IMPACT OF AI-ENABLED SOFTWARE ON ORGANIZATIONAL COST REDUCTION

ВЛИЯНИЕ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ С ИСКУССТВЕННЫМ ИНТЕЛЛЕКТОМ НА СНИЖЕНИЕ ЗАТРАТ ОРГАНИЗАЦИИ

Bukhtueva I.

31.03.2024 214

4(114)

02. Экономика и управление народным хозяйством

Цитировать:

Bukhtueva I. IMPACT OF AI-ENABLED SOFTWARE ON ORGANIZATIONAL COST REDUCTION // Universum: экономика и юриспруденция : электрон. научн. журн. 2024. 4(114). URL: https://7universum.com/ru/economy/archive/item/17041 (дата обращения: 21.11.2024).

Прочитать статью:

DOI - 10.32743/UniLaw.2024.114.4.17041

ABSTRACT

This article analyzes the impact of Artificial Intelligence (AI) enabled software (SW) on reducing organizational costs. The focus is on various types of AI SW and their applications in enhancing efficiency and automating processes across different industries. Analyses include case studies demonstrating significant savings and a discussion on the direct and indirect impacts on costs. The article also addresses the challenges and limitations of AI implementation, underscoring the importance of overcoming these hurdles to realize AI's full cost-saving potential.

АННОТАЦИЯ

Данная статья анализирует влияние программного обеспечения (ПО), оснащенного искусственным интеллектом (ИИ), на снижение затрат в организации. Основное внимание уделяется различным типам ПО с применением ИИ и их применению для повышения эффективности и автоматизации процессов в различных отраслях. Анализируются кейс-исследования, демонстрирующие значительные экономии, а также обсуждаются прямые и косвенные воздействия на затраты. Статья также затрагивает проблемы и ограничения внедрения ИИ, подчеркивая важность преодоления этих препятствий для реализации полного потенциала снижения затрат с помощью ИИ.

Keywords: Artificial Intelligence (AI), cost reduction, efficiency, automation, business process, AI implementation, organizational savings.

Ключевые слова: искусственный интеллект, снижение затрат, повышение эффективности, автоматизация, бизнес-процессы, внедрение искусственного интеллекта, экономия для организации.

Introduction

The advent of Artificial Intelligence (AI) has ushered in a new era in the realm of business operations and management. AI-enabled software (SW), with its capacity for learning, automation, and data analysis, is increasingly being recognized for its potential to streamline organizational processes and reduce operational costs.

The objective of this study is to examine the impact of AI-enabled SW on organizational cost reduction. The relevance of this research lies in the growing integration of AI technologies in various business sectors and the need to understand their financial implications. In an era where efficiency and cost-effectiveness are paramount, AI presents an opportunity for organizations to optimize their operations. However, the extent and manner in which AI-enabled SW contributes to cost reduction remain areas that require in-depth exploration. This study seeks to elucidate the mechanisms by which these technologies can lead to more efficient and economically sustainable business practices.

Main part

The integration of AI-enabled SW in organizations has revolutionized various business processes, leading to significant cost reductions and efficiency improvements. According to a survey conducted in the USA in June 2023, approximately 30% of small business owners expect to save between $1 000 and $4 999 in 2024 using AI. About 28 % anticipate savings of at least $5 000 [1].

In February 2023, the research company conducted a survey among 1 000 business leaders in the USA. About 99 % of companies using ChatGPT report that they have saved money. Of this amount, 48 % of companies saved more than $50 000, while 11 % saved over $100 000 (fig. 1).

Figure 1. Savings of companies using ChatGPT, dollars [2]

Companies utilize ChatGPT for coding, copywriting and content creation, customer support, and generating resumes, meeting summaries, or documents. AI assists in drafting job descriptions, interview requests, and responding to candidates.

Analysis of the implementation of AI in business processes

Among the exponentially developing technologies impacting business, the following directions stand out: AI, machine learning, predictive analytics, the Internet of Things, high-performance computing systems, digital twins, big data, and robotics. Let's analyze some of these in more detail.

Digital twins (DT) are virtual replicas of physical systems, allowing businesses to simulate, predict, and manage assets without interacting with the real system. This technology can lead to cost savings by optimizing maintenance schedules, reducing downtime, and enhancing product development. For example, one of the large international companies specializing in software development with the integration of digital twins, monitors over 1.2 million DT of jet engines, wind farms, offshore oil platforms, power generation equipment, pumps, and compressors in real-time. The company identified an increase in the exhaust gas temperature in a heat recovery steam generator at a power plant. With an early warning from the company's specialists, the client was able to timely order the repair of a damaged system tube, avoiding significant leaks leading to power generation loss and equipment damage. The customer estimated this prevention saved approximately $24 000 in production and mechanical costs [3].

