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TYPE Opinion
PUBLISHED 24 March 2025
DOI 10.3389/frai.2025.1568210
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Francesc Pozo,
Universitat Politecnica de Catalunya, Spain
REVIEWED BY
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University of Naples Federico II, Italy
*CORRESPONDENCE
Sarfraz Aslam
Sarfraz.aslam@unitar.my;
sarfrazmian@nenu.edu.cn
RECEIVED 29 January 2025
ACCEPTED 10 March 2025
PUBLISHED 24 March 2025
CITATION
Shahidi Hamedani S, Aslam S and Shahidi
Hamedani S (2025) AI in business operations:
driving urban growth and societal
sustainability. Front. Artif. Intell. 8:1568210.
doi: 10.3389/frai.2025.1568210
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©2025 Shahidi Hamedani, Aslam and Shahidi
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which does not comply with these terms.
AI in business operations: driving
urban growth and societal
sustainability
Sharareh Shahidi Hamedani1, Sarfraz Aslam2*and
Shervin Shahidi Hamedani3
1Faculty of Business, UNITAR International University, Petaling Jaya, Malaysia, 2Faculty of Education
and Humanities, UNITAR International University, Petaling Jaya, Malaysia, 3Department of
Environmental Sciences and Engineering, NOVA School of Science and Technology, Universidade
NOVA de Lisboa, Lisbon, Portugal
KEYWORDS
artificial intelligence, sustainability, business operations, societal dynamics, urban
growth
1 Introduction
Approximately 30% of smart city applications will use artificial intelligence (AI) by
the end of 2025, thereby radically altering the urban sustainability landscape in the
future (Yan et al., 2023). The advent of AI in reshaping traditional businesses into
sustainable operations is evident. Whenever AI is brought to the forefront, it is considered
a cornerstone in the business domain, enabling a transition toward more innovative and
sustainable practices (Appio et al., 2024). Incorporating AI into business practices has
many facets. According to Grand View Research (2023), the global AI market size was
anticipated at USD 196.63 billion in 2023 and is expected to grow at a CAGR of 36.6%
from 2024 to 2030. The recent fanfare surrounding AI has elevated it to a key enabler
of sustainable development, prompting many companies to prioritize and integrate it
into their business operations; hence, there is a stark difference between traditional and
new practices. In tandem with this evolution, urban growth and societal dynamics are
experiencing profound changes as AI-driven solutions come to the fore in various aspects
of modern society (Shahidi Hamedani et al., 2024).
AI applications in city government, transforming conventional cities into efficient
ones (Ortega-Fernández et al., 2020), have significantly shifted from functional systems
to more sustainable and intelligent ones. Furthermore, from another perspective, the role
of AI in optimizing business processes has surpassed comparison with its implication for
improving logistics operational capabilities and reducing environmental impacts (Jorzik
et al., 2024) till manufacturing reduces downtime, all of which contribute to the growth of
urban economics. In the meantime, with the speedy pace of adoption of AI in business
operations, it is also imperative to amalgamate with sustainable practices. Acting on
this matter requires a thoughtful approach that aligns AI with social, economic, and
environmental sustainability.
2 AI in business operations
The intersection of AI role and business operations has recently gained widespread
attention. Some studies (Chen et al., 2024;Shahzadi et al., 2024) focused on AI’s role in
supply chain management, highlighting its role in minimizing inefficiencies and improving
logistics by utilizing AI more often; supply chains become leaner and reduced carbon
Frontiers in Artificial Intelligence 01 frontiersin.org
Shahidi Hamedani et al. 10.3389/frai.2025.1568210
footprints, paving the path to sustainable operations. It is estimated
that by 2026, 60% of businesses will adopt AI-powered warehouse
solutions instead of just 10% in 2020 (MHI, 2024).In line with
this shift (Dilmegani and Ermut, 2025), note that businesses
also invest heavily in warehouse robots to enhance their supply
chain management through AI technology. Robots can manage
operations more efficiently and accurately by automating picking,
packing, sorting, and inventory management, thus saving labor
costs and accelerating order processing. Amazon, for instance,
has deployed more than 200,000 robots in its warehouses to
optimize operations.
