the use of generative AI also brings
undesirable consequences. These include perpetuating existing biases, concerns about
intellectual property control and the ability to create convincing disinformation [8]. The issue
of regulating generative AI and ethical questions are the most pressing tasks facing researchers
[9].
3. Study results
Generative AI uses deep learning models proficient in generating text, images, or other types
of informational content that mirror the data they were trained on. By deciphering patterns and
dependencies, these algorithms develop the capability to generate unique outcomes by
constructing new samples in analogous formats. Generative AI models can be assorted in
diverse ways, such as by the type of outcome they generate (texts, images, multimodal objects,
etc.) or by the foundational architecture they employ. Generally, models that produce images
are recognized as Generative Adversarial Networks (GANs) or diffusion models, whereas those
generating text or audio are typically autoregressive, predicting future outcomes by leveraging
previous data inputs. Each of these methodologies has culminated in the development of
cutting-edge products that now promote the application of AI across various life spheres.
Among the various families of deep learning models capable of generating new data samples,
autoregressive and diffusion models have showcased the highest quality results in recent years.
Diffusion models are exceptionally suited for creating visual and multimedia content or
performing tasks such as image inpainting and coloring when provided with textual prompts
describing the desired outcome. Noteworthy models for image creation include DALL-E 2,
Image GPT, Midjourney, and Stable Diffusion. On the other hand, there are large language
models (LLMs) that generate responses word-by-word, using the text input by the user as well
as the text previously generated by the model. Typically, these are models with a transformer
decoder architecture, and they excel in various natural language processing tasks. This high
performance is achieved through knowledge acquired during training on vast amounts of public
data available on the internet.
3.1. The evolution and current dynamics of generative AI
Over the past year, generative AI has transformed from a compelling concept into a
mainstream technology, attracting attention and investments on an unprecedented scale,
considering its brief history. Generative AI demonstrates extraordinary proficiency in creating
coherent text, images, code, and a variety of other outputs based on simple textual prompts.
This capability has captivated the world, fueling a growing interest that intensifies with each
iteration of generative AI model release. It is important to note that the true potential of
generative AI extends far beyond traditional natural language processing tasks. This technology
has found applications across numerous industrial sectors, paving the way for complex
algorithms that can be distilled into clear, concise explanations. This helps in creating bots,
developing software, and conveying complex academic concepts with unprecedented ease.
Creative fields such as animation, gaming, art, cinema, and architecture are undergoing
profound transformations, spurred by powerful programs for converting text into images, such
as DALL-E, Stable Diffusion, and Midjourney. The groundwork for contemporary AI was laid
over more than a decade; however, 2022 marked a significant turning point—a key moment in
the history of AI. That was the year when ChatGPT was launched, inaugurating a promising
era of collaboration between humans and machines [10]. This significantly accelerated the
development and implementation of generative AI across various sectors of life and business.
Currently, the development of generative AI is vigorously pursued by the American
company OpenAI, often hailed as a pioneer in the field of AI. Founded with the goal of providing
broad benefits of general AI to humanity, OpenAI has achieved significant progress. Their
contributions extend beyond simple technologies; they facilitate open collaboration and ethical
development of AI. Besides OpenAI, leaders in the development of generative AI include
DeepMind, Google AI, Meta, Microsoft AI, LeewayHertz, Markovate, Stability AI, Anthropic,
Cohere, Accubits, InData Labs, ExoMetrics Inc, and Sentient. The contemporary landscape of
AI resembles the battle for search engines in the late 1990s. Currently, many companies are
engaged in this process.
Today, generative AI enables systems to create high-value artifacts such as videos,
narratives, training data, and even designs and schematics. For instance, the generative pre-
trained transformer (GPT) is a large-scale natural language technology that uses deep learning
to produce human-like text. This technology predicts the most likely next word in a sentence
based on accumulated learning, can write stories, songs, and poems, and even computer code.
Besides text, digital image generators such as DALL-E 2, Stable Diffusion, and Midjourney can
produce images from text. There are a variety of methods used for generative AI, but recently,
foundational models have attracted attention. Foundational models are pre-trained on general
data sources in a self-supervised manner, which can then be adapted to solve new problems.
Mainly based on transformer architectures, which embody a type of deep neural network
architecture that computes a numerical representation of the training data, transformer
architectures study context and thus meaning, tracking connections in sequential data.
