Using Artificial Intelligence Systems to Improve the Performance and Sustainability of Production Processes in The Iraqi Oil and Gas Sector

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 45

USING ARTIFICIAL INTELLIGENCE SYSTEMS TO IMPROVE THE
PERFORMANCE AND SUSTAINABILITY OF PRODUCTION
PROCESSES IN THE IRAQI OIL AND GAS SECTOR
Abduljalil Majeed Al-Hassan, R.1, Jasim Mohammed Alsalman, M.2, and Basil
Abdulkareem Albasri, A.3
1,2&3 Department of Business Administration, Shatt Al-Arab University College, Basra, Iraq.
1rafid.abduljalil@sa-uc.edu.iq

ABSTRACT
Purpose: The purpose of this study is to analyse the relationship between artificial intelligence
(AI) applications and the achievement of sustainability in the Iraqi oil and gas sector by
exploring the available opportunities and potential risks associated with adopting intelligent
technologies and proposing a roadmap for their effective utilisation.
Design/Methodology/Approach: The study adopts a descriptive analytical approach
supported by an applied exploratory method. AI is considered the independent variable and is
measured through five dimensions: predictive and intelligent analytics systems, predictive
maintenance, robotics and autonomous systems, big data analytics, and intelligent decision
support systems. Sustainability is the dependent variable and is measured through its
environmental, economic, and social dimensions. The study was conducted in 2025 within the
Iraqi oil and gas sector, particularly companies operating in Basra Governorate. A total of 165
questionnaires were distributed, of which 151 valid responses were received, representing a
response rate of 91.5%. The data were analysed using SPSS through reliability and validity
tests and multiple regression analysis.
Research Limitation: The study is limited to the Iraqi oil and gas sector, particularly
companies operating in Basra Governorate, and the data were collected during 2025. The
findings are based on questionnaire responses and may therefore have limited generalizability
to other sectors or geographical contexts.
Findings: The results indicate a statistically significant relationship between the application of
AI technologies and the achievement of sustainability. The findings confirm that AI
technologies contribute to improving operational performance while enhancing environmental,
economic, and social sustainability within the Iraqi oil and gas sector.
Practical Implication: The study recommends establishing an integrated system to
periodically and systematically measure the impact of AI technologies on sustainability,
enabling organisations to improve operational efficiency and support sustainable development
objectives.
Social Implication: The adoption of AI technologies can enhance environmental protection,
improve workplace safety, and promote sustainable practices that contribute to broader social
and economic development within the Iraqi oil and gas sector.

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 46

Originality/Value: The study contributes to the limited body of research on AI adoption and
sustainability in the Iraqi oil and gas sector by examining AI through five technological
dimensions and sustainability through its environmental, economic, and social dimensions,
providing empirical evidence and a practical roadmap for the effective implementation of AI
to support sustainability.

Keywords: AI. autonomous systems. big data analytics. robotics. sustainability

INTRODUCTION

The oil and gas sector is one of Iraq's most important economic sectors , contributing
significantly to national revenues, industrial development, and energy security (Umran, Lu,
Abduljabbar, & Nyangaresi, 2023). However, the sector operates in an increasingly complex
environment characterised by growing production requirements, operational challenges,
resource constraints, environmental pressures, and the need to improve the efficiency and
sustainability of production processes. These challenges have increased the need for modern
technological approaches that help organisations improve operational performance while
maintaining sustainable production practices.

Artificial Intelligence (AI) has emerged as one of the most significant technological
developments capable of transforming production and operational processes across various
industries (Sakhno, Tkal, Sushko, Ocheret'ko, & Shevchenko, 2025) . AI systems can process
large volumes of data, identify patterns, support decision -making, predict potential problems,
and optimise operational activities. In the oil and gas sector, AI can improve production
efficiency, reduce operational losses, support predictive maintenance, optimise resource
utilisation, and enhance organisations' ability to respond to changing production conditions
(Halwe-Pandharikar et al., 2025).

At the same time, sustainability has become an increasingly important consideration for
organisations operating in energy -intensive industries. Sustainable production requires
organisations to balance economic and operational performance with efficient resource use and
reduced negative environmental impacts (Nyasapoh et al., 2025). Consequently, integrating AI
systems into production processes may improve both organisational performance and
sustainability by enabling more accurate decisions, better resource allocation, and more
efficient management of production activities.

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 47

In the Iraqi oil and gas sector, investigating the relationship between AI systems, production
performance, and sustainability is particularly relevant because of the sector's strategic
importance to the Iraqi economy and the ongoing need to improve production efficiency .
Understanding how AI can improve production performance and support sustainable practices
may help oil and gas organisations identify technological opportunities that enable more
efficient and sustainable production processes (Sabhan et al., 2026; Kadhim, Sadeghi,
Abdalrada, Arandian, & Khorsand, 2025; Abolghasemi, Emadi, & Karimi, 2024). .

Therefore, this study examines the role of Artificial Intelligence systems in improving the
performance and sustainability of production processes in the Iraqi oil and gas sector. The study
focuses on the relationship between AI adoption and improvements in production performance
and sustainability, providing an empirical basis for understanding how AI can contribute to
more efficient and sustainable production practices in the Iraqi oil and gas industry.

The oil and gas sector constitutes the backbone of the Iraqi economy, as the country relies
heavily on oil revenues to finance its national budget and support development programs.
Despite its economic importance, the sector faces growing challenges, inclu ding rising
operational costs, ageing infrastructure, declining efficiency, and increasing environmental and
social pressures associated with extraction and production activities. In recent years, the need
for innovative solutions that can enhance productivity, reduce waste, improve safety standards,
and meet sustainability requirements has become increasingly urgent. Sustainability has now
become a central objective within global energy policies (Abdul-Jabbar et al., 2026; Zanjirei,
Davoodi, Sadeghi, & Ansarisamani, 2025).

In this context, AI technologies have emerged as one of the most significant transformative
forces in the global oil industry. These technologies provide advanced capabilities in predictive
and intelligent analytics, big data management, predictive mainten ance, robotics and
autonomous systems, and data -driven decision-making. While many oil -producing countries
have already taken practical steps toward integrating these technologies into their operations,
the application of AI within the Iraqi oil and gas sector remains at a relatively early stage
(Alaidany & Lakizadeh, 2025; Karimi et al., 2025).