AI can be used to optimize resource utilization, automate
processes for improved efficiency, and enable real-time monitoring
that aligns with sustainability goals (Waltersmann et al., 2021). As
sustainable supply chain management focuses on reducing waste
and enhancing traceability, AI-driven technologies such as machine
learning and big data analytics have been pivotal in achieving these
goals (Tsolakis et al., 2023).
Companies like eBay leverage AI for machine translation,
enhancing decision-making and operational efficiency. Similarly,
Vodafone employs AI-driven analytics to personalize services,
exemplifying its transformative impact (Jorzik et al., 2024). These
technologies help reduce forecasting errors, minimize excess
inventory, and lower energy consumption (Sharma et al., 2020).
Likewise, Smart grid protection sensors can detect defects up to
80% more accurately than traditional sensors, reducing losses and
improving the system’s reliability by adjusting to grid conditions
dynamically (Mahadik et al., 2025). These applications contribute
to urban economic growth by fostering technological innovation.
AI leverages advanced techniques like deep reinforcement learning
(DRL) to optimize dynamic business operations (Shuford, 2024).
DRL improves supply chain management through adaptive
routing and inventory optimization, dynamically adjusting to
real-time changes in demand and logistics; with the help of
DRL, researchers can develop systems that can dynamically
adapt to changes, optimize resource utilization, and facilitate
multi-objective decision-making for instance (Dehaybe et al.,
2024).
In addition, it enables businesses to prevent equipment
failures and minimize downtime, thereby streamlining workflows
significantly (Mohan et al., 2021). Moreover, in urban centers, these
advancements catalyze economic growth and foster innovation. In
other words, a key contribution of AI is to facilitate smart urban
development and efficient resource allocation, thereby ensuring
that cities are resilient and economically prosperous (Li et al.,
2024). In developing smart cities, AI has a transformative impact
on urbanization trends. Through the application of AI, urban
infrastructure can be optimized by improving energy efficiency,
streamlining transportation, and managing housing needs; AI
makes it possible to reduce traffic congestion and advance mobility
in transportation systems, such as prescriptive traffic management
and autonomous vehicles (Regona et al., 2024).
In cities like Singapore, AI manages real-time traffic
and monitors energy consumption, setting urban efficiency
benchmarks (Padhiary et al., 2025). On a similar note, Tennet
TSO, a German transmission system operator, has been utilizing
AI-based forecasting and IBM Watson’s cognitive computing
platform to anticipate renewable energy generation in real time,
allowing real-time grid adjustments and maximizing clean energy
use (Mahadik et al., 2025).
3 Sustainability and AI-driven business
practices
Nowadays, sustainability is a debatable topic, and the role of AI
in sustainability is inevitable. Reducing waste and environmental
food print, optimizing resource utilization, and fostering a circular
economy is the sprout of AI role which assists in a sustainable
environment (Onyeaka et al., 2023); for example, in the agriculture
industry, enhancing operational automation, a prediction model
for the total agricultural output value (Sachithra and Subhashini,
2023), improving yields while reducing environmental impact.
Moreover, this is apparent regarding the implications of AI and
IoT in agriculture due to their ability to improve efficiency
and sustainability. Agriculture leads the way with 35% of
these technologies, followed by precision farming and irrigation
monitoring at 16% each. Farming practices are becoming smarter
and more sustainable due to these innovations, which increase
yields, reduce waste, and conserve resources (Market.Us, 2024).
Similarly, smart grid technologies optimize energy distribution,
lowering carbon footprints (Bhattacharya et al., 2022). As
manufacturing and logistics become increasingly automated,
energy consumption and operational inefficiencies will be
minimized and aligned with global sustainability goals (Garrido
et al., 2024).