Transformer models employ an evolving set of mathematical methods called attention or self-
attention to detect subtle ways in which even distant elements of data in a series affect and
depend on each other.
The pace at which generative AI technologies are evolving is accelerating. ChatGPT was
released in November 2022. In early 2023, OpenAI launched a new large language model (LLM),
named GPT-4, with notably improved capabilities. Similarly, by May 2023, the generative AI
Anthropic, Claude, was able to process 100,000 tokens of text, equivalent to approximately
75,000 words per minute—the length of an average novel—compared to about 9,000 tokens when
it was introduced in March 2023. In May 2023, Google announced several new features based
on generative AI, including the Search Generative Experience and a new LLM named PaLM.
3.2. Applications of Generative AI in the business operations
Generative AI technologies are used widely in business to improve efficiency and drive
innovation. They are capable of executing a multitude of tasks, including image creation and
the synthesis of speech and text. This broad applicability makes generative AI a valuable asset
across many industry sectors, helping businesses streamline operations and innovate in
multiple areas.
3.2.1. Sales and marketing
Sales and marketing teams became early adopters of generative AI, recognizing its potential
to streamline and enhance their operations. In sales, generative AI is employed extensively to
automate generation of custom sales scripts, email templates, and communications that are
precisely tailored to meet the needs and interests of prospective customers. This technology not
only saves time but significantly boosts the efficacy of sales strategies by ensuring messages
are both engaging and relevant. Marketing teams utilize this technology to create both text and
visual content, conduct proofreading, brainstorm innovative ideas, and personalize
advertisements. Generative AI also plays a crucial role in market research and opportunity
validation. It can analyze large datasets to identify trends, forecast customer behaviors, and
pinpoint profitable opportunities, equipping businesses with the insights needed to stay ahead
in competitive markets [11].
3.2.2. Customer service
In the sphere of customer service, generative AI is revolutionizing the way businesses
interact with their clients. AI-driven chatbots and virtual assistants, which are underpinned by
sophisticated generative models, have the capacity to deal with a broad spectrum of customer
inquiries. Their design allows them to learn and evolve from each interaction, leading to
progressively more adept service. These intelligent systems efficiently handle routine tasks such
as managing bookings, providing personalized recommendations, and swiftly resolving
complaints [12]. This automation enables human customer service agents to concentrate on
more intricate issues that require human empathy and complex problem-solving skills.
Beyond direct customer interaction, generative AI has a pivotal role in preparing customer
service teams for the challenges of their role. By simulating diverse customer service scenarios,
generative AI offers a dynamic training environment for representatives. This method of
training allows customer service teams to experience and respond to a variety of potential
situations, thus improving their preparedness and the quality of their responses when real-life
customer interactions occur. This advanced approach to training with AI simulation results in
a more competent and confident customer service workforce, equipped to uphold high service
standards.
3.2.3. Financial services
In the financial services sector, the application of generative AI is making significant strides,
particularly in enhancing security and decision-making processes. One of the standout uses of
generative AI is in the realm of fraud detection. Here, AI algorithms are employed to create
simulations of fraudulent activities, which not only helps in understanding the mechanisms of
such activities but also aids in developing predictive measures that can be applied to real-world
scenarios to prevent potential fraud [13].
Generative AI also contributes to the efficiency of high-frequency trading. It achieves this
by crafting predictive models that are capable of simulating a variety of market conditions,
enabling traders and financial analysts to anticipate market movements and make informed
decisions rapidly.
Another innovative use of generative AI in this domain is the personalization of financial
advice. AI systems are designed to analyze individual risk preferences and financial objectives
to generate customized investment portfolios. This approach ensures that financial advice is not
one-size-fits-all but rather tailored to the specific needs and goals of each client, thereby
optimizing their financial planning and investment strategies.
3.2.4. Healthcare
Generative AI is transforming healthcare by accelerating drug discovery and personalizing
patient care. AI models can simulate the effects of drugs to predict efficacy and side effects,
dramatically speeding up the drug development process [14].
Furthermore, generative AI is also expanding into areas such as medical imaging and
diagnostics, where it aids in interpreting complex images more quickly and accurately than ever
before, and in developing patient-centric health monitoring systems that can anticipate health
events before they occur. The culmination of these advancements in generative AI is set to offer
significant benefits in terms of patient care efficacy, the efficiency of healthcare providers, and
the well-being of patients.