This situation calls for a systematic scientific investigation to assess available opportunities,
potential risks, and the requirements for transitioning to a more sustainable system supported
by AI. This study addresses a key knowledge gap regarding the extent to which AI technologies
can enhance environmental, economic, and social sustainability in a sector vital to Iraq’s
development. In addition, the study aims to contribute to the development of a clear roadmap
for decision-makers, helping to improve operational efficiency, reduce emissions, minimise

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 48

risks, strengthen transparency, and empower the workforce through digital transformation (Al-
shareeda et al., 2026; Aliabadi, Karimi, Karimi, & Fard, 2025; Alaidany & Mahdi, 2024).

By examining the impact of several AI tools such as predictive and analytics systems,
predictive maintenance, autonomous robotics, big data analytics, and intelligent
decision-support systems , the study provides a comprehensive perspective on how these
technologies can be utilised to promote sustainable and inclusive development in the sector.

This study aims to explore the role of AI in supporting sustainability and advancing sustainable
development goals within the Iraqi oil and gas sector, while highlighting key opportunities and
challenges and proposing a strategic roadmap. To achieve this purpose, the research seeks to:

1. Assess the impact of AI systems on production process performance.
2. Evaluate the role of AI in improving production efficiency and reducing downtime.
3. Examine the contribution of AI to sustainable production practices.
4. Develop a practical roadmap for AI adoption in the Iraqi oil and gas sector.

LITERATURE REVIEW
The researcher conducted a comprehensive review of relevant prior studies to situate the
current research within the existing body of knowledge. This review bridges theoretical gaps
and establishes a robust scientific framework for investigating the relationship between AI
technologies and the sustainability of Production Processes in the Iraqi oil and gas industry.

Manikandan et al. (2025) explored integrating AI with carbon capture technologies to improve
emission-reduction efficiency and resource management. The findings highlight AI’s capacity
to analyse big data from capture plants, optimise injection processes, and design innovative
carbon-absorbing materials. It also emphasises how AI can work alongside smart grids and
renewable energy to minimise environmental impact.

Olawade et al. (2024) examined AI’s role in supporting “Net -Zero” goals. The study
demonstrated how AI can optimise energy systems, enhance climate forecasting, and improve
sustainability in transport, agriculture, and waste management. It also addressed critical
challenges—such as data privacy, energy consumption, and algorithmic bias —identifying
policy support and investment as vital drivers for scaling these technologies.

Focusing on the socio -environmental and economic nuances of AI, Rohde et al. (2024)
analysed risks such as opaque decision -making, discrimination, and increased energy
consumption. To promote “Sustainable AI,” the authors introduced the SCAIS framework,
which consists of 19 criteria and 67 indicators. This multidisciplinary framework provides a

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 49

structured approach to developing AI applications in a socially and environmentally conscious
manner.

Nikseresht et al. (2022) investigated the role of AI in supporting decision -making for
sustainable development under Volatility, Uncertainty, Complexity, and Ambiguity ( VUCA)
conditions. Through a systematic literature review and bibliometric analysis of 534 articles, the
study identified key decision models and provided a roadmap for practical applications in the
circular economy and multi-dimensional sustainable development.

Similarities and Differences Between the Current Study and Previous Literature
While previous studies have broadly addressed the intersection of AI and sustainability, they
vary significantly in scope and focus. For instance, Manikandan et al. (2025) and Olawade et
al. (2024) focused on specific environmental targets such as carbon capture and net -zero
emissions across sectors. Rohde et al. (2024) provided a governance -heavy framework
(SCAIS) for ethical application, while Nikseresht et al. (2022) utilised bibliometric tools to
map decision-making under uncertainty.

In contrast, the present study focuses specifically on the Iraqi oil and gas sector. By grounding
the investigation of AI and sustainability within this local context, the research evaluates
unique opportunities and risks while proposing a practical, industry -specific roadmap. Unlike
previous global or cross -sectoral studies, this research addresses the specific contextual
challenges and strategic needs of the Iraqi energy industry, filling a critical gap in the regional
literature (Dahoos et al., 2026; Faleh et al., 2025).

Theoretical Foundations
Sustainability
From Moreira et al. (2025), sustainability has become a cornerstone of modern organisational
strategy, reflecting the capacity to preserve resources and systems over the long term. While
the core principles of sustainability have ancient roots, the first formal scientific documentation
is often traced back to Hans Carl von Carlowitz’s 1713 princi ples of forestry management,
which succinctly stated: “Do not use more resources than are available.”

Sustainability, however, extends beyond mere conservation; it encompasses maintaining
multifaceted systemic functions such as air and water purification, soil protection, biodiversity,
and the provision of social spaces across various timescales. This aligns with the Brundtland
definition, which emphasises meeting the needs of today’s marginalised populations while
safeguarding resources for future generations. Achieving this balance requires navigating
complex management questions: What must be preserved? For whom? For how long? And at
what cost?

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
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Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
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As Fonseca et al. (2024) note , true sustainability is realised through integrated practices that
address environmental, social, and economic dimensions. In the environmental sphere, formal
and informal management systems work to optimise performance and minimise waste.
Socially, it fosters community engagement and strengthens local relationships, while the
economic dimension focuses on strategic partnerships that drive business growth and
resilience.

In the oil and gas industry, Egbumokei et al. (2024) argue that the transition to low -carbon
operations is particularly complex. This difficulty stems from deep economic reliance on fossil
fuels, technical and financial barriers, and entrenched organisational resistance. Under
mounting pressure from governments, regulators, and civil society, it has become imperative
for the sector to adopt sustainable strategies centred on clean -tech innovation, energy
efficiency, and the integration of renewables. Ultimately, the industry’s future hinges on its
ability to restructure business models to align with the global shift toward a low -carbon
economy, balancing commercial viability with environmental stewardship.

Artificial Intelligence
Wang et al. (2025) define AI as a broad field dedicated to developing computational systems
capable of performing tasks that typically require human intelligence. Within this domain,
Machine Learning (ML) and Deep Learning (DL) stand out as transformative subfields.
Inspired by the human brain’s neural networks, these technologies enable systems to analyse
vast datasets, recognise patterns, and generate precise predictions, improving through iterative
training to support more efficient decision-making across sectors.

Bickley et al. (2025) further describe AI as a discipline focused on solving problems that
humans handle with ease but which computers have historically struggled to perform
efficiently, ranging from basic logical reasoning to complex medical diagnostics via
radiographic imaging. AI remains a dynamic field that bridges the gap between theoretical
scientific structures and practical industrial application.

In vital industries, Ochieng et al. (2024) emphasised that the oil and gas sector faces unique
risks and complexities that demand innovative solutions. AI has emerged as a potent tool for
managing these challenges; specifically, Generative AI (GAI) and ML algorithms enhance
operational resilience and adaptability in volatile environments. Similarly, Arinze et al. (2024)
argue that AI has fundamentally reshaped management and operations in the petroleum sector.
Applications such as predictive maintenance reduce unplanned downtime, while real-time data
analytics optimise operations to maximise output at minimal cost. Furthermore, AI streamlines

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 51

resource allocation and geological data analysis, significantly mitigating exploration and
drilling risks.

Finally, Povoas et al. (2024) conclude d that AI has become a strategic pillar for industrial
development. It supports performance optimisation across the value chain, improves the
accuracy of operational forecasting, and strengthens the ability to respond swiftly to
disruptions. By processing massive amounts of data to produce predictive insights, AI increases
operational reliability and minimises waste. Integrating AI into industrial systems is a critical
step toward sustainable digital transformation, enabling self-learning algorithms that help the
sector adapt to complex environments and maintain global competitiveness.

Underlying Theory
Recent scholarship suggests that AI emulates human cognitive capabilities in machines,
allowing them to perform advanced functions such as learning, reasoning, and problem-solving
(Adewoyin et al., 2025). Technologies such as computer vision and natural la nguage
processing allow for the extraction of meaningful predictions from complex datasets. Wang et
al. (2025) posit that AI is now a primary driver of global economic development, enhancing
production efficiency and resource distribution.

While AI offers significant opportunities to reduce energy consumption and promote “green
growth,” the technology's energy demands present a challenge that requires deeper
investigation into the relationship between AI and environmental sustainability. Despite being
recognised as a vital tool for institutional sustainability, a comprehensive assessment of how
to deploy these technologies effectively to achieve long-term goals is still lacking (Schwaeke
et al., 2025).

Sustainability is a strategic approach that balances economic, social, and environmental
outcomes (Kutaula et al., 2025). By adopting eco -friendly measures like waste management
and resource efficiency, organisations not only improve operational quality but also secure a
long-term competitive advantage. In the oil and gas sector, sustainability and the “circular
economy” have become essential pathways to ensure operational continuity and meet global
development targets (Digitemie et al., 2025). Thus, merging AI with sustainability creates a
robust theoretical fra mework for analysing future roadmaps and supporting sustainable
decision-making amid increasing global pressures.

The Theoretical Relationship Between Variables
Wang et al. (2024) identify AI as one of the most influential transformative technologies of the
modern era, contributing directly to Sustainable Development Goals (SDGs) by optimising

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
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Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
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resource use and supporting data-driven innovation. Beyond industrial efficiency, AI enhances
environmental integration and economic performance across green supply chains.

According to Ofongo (2024), AI plays a pivotal role in optimising Carbon Capture, Utilisation,
and Storage (CCUS). By improving the selection of geological storage sites and increasing the
accuracy of reservoir characterisation, AI reduces both operational risks and costs. In post -
combustion capture, reinforcement learning algorithms dynamically adjust operational
parameters to lower energy consumption. Furthermore, AI accelerates the development of
technologies that convert CO ₂ into fuel or construction materials more cost -effectively.
Combined with the Internet of Things (IoT), it enables real-time facility monitoring and failure
prediction, bolstering operational sustainability. These applications illustrate AI’s capacity to
enhance the reliability of low -emission solutions, driving the transition toward a more
sustainable oil and gas industry.

The proposed conceptual framework can be illustrated as follows, where it represents:

Figure 1: The Proposed Research Model
Source: Prepared by the researcher based on previous studies.

Main Independent
Variable:
Artificial Intelligence

Main Dependent Variable:
Sustainability of Production
Processes

• Predictive and intelligent
analytics systems
• Predictive maintenance of
equipment
• Robotics and autonomous
systems
• Big data analytics
•Intelligent decision -support
systems
Environmental
Economic

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
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METHODOLOGY
This study adopts an analytical approach that combines a structured review of the relevant
literature with the collection of primary data through a questionnaire designed to examine the
relationship between AI and sustainability in the Iraqi oil and gas sector.

The literature review defined the study scope and selected key academic sources, with
particular attention to research on the oil and energy sectors. This review helped identify
prevailing research trends, extract theoretical insights, and highlight existing knowledge gaps,
which informed the questionnaire design.

Population and Sample
The study population consisted of employees working in the Iraqi oil and gas sector. The study
selected 165 individuals from this population to receive the questionnaire. Out of these, 151
valid responses were obtained and used in the analysis, representing a response rate of
approximately 91.5%.

The sample was selected randomly from the target population to ensure representation of
different occupational categories within the sector, including production, operations,
maintenance, and senior management staff. This distribution allowed the study to analyse the
data accurately and produce findings that reflect the actual impact of AI technologies on
environmental, economic, and social sustainability within the Iraqi oil and gas sector.

Research Boundaries
Temporal Boundaries
The study covers the period during which the research was conducted and the data were
collected, namely the year 2025. The questionnaires were distributed and collected during this
period to ensure that the results reflect the current state of AI applications in the Iraqi oil and
gas sector.

Spatial Boundaries
The research focuses on the Iraqi oil and gas sector in Basra Governorate, where the
questionnaires were distributed among employees working in this region to ensure accurate
representation of the Iraqi petroleum sector.

Thematic Boundaries
The study examines the relationship between AI technologies and sustainability in the oil
sector, specifically addressing environmental, economic, and social aspects. It excludes
technologies outside the scope of AI and issues unrelated to sustainability.

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 54

Human Boundaries
The study includes employees working in the oil and gas sector in Basra. The study selected
165 employees and analysed 151 valid questionnaires , without extending the study to
individuals outside this sample.

Data Collection Tools
The questionnaire served as the primary data-collection instrument. In addition, the researcher
conducted several personal interviews with participants from the study sample.
The questionnaire aimed to collect data on the relationship between AI and sustainability in the
Iraqi oil and gas sector. A five-point Likert scale was adopted.