By placing AI at the heart of sustainability, industries can
grow while solving environmental and social issues. Moreover,
businesses shift from traditional linear operations to circular,
innovative, and efficient models (Pathan et al., 2023). The paradigm
shift of AI contributes to sustainability from various aspects; for
site surveying and progress monitoring, AI power drones are
used to enhance decision-making, reduce energy consumption and
minimize waste, and facilitate green finance in the agriculture
sector and its application in the cultivation and harvesting phases
(Fuentes-Peñailillo et al., 2024). While AI is crucial in ensuring
sustainable business operations, implementing it brings several
challenges, including ethical and privacy concerns (Fan et al., 2023).
In urban planning and infrastructure, there are also notable
examples; by using data and knowledge acquired by AI, cities
can shift to another level and have the potential to revolutionize
city development, which will enable over 30% of smart city
applications by 2025, including urban transportation solutions,
significantly enhancing urban sustainability, social welfare, and
vitality (Herath and Mittal, 2022). Furthermore, AI-enabled robots
are deployed in the hospitality sector to provide personalized
services and facilitate seamless guest experiences (Szpilko et al.,
2023). Similarly, in the healthcare industry, AI can detect and
predict diseases rapidly and accurately (Rashid and Kausik, 2024).
For instance, the PRAIM study in Germany assessed AI-supported
mammography screening vs. standard double reading. Out of
463,094 women screened, 260,739 were assisted by AI. With AI-
supported screening, 6.7 cancers were detected out of 1,000, which
is 17.6% higher than in standard screening (Eisemann et al.,
2025).
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Shahidi Hamedani et al. 10.3389/frai.2025.1568210
4 Societal implications
Policies are needed to protect individual privacy in urban
settings and solve concerns (Dong and Liu, 2023). AI technologies
rapidly gain momentum in various industries but present
challenges, including significant data security and privacy concerns.
Data privacy and security protection are becoming an urgent
concern (Saura et al., 2022). Acknowledging that AI adoption will
have significant societal consequences, particularly when shifting
employment patterns and consumer behaviors, is important (Yu
et al., 2023). The rise of automation has displaced traditional jobs
and created a demand for AI-specialized workers (Betts et al., 2024).
AI’s role in personalizing consumer experiences underscores
the ethical responsibility to protect data privacy and mitigate
algorithmic biases, maintaining public trust and equity.
Governments and businesses must work together to implement
reskilling programs to seamlessly transition to an AI-driven
world. AI’s Ethical concerns, like data privacy and the digital
divide, underscore the need for transparent and inclusive AI
solutions (Bouhouita-Guermech et al., 2023). These challenges are
amplified in urban areas, where disparities in digital access can
marginalize vulnerable populations. These issues can be solved
only by collaborative efforts to design AI systems prioritizing
societal wellbeing and inclusiveness.
5 Challenges and limitations
Several challenges exist, including data integration issues, AI
literacy issues, resistance to technological change, data availability,
and reliance on data (Uwaoma et al., 2024). In many industries,
getting clean and actionable data is time-consuming and costly.
As a result, AI models cannot produce satisfactory results
without robust data, undermining their potential for sustainability.
Moreover, AI adoption is complicated by ethical issues (Bouhouita-
Guermech et al., 2023). Ensuring equal access to technology and
data privacy must be addressed so that AI benefits all sectors of
society. Additionally, fostering AI literacy within organizations is
of utmost importance. Many organizations resist to change due
to a lack of understanding, making it difficult for them to adopt
AI-driven sustainability practices in the future.
Moreover, lack of data (availability and quality) also remains
a hurdle for implementing sustainability in business operations; in
other words, accessing clean data is also opaque (Jorzik et al., 2024);
for instance, for training DRL’s models, quality datasets are critical,
and data within several sustainability contexts is both sparse and
expensive to collect (Equihua et al., 2024). On the other hand, the
reliability of data is also another concern; according to Choudhuri
(2023), 30 % of sustainability data is unreliable or has poor quality;
having said that, incomplete data can fail any method of analysis
and affect the decision-making process in other words without
data—especially high-quality data—sustainable development is
doomed to falter. A further concern is ensuring equitable access
to AI since marginalized communities often face barriers to
taking advantage of these developments (Kasun et al., 2024). The
challenges highlighted here highlight the need for a balanced
approach to AI deployment.