Another direction of successful AI implementation is associated with establishing digital pathways for customer acquisition, for example, in banks. AI can predict customer needs by personalizing marketing messages and automating interactions using chatbots. This leads to improved customer experiences, higher engagement rates, and more efficient targeting of potential clients, ultimately resulting in cost-effective customer acquisition and retention strategies. According to a study by one of the American consulting firms, banks could save $70 billion by 2025 using technologies like automation and AI to reduce staff and increase productivity. The financial services industry, including banking, insurance, and capital markets, could see savings of $140 billion during the same period. The study estimates that between 7% to 10% of tasks will be automated by 2025, enhancing cost and productivity savings [4].

For instance, some USA banks utilize AI to enhance customer service quality, reduce risks, combat fraud and money laundering, and make credit issuance decisions. Banks offer consultations o through voice assistants, leveraging AI technology. It collects data on customers and transactions, employing deep machine learning to identify patterns that help detect fraudulent clients, reduce fraud cases, and ensure cybersecurity.

The world's largest American e-commerce platform has successfully optimized its operations through robotization, with robots in its fulfillment centers proving to be four times more efficient than human employees, reducing operational expenses by $22 million per warehouse as early as 2016. In 2023, it introduced the robotic system, enhancing inventory listing speed and delivery estimations. The system is expected to reduce order processing time by up to 25% and identify stock levels 75% faster, leading to significant cost savings. Furthermore, e-commerce platform is working to reduce the operating cost of the Digit robot to $2-3 per hour plus software overheads, aiming to minimize expenses and reduce staff numbers [5].

One prominent category of AI SW is in the domain of data analytics and business intelligence.

Using natural language processing and machine learning, analysis and visualization tools allow users to interact with data in a conversational mode, making complex data analysis accessible to those without deep technical knowledge [6]. Predictive analytics capabilities are key, allowing organizations to identify patterns and trends from large data sets to make informed decisions. Many companies across various industries are using such intelligent services to improve their business operations. For example, in the healthcare sector, some American clinics are improving the processes of matching clinical trial outcomes. In the financial sector, banks and insurance companies are using analytics tools to detect fraud and analyze risks. Retail companies use intelligent tools to personalize purchases for their customers.

In customer service and engagement, AI-powered platforms are increasingly prevalent. One of these platforms is focused on creating products that improve customer relationships. One of the key features is its AI-powered automated responses system, which helps businesses manage large volumes of customer inquiries efficiently. This system uses machine learning algorithms to analyze incoming customer queries. According to a study conducted in the American business sector in 2023, 80% of company executives plan to increase their budgets for the implementation of artificial intelligence technologies to improve user experience in 2024 [7]. One of the American taxi companies uses AI-powered platforms for its customer support operations. The company can effectively manage the vast number of customer interactions it receives daily, providing support for both passengers and drivers.

Supply chain management is another critical area where AI SW plays a transformative role [8, 9]. Applications use predictive analytics to optimize inventory management and logistics. For example, a major American non-alcoholic beverage manufacturing company has implemented such an approach to optimize supply chain planning processes. This has allowed the company to better manage its complex distribution network, improve demand planning accuracy, and reduce inventory costs, thereby increasing overall operational efficiency.

These diverse applications of AI-enabled SW in organizations underscore the technology's versatility and its capacity to add value across various business functions. By automating routine tasks, providing deep insights from data, and optimizing decision-making processes, AI SW not only fosters operational efficiency but also drives innovation and strategic growth in organizations.

Impact on cost reduction

The impact of AI SW on organizational cost reduction is multifaceted, encompassing both direct and indirect financial benefits. AI-driven solutions streamline operations, enhance productivity, and optimize resource allocation, leading to substantial cost savings for businesses.

Direct cost reductions are most evident in areas where AI automates routine and repetitive tasks. For instance, AI in customer service, exemplified by chatbots and automated response systems, significantly reduces the need for extensive human customer support teams [10, 11]. This automation not only cuts down on labor costs but also increases efficiency, as AI systems can operate continuously without breaks, handling a large volume of queries simultaneously.

According to one of the relevant studies, the introduction of chatbots in the retail, banking and healthcare sectors will allow enterprises to save $11 billion in costs in 2023, compared with about $6 billion in 2018 [12].

In manufacturing and production, AI-driven predictive maintenance tools forecast equipment malfunctions before they occur. This proactive approach prevents costly downtime and extends the lifespan of machinery, translating into direct savings on repair and replacement expenses. Similarly, in supply chain management, AI algorithms optimize inventory levels, reducing holding costs and minimizing waste due to overstocking or product obsolescence.

Indirect cost impacts, though less immediately apparent, are equally significant. AI-enabled data analytics tools provide deep insights into market trends, customer preferences, and operational inefficiencies [13]. These insights enable businesses to make informed strategic decisions, leading to better product development, targeted marketing strategies, and efficient resource allocation. Such informed decision-making can result in higher revenue generation and reduced operational costs over time [14].

AI applications in HR and talent management streamline recruitment processes, reducing the time and cost involved in hiring. By quickly analyzing a large pool of applicants and identifying the most suitable candidates, AI tools minimize the expenses associated with prolonged recruitment processes and potential turnover costs.

The use of AI in energy management, especially in large facilities, leads to significant savings in utility costs. AI systems can optimize energy consumption by analyzing usage patterns and adjusting resource allocation in real-time, thereby reducing overall energy expenditures.

