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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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Algorithmic Leadership through Artificial Intelligence in
Organizational Strategy: A Case Study of Papua Pegunungan
Province, Indonesia
Rudihartono Ismail
1
, Mangesti Sedjati
2
, Saling
3
, Lulu Indriaty
4
, Nurhidayah Yahya
5
,
Iylia Dayana
Mohamed Izwan
6
1
Universitas Amal Ilmiah Yapis Wamena
2
Universitas Brawijaya
3
Faculty of Economics and Business, Universitas Yapis Papua
4
Sekolah Tinggi Ilmu Ekonomi Yapis Merauke
5
Accounting Research Institute, University Teknologi MARA, Malaysia
6
Management and Science University, Malaysia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000160
Received: 22 September 2025; Accepted: 28 September 2025; Published: 06 November 2025
ABSTRACT
The emergence of algorithmic leadership based on Artificial Intelligence (AI) is reshaping organizational
strategies, particularly in the context of the New Autonomous Regions (DOB) in Papua. This article explores
how AI-driven leadership models can optimize governance and organizational success in Papua’s DOB, where
digital transformation, infrastructure development, and socio-political stability are key factors. By integrating
machine learning, predictive analytics, and AI-based decision-making, algorithmic leadership can enhance
efficiency, transparency, and strategic foresight. The study employs a mixed-method approach by analyzing
governance frameworks, stakeholder adaptation, and the challenges of AI adoption in Papua’s DOB. The
findings indicate that AI-based leadership promotes data-driven decision-making, addresses bureaucratic
inefficiencies, and accelerates economic growth. However, challenges such as technological readiness, ethical
concerns, and local community acceptance remain central issues. The article proposes a hybrid leadership model
that combines algorithmic intelligence with human decision-making to ensure inclusivity, cultural adaptation,
and sustainable governance in Papua.
Keywords: Algorithmic Leadership, Artificial Intelligence, Digital Governance, New Autonomous Regions,
Papua, Organisational Strategy.
INTRODUCTION
Digital transformation has become a central pillar in the development strategies of modern governments across
the world. The advancement of Artificial Intelligence (AI), machine learning, and predictive data analytics has
triggered a paradigm shift in decision-making and the governance of public organizations. Globally, the
application of AI in public governance has demonstrated significant success, including in the optimization of
resource distribution, fraud detection, and the enhancement of public service efficiency in countries such as
Estonia, South Korea, and the United Arab Emirates (OECD, 2023; United Nations E-Government Survey,
2022).
In Indonesia, the direction of national digital transformation policy, as articulated in the Indonesia Digital Master
Plan 2024, emphasizes strengthening data-driven governance, cultivating digital leadership, and integrating AI
within government systems. Yet, the implementation of AI in newly established administrative regions, known
as Daerah Otonom Baru (DOB), particularly in Papua, presents unique challenges. The geographical complexity
of Papua, marked by vast and difficult-to-reach areas, coupled with limited digital infrastructure and diverse
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socio-cultural dynamics, requires leadership models that are not only technologically advanced but also
adaptable to local realities.
Beyond infrastructure, human resource readiness is another critical factor. The capacity of civil servants and
local administrators to interpret and utilize AI tools determines the effectiveness of digital governance initiatives.
Without adequate training and institutional support, AI adoption risks creating new inequalities, where only
central or urban areas benefit, while remote regions remain marginalized. Furthermore, the cultural and social
fabric of Papua demands that technological innovation be aligned with local traditions, values, and community
engagement mechanisms. Ignoring these aspects could result in resistance, mistrust, or even the rejection of AI-
driven governance.
At the same time, the emergence of algorithmic leadership offers new opportunities. Leaders equipped with AI-
driven insights can improve transparency, enhance policy responsiveness, and build resilience in governance
systems. By combining predictive analytics with participatory decision-making, algorithmic leadership has the
potential to bridge the gap between global digital standards and local governance needs. Therefore, the study of
AI-based leadership in the New Autonomous Regions of Papua not only contributes to the discourse on digital
governance in Indonesia but also offers lessons for other developing regions facing similar constraints of
geography, infrastructure, and social diversity.
Research Background
Papua Pegunungan Province, as one of the newly established New Autonomous Regions (DOB) in 2022, is
currently at a critical phase in building its governmental institutions. As a new province carved from a broader
administrative reform, the region faces multi-layered challenges that influence its ability to deliver effective
governance.
One of the foremost challenges lies in administrative efficiency. Bureaucratic processes in Papua Pegunungan
remain lengthy, fragmented, and largely undigitized. This condition limits the capacity of government agencies
to respond swiftly to citizen needs and often results in delays in service delivery, duplication of tasks, and weak
monitoring capabilities. The lack of integrated digital systems not only reduces the speed of administrative
functions but also undermines the credibility of institutions that should serve as the backbone of the new
province.
A second challenge concerns transparency and accountability. Weak institutional mechanisms and the absence
of data-based monitoring systems have created conditions in which corruption, misuse of authority, and policy
inefficiency can flourish. Without reliable reporting systems and transparent oversight structures, public trust in
government institutions risks being eroded. For a province still at the formative stage of its administrative
identity, this risk is particularly acute, as credibility and legitimacy are essential for securing long-term stability.
Another equally pressing challenge is inclusive economic development. Although Papua Pegunungan is
endowed with significant natural resources and diverse human capital, the region has struggled to fully realize
its economic potential. Planning and investment initiatives remain uneven, often favoring urban or resource-rich
pockets while leaving rural and marginalized communities behind. The underutilization of local talents and
resources hinders the possibility of building a sustainable and inclusive economy that can serve as the foundation
for the province’s growth.
Traditional leadership models rooted in hierarchical decision-making have proven insufficient to meet these
pressing demands. Such models tend to be rigid and slow in responding to the rapid changes characteristic of the
digital era. In light of these challenges, the concept of algorithmic leadership, understood as the deployment of
Artificial Intelligence (AI) as a co-decision maker to support human leaders, presents itself as a promising
alternative. By embedding AI into decision-making processes, local governments could potentially improve
efficiency, enhance transparency, and design policies that are both responsive and inclusive. In the specific
context of Papua Pegunungan, algorithmic leadership holds the potential to modernize governance practices
while simultaneously serving as a strategic instrument for economic growth, social stability, and sustainable
regional development.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Research Gap
Although global studies on AI-based leadership have expanded rapidly in recent years (Avolio et al., 2021;
Brynjolfsson & McAfee, 2017), research that specifically investigates its application in the context of
Indonesia’s New Autonomous Regions remains scarce. Much of the existing scholarship has concentrated on
metropolitan areas or advanced economies where digital infrastructure, policy frameworks, and organizational
capacity are already relatively well established. In contrast, Papua’s DOB governance represents a unique case
where technological readiness intersects with geographic isolation, fragile institutional development, and diverse
socio-cultural contexts.
Previous Indonesian research on digital governance, such as Indrawan et al. (2022), has generally focused on
urban centers with higher levels of connectivity and resource availability. These studies have paid limited
attention to remote regions, where poor internet penetration, underdeveloped infrastructure, and multi-ethnic
populations present an entirely different set of challenges. Furthermore, to date, no comprehensive conceptual
model has been formulated that integrates algorithmic leadership with local wisdom as the foundation for
governance in Papua’s DOB. This absence of contextualized models indicates a critical research gap. Without
governance approaches that reflect local realities, national digital transformation policies risk being ineffective
at the grassroots level, leaving remote communities further marginalized. Addressing this gap requires not only
technological adaptation but also theoretical innovation that accounts for cultural and geographic complexity.
Research Objectives and Contributions
The present study pursues three main objectives. First, it seeks to analyze the potential of algorithmic leadership
based on Artificial Intelligence to optimize governance in the New Autonomous Region of Papua. This analysis
covers the ability of AI tools to streamline bureaucratic procedures, support evidence-based policy design, and
enhance service delivery within a geographically challenging environment. Second, it aims to identify the ethical
and operational challenges associated with AI deployment in newly formed autonomous regions. Particular
attention is given to issues of fairness, transparency, accountability, privacy, and inclusivity, all of which must
be critically assessed before recommending large-scale adoption. Third, the research intends to propose a hybrid
leadership model that combines algorithmic intelligence with human decision-making, thereby ensuring that
governance in Papua remains both technologically progressive and culturally legitimate.
The contribution of this study is twofold. From a theoretical perspective, it advances the discourse on algorithmic
leadership by introducing the element of cultural adaptability, a dimension largely overlooked in global
literature. This enriches academic debates on digital governance by highlighting the necessity of situating AI
applications within their local socio-cultural contexts. From a practical perspective, the research develops a
policy framework that local governments can use to guide the implementation of AI-based decision-making
systems. This framework ensures that the integration of new technologies does not marginalize local traditions
and values but instead complements them, thereby fostering legitimacy, inclusivity, and sustainable
development.
Research Novelty
The novelty of this research lies in its attempt to formulate a hybrid leadership model that integrates Artificial
Intelligence with human judgment, specifically designed for the conditions of Papua’s New Autonomous
Regions. Unlike existing models that presume homogeneity across governance contexts, this study recognizes
the distinct structural, cultural, and infrastructural realities of remote provinces such as Papua Pegunungan. The
hybrid approach is positioned not only as a theoretical innovation but also as a practical tool to address the
inefficiencies and vulnerabilities of traditional bureaucratic systems.
Another distinctive feature of this study is its integration of global principles of AI governance, including
transparency, fairness, and accountability, with the indigenous wisdom of Papua. Such integration ensures that
algorithmic leadership is not imposed as a purely technological solution but is instead embedded within the
values, norms, and traditions of local communities. By doing so, the study demonstrates how digital
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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transformation can be both inclusive and sustainable when aligned with cultural identity and community
engagement.
Methodologically, the research is novel in its use of a mixed-methods approach that combines quantitative
assessments of digital readiness with qualitative analyses of stakeholder perceptions. The quantitative dimension
provides measurable insights into the availability of infrastructure and institutional capacity, while the qualitative
inquiry captures the perspectives, concerns, and expectations of local actors. Together, these methods allow the
study to generate a holistic understanding of both the technical and social dimensions of AI adoption in Papua.
In summary, the novelty of this research lies in its capacity to offer a theoretically grounded, culturally sensitive,
and methodologically integrative model of algorithmic leadership. The proposed framework not only extends
current debates on digital governance but also offers concrete strategies for policymakers seeking to strengthen
governance capacity in Papua Pegunungan and other similarly marginalized regions.
RESEARCH METHODOLOGY
Research Design
This study is designed as a qualitative case study focusing on the application of algorithmic leadership in Papua
Pegunungan, a newly established New Autonomous Region (DOB) in Indonesia. The qualitative approach was
chosen because the research seeks to understand governance dynamics, cultural adaptation, and institutional
readiness in a complex local setting. To strengthen contextual interpretation, the study also employed
quantitative descriptive support through a small-scale survey, providing numerical indicators that complemented
the qualitative findings. This combination allowed for deeper exploration of how Artificial Intelligence (AI) can
be integrated into governance while maintaining sensitivity to cultural and infrastructural realities.
Research Location and Case Selection
The case study was conducted in Papua Pegunungan Province, established in 2022 as part of Indonesia’s regional
expansion. The province was selected deliberately due to its unique challenges: geographical isolation,
underdeveloped digital infrastructure, and high socio-cultural diversity. These conditions made it an important
case for exploring whether algorithmic leadership, supported by AI, could address governance inefficiencies
while respecting local traditions. Lessons drawn from this case are expected to provide valuable insights for
other newly formed or remote regions in Indonesia.
Data Sources
The study employed both primary and secondary data. Primary data was collected through semi-structured
interviews with local government officials, traditional leaders, and civil society actors. These interviews explored
perceptions, expectations, and resistance related to AI adoption. In addition, a small-scale survey involving 50
respondents was conducted to provide descriptive statistics on digital readiness, internet access, and public
perceptions of AI in governance. While not intended for inferential analysis, the survey offered valuable
supporting evidence that contextualized the qualitative findings.
Secondary data included government policy documents on digital transformation, reports on the Master Plan
Indonesia Digital 2024, academic literature on AI governance, and international benchmarks such as the OECD
AI Principles and the United Nations E-Government Survey. These documents were used to triangulate and
enrich the primary data.
Research Instruments
The main instruments consisted of interview guides structured around themes of governance readiness, AI
adoption challenges, and socio-cultural perspectives. Survey questionnaires using Likert scales were designed
to measure digital literacy, infrastructure availability, and perceptions of AI. Observation checklists were also
employed to document infrastructural conditions such as internet coverage, administrative practices, and data
management processes.