3.2.5. Game development
In the realm of game development, generative AI offers the potential to dramatically
transform the creation of non-playable characters (NPCs). By incorporating AI, developers can
create NPCs with a level of autonomy, complexity, and interactivity previously unachievable.
This enhancement could lead to more immersive gaming environments where NPCs respond
and adapt to player actions with realistic finesse.
Generative AI enables dynamic behavior modeling in NPCs [15], allowing them to exhibit
nuanced and context-dependent behaviors that contribute to a more engaging and believable
gaming world. Additionally, NPCs can utilize AI for dynamic dialogue generation in real-time,
tailored to the individual player’s journey and actions, thus making each interaction distinct
and personal.
Generative AI also can personalize the gaming experience by analyzing a player's style and
preferences to adapt NPC behavior and game narratives accordingly. This personalization not
only aims to enhance player engagement but also reduces the resources traditionally required
in the creative processes of game development.
3.2.6. E-commerce
Generative AI is significantly altering the e-commerce industry by introducing various
useful applications. One key application is in product description generation. Large language
models enable online stores to quickly generate detailed and appealing descriptions that are
specifically tailored to each product and its intended audience. This technology saves merchants
a considerable amount of time and ensures that all product listings maintain a high standard of
quality and consistency. For instance, LLMs can be trained to incorporate essential product
features, adherence to brand style, and search engine optimization keywords into descriptions,
making them not only informative but also more likely to appear in search results [16].
Another important use of generative AI is in analyzing customer reviews through sentiment
analysis. This allows merchants to understand customer opinions and satisfaction levels by
systematically examining large volumes of review text to identify trends and overall sentiments.
This process provides deeper insights than manual analysis, enabling the identification of
specific elements that customers like or dislike, which can guide product improvements and
marketing approaches.
Additionally, generative AI helps with product tagging and categorization, which are crucial
for making products easier to find on e-commerce sites. This technology automates the process
of assigning tags and placing products into categories based on their descriptions, images, and
other related data. This not only enhances the customer shopping experience by improving
search and filtering options but also aids merchants in managing their inventories more
effectively. By using LLMs to identify relevant product attributes, the process of categorization
becomes more efficient and less prone to the errors often seen with manual tagging.
3.2.7. Manufacturing
Generative AI is becoming indispensable in the manufacturing sector, significantly
enhancing productivity and fostering innovation across various processes. In the area of
component development [17], generative AI is used to design and produce components that
meet specific business objectives. This application of AI boosts productivity by overcoming
traditional manufacturing constraints, enabling manufacturers to achieve greater precision and
customization. This leads to more efficient production processes and potentially higher quality
in the final products [18].
Material innovation also benefits from the use of generative AI. This technology assists in
the creation of new materials and chemicals, advancing the field of material science. AI
accelerates the development of innovative materials with improved performance or reduced
costs, which is crucial for industries aiming to integrate new technologies and enhance product
sustainability.
In quality control, generative AI's impact is particularly evident in high-precision industries
like automotive manufacturing. For example, companies such as the BMW Group and Tesla
utilize AI-driven automated image recognition and other AI capabilities [19] to maintain strict
quality standards. This enables the detection of subtle imperfections that are often invisible to
the human eye, ensuring consistent high-quality manufacturing outputs.
3.3. Forecasted growth of generative AI and its economic implications
Economists predict that the growing popularity of generative AI tools, such as ChatGPT
from OpenAI, could revolutionize workplaces and stimulate economic growth by enhancing
productivity [20]. However, the actual impact will depend on various factors, including
widespread adoption and effective use of technologies by companies. Economists at Goldman
Sachs have calculated that once generative AI is widely adopted, it could contribute an
additional 1.5% annual growth in productivity in the U.S. over a 10-year period. This increase
in productivity could lead to a corresponding growth in the U.S. gross domestic product.
However, it is difficult to predict when widespread adoption will occur. There may be
uncertainties regarding the timing of acceptance by companies and the ultimate capabilities of
AI that could lead to productivity increases ranging from 0.3 to 2.9 percent. Senior economist
Joseph Briggs from Goldman Sachs suggests that in the second half of this decade and in the
2030s, AI will begin to have a broad macroeconomic impact. Historical comparisons with
technologies such as electricity and the Internet show that the impact on productivity growth
may occur gradually. It took several decades for electricity to have a significant impact on
productivity growth in manufacturing. Similarly, the integration of generative AI into
industries and companies may take time, as employees and organizations must incorporate the
technology into their work processes.