Without AI, the prospects of adopting sustainable business
practices are becoming increasingly bleak. However, Sustainable
business demands the involvement of the government and the
public sector. Governments must establish policies and regulations
to promote transparency and collaboration to ensure high-quality
data transfer to the private sector. Policies of this kind can
foster cooperation between industries, facilitating the use of AI
technologies responsibly and efficiently while addressing broader
sustainability goals.
The advancement of AI, however, is hindered by several
limitations, including an unwillingness to change, ethical privacy
concerns, and the difficulty of integrating new technology into
pre-existing HR systems (Madanchian and Taherdoost, 2025). In
addition, AI advancements are hampered by algorithms without
common sense that cannot interpret data properly, resulting in
flawed decisions (Nishant et al., 2024). As a result, clinicians’
decision-making can be negatively impacted (Dratsch et al.,
2023); for example, when prescribing antidepressants, clinicians
were less accurate when following incorrect AI recommendations
compared to a baseline or correct advice condition (Jacobs
et al., 2021). The high cost of implementing AI in resource-
intensive settings makes it difficult to reach a broad audience
(Sommer et al., 2023). Additionally, organizational resistance to
change creates a significant barrier to adopting AI in HRM
since employees are reluctant to adopt AI due to concerns about
data security, privacy, and possible job losses (Hassan et al.,
2024).
6 Summary
Businesses and industries are witnessing the impact of
AI as a key driver of growth, which profoundly impacts
businesses in various sectors. For instance, In the context of
urban development, it can be implemented to improve traffic
management, infrastructure, and public transportation scheduling
in a way that contributes to more livable and sustainable urban
development. AI can provide businesses with the means to
optimize resources, reduce inefficiencies, and embrace innovative
practices, enabling them to tackle urgent environmental and
economic concerns. The full benefits of AI can only be realized if
businesses align their operations with clearly defined sustainability
targets. Achieving this requires a strategic approach to AI,
not just a technical tool for generating short-term benefits.
Policymakers must develop a reliable model that fairly and
equitably fosters the use of AI in a broad range of sectors.
Additionally, it would be beneficial for both the public and private
sectors to work together to create inclusive solutions that will
reduce societal disparities and protect the environment at the
same time.
As AI becomes increasingly integral to sustainability, it presents
opportunities and challenges. A more sustainable market requires
businesses to adopt AI to reduce costs; as McKinsey (2022),
several companies have reported that AI forecasting engines
reduce costs by 10%−15% and improve their competitive position
by automating up to 50% of workforce management tasks.
However, the role of policymakers and urban planners in creating
the conditions for AI innovations to thrive responsibly and
Frontiers in Artificial Intelligence 03 frontiersin.org
Shahidi Hamedani et al. 10.3389/frai.2025.1568210
inclusively cannot be overstated. Integrating AI into sustainable
practices requires balancing technological advancements with
ethical considerations. AI can be a powerful force for sustainable
development if stakeholders create a collaborative atmosphere,
address barriers, and promote transparency. As a result, businesses,
societies, and the environment will all benefit. By examining the
intersection of AI and urban sustainability in a new manner, the
article introduces a fresh perspective to the literature because its
analysis is not comprehensively covered in the current literature.
It is valuable to synthesize existing literature to highlight trends
and develop a strong foundation for understanding AI’s role
in business.
Author contributions
ShaS: Conceptualization, Investigation, Validation, Writing –
original draft, Writing – review & editing. SA: Conceptualization,
Methodology, Resources, Supervision, Validation, Writing – review
& editing. SheS: Investigation, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the
research and/or publication of this article. UNITAR International
University Supported the publication Fee for this article.
Acknowledgments
The authors sincerely thank UNITAR International University
for their invaluable support for this study.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation
of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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