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Data Analysis
Data analysis followed a qualitative thematic approach, beginning with open coding of interview transcripts,
followed by axial and selective coding to identify recurring themes such as infrastructure gaps, ethical concerns,
and opportunities for hybrid leadership models. The survey results were analyzed using descriptive statistics to
illustrate the broader patterns of digital readiness and perceptions among respondents. The integration of these
two sources created a richer understanding of the case, allowing quantitative data to provide supportive evidence
without overshadowing the qualitative narratives.
Trustworthiness
To ensure the credibility and trustworthiness of findings, several validation strategies were applied. Interview
interpretations were verified through member checking, where participants reviewed the summaries of their
statements. Triangulation was employed by comparing insights from interviews, surveys, and secondary
documents. The qualitative coding process was peer-reviewed by academic colleagues to reduce researcher bias.
These measures strengthened confidence that the results reflect the realities of governance and digital
transformation in Papua Pegunungan.
FINDINGS AND DISCUSSION
Overview of Digital Readiness in Papua Pegunungan New Autonomous Region (DOB)
The survey of 50 respondents provides an important snapshot of the digital readiness of Papua Pegunungan
Province. The overall digital readiness index stands at an average score of 3.1 out of 5, reflecting a moderate
level of preparedness that shows promise but also reveals structural weaknesses requiring urgent attention.
From an infrastructural perspective, only 45 percent of the region benefits from relatively stable internet
connectivity, while the remaining areas depend on satellite connections that are often unstable and fluctuating in
quality. This disparity highlights the digital divide within the province. Areas with more stable internet access
are better positioned to adopt Artificial Intelligence (AI) in governance processes, while those dependent on
unstable networks risk being left behind. For instance, one respondent noted, “AI could speed up services, but
in our district, the internet is often down for hours, so the system would not function consistently.” This statement
underscores that enthusiasm for digital transformation exists but is tempered by infrastructural barriers.
In terms of human resource capacity, the data indicates that only 37 percent of local government employees
possess basic digital data management skills. This limited digital literacy constrains the ability of staff to interact
effectively with AI-driven systems. The situation is further complicated by the uneven distribution of skills, with
a few trained staff concentrated in urban centers and very limited expertise in rural administrative offices. Several
respondents expressed concern, with one stating, “We support AI in principle, but we need training first. Right
now, even managing spreadsheets is difficult for many colleagues.” Such reflections suggest that without
significant capacity-building initiatives, AI adoption will be superficial and ineffective.
Perceptions toward Artificial Intelligence itself present a mix of optimism and apprehension. A strong majority
of respondents, around 72 percent, expressed confidence that AI could improve bureaucratic efficiency,
especially in speeding up document verification and reducing manual workloads. As one participant put it, “If
AI can cut down the time we spend on paperwork, it will give us more time to actually serve the public.”
However, 58 percent voiced fears about job security, worrying that automation might replace human roles.
Another respondent cautioned, “We do not want AI to mean fewer jobs for our people; we want it to help us, not
to take over.” These mixed perceptions illustrate both the perceived utility of AI and the anxieties it triggers,
especially in a region where employment opportunities are already limited.
The findings suggest that while the optimism for AI-driven governance is high, infrastructural deficiencies and
limited digital skills constitute significant obstacles. Furthermore, concerns about displacement must be
addressed proactively through communication strategies and assurances of hybrid human AI leadership models
that emphasize augmentation rather than replacement of human roles.
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Application of Algorithmic Leadership in the Global Context
A comparative review of international case studies provides critical insights into the application of algorithmic
leadership in different governance contexts. Estonia has become a leading example by implementing the X-Road
platform, which enables integration of public data systems and has shortened bureaucratic processes by up to 70
percent. This success is attributed not only to technological innovation but also to a supportive regulatory
environment and consistent public investment in digital literacy.
The United Arab Emirates presents another model by institutionalizing its AI Strategy 2031, which included the
creation of a dedicated position of Minister of State for Artificial Intelligence. This high-level political
appointment facilitated cross-sectoral coordination and ensured that AI adoption became a strategic priority
across all branches of government. By elevating AI leadership to ministerial level, the UAE signaled both
domestically and internationally that AI is not merely a technical matter but a pillar of national governance
strategy.
South Korea demonstrates the application of AI in urban planning, particularly in optimizing public transport
systems and reducing congestion in major cities. By embedding predictive analytics into transport policy, the
government has created tangible improvements in the daily lives of citizens. Such examples highlight that the
success of algorithmic leadership depends on three intersecting elements: robust technological infrastructure,
effective human capital development, and strong regulatory and institutional frameworks.
Comparing these cases with Papua Pegunungan underscores the gaps. While Estonia, the UAE, and South Korea
had relatively high baseline infrastructure and human capital before adopting AI, Papua begins from a position
of infrastructural and literacy constraints. This comparison suggests that adopting AI in Papua requires a phased
and adaptive strategy, tailored to local capacities and cultural contexts rather than attempting wholesale
replication of global models.
The Potential of Algorithmic Leadership in Papua Pegunungan DOB
The combination of quantitative and qualitative data indicates that the adoption of AI-driven algorithmic
leadership in Papua Pegunungan has the potential to generate meaningful transformations in governance.
First, AI has the capacity to enhance administrative efficiency. Processes such as document verification,
budgeting, and the distribution of social assistance could be automated, significantly reducing the time currently
spent on manual procedures. Respondents noted that “verification of social assistance now takes weeks, but with
AI it could be done in hours.” This efficiency would free up human resources for higher-order tasks such as
community engagement and policy planning.
Second, AI promises improvements in transparency and accountability. Real-time monitoring systems could
minimize opportunities for corruption by creating immutable records of transactions and decisions. In a context
where accountability deficits are a major concern, such systems could rebuild public trust. As one local official
emphasized, “If people can see the process in real time, they will trust that funds are being used correctly.” This
illustrates how algorithmic systems can strengthen the social contract between government and citizens.
Third, predictive analytics could improve the quality of public policy, particularly in logistics planning for
remote regions. For example, AI models could anticipate supply chain needs ahead of the rainy season or before
potential natural disasters, ensuring that resources reach communities on time. This capacity is especially
relevant in Papua, where geographical barriers frequently disrupt aid and service delivery. A respondent
highlighted this need, stating, “When the rains come, villages are cut off. If AI can help us predict and prepare
earlier, it will save lives.”
Taken together, these findings indicate that algorithmic leadership in Papua Pegunungan can address
inefficiencies, reduce corruption risks, and enhance resilience in public service delivery. However, the
effectiveness of these benefits will depend on addressing infrastructural and literacy constraints, as well as
ensuring that AI is implemented as a complement to, rather than a replacement for, human decision-making. A
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hybrid model that balances algorithmic intelligence with local human judgment appears to be the most viable
path forward for ensuring inclusive, culturally sensitive, and sustainable governance in the region.
Challenges of Implementation
The field findings reveal that the adoption of algorithmic leadership in Papua Pegunungan faces several
interconnected challenges that can be grouped into infrastructural, human capital, and socio-ethical dimensions.
The first and perhaps most pressing challenge is the limitation of infrastructure. Internet connectivity across the
province is not yet evenly distributed, with urban centers enjoying relatively more stable access while remote
districts continue to depend on weak or intermittent satellite connections. Such unequal distribution hampers the
integration of AI-based governance systems, as the effectiveness of algorithms depends on a consistent and
reliable flow of data. Furthermore, the lack of local data centers limits the storage, security, and availability of
sensitive governmental information. Without adequate data infrastructure, the province risks relying on external
servers, which may raise concerns regarding data sovereignty and long-term sustainability.
The second challenge relates to the digital divide among civil servants and local administrators. There is a
significant disparity between the digital competencies of staff working in urban government offices and those in
remote districts. While some employees are familiar with basic digital tools, many others lack even foundational
skills in data management, let alone the capacity to interact with AI-driven systems. This gap is compounded by
the absence of continuous training programs. Without structured capacity-building initiatives, adoption of new
technologies is slow and fragmented. As one respondent explained, “We are enthusiastic about technology, but
without proper training, many of us cannot use it effectively.” This highlights the need for sustained investment
in human resource development rather than one-off workshops.
The third challenge is rooted in ethical and social considerations. Community members have voiced concerns
about the potential for algorithmic bias, particularly in the distribution of social assistance and other welfare
programs. If AI systems are not designed with sensitivity to local contexts, they risk reinforcing inequalities or
creating new forms of exclusion. Moreover, there is visible resistance from some traditional leaders, who fear
that increased reliance on technology may erode the role of social interaction and cultural practices in community
decision-making. A village elder expressed this apprehension by stating, “Machines cannot understand our
customs. Decisions should always involve people who know our traditions.” Such sentiments underscore the
importance of ensuring that AI adoption is seen not as a replacement for human and cultural values but as a
complementary tool.
Together, these challenges illustrate that the adoption of AI in governance cannot be treated as a purely
technological project. It must instead be approached as a socio-technical transformation that requires investment
in infrastructure, systematic human capital development, and careful negotiation with local cultural norms.
Hybrid Leadership Model (Artificial Intelligence and Human Leadership)
The findings of this study suggest that a hybrid leadership model represents the most viable pathway for
integrating Artificial Intelligence into governance in Papua Pegunungan. This model recognizes that while AI
can provide advanced analytical and predictive capabilities, human leaders remain essential in interpreting,
contextualizing, and legitimizing decisions within local socio-cultural realities.
Within this model, Artificial Intelligence functions as an analyst and recommender. It is tasked with processing
large volumes of data, identifying trends, generating insights, and offering policy recommendations. For
example, predictive analytics can be used to anticipate logistical needs during the rainy season or to detect
anomalies in budget allocations that may indicate potential fraud. In this capacity, AI operates as a support
system rather than as a substitute for leadership, ensuring that decisions are informed by accurate, timely, and
comprehensive data.
Human leaders, by contrast, maintain their position as the ultimate decision-makers. They are responsible for
weighing AI-generated recommendations against social, cultural, and ethical considerations. In the context of
Papua, this means integrating AI insights with local wisdom, community participation, and the lived realities of
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citizens. Human leaders bring the flexibility, empathy, and cultural sensitivity that AI systems inherently lack.
Their role is to ensure that technological solutions align with the aspirations and traditions of the community
while preserving social legitimacy.
An equally critical element of the hybrid model is the integration of local knowledge into AI systems. For AI to
be accepted and trusted, it must reflect the cultural identity of the communities it serves. This could include
adapting systems to local languages, aligning decision-making processes with traditional calendars, or designing
interfaces that mirror community patterns of interaction. By embedding cultural markers into digital tools, AI
systems become more relatable and reduce resistance from traditional authorities.
This approach resonates strongly with the principle of “human-in-the-loop” governance recommended by the
OECD (2021). Under this principle, AI is used to augment rather than replace human judgment, ensuring
inclusivity, transparency, and accountability. In Papua Pegunungan, adopting such a model could help overcome
community fears of being marginalized by technology while also ensuring that AI strengthens, rather than
disrupts, existing governance practices.
In summary, the hybrid leadership model positions AI as a powerful analytical tool and human leaders as
custodians of culture and legitimacy. By combining the strengths of both, Papua Pegunungan can build a
governance system that is not only technologically advanced but also socially inclusive and culturally grounded.
This integration offers a pathway toward governance that is efficient, accountable, and resilient, while remaining
responsive to the unique context of Papua.
CONCLUSION AND IMPLICATIONS
Conclusion
This study demonstrates that algorithmic leadership based on Artificial Intelligence (AI) holds significant
potential to enhance the effectiveness of governance in the newly established Papua Pegunungan Province. The
findings suggest that the integration of AI into public administration can accelerate bureaucratic processes,
reduce redundancy, and improve overall efficiency. By enabling real-time monitoring systems, AI also has the
capacity to strengthen transparency and accountability, creating greater public trust in government institutions.
Furthermore, the application of predictive analytics allows governments to anticipate future challenges, optimize
logistics, and design more proactive public policies.
Nevertheless, the study also reveals that full-scale implementation faces considerable barriers. Unequal digital
infrastructure, limited technological literacy among civil servants, and social-cultural resistance remain
persistent obstacles. These challenges indicate that the adoption of AI cannot simply be transplanted from global
models but must be carefully adapted to the realities of Papua. For this reason, the hybrid leadership model,
which positions AI as a decision-support system and human leaders as the ultimate decision-makers, emerges as
an adaptive and context-sensitive solution. This hybrid approach not only balances technological innovation with
local wisdom but also ensures that governance in Papua Pegunungan is inclusive, legitimate, and sustainable.
Theoretical Implications
Theoretically, this research contributes to the expansion of the concept of algorithmic leadership by introducing
the dimension of cultural adaptability into the framework of AI governance. Previous studies have often treated
algorithmic systems as universally applicable, overlooking the socio-cultural dynamics of local governance. By
situating AI adoption in Papua Pegunungan, this study highlights the importance of embedding cultural values
and indigenous traditions into algorithmic models to ensure social legitimacy.
The research also offers a conceptual model that is particularly relevant for regions with limited infrastructure
and ethnographic diversity. Unlike urban or technologically advanced settings, Papua Pegunungan presents a
case where the intersection of geography, technology, and culture must be carefully managed. This model
demonstrates that algorithmic leadership is not merely about efficiency but also about cultural alignment and
legitimacy. Finally, the study integrates the principle of “human-in-the-loop” in public decision-making,
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showing that human leaders must remain central in interpreting, contextualizing, and legitimizing AI
recommendations.
Practical Implications
In practical terms, the findings hold implications for multiple stakeholders. For local governments, the
implementation of AI can serve as a strategic instrument to improve the quality of public services, ranging from
administrative record-keeping to the distribution of social assistance. Governments that strategically integrate
AI into their workflows stand to reduce inefficiencies and strengthen citizen trust.
For technology developers, the study emphasizes the importance of adapting AI algorithms to local languages,
cultural contexts, and modes of interaction. Systems that reflect the identity and practices of the community are
more likely to gain acceptance and reduce resistance. Developers must therefore move beyond generic designs
and instead produce culturally responsive AI applications.
For local communities, the research highlights that improvements in digital literacy will be a determining factor
in accelerating the acceptance and effective use of technology. Citizens who understand the functioning and
limitations of AI are more likely to trust its outcomes, while also developing the skills to hold governments
accountable for the use of technology in decision-making.
Policy Implications
The study also offers policy-level recommendations. Governments at both the national and regional levels must
prioritize investments in digital infrastructure, including the expansion of internet coverage, the establishment
of local data centers, and the strengthening of cybersecurity systems. Without such infrastructural investments,
the promises of AI will remain theoretical.
Equally important is the development of comprehensive education and training programs for civil servants.
Sustainable adoption of AI requires continuous capacity-building, not isolated training workshops. By equipping
public officials with the necessary skills, governments can ensure that AI is used effectively and responsibly.
The research further suggests the need for clear ethical regulations governing the use of AI in public decision-
making. Guidelines on transparency, accountability, and anti-bias mechanisms must be established to prevent
misuse and to ensure that AI serves the interests of all citizens, particularly vulnerable communities.
Finally, the implementation of pilot projects in selected sectors such as civil registry management or social
assistance distribution offers a practical approach to testing AI systems before full-scale deployment. These
pilots would provide valuable lessons, build institutional learning, and allow for incremental adjustments that
reduce risks and improve outcomes.
Recommendations for Future Research
Future studies could build upon this research by empirically testing the effectiveness of the hybrid leadership
model in other sectors of local governance. Comparative analysis with other New Autonomous Regions outside
Papua would also be valuable for identifying common success factors and shared challenges. Such comparative
studies could help to refine the model and enhance its applicability across diverse contexts.
Longitudinal research is also recommended to track the long-term impacts of AI adoption on governance quality,
citizen trust, and socio-economic outcomes in Papua Pegunungan. Monitoring these dynamics over time will
provide a deeper understanding of how algorithmic leadership evolves in practice and how it interacts with local
political and cultural systems.
Algorithmic leadership should not be viewed merely as a technological transformation but rather as a paradigm
shift in the way governance is conceived and practised. In Papua Pegunungan, the success of AI implementation
will ultimately depend on the capacity to integrate artificial intelligence with cultural intelligence. This synergy,
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 1915
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between digital systems and indigenous wisdom, offers a pathway toward governance that is not only effective
and transparent but also inclusive, resilient, and sustainable.
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