Continuously evolving, generative AI will continue to influence product development, user
experience, employee productivity, and innovation, having a significant effect on business
productivity and development. According to Gartner forecasts:
• By 2025, 70% of enterprises will identify sustainable and ethical use of AI among their
top concerns [21].
• By 2025, 45% of B2B revenue organizations will list “prompt engineering” as a required
skill on job descriptions for messaging strategist roles [22].
• By 2025, 30% of outbound marketing messages from large organizations will be
synthetically generated, up from less than 2% in 2022 [4].
• By 2026, more than 80% of enterprises will have used generative AI APIs or deployed
generative AI-enabled applications [23].
• By 2030, AI could consume up to 3.5% of the world’s electricity [24].
Overall, the business community is at the beginning of the journey to understanding the
power, scope, and opportunities of generative AI. It transforms and enhances the efficiency of
business functions such as sales and marketing, customer service, and software development.
Research conducted by McKinsey & Company [25] suggests that generative AI has the potential
to contribute up to $7.9 trillion annually to the global GDP, as shown in Figure 2.
Figure 2: The potential impact of AI on the global economy, $ trillion.
4. Discussion
This paper provides a comprehensive examination of the transformative role of generative
AI in enhancing labor productivity across various business sectors and its broader economic
implications globally. The findings confirm that generative AI significantly boosts operational
efficiency, reduces costs, and speeds up service delivery, particularly in healthcare,
manufacturing, and education. This enhancement stems largely from its capacity to automate
and innovate within creative processes.
The research also uncovers discrepancies between the anticipated benefits of generative AI
and its actual utilization. These discrepancies highlight that the efficacy of AI technology is not
solely technology-driven but also depends heavily on the rate of its adoption and the specific
needs of each sector. For instance, while some sectors swiftly integrate new AI technologies to
gain competitive advantage, others may lag due to various barriers such as lack of
infrastructure, regulatory challenges, or skills.
The findings prompt a reconsideration of the broader implications of generative AI. While
the technology promises substantial economic benefits, it also poses challenges, such as
potential job displacement and ethical concerns, which require careful management.
5. Conclusions
The capability of generative AI technologies to exhibit creativity significantly impacts the
business environment and reforms it in many dimensions. Generative AI unveils not only a
broad spectrum of possibilities for businesses but also presents associated real and tangible
threats. Among these threats are the potential for creating deep fakes, intellectual property
issues, and other malicious uses of generative AI technology within the business context.
The current landscape of generative AI is experiencing a phase of remarkable growth
unparalleled in history. Leading companies are drawing billions of dollars in investments and
are pioneers in shaping the future of AI. The advancement of generative AI holds the potential
to spark a new economic revolution that could fundamentally alter perceptions of business
productivity. With effective implementation, enterprises may anticipate revolutionary changes
in business processes and strategies. However, despite the significant potential of generative AI
and its contribution to economic growth, the actual impact and the timing of its widespread
deployment remain subjects of active research and discussion.
This transformative phase mirrors the proactive adaptation seen in the implementation of
digital twins with augmented reality interfaces, poised as a powerful catalyst for unlocking
human creative potential in smart manufacturing—a key stepping stone toward the transition
to Industry 5.0 [26]. Digital twins prove particularly invaluable in enhancing manufacturing
workflows, where decisions need to be made by human operators. Concurrently, the gathering
of pertinent information facilitates ongoing process optimization, which can yield numerous
advantages, such as enhanced product quality, increased energy efficiency, and effective
predictive maintenance, integrating seamlessly with smart city ecosystems.
Various levels of digitalization and stages in the implementation of digital twins can be
augmented by reality assets—from a virtual copy of an individual object, which can be remotely
monitored to perform quality checks and control its behavior, to a virtual twin of the entire
production pipeline. This not only enables remote control in real time but also opens up
opportunities to employ novel big data processing methods for predictive analytics and process
optimization. As we transition to the principles and practices of Industry 5.0, where human
creativity will play a central role in production processes, innovative human-centered
interfaces, such as those based on augmented reality technology, will be crucial. The specific
examples of digital twins discussed in the literature underscore the importance of adopting and
properly implementing a secure-by-design approach for digital twins' design, highlighting their
characteristic features and the potential paths for further implementation in smart
manufacturing. This ongoing dialogue and exploration underscore the importance of
maintaining a vigilant and proactive approach to understanding and harnessing the capabilities
of generative AI within the business environment.
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