Validity and Reliability of the Measurement Tool
Ensuring the questionnaire's accuracy before administering it to the target group is a key step
in developing scientific research instruments, as it supports result reliability and the potential
to generalise findings.

To verify the validity and reliability of the measurement tool, several procedures were
employed to ensure that the questionnaire items accurately measure the intended concepts. Face
validity was assessed by reviewing relevant literature and consulting subject-matter experts.
Content validity was also considered to ensure that all dimensions of the studied concepts were
adequately represented.

Reliability was measured using Cronbach’s Alpha, which assesses the internal consistency of
the questionnaire items. The minimum acceptable value for this coefficient is generally 0.60.
The analysis showed an overall reliability coefficient of 0.933, indicating a high level of
internal consistency among the questionnaire items. This result confirms the stability of the
research instrument and suggests that no items needed to be removed.

In addition, the validity coefficient, calculated as the square root of the reliability coefficient,
was 0.966, a very high value indicating strong validity in measuring the targeted variables.

Table 1: Reliability and Validity Coefficients
Validity Coefficient Cronbach’s Alpha Number of Items
0.966 0.933 39

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
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Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
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FINDINGS AND DISCUSSION
The results are presented in the following table:
Table 2: Cronbach’s Alpha and Validity Coefficients for Each Variable
Variable
Code
Variable Cronbach’s
Alpha
Number of
Items
Validity
Coefficient
Q1 Predictive and Intelligent
Analytics Systems 0.913 4 0.956
Q2 Predictive Maintenance of
Equipment 0.894 4 0.946
Q3 Robotics and Autonomous
Systems 0.924 4 0.961
Q4 Big Data Analytics 0.700 4 0.837
Q5 Intelligent Decision-Support
Systems 0.742 4 0.861
Q AI 0.933 20 0.966
S1 Environmental Sustainability 0.711 5 0.843
S2 Economic Sustainability 0.915 5 0.957
S3 Social Sustainability 0.712 5 0.844
S Sustainability (Overall) 0.856 15 0.925

Description and Diagnosis of Research Variables
The independent variable in this study is AI, measured through several dimensions: predictive
and intelligent analytics systems, predictive maintenance of equipment, robotics and
autonomous systems, big data analytics, and intelligent decision-support systems.
The dependent variable is sustainability in the oil and gas sector, which was measured through
three dimensions: environmental, economic, and social sustainability.

Description of Demographic Variables
The researcher s examined the distribution of the study sample by several demographic
characteristics, including gender, age, educational level, and work experience. Table 3 presents
the results.

African Journal of Applied Research
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Table 3: Description of the Study Sample
Characteristic Category Frequency %
Gender
Male 99 65.6
Female 52 34.4
Total 151 100.0
Age
25–34 5 3.3
35–44 98 64.9
45–54 29 19.2
55 and above 19 12.6
Total 151 100.0
Educational Level
Secondary or below 7 4.6
Bachelor’s degree 133 88.1
Master’s degree 9 6.0
PhD 2 1.3
Total 151 100.0
Work Experience
Less than 2 years 96 63.6
2–5 years 10 6.6
6–10 years 25 16.6
More than 10 years 20 13.2
Total 151 100.0

The statistical results indicate that most respondents in the study sample were male (65.6%),
reflecting the male-dominated nature of the Iraqi oil and gas sector. Regarding age distribution,
the 35–44 age group made up the largest proportion of participants (64.9%), suggesting that
most respondents fall into a middle-age category characterised by accumulated professional
experience.

In terms of educational attainment, individuals holding a bachelor’s degree accounted for the
largest share (88.1%), indicating a sufficiently strong academic background among participants
to comprehend topics related to AI and sustainability. Regarding professional experience,
63.6% of respondents reported less than two years of work experience, suggesting the growing
entry of young and newly recruited personnel into the sector. At the same time, a smaller
proportion of highly experienced employees remains present, highlighting the need for a
balanced workforce that combines traditional industry expertise with modern technological
knowledge to support digital transformation in the oil and gas sector.

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
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Statistical Description of the Study Variables
AI Dimension
Table 4: Means and Standard Deviations of AI Variable Items (One-Sample Statistics)
Statement N Mean Std.
Deviation
Std.
Error
Mean
Sig.
(2-tailed)
Intelligent prediction systems help
improve daily operational planning.
151 3.75 1.166 0.095 0.000
These systems support accurate
decision-making to reduce operational
risks.
151 3.47 1.082 0.088 0.000
They help predict failures or problems
before they occur.
151 3.79 1.117 0.091 0.000
They enhance the ability to respond
quickly to sudden production changes.
151 3.55 1.170 0.095 0.000
Predictive and Intelligent Analytics
Systems
151 3.64 1.010 0.082 0.000
Predictive maintenance reduces
unexpected equipment failures.
151 3.62 1.170 0.095 0.000
It improves resource efficiency and
reduces operational costs.
151 3.50 1.183 0.096 0.000
It provides accurate data for
evidence-based maintenance decisions.
151 3.93 0.974 0.079 0.000
It helps extend equipment lifespan and
reduce downtime.
151 3.81 1.044 0.085 0.000
Predictive Maintenance of Equipment 151 3.72 0.955 0.078 —
Robotics and autonomous systems
improve operational productivity.
151 3.51 1.038 0.085 0.000
They reduce risks to workers by
performing hazardous tasks.
151 3.70 1.108 0.090 0.000
They automate routine processes, saving
time and effort.
151 3.13 1.235 0.100 0.212
They enhance precision and quality in
executing complex tasks.
151 3.46 1.075 0.088 0.000
Robotics and Autonomous Systems 151 3.45 1.007 0.082 0.000
Big data analytics helps uncover hidden
patterns in operational processes.
151 3.56 1.037 0.084 0.000

