Citation
Ilo, K. O., Chris-Sanctus, O. E., Ohabuenyi, J., & Oluebube, U. R. (2026). Investigating the Emergence of AI Startups and Public-Sector Administration in Nigeria. International Journal for Social Studies, 12(1), 1–31. https://doi.org/10.26643/ijss/2026/1
1Ilo, Kingsley Obumunaeme
Department
of Political Science, Faculty of the Social Sciences,
University
of Nigeria, Nsukka
Email:
kingsley.ilo@unn.edu.ng
https://orcid.org/0000-0002-4712-9371
2**Ogu Esomchi Chris-Sanctus**
Department
of Political Science, Faculty of the Social Sciences,
University
of Nigeria, Nsukka
Email:
esomchi.ogu@unn.edu.ng
Corresponding author
https://orcid.org/0000-0002-9628-4989
3Jonas Ohabuenyi
Department
of Political Science, Faculty of the Social Sciences,
University
of Nigeria, Nsukka
Email:
Jonas.ohabuenyi@unn.edu.ng
4Ukairo Rejoice Oluebube
Department
of Political Science, Faculty of the Social Sciences,
University
of Nigeria, Nsukka
Email:
rejoice.ukairo.241497@unn.edu.ng
Abstract
This study investigates the
integration of AI into public sector administration in Nigeria from 2013 to
2023. The study adopts the neo-institutional theory, integrating historical and
sociological institutionalism to analyse how institutional structures, cultural
norms, and path dependence shape policy outcomes. Adopting an ex post facto
research design, the research utilises documentary data collection and content
analysis to analyse the research questions. The findings revealed that
government investment in AI R&D during the review period was minimal,
fragmented, and lacking in strategic direction. This insufficient investment
directly correlated with Nigeria’s peripheral and reactive participation in
global AI governance forums. In this light, the government should foster
structured public-private partnerships to integrate AI startups and
capacity-building outcomes into public sector governance. To bridge the gap
between private sector innovation and public sector adoption, the government
must create institutionalised pathways for AI startups and capacity-building
initiatives to contribute to governance reform. This requires moving beyond ad
hoc collaborations to structured, sustainable partnerships.
Keywords: AI, Public sector governance, Government investment, Innovation
Introduction
The emergence
and rapid evolution of artificial intelligence (AI) over the past decade has
transformed global governance frameworks, presenting both unprecedented
opportunities and significant challenges. Globally, AI has begun to reshape
decision-making processes, influence state sovereignty, redefine accountability
mechanisms, and generate new domains of power and inequality (Tinnirello,
2021). In Nigeria, however, the integration of AI into governance remains
underdeveloped, fragmented, and poorly regulated, reflecting broader trends of
digital inequality and institutional inertia in the Global South (UNDP, 2021;
Oxford Insights, 2021).
Between 2013
and 2023, Nigeria witnessed a surge in digital technologies, but AI adoption in
public administration has been slow, largely driven by private sector
initiatives rather than coordinated government policy (Akanbi & Adebayo,
2021; Afolabi, 2022). This technological gap is compounded by structural
deficits in digital infrastructure, weak regulatory institutions, low AI
literacy among policymakers, and the absence of a binding national framework
for AI governance (NITDA, 2020; Olayinka & Mohammed, 2020). The National
Artificial Intelligence Policy (Draft) of 2020 was a step towards better
regulation, but implementation is still on hold, and there are almost no ways
to enforce it.
The lack of a
robust governance framework has exposed Nigeria to a range of ethical,
political, and social risks, including algorithmic bias, surveillance
overreach, data insecurity, and threats to democratic accountability (Raji
& Buolamwini, 2019; Olatunji, 2020; Akinwale, 2021). Moreover, the
influence of foreign AI models and big tech platforms, often operating without
sufficient oversight, undermines national sovereignty and perpetuates digital
dependency. The consequence is a regulatory vacuum that inhibits innovation
while failing to protect public interests.
Additionally,
AI’s potential to enhance transparency, citizen engagement, and service
delivery in Nigeria's governance structures is largely unrealised due to
institutional resistance, political interference, and systemic corruption
(Idowu & Adebayo, 2023; Okon & Ibrahim, 2021). Despite its promises, AI
is being deployed unevenly, exacerbating existing governance disparities and
marginalising already vulnerable populations (Mbah & Okocha, 2022). The
result not only undermines public trust but also jeopardises the legitimacy of
democratic institutions in an era where technology increasingly mediates
state-citizen relations (Floridi, 2019; Okolo & Edeh, 2022).
While global
actors like the UN and World Economic Forum have called for inclusive and
accountable AI governance (WEF, 2020; UNDP, 2021), Nigeria’s approach remains
reactive rather than strategic. With the country poised to become a leading
tech hub in Africa, the absence of coherent AI policy risks placing it at the
periphery of global digital governance regimes (World Bank, 2022). There is an
urgent need to examine how Nigeria can develop a locally relevant, ethically
sound, and globally informed AI governance framework that addresses the
political, legal, and societal challenges posed by artificial intelligence. In
light of this, this study therefore seeks to critically analyse the integration
of artificial intelligence into public governance in Nigeria from 2013 to 2023.
Development of AI Startups, Capacity Building
Programs and the Integration of AI into Public Sector Governance
In analysing
the development of Artificial Intelligence (AI) startups and capacity-building
programmeses in Nigeria, a robust critical literature has emerged that
questions the sustainability and equity of the current innovation model.
Olanrewaju (2020) offers a trenchant critique of the venture capital
(VC)-driven startup model, warning of a “venture capital trap”. He argues that
the need to provide outsized returns to foreign investors forces startups to
prioritise rapid scaling and monetisation over deep, