AI in Governance; challenges & opportuinites South Punjab.docx

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1. 1. Introduction
1.1 Background of Study
Artificial Intelligence (AI) has become a revolutionary tool in the latest governance systems
with better decision making, optimization of operations and better delivery of Public Service
(Mishra et al., 2024). The use of Artificial Intelligence (AI) applications in Government is
receiving increasing attention from global research and practice communities. Governance
wise, applications of algorithms, machine learning, natural language processing and
predictive analytics are the components of AI that automate Administrative Functions to
identify inefficiencies and improve citizen’s services. On a global level, different
governments are using AI to administer public infrastructure, allocate the distribution of
resources and increase the efficiency of their responsiveness to the provision of economic
services. For example, Estonia and Singapore have formalized AI to enhance bureaucratic
operations and citizen experiences (Wibowo, 2025). AI is still an emerging and fast growing
tool in Pakistan. The Presidential Initiative for Artificial Intelligence and Computing (PIAIC)
by Ex-President Dr. Arif Alvi was initiated to integrate AI technologies into national
developmental priorities (Adams, 2024). However, the regional / local level implementation
of AI in Governance / Public Administration, especially in underdeveloped regions such as
South Punjab, is still limited due to many socio-technical barriers.
In the past, poor administration, corruption, weak infrastructure, and a shortage of skilled
staff have been major obstacles to delivering public services in South Punjab. South Punjab, a
less urban and under-served region, has unevenness in Healthcare, Education and Municipal
Services compared to North and Central Punjab (Shergill & Mehta, 2023). For example, the
Health and Education indices for districts such as Rajanpur, Bahawalpur, and Dera Ghazi
Khan are consistently below the provincial averages (Mohey-Ud-Din & Ikram, 2023). These
challenges in the provision of services are well grounded in institutional confinements and
bureaucratic red tape. In this regard, AI has the potential to circumvent classical inefficiencies
by using automation to make decisions, minimize human bias and model predictive policy.
However, when it comes to implementation, this setup has to be viewed within the context of
regional differences regarding technology availability and readiness for Governance.
The potential of AI in improving Service Delivery is created by its ability to process and
analyze large amounts of data in real time which will help policymakers to make decisions. In
health services, the tools of AI can be used to forecast outbreaks, manage patients records and

2. enhance the chain of supply (Khan et al., 2024). Machine learning models in education can
detect a vulnerable learner, suggest interventions and individualize the learning material
(Huang et al., 2023). In the identification of complaints, AI can also complement the
municipal systems of complaint management and continuously monitor waste collection. It
can also help predict failures in the infrastructure. From a theoretical standpoint, AI adoption
is set to build state capacity through increasing transparency, reducing arbitrary attributions
and growing trust between the citizens and the state. However, political will, regulatory
support, ethical data handling and digital infrastructure are the areas in which South Punjab is
far from being at the target.
Technical, institutional and socio-political barriers limit the implementation of AI in South
Punjab. In addition to these, other significant challenges include limited digital literacy,
insufficient funding, lack of reliable internet connectivity, resistance to change among public
officials, weak data management systems and the absence of clear regulatory frameworks to
guide AI integration in governance. The lack of adequate digital infrastructure initially
characterizes the area. Still, many rural tehsils have no stable internet coverage and digital
public services and there is a foundational gap for intervention based on AI (Sindakis &
Showkat, 2024). Secondly, there is a lack of skilled human resources equipped with
knowledge of data science, computer programming and AI system integration. Most public
servants in South Punjab have no formal training in digital governance. So far, the
government has not set up AI training centers at the regional level (Aman, 2022). Third,
inertia on the part of the institutions, as well as the lack of trust in technology among
bureaucrats and citizens alike, poses an additional challenge to adoption. Further, AI needs
vast amounts of data black boxes, threatening data privacy, cybersecurity and algorithmic
bias, particularly without national data protection laws (Chaudhary, 2024). Such challenges
are a call to the necessity of a context sensitive guide around AI use in public service
delivery.
Through these barriers, the South Punjab also provides fertile land for innovation. First, the
government’s digitalization program, run by the Punjab Information Technology Board
(PITB), comprises e-governance reforms and digitalization of land records which can be a
stepping stone for the further adoption of AI (Pushpa, 2024). Second, public-private
partnerships can leverage resources and technical expertise to create AI solutions to local
issues. Several universities in Multan and Bahawalpur now offer AI-related courses which
may initiate the development of regional talent pools. Third, donor agencies such as the

3. World Bank and UNDP have voiced interest in sponsoring technology driven governance
efforts within underdeveloped regions, contributing through finances and technology in pilot
AI projects (Amoah, 2024). Finally, AI offers a distinct chance for youth involvement in
public innovation. The native populations of conventional digital environments can become
users and further developers of AI-driven governance solutions.
The importance of this research lies in its attempt to close the empirical gap in understanding
how the adoption of AI unfolds in a region that suffers from governance deficits. Although
the volume of research on AI in public administration is increasing worldwide, there is a lack
of scholarly interest in localizing it in South Punjab. The conclusions of this research will not
only inform the regional policy and allow entry into the larger debate on inclusive
technological governance within developing states. This research can help policymakers, civil
society, and development partners develop an AI-driven public service model suitable to
South Punjab’s actualities by defining pragmatic pathways, institutional barriers and
marketable opportunities.
In South Punjab, adopting Artificial Intelligence (AI) to improve the quality of governance
and public service delivery is scant and poorly understood. However, globally, AI is receiving
much recognition as a critical tool for enhancing the quality of governance and public service
delivery. The region is faced with weak institutional capacity, insufficient digital
infrastructure, and a lack of competent staff which prevents the implementation of AI in
public administrative systems. Moreover, the national and provincial governments have now
launched the digital reforms. However, there is a tremendous void of empirical studies
investigating the practical challenges and enablers regarding South Punjab’s socio-political
and economic context. The absence of localized evidence hinders effective policy
formulation, prioritization of investment and strategic development for AI-powered
transformation of the public sector. Therefore, it is essential to explore barriers and
opportunities regarding adopting AI in public service delivery in South Punjab to provide
context-sensitive, sustainable and inclusive governance strategies.
1.2 Problem Statement
While the global discourse on Artificial Intelligence (AI) adoption in public governance is
rapidly expanding, there remains a vibrant research gap concerning its application in
underdeveloped and contextually complex regions such as South Punjab, Pakistan. Existing
literature predominantly focuses on AI integration in technologically advanced settings or at

4. the national level, often overlooking the unique institutional, infrastructural, and socio-
cultural challenges faced by marginalized / under developed regions. Moreover, most studies
emphasize technical feasibility or economic impact with limited attention to public sector
readiness, stakeholder perceptions and ethical concerns in local governance contexts. In
South Punjab, where public service delivery is already hindered by bureaucratic
inefficiencies, digital illiteracy and uneven resource distribution, the absence of empirical
studies examining AI adoption further exacerbates the policy and implementation vacuum.
Additionally, there is little evidence on how localized AI strategies can be tailored to improve
service delivery outcomes in education, healthcare and municipal services within such
regions. This lack of granular, region-specific research hinders the development of practical
frameworks and policy interventions that are essential for sustainable and inclusive AI-driven
governance.
1.3Research Objectives:
i.To identify and analyze the institutional, infrastructural and socio-political factors that
influence the adoption of Artificial Intelligence (AI) in public service delivery within
the South Punjab region of Pakistan.
ii.To explore the perceptions, attitudes and experiences of public administrators and
frontline service providers regarding the implementation of AI technologies in South
Punjab.
iii.To assess the implications of stakeholder perceptions for institutional readiness,
governance practices and policy formulation related to AI adoption.
iv.To propose context-sensitive policy frameworks and strategic interventions that can
support the effective, inclusive and ethical integration of AI in public sector service
delivery across underdeveloped regions.
1.4Research Question:
i. What institutional, infrastructural and socio-political factors influence the
adoption of Artificial Intelligence in public service delivery within the South
Punjab region of Pakistan?
ii.How do public administrators and frontline service providers in South Punjab
perceive the implementation of AI technologies and what implications do these
perceptions have for policy design and institutional readiness?

5. iii.What context-sensitive policy frameworks and strategic interventions can be
proposed to enhance AI’s effective and ethical deployment in public service
delivery systems across underdeveloped regions like South Punjab?
1.5 Significance of Study
This study is significant in bridging the empirical / policy gap for the adoption of Artificial
Intelligence (AI) based public service delivery in South Punjab which is an underdeveloped
region of Pakistan. Despite the growing global acceptance of AI as a transformative tool, its
localized application in Pakistan is poorly understood when it comes to areas that have
infrastructural deficits, institutional inertia and low levels of digital capacity. This study will
uncover frontline administrators’ perceptions, as well as region-specific barriers and enablers,
to provide such information and offer actionable insights to policymakers, development
practitioners, and civil society actors. It will make a meaningful contribution to discourse
around inclusive digital transformation by proposing context-sensitive, ethical, and
sustainable strategies for the integration of AI to temper the current consideration of and
contribution towards regional inequalities exacerbated through technological advancement
while promoting equitable governance outcomes.
1.6 Research Gaps
Because there is a growing global interest in applying Artificial Intelligence (AI) in public
sector governance and most of the research work is being conducted in developed areas,
research, especially in underdeveloped areas like South Punjab, in particular the Pakistan
context, is lacking. Current studies primarily concern national-level policy frameworks or the
technical side of AI, with little or no attention paid to the complex socio-political and
institutional environments in which implementation occurs in marginalized regions. In
addition, there is little empirical work that provides a voice and captures the lived experience
of frontline public officials and service providers who are directly engaged in the governance
they promote. This leads to a critical knowledge void about localized studies of
infrastructural limitations, bureaucratic resistance and public trust in digital systems.
Furthermore, few studies provide actionable, context-specific policy models that are specific
to the South Punjab’s specific administrative, cultural, and technological realities. To fill
these gaps, this study presents a grounded, qualitative understanding of AI adoption
dynamics, which will be informed by both academic scholarship and practical policy
development.

6. 1.7 Structure of the Thesis
Chapter 1: Introduction
This chapter will introduce the research topic, outline the problem, objectives, and research
questions, and highlight the study’s significance. It will also define the scope and provide a
roadmap for the thesis.
Chapter 2: Literature Review
This chapter will critically review global and national literature on AI in governance, identify
regional challenges in South Punjab, and present relevant theoretical frameworks. It will
conclude by highlighting research gaps the study aims to address.
Chapter 3: Research Methodology
This chapter will explain the philosophical approach, research design, sampling method, data
collection, and analysis techniques. It will also address issues of trustworthiness, ethics, and
methodological limitations.
Chapter 4: Findings and Analysis
This chapter will present and interpret the empirical data collected through interviews,
organized into key themes. It will reflect how institutional, infrastructural, and socio-political
factors shape AI adoption in South Punjab.
Chapter 5: Discussion
This chapter will compare the findings with existing literature and theoretical frameworks. It
will also discuss broader implications for public sector innovation and digital transformation
in underdeveloped regions.
Chapter 6: Conclusion and Recommendations
This chapter will summarise the key findings and their contributions to knowledge. It will
offer practical, context-sensitive policy recommendations and suggest areas for future
research.

7. 2. Literature Review
Artificial Intelligence (AI) is becoming an important tool that can transform governance and
delivery of public services. Its global adoption is changing how administrative efficiency,
resource allocation, and citizen engagement are taking place globally. As such, AI
technologies such as algorithms, Machine Learning (ML), and Natural Language Processing
(NLP) are being integrated into governance systems for automating decision-making,
predicting policy outcomes, and personalizing public service delivery (Poudel, 2024). For
example, Estonia and Singapore have institutionalized AI to oversee citizen databases,
improve traffic, enhance healthcare, and improve judicial processes so that the most advanced
digital infrastructures and a committed nation can establish sound e-governance systems
(Bejarano-Murillo, 2025). These cases are models of what can be done when AI is inserted
into well-constituted policy frameworks.
2.1 Global to National AI Governance Trends
Pakistani national policy has followed the global movement in favour of AI-enabled
governance. With (Abisoye & Akerele 2022), a key step is taken to embrace AI into national
development plans intended to augment digital literacy and ecosystem de innovation. Still,
implementation is uneven across regions, even when top-down commitments promise
otherwise. The deployment of AI in underdeveloped and semi-urban areas like South Punjab
also addresses many systemic hurdles, such as poor infrastructure, low institutional capacity,
and generally low digital literacy (Razzaq & Wali, 2025).
Applying Rogers (2003) Diffusion of Innovation (DoI) Theory, the spread of innovation (like
AI) depends on innovation, communication channels, time, and social systems in that regard
(Patnaik & Bakkar, 2024). The diffusion is slowed by bureaucratic inertia and low trust in
innovation in the social system, which, already marked by a digital divide and weak
communication channels, makes for even slower diffusion in South Punjab. The relative
advantage and compatibility of the innovation to existing values and practices remain
unarticulated to public administrators in the region.

8. 2.2 Regional Governance Challenges in South Punjab
Pakistan’s South Punjab, both geographically vast and demographically weighty, has a
history of being marginalized in development strategy and resource allocation (Ahmad,
2022). As a result of this persistent neglect, there is a host of governance challenges that have
occasioned administrative inefficiency, infrastructure deficits, poor public service delivery,
and weak institutional capacity. With Artificial Intelligence (AI) emerging as a strategic tool
for worldwide system transformation in governance, South Punjab’s systemic weaknesses
make a critical case for regional preparedness, diffusion of innovation in an equitable
manner, and prerequisites of deploying AI in underdeveloped contexts (Sager et al., 2025).
2.2.1 Institutional Inefficiency and Bureaucratic Inertia
Institutional inefficiency is a central governance issue in South Punjab. In government,
departments across sectors like health, education, and municipal services suffer from opaque
or lack of streamlined processes, use outdated systems, and are bedevilled by delays in
services that citizens choose to put up with. The frustration is born from hierarchical
command structures, under-resourced departments, and a lack of accountability mechanisms.
For instance, development funds tend to be disbursed later and are suffocated in bureaucratic
red tape in districts like Rajanpur, Layyah, and Bahawalnagar, undermining public trust and
operational effectiveness (Awan, 2022).
Another key hurdle is institutional inertia, which is resistance to change among public
officials (Rahman et al., 2023). Mid-level bureaucrats often look at digital transformation
skeptically, afraid of losing their jobs and suspicious of surveillance or presentation of their
potent ineffectiveness. Socio-technical Systems Theory tells us that effective technological
innovation requires social system adaptation. In South Punjab, there is a tendency to have a
broken fit between technical possibilities and the administration culture. Institutions are ill-
prepared to absorb emerging technology such as AI because they do not have incentive
structures, performance-linked evaluations, and digital change management programs.
2.2.2 Infrastructure Deficits and Digital Exclusion
One of the greatest challenges to AI adoption in South Punjab is arguably infrastructure-
related limitations (Salamat, 2024). However, although there are national initiatives to

9. improve broadband penetration, (many) rural tehsils in South Punjab remain digitally
excluded. Some of Punjab’s lowest internet coverage rates are in some districts like
Muzaffargarh and Rahim Yar Khan, as reported by the Pakistan Telecommunication
Authority (2023) (Ahmad et al., 2021; Waqar et al., 2024). Even minimal e-governance
services are cut out by this digital divide and all the complex data infrastructures that make
up AI deployment.
Electricity is also inconsistent in remote areas, a prerequisite for sustained digital operations.
Schools and health centres experience recurring power outages that disturb the management
of digital tools and systems. Moreover, the raw data for AI algorithms is unavailable because
data analytics does not have centralized data repositories, cloud infrastructure, or a secure
server system. However, supporting infrastructure in the ‘early majority’ adoption phase (as
propounded by Diffusion of Innovation (DoI) Theory) is still lacking in South Punjab.
2.2.3 Human Capital Deficiencies
To deploy AI effectively in governance, we need a workforce equipped with data analysis,
programming, systems integration, and ethical AI management. In these areas, South Punjab
suffers a major human resource gap. Public servants in the region are the majority, with little
or no exposure to digital technologies. Since the focus of digital governance in Pakistan has
concentrated somehow in urban centres like Lahore and Islamabad (Ahmed et al., 2024), the
technical training programs and continuous professional development programs for the digital
economy have barely penetrated South Punjab.
Aside from the theory courses offered at these educational institutions introduced into the
region, courses related to AI have only lately been provided, and these are often presented at
an undergraduate level (Chiu et al., 2021). Unless there is a targeted effort to develop human
capital in the region (through technical training scholarships, digital literacy programs, and
AI fellowships), the skill gap will continue to grow. Based on this, the Technology
Acceptance Model authors show that individuals’ technology adoption depends on perceived
ease of use and usefulness. In South Punjab, however, due to a dearth of exposure, public
officials often do not see AI as either accessible or beneficial – it is also an impediment to
adoption.

10. 2.2.4 Trust Deficit and Resistance to Technology
Historically determined patterns of exclusion, misuse of technology, and poor service
delivery have continued to drive low public trust in South Punjab’s digital systems. New
technologies are viewed with suspicion by communities where people have had their land
records fraudulently manipulated, where complaint systems cannot be accessed, or where
group targeting by surveillance tools has been biased. The same issue of trust applies to
bureaucrats, who worry AI is too unknown. They call it the ‘black box’ problem to be
entrusted to make decisions that will affect the lives of their citizens. Algorithmic opacity has
particular ethical implications in sensitive areas like public welfare distribution or criminal
justice, as highlighted by (Purves & Davis, 2022).
In this case, the deployment of AI should, therefore, be preceded by robust awareness
campaigns, participatory planning, and transparent, resilient, and strong governance protocols
(Sanchez et al., 2025). Trust-building mechanisms such as open data policies, communities
that audit AI algorithm decisions, and community consultations are necessary to obtain local
legitimacy. Such disruptive technologies are rarely reported in South Punjab, so this practice
is poorly prepared at the institutional governance level.
2.2.5 Lack of Legal and Regulatory Frameworks
In addition, there is no coherent legal and regulatory framework governing AI and digital
governance, and it’s unclear when existing regulations should or shouldn’t apply to AI
systems. There is neither comprehensive legislation on data protection nor cyber security and
transparency of algorithms in Pakistan (Masudi & Mustafa, 2023). In South Punjab, where
the capacity for oversight is limited, this regulatory vacuum is particularly serious. The
absence of legal clarity on data ownership, user consent, and AI accountability will create
risks for misuse or discrimination, aggravating marginalization or maintaining a governance
deficit.
Additionally, many of the policy frameworks at the national and provincial levels have still
not articulated national and provincial-specific strategies for digital transformation. Although
Punjab’s Digital Policy emphasizes the need for e-services, it fails to formulate disaggregated
South Punjab plans and budgets for e-services (Aman, 2022). This makes provincial

11. programs generally not reflect the region’s socio-political complexities and infrastructure
constraints.
2.2.6 Cultural and Linguistic Barriers
Many AI applications are developed in English or Urdu without accounting for the
multilingual world of South Punjab, where Saraiki and Punjabi are the major lingua franca
(Ayoub et al., 2023). Low user engagement and alienation are normally caused by an
inability to localize digital services. AI-driven public services should be accessible to all, and
culturally appropriate and linguistically inclusive interfaces are critical to that goal. Without
such adaptations, even the most well-intentioned AI applications will fail to take hold within
local populations.
Moreover, in many South Punjabi communities, gender norms and other socio-cultural
constraints stop women from getting access to digital technology (Khan, 2021). Without
gender equity and inclusive design imbibed into AI strategy, there is a risk of exacerbating
inequalities instead of solving them.
Institutional inefficiencies, infrastructural deficits, human capital deficits, and socio-cultural
barriers challenge the adoption of AI in South Punjab (Shergill & Mehta, 2023). To these
problems is added a lack of trust, inadequate regulation, and insufficient localization of AI
tools. As a result, AI does not scale in the region and cannot be superimposed on it without
substantial preparatory investments. For AI to truly be adopted and utilized, even in
governance, a successful model will be contextualized appropriately within the constraints of
South Punjab in a way that does not perpetuate the same level of exclusion that the current
state of digital lacks (Ahmad & Sharoon, 2025). To promote an enabling environment for the
integration of sustainable AI, the pathway forward must put bottom-up planning, targeted
resource allocation, public sector training, and community trust building at the forefront.
2.3 Infrastructural and Human Resource Limitations
The first foundational constraint is an infrastructure deficit for digital infrastructure. South
Punjab’s rural tehsils are days behind regarding reliable internet connectivity, electricity, and
digital devices (Abid & Chaudhry, 2024). The absence of an infrastructure makes basic
digital public service nonoperational. In addition, there is a shortage of skilled personnel,

12. such as data scientists, programmers, and systems integrators (Romanenkov, 2021). A policy
gap was found, including in the human resource development of most public officials without
formal training on e-government or AI systems.
Citing the Technology Acceptance Model (TAM) (Davis, 1989), individuals embrace new
technologies based on the relevance of perceived usefulness and perceived ease of use
(Katebi et al., 2022). In South Punjab, the perception of AI applications is extremely low
among public administrators and service providers due to limited awareness about AI
applications and the fact that no successful pilot demonstrations have been carried out.
Skepticism towards AI is uncalled if they aren’t trained carefully and we aren’t getting the
results they need.
Many such concerns, ethical worries about data privacy, algorithmic bias, and cybersecurity
threats also increase the pushback. There are no data protection laws beyond a national
framework, and artificial intelligence (AI) remains a secretive force in a data black box
(Müller, 2021). Affirmation, surveillance, job redundancy fears, and slow acceptance in a
culture with discretion and informality. Other than the regulations and decisions that indicate
environmental policy, policy refers to opportunities for development.
However, South Punjab is also full of potential for AI-enabled transformation. Digital
reforms in land record management and public grievance systems have been undertaken by
Punjab Information Technology Board (PITB) initiatives (Ullah et al., 2021). However, these
foundational projects have set the infrastructural and institutional precedent necessary for
integration of Artificial intelligence (Ajirotutu et al., 2024). In addition, the resource and
expertise gap is increasingly encouraged to be filled by public-private partnerships (PPPs).
Lately, the conversation about AI has risen, and the universities in Multan and Bahawalpur
are offering courses related to AI, which in turn is likely to bring up the local tech
ecosystems.
Another enabler is support from international development agencies such as the UNDP and
World Bank. Because of this, these organizations are funding pilot projects in digital
governance; by investing in targeted investments and training programs, they can import
global best practices to South Punjab (Giles et al., 2022). Second, the youth population a
digitally literate and more innovation-driven portion of the population compared to senior
bureaucratic staff—also offers a fertile field for AI diffusion due to their greater impatience

13. with social norms and propensity for trying out new things, corresponding to the DoIs focus
on these early adopters.
2.4 Conceptual and Empirical Gaps in Current Literature
There is little work on local AI adoption issues within Pakistan’s governance framework,
particularly in South Punjab (Khan & Ullah, 2024). There haven’t been any previous studies
that are either overly technical or national-level focused. However, there is little empirical
work to show how regional disparities matter in the deployment of AI or how frontline public
officials interpret AI’s utility and risks.
Therefore, this research fills a critical gap between macro-level analysis of policy and micro-
level analysis of institutional dynamics. Some particularities for adopting AI include focusing
on a sense of context, in which AI can be adopted technologically and about institutions, the
political economy, and cultural traditions. The study uses her theoretical lenses of TAM, DoI,
and Socio-technical Systems Theory to provide a multi-dimensional understanding of what
drives or limits AI integration in governance ecosystems that are yet to be developed (Zhai &
Gao, 2024).
Finally, having determined that AI has enormous potential to change how public governance
and service delivery are carried out, we find that structural, technical and cultural obstacles
have blocked its adoption in South Punjab. Per the literature, AI’s promise will likely be
unfulfilled unless AI is employed with targeted interventions, including infrastructural
development, capacity building, and reform of the regulatory environment. But the whole
region does not have hope. Digital reforms continue, educational opportunities continue to
blossom, and donor interest continues to rise, all collectively supporting the infrastructure
from which inclusive, ethical, and effective AI governance can grow. Studies such as these
must be localized, participation should be applied for policy design, and adaptive strategies
should respond to the complex realities of South Punjab.

14. 3. Methodology
In this research, the adoption of artificial intelligence (AI) in public service delivery in South
Punjab and factors that inhibit or facilitate adoption will be qualitatively investigated based
on institutional, infrastructural, and socio-political factors. Uncovering the complex, context-
dependent realities that frame the use of such technologies is probably best understood and
served by a qualitative methodology. First, it will permit the researcher to engage directly
with stakeholders’ experiences of living in, working in or governing a river city and thereby
generate a rich, in-depth understanding of local dynamics that quantitative methods cannot
deliver.
3.1 Research Paradigm
An interpretive paradigm of study will guide the study as reality is seen as a socially
constructed phenomenon and the product of perceptions, meanings and actions of individuals
in particular institutional and cultural milieus. In keeping with this, the goal of this thesis is to
pursue a philosophical approach that considers how public officials, service providers, and
administrators perceive the role of AI in governance and service delivery. By using the
interpretivist lens, the study can look at how subjective understanding, social norms, and
institutional logic frame the behaviour and decision-making of the responsible in the public
sector of South Punjab.
3.2 Sampling Technique
The research will draw on a purposive sampling method that will choose participants with
related knowledge and experience from covey areas like public administration, information
technology, and, among others, digital service delivery. Local government officials, e-
governance personnel, public service managers, and technology professionals from
administrative departments will take part as participants. According to the principle of
relevance, the sampling will be done by the people who are involved or influenced by AI-
related decisions. In order to achieve data saturation and still keep the research manageably
sized, it is known that 25 to 30 participants should be enough to interview. To increase the
depth and breadth of views collected, demographic diversity regarding gender, professional
role, and geographic representation will be considered within South Punjab.