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

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It supports improved strategic and
operational decision-making.
151 3.42 1.219 0.099 0.000
It enhances accurate forecasting of
resource and service demand.
151 3.54 1.365 0.111 0.000
It improves production efficiency and
reduces resource waste.
151 4.23 0.634 0.052 0.000
Big Data Analytics 151 3.69 0.797 0.065 0.000
Decision-support systems help identify
the best operational alternatives.
151 4.32 0.570 0.046 0.000
They provide accurate insights to reduce
risks and increase efficiency.
151 4.09 0.786 0.064 0.000
They support long-term planning and
sustainable production goals.
151 4.18 0.612 0.050 0.000
They enhance the ability to make rapid
and flexible decisions in changing
conditions.
151 4.04 0.610 0.050 0.000
Intelligent Decision-Support Systems 151 4.16 0.488 0.040 0.000
AI (Overall) 151 3.73 0.686 0.056 0.000

Interpretation of AI Variable Results
The results presented in the table relating to the AI variable highlight the growing importance
of intelligent technologies in strengthening sustainability practices and improving operational
performance within the Iraqi oil and gas sector. The calculated means indicate that all
statements associated with predictive analytics systems, predictive maintenance, robotics and
autonomous systems, big data analytics, and intelligent decision -support systems received
generally positive evaluations. Mean scores ranged from 3.13 to 4.32 on the Likert scale,
suggesting respondents clearly recognised AI’s contribution to improving planning processes,
reducing operational risks, and enhancing resource efficiency.

The reported standard deviations indicate a reasonable degree of variation in participants’
responses, reflecting a relatively consistent perception regarding the role of AI technologies in
the sector. However, some variation was observed in statements rel ated to the automation of
routine operational processes. In particular, the statement “It helps automate routine processes
to save time and effort” recorded the lowest mean (3.13) alongside the highest standard
deviation (1.235). This pattern may reflect d iffering views among respondents regarding the
sector’s readiness to fully adopt such automation technologies.

Moreover, the statistical results reveal strong significance levels (Sig. < 0.05) for most items,
reinforcing the reliability of these findings and underscoring the importance of AI as a key

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driver in advancing sustainability, minimising waste, and improving equipment maintenance.
The highest mean values were associated with statements related to intelligent decision-support
systems (“Decision support systems help identify the best operational alternatives”, 4.32) and
big data analytics (“It contributes to improving production efficiency and reducing resource
waste”, 4.23). These results suggest a strong acknowledgement of the strategic role of
data-driven technologies in supporting decision -making and achieving both economic and
environmental efficiency.

Overall, the findings indicate broad consensus among professionals and practitioners on the
added value of AI in the Iraqi oil and gas sector, both in enhancing day -to-day operational
performance and supporting long-term strategic planning aligned with sustainability objectives
and risk-reduction efforts.

Sustainability Dimension
Table 5: Means and Standard Deviations of Sustainability Variable Statements (One-Sample Statistics)
Statement N Mean Std. Deviation Std. Error Mean Sig. (2-tailed)
AI contributes to reducing harmful
emissions from industrial operations. 151 3.97 0.725 0.059 0.000
It helps improve waste and residue
management in petroleum operations. 151 3.80 0.872 0.071 0.000
It promotes more efficient and
sustainable use of natural resources. 151 3.56 0.906 0.074 0.000
It supports monitoring the
environmental impact of operational
activities.
151 4.12 0.673 0.055 0.000
It contributes to the adoption of clean
and renewable energy technologies
within operations.
151 3.95 0.755 0.061 0.000
Environmental Dimension 151 3.88 0.539 0.044 0.000
AI improves production efficiency
and reduces waste. 151 3.17 1.151 0.094 0.079
It contributes to reducing operational
and maintenance costs. 151 3.71 0.963 0.078 0.000
It improves the organization’s
financial returns through performance
optimization.
151 3.49 0.951 0.077 0.000

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It strengthens the company’s
competitiveness in local and
international markets.
151 3.21 1.085 0.088 0.021
It supports long-term financial and
operational planning. 151 3.46 0.936 0.076 0.000
Economic Dimension 151 3.41 0.881 0.072 0.000
AI improves working conditions and
occupational safety. 151 3.60 1.008 0.082 0.000
It enhances employee satisfaction by
reducing strenuous and hazardous
tasks.
151 3.54 1.038 0.084 0.000
It supports training and technical
capacity building for employees. 151 3.88 0.515 0.042 0.000
It improves human–machine
interaction in the workplace. 151 3.93 0.585 0.048 0.000
It contributes to developing socially
responsible workplace policies. 151 4.32 0.570 0.046 0.000
Social Dimension 151 3.85 0.531 0.043 0.000
Total Sustainability 151 3.71 0.501 0.041 0.000

Interpretation of Sustainability Results
The table above presents the mean and standard deviation results for the sustainability
variable in the Iraqi oil and gas sector, with particular attention to AI's role in enhancing
sustainability across environmental, economic, and social dimensions.

Environmental Dimension
The findings indicate that AI exerts a notable positive influence on environmental
sustainability within petroleum operations. The statement “It supports monitoring the
environmental impact of operational activities” received the highest mean score (4.12)
and a relatively low standard deviation (0.673), suggesting strong agreement among
respondents on the importance of AI in environmental monitoring. Similarly, the
statement “AI contributes to reducing harmful emissions from industrial operations”
recorded a relatively high mean (3.97) and a modest standard deviation (0.725),
reflecting a strong perception of the technology’s role in mitigating environmental
impacts. Overall, the environmental dimension achieved an average mean of 3.88,
indicating a generally positive orientation among respondents toward AI -supported
environmental sustainability.

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Economic Dimension
The economic dimension showed greater variability . The statement “ AI improves
production efficiency and reduces waste” recorded a comparatively lower mean (3.17)
and a higher standard deviation (1.151), indicating differing perspectives among
participants regarding the direct financial impact of AI technologies. Conversely , the
statement “It contributes to reducing operational and maintenance costs” achieved a
higher mean (3.71) with a smaller standard deviation (0.963), suggesting a moderate
level of agreement regarding the economic benefits associated with AI adoption.
Overall, the economic dimension recorded a mean of 3.41, reflecting a moderate
assessment of AI’s influence on financial performance and economic sustainability
within the sector.

Social Dimension
The social dimension demonstrated relatively positive evaluations. The statement “It
contributes to developing socially responsible workplace policies within the company”
achieved the highest mean (4.32) with a relatively low standard deviation (0.570),
indicating strong agreement among respondents regarding AI’s role in enhancing social
responsibility within organisational practices. Other statements related to improving
working conditions and building technical capabilities among employees recorded
means ranging between 3.54 and 3.93, suggesting a balanced perception of the social
benefits associated with technological ado ption. The overall mean for the social
dimension was 3.85, reflecting strong appreciation of AI’s contribution to workplace
well-being and improved human–technology interaction.