Challenges and limitations

While AI technology offers transformative potential across various sectors, its implementation is not without challenges and limitations. Understanding these barriers is crucial for organizations aiming to integrate AI effectively into their operations. The following table provides an analysis of the common challenges and limitations in AI implementation (table 1).

Table 1.

Challenges and limitations in AI implementation

Challenge/Limitation	Description	Example(s)
High implementation costs	AI projects often require significant investment in technology, infrastructure, and expertise.	Start-up costs for AI systems can be prohibitive for small and medium-sized enterprises.
Data privacy and security	Ensuring the security and privacy of data used in AI systems.	Risk of data leakage and privacy violations.
Lack of skilled personnel	There is a shortage of professionals with the necessary AI expertise.	Companies often struggle to find and retain AI talent due to high demand and competition.
Integration with existing systems	Integrating AI with current organizational processes and systems can be challenging.	Legacy systems may not be compatible with new AI technologies.
Dependence on quality data	AI systems require large amounts of high-quality data to function effectively.	Poor data quality can lead to inaccurate AI predictions and decisions.
Regulatory and compliance issues	Navigating the regulatory landscape for AI technologies can be complex.	Different countries have varying regulations regarding AI, affecting global operations.
Resistance to change	Employees and management may resist adopting AI due to fear of job loss or mistrust of technology.	Organizations may face internal pushback when introducing AI systems.

Despite the challenges and limitations in implementing AI, the integration of these technologies into business operations offers significant potential for cost savings and efficiency improvements.

Conclusion

The integration of AI SW in business operations leads to cost savings through the automation of tasks, predictive analytics, efficient resource management, and informed decision-making. These savings manifest as both direct reductions in operational expenses and indirect impacts through enhanced efficiency, productivity, and strategic planning. The evolving landscape of AI technology presents a promising path for businesses seeking to modernize their operations and achieve substantial economic benefits.

References:

Expected savings from using artificial intelligence (AI) // Statista URL:https://www.statista.com/statistics/1412702/expected-savings-smbs-using-artificial-intelligence-automation-marketing-united-states/ (date of application: 23.02.2024).
1 in 4 companies have saved $75k+ already with ChatGPT//ResumeBuilder URL: https://www.resumebuilder.com/1-in-4-companies-have-already-replaced-workers-with-chatgpt/ (date of application: 23.02.2024).
Using Digital Twin blueprints, GE's Industrial URL: https://www.ge.com/digital/industrial-managed-services-remote-monitoring-for-iiot/article/336849-early-notification-for-an-electrical-issue-on-a-hrsg (date of application: 24.02.2024).
Wadley D. Technology, capital substitution and labor dynamics: global workforce disruption in the 21st century? //Futures. – 2021. – Т. 132. – С. 102802.
Gutelius B., Theodore N. The future of warehouse work: Technological change in the US logistics industry. – 2019.
According to the 2023 Fact.MR report, the global rating of IBM Watson services URL: https://www.factmr.com/report/759/ibm-watson-services-market#:~:text=According%20to%20the%20latest%20detailed,(AI)%20and%20cognitive%20computing%20technologies (date of application: 20.02.2024).
Zendesk CX Trends Report 2023 URL: https://cxtrends.zendesk.com/ (date of application: 23.02.2024).
Artemov A. PROGRAMMING LANGUAGES IN DATA ENGINEERING: OVERVIEW, TRENDS AND PRACTICAL APPLICATION // Innovacionnaya nauka. – 2023. – №10-2.
Kudrenko I. The new era of American manufacturing: evaluating the risks and rewards of reshoring //E3S Web of Conferences. – EDP Sciences, 2024. – Т. 471. – С. 05020.
Gerasimov, A. S. Informacionnye tekhnologii v malom i srednem biznese / A. S. Gerasimov // Ekonomika i predprinimatel'stvo. – 2022. – № 11(148). – S. 868-871. – DOI 10.34925/EIP.2022.148.11.169. – EDN DZDFRS. [in Russian].
Davletov A.R. SOVREMENNYE METODY MASHINNOGO OBUCHENIYA I TEKHNOLOGIYA OCR DLYA AVTOMATIZACII OBRABOTKI DOKUMENTOV // Vestnik nauki. 2023. №10 (67). [in Russian].
Lapan M. Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more. – Packt Publishing Ltd, 2020.
Petrova, N. S. Iskusstvennyj intellekt kak mekhanizm predotvrashcheniya ottoka klientov / N. S. Petrova // Ekonomika i biznes: teoriya i praktika. – 2023. – № 11-2(105). – S. 201-204. – DOI 10.24412/2411-0450-2023-11-2-201-204. – EDN OIWSIE. [in Russian].
Kendzhaev D.A. Razrabotka AR-reshenij dlya povysheniya kvalifikacii v bystro razvivayushchihsya otraslyah ekonomiki // Konkurentosposobnost' v global'nom mire: ekonomika, nauka, tekhnologii. 2024. №1(3). [in Russian].