15. 3.3 Data Collection Methods
Semi-structured interviews will serve as the main data used in this study, which will afford
the flexibility to expose key themes but also allow participants to express their views in their
own terms. Open-ended questions based on the guide will be provided to probe participants’
awareness, attitudes, and experiences with AI in public sector operations. Benefits and risks
perceived in AI, institutional readiness, infrastructural limitations, policy support, ethical
implications and how AI could be implemented effectively will be discussed. Face-to-face
interviews will be conducted if possible or by secure digital platforms if participants are
available and logistical feasibility is possible. Interviews will be about 45 to 60 minutes long
and recorded, with participants informed consent. During and immediately after interviews,
non-verbal cues and contextual observations will be taken down in notes that could assist in
the interpretation of data.
3.4 Data Analysis
Thematic analysis will be used to analyze the data as it is appropriate to identify, organize
and infer meaning from patterns of meaning within qualitative data. We will use the
transcription of interview recordings and then repeatedly read through them to ensure full
immersion within the data. Afterwards, the researcher will generate initial codes that are the
recurrent features of the responses. The groups of these codes will be divided into higher-
level themes that reflect major conceptual and practical AI adoption issues in the region.
Following Braun and Clarke’s (2006) six-step table, from familiarising with data to theme
refinement and final narrative development, will be the analysis. We will use NVivo software
to ease consistency and transparency in coding and data management. Analytical memos will
be written to record evolving insights, decisions to manage the method and reflexive notes
throughout the process.
3.5 Trustworthiness and Rigor
Several strategies will be taken to ensure the credibility and trustworthiness of the study.
Members will check the accuracy and authenticity of the interviews by giving back
summaries of them to the participants. Academic supervisors and qualitative research experts
will sit in on the peer debriefing sessions to assess critically the coding process and thematic
interpretations. The audit trail will document the entire research process, including data

16. collection and final analysis, and will be maintained to improve construct dependability.
Detailed, thick descriptions of the research context, participant backgrounds, and thematic
patterns will provide support for transferability. Throughout the study, methods of reflexivity
will be practiced in order to critique the researcher’s assumptions and positionality and how
these assumptions may shape the data interpretation.
3.6 Ethical Considerations
Research ethics considerations for human participants will strictly apply to this study. Before
the data collection starts, the relevant institutional review board will be acquired (or
provided) for either ethical clearance or clearance (approval). All participants will also be
given detailed information concerning the reasons for the study, their function in the research,
the voluntary nature of participation, and their right to withdraw again without consequence.
Participant written informed consent will be obtained for each study of the participant that
involves protecting the participant’s identity. Transcripts and published materials will be de-
identified, and pseudonyms will be used to protect privacy. Audio recordings and transcripts
will be securely stored in encrypted digital folders, accessible only by the researcher. Finally,
ethical issues in terms of power dynamics when interacting with government officials,
respecting others, avoiding coercion and remaining neutral will be addressed by the research.
3.7 Limitations
The study is qualitative and focused on a specific regional context, so results can not be
generalized for all the regions of Pakistan or other countries. The goal of the study is not a
statistical generalization but a theoretical and analytical generalization. Insights from the
participants’ experiences will help shape a larger understanding of how AI might be applied
within governance systems in underdeveloped settings. Likewise, while their best efforts will
be made to include many voices, there are access constraints, institutional gatekeeping, and
political sensitivities that will constrain who can participate. In the final analysis, these
limitations will be discussed transparently and comprehensibly.

17. References
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Abisoye, A., & Akerele, J. I. (2022). A practical framework for advancing cybersecurity,
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