Overall Sustainability Assessment
The overall sustainability mean reached 3.71, accompanied by a relatively low standard
deviation (0.501), indicating a generally positive and stable evaluation of the role of AI
in promoting sustainability within the Iraqi oil and gas sector.

In general, the analysis suggests that respondents perceive the environmental and social
benefits of AI more clearly than its immediate economic returns. This pattern may point
to the need for further empirical studies or practical implementations to strengthen the
measurable financial impact of AI technologies within the sector.

Specialised Analytical Perspective
This study falls within the intersection of managerial, operational, and technological
research, as it examines the impact of AI as an independent variable on sustainability
as a dependent variable within the Iraqi oil and gas sector. The analysis focuses on
several AI dimensions, including predictive analytics systems, predictive maintenance,

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robotics and autonomous systems, big data analytics, and intelligent decision -support
systems, all of which aim to improve organisational performance and reduce
operational risks.

On the other hand, sustainability is examined through its environmental, economic, and
social dimensions, making the study inherently multidisciplinary, integrating
perspectives from management, technology, and economics. The research therefore
emphasises the alignment between technological advancement and the implementation
of sustainable practices within the oil and gas industry.

Hypothesis Testing
To achieve the objectives of the study and determine the extent to which AI
technologies influence sustainability in the Iraqi oil and gas sector, the hypotheses were
tested using multiple regression analysis. The results show the strength of the
relationship between the independent variables and the dependent variables, as well as
their statistical significance.

First Sub-Hypothesis
There is a statistically significant relationship between AI technologies represented by
predictive and intelligent analytics systems, predictive maintenance of equipment,
robotics and autonomous systems, big data analytics, and intelligent decision -support
systems and the achievement of environmental sustainability in the Iraqi oil and gas
sector.

To test this hypothesis, the researcher applied multiple regression analysis , and the
results are presented in Table (6).
Table 6: Multiple Regression Analysis Results for the Relationship Between AI
Technologies and Environmental Sustainability
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.681 0.463 0.445 0.401

Interpretation of Multiple Regression Results for Environmental Sustainability
The results of the multiple regression analysis indicate the presence of a moderately
positive relationship between the independent variables representing different AI
technologies namely predictive and intelligent analytics systems, predictive
maintenance of equipment, robotics and autonomous systems, big data analytics, and
intelligent decision -support systems and the dependent variable of environmental
sustainability in the Iraqi oil and gas sector.

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The overall correlation coefficient (R = 0.681) reflects a meaningful association
between the variables included in the model. Furthermore, the coefficient of
determination (R² = 0.463) indicates that these independent variables explain
approximately 46.3% of the variance in environmental sustainability, suggesting a
reasonable explanatory capacity of the regression model. The adjusted coefficient of
determination (Adjusted R² = 0.445) confirms the relative stability of the model after
accounting for the nu mber of predictors included in the analysis. Meanwhile, the
standard error of the estimate (0.401) reflects an acceptable level of prediction accuracy
for the dependent variable.
Table 7: ANOVA Results for Testing the Relationship Between AI Technologies and
Environmental Sustainability
Model Sum of Squares df Mean Square F Sig.
Regression 20.170 5 4.034 25.044 0.000
Residual 23.356 145 0.161

Total 43.526 150

Dependent Variable: S1

Table 7 shows that the regression model used to examine the relationship between AI
technologies and environmental sustainability is statistically significant. The calculated
F-value (25.044) with a significance level of Sig = 0.000 which is considerably lower
than 0.05 indicates that the model as a whole explains the variance in environmental
sustainability in a statistically meaningful way.

Additionally, the regression sum of squares (20.170) compared with the residual sum
of squares (23.356) demonstrates that a substantial portion of the variation in the
dependent variable can be explained by the independent variables included in the
model. This strengthens the reliability of the findings obtained from the multiple
regression analysis.

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Table 8: Regression Coefficients for the Effect of AI Technologies on Environmental
Sustainability
Model B Std. Error Beta t Sig.
Constant 0.844 0.402 — 2.099 0.038
Q1 0.125 0.061 0.234 2.032 0.044
Q2 -0.055 0.101 -0.098 -0.547 0.585
Q3 -0.154 0.105 -0.288 -1.463 0.146
Q4 0.127 0.086 0.189 1.487 0.139
Q5 0.685 0.079 0.620 8.647 0.000
Dependent Variable: S1

The regression coefficients indicate that some AI variables significantly influence
environmental sustainability, while others do not . Specifically, Q1 (Predictive and
Intelligent Analytics Systems) and Q5 (Intelligent Decision-Support Systems) recorded
significance values below 0.05, indicating a positive and statistically significant impact
on environmental sustainability.

In contrast, Q2, Q3, and Q4 did not show statistically significant effects at the 0.05
significance level, suggesting their direct contribution to explaining environmental
sustainability in this model is limited.

Based on the overall regression results, the first sub -hypothesis can therefore be
accepted, indicating a statistically significant relationship between certain AI
technologies and the achievement of environmental sustainability in the Iraqi oil and
gas sector, while acknowledging that the strength of influence varies among different
AI technologies.

Second Sub-Hypothesis
There is a statistically significant relationship between AI technologies represented by
predictive and intelligent analytics systems, predictive maintenance of equipment,
robotics and autonomous systems, big data analytics, and intelligent decision -support
systems and the achievement of economic sustainability in the Iraqi oil and gas sector.
To test this hypothesis, multiple regression analysis was employed, and the results are
presented in Table (9).

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Table 9: Multiple Regression Results for the Relationship Between AI Technologies and
Economic Sustainability in the Iraqi Oil and Gas Sector
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.844 0.713 0.703 0.481

The results of the multiple regression analysis indicate a strong relationship between
the AI variables predictive analytics systems, predictive maintenance, robotics and
autonomous systems, big data analytics, and intelligent decision -support systems and
economic sustainability in the Iraqi oil and gas sector.

The overall correlation coefficient (R = 0.844) reflects a strong association between the
variables. Meanwhile, the coefficient of determination (R² = 0.713) indicates that these
variables explain about 71.3% of the variance in economic sustainability, demonstrating
the model's strong explanatory power.

Furthermore, the adjusted coefficient of determination (Adjusted R² = 0.703) confirms
the stability of the model after adjusting for the number of predictors, while the standard
error of the estimate (0.481) indicates an acceptable level of predictive accuracy.
Table 10: ANOVA Results for Testing the Relationship Between AI Technologies and
Economic Sustainability
Model Sum of Squares df Mean Square F Sig.
Regression 83.076 5 16.615 71.960 0.000
Residual 33.480 145 0.231

Total 116.556 150

Dependent Variable: S2

Table 10 show that the regression model examining the relationship between AI
technologies and economic sustainability in the Iraqi oil and gas sector is highly
statistically significant. The calculated F-value (71.960) with a significance level of Sig
= 0.000, well below 0.05, indicates that the model as a whole explains the variation in
economic sustainability in a statistically meaningful manner.

Moreover, the regression sum of squares (83.076) compared with the residual sum of
squares (33.480) suggests that the independent variables explain a substantial
proportion of the variance in the dependent variable, reinforcing the reliability of the
regression results in this analysis.

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Table 11: Regression Coefficients for the Effect of AI Technologies on Economic
Sustainability
Model B Std. Error Beta t Sig.
Constant 0.068 0.481 — 0.141 0.888
Q1 0.239 0.074 0.274 3.247 0.001
Q2 0.268 0.121 0.290 2.221 0.028
Q3 -0.151 0.126 -0.173 -1.199 0.232
Q4 0.568 0.103 0.514 5.536 0.000
Q5 -0.024 0.095 -0.013 -0.254 0.800
Dependent Variable: S2

The results indicate that some AI variables have a statistically significant effect on
economic sustainability, while others do not. Specifically, Q1 (Predictive and
Intelligent Analytics Systems), Q2 (Predictive Maintenance of Equipment), and Q4
(Big Data Analytics) recorded significance values below 0.05, indicating a statistically
significant positive impact on economic sustainability.

In contrast, Q3 (Robotics and Autonomous Systems) and Q5 (Intelligent
Decision-Support Systems) did not show statistically significant effects at the 0.05
level. Accordingly, based on the overall regression results, the second sub -hypothesis
can be accepted, confirming the existence of a statistically significant relationship
between certain AI technologies and the achievement of economic sustainability in the
Iraqi oil and gas sector, while acknowledging differences in the magnitude of influence
among technologies.

Third Sub-Hypothesis
There is a statistically significant relationship between predictive and intelligent
analytics systems, predictive maintenance of equipment, robotics and autonomous
systems, big data analytics, and intelligent decision -support systems, and the
achievement of social sustainability in the Iraqi oil and gas sector.
To test this hypothesis, multiple regression analysis was employed, and the results are
presented in Table 12.
Table 12: Multiple Regression Analysis for the Relationship Between AI Technologies and
Social Sustainability
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.812 0.659 0.647 0.315

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The results reveal a strong relationship between AI technologies and social
sustainability in the Iraqi oil and gas sector. The overall correlation coefficient (R =
0.812) reflects a strong association between the variables. Moreover, the coefficient of
determination (R² = 0.659) indicates that the independent variables explain
approximately 65.9% of the variance in social sustainability, demonstrating the model's
strong explanatory power.

The adjusted R² value (0.647) confirms the model's stability after accounting for the
number of predictors, while the standard error of the estimate (0.315) indicates an
acceptable level of predictive accuracy.
Table 13: ANOVA Results for Testing the Relationship Between AI Technologies and
Social Sustainability
Model Sum of Squares df Mean Square F Sig.
Regression 27.826 5 5.565 56.076 0.000
Residual 14.390 145 0.099

Total 42.216 150

Dependent Variable: S3

Table 13 demonstrates that the regression model examining the relationship between
AI technologies and social sustainability is highly statistically significant. The F-value
(56.076) with a significance level of Sig = 0.000, well below the conventional threshold
of 0.05, confirms that the model explains variation in social sustainability in a
statistically meaningful manner.

Furthermore, the regression sum of squares (27.826) substantially exceeds the residual
sum of squares (14.390), indicating that a large proportion of the variance in the
dependent variable is attributable to the independent variables included in the model,
thereby reinforcing the robustness of the regression results.
Table 14: Regression Coefficients for the Effect of AI Technologies on Social
Sustainability
Model B Std. Error Beta t Sig.
Constant 0.758 0.316 — 2.403 0.018
Q1 0.137 0.048 0.261 2.842 0.005
Q2 -0.153 0.079 -0.275 -1.933 0.055
Q3 0.034 0.083 0.064 0.406 0.685
Q4 0.493 0.067 0.740 7.322 0.000

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Q5 0.296 0.062 0.272 4.763 0.000
Dependent Variable: S3

The findings indicate that most AI variables exert a statistically significant influence on
social sustainability, with a few exceptions. Specifically, Q1 (Predictive and Intelligent
Analytics Systems), Q4 (Big Data Analytics), and Q5 (Intelligent Decision -Support
Systems) recorded significance values below 0.05, reflecting a positive and statistically
significant effect on social sustainability.

Conversely, Q3 did not demonstrate a significant effect, while Q2 was close to the
significance threshold (Sig = 0.055) and is therefore considered weak and statistically
insignificant at the 0.05 level. Based on the overall results, the third sub -hypothesis is
accepted, confirming a statistically significant relationship between AI technologies
and social sustainability in the Iraqi oil and gas sector, with varying levels of influence
across technologies.

Hypothesis
There is a statistically significant relationship between the application of AI
technologies and sustainability in the Iraqi oil and gas sector.
Table 15: Multiple Regression Results for the Relationship Between AI Application and
Overall Sustainability
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.700 0.489 0.486 0.359

The results indicate a moderate -to-strong relationship between AI technologies and overall
sustainability in the Iraqi oil and gas sector. The correlation coefficient (R = 0.700) reflects a
meaningful association, while the coefficient of determination (R² = 0.489) shows that
approximately 48.9% of the variance in sustainability is explained by the independent variable.
The adjusted R² (0.486) confirms the stability of the model, and the standard error of the
estimate (0.359) indicates an acceptable level of predictive accuracy.
Table 16: ANOVA Results for Testing the Relationship Between AI Application and Sustainability
Model Sum of Squares df Mean Square F Sig.
Regression 18.415 1 18.415 142.808 0.000
Residual 19.213 149 0.129

Total 37.628 150

Dependent Variable: S

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Table 16 confirms that the regression model assessing the relationship between AI
application and overall sustainability is highly statistically significant. The F-value (142.808)
with Sig. = 0.000, far below the 0.05 threshold, demonstrates that the model explains
variation in sustainability in a statistically meaningful manner.
Table 17: Regression Coefficients for the Effect of AI Application on Sustainability
Model B Std. Error Beta t Sig.
Constant 1.807 0.162 — 11.150 0.000
Q 0.511 0.043 0.700 11.950 0.000
Dependent Variable: S

The coefficients indicate that the application of AI technologies has a strong and statistically
significant effect on sustainability in the Iraqi oil and gas sector. The standardised Beta value
(0.700) reflects a substantial effect size, while the significance level (Sig = 0.000) confirms the
robustness of this relationship. Accordingly, the main hypothesis is accepted, establishing a
statistically significant relationship between AI application and sustainability.

Discussion of Results
The findings show that AI technologies have a positive and statistically significant impact on
sustainability in the Iraqi oil and gas sector across its environmental, economic, and social
dimensions.

At the environmental level, the sub -hypotheses indicate that certain AI tools, particularly
predictive and intelligent analytics systems and intelligent decision -support systems, play a
pivotal role in enhancing environmental sustainability by improving emissions monitoring,
analysing operational data, optimising maintenance processes, and reducing resource waste.
These findings align with results reported by Manikandan et al. (2025) and Ofongo (2024),
who highlighted AI's capacity to improve carbon capture efficiency, resource management, and
environmental risk mitigation.

From an economic perspective, the results show that AI contributes effectively to economic
sustainability by supporting predictive maintenance and big data analytics for optimised
operational planning. This, in turn, enhances productivity and reduces operating costs. These
outcomes align with the findings of Povoas et al. (2024) and Wang et al. (2024), who
emphasised AI's role in strengthening industrial value chains and improving operational
resilience, thereby enabling sustainable economic returns.

At the social level, the study shows that AI supports social sustainability by promoting
informed, transparent decision-making, enhancing community interaction, and providing safer,

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more efficient working environments. These findings are consistent with the SCAIS framework
proposed by Rohde et al. (2024), which underscores the importance of implementing AI in a
socially responsible manner that minimises inequality and bias.

When sustainability is considered as an integrated concept, the results indicate that AI
contributes in a balanced manner across the three sustainability dimensions, although the
strength of impact varies among technologies. Predictive analytics and intelligent
decision-support systems emerge as strategic tools, whereas applications such as r obotics and
autonomous systems show relatively limited influence in the current Iraqi context. This may
be due to regulatory challenges, skill shortages, or underdeveloped infrastructure, consistent
with the recommendations of Egbumokei et al. (2024), who emphasised the need for
innovative, low-emission technologies adapted to local conditions.

Overall, the study confirms AI's transformative value as a key enabler of sustainable
development in the oil and gas sector. Its role extends beyond operational efficiency to
encompass environmental protection, economic value creation, and social responsibility. The
findings highlight the importance of integrating AI technologies with institutional policies and
strategic investments to establish a long -term, viable sustainability model, aligned with the
global transition toward a low-carbon economy and the Sustainable Development Goals (Wang
et al., 2025; Fonseca et al., 2024).
1-

CONCLUSION
The study found a statistically significant relationship between the application of AI
technologies and sustainability in the Iraqi oil and gas sector, confirming that these
technologies are a key driver of improved operational, environmental, and social performance.

The results showed that AI technologies do not exert the same level of influence. Intelligent
decision-support systems, big data analytics, and predictive and intelligent analytics systems
showed the strongest effects on environmental and social sustainability, while other
technologies had a more limited effect on the direct economic dimension.

The regression analysis confirmed that the independent variables explain a substantial
proportion of the variation in environmental, economic, and social sustainability, reflecting AI
technologies' strong capacity to enhance efficiency and reduce risks and resource waste.

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The findings also indicated that respondents value the environmental and social benefits of AI
more strongly than its economic benefits, highlighting the need to develop policies and
strategies that can maximise the direct economic return of these technologies.

The study confirms that AI constitutes a strategic instrument for strengthening sustainability in
the Iraqi oil sector, contributing to a more balanced integration of economic, social, and
environmental performance.

Recommendations
i. Oil and gas companies should adopt intelligent decision-support systems and big data
analytics in planning and production processes in order to improve performance and
reduce waste.
ii. Advanced training programs should be introduced to equip employees with the skills
needed to use intelligent technologies effectively, thereby enhancing operational
efficiency and maximising the benefits derived from these systems.
iii. Greater investment should be directed toward predictive maintenance and intelligent
robotics to improve equipment reliability and reduce operational risks, with positive
implications for both environmental and economic sustainability.
iv. Policies and procedures should be developed to link the use of AI directly to
improvements in financial efficiency, through lowering operating costs, minimising
waste, and increasing productivity.
v. Research and development initiatives should be supported to assess the impact of
emerging technologies and explore additional applications of AI in the fields of energy
and sustainability.
vi. An integrated system should be established to measure the impact of AI on
environmental, social, and economic sustainability on a regular and systematic basis.
Future Research Directions
Future studies should examine the long -term impact of AI on economic and social
sustainability, with particular attention to direct financial returns and operational sustainability
of Production Processes . Further research should investigate the integration of AI with the
Industrial Internet of Things (IIoT) and renewable energy systems in order to improve resource
efficiency and reduce environmental impacts in the oil sector.

African Journal of Applied Research
Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 72

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Vol. 12, No. 6 (2026), pp.45-75
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African Journal of Applied Research
Special Issue: Artificial Intelligence and
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Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
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ISSN: 2408-7920
Copyright ⓒ African Journal of Applied Research
Arca Academic 74

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Special Issue: Artificial Intelligence and
Engineering: Emerging Technologies and
Applications
Vol. 12, No. 6 (2026), pp.45-75
http://www.ajaronline.com
Received January 1, 2026
Peer reviewed: April 25, 2026
Revised: August 15, 2026
Published: September 2026

ISSN: 2408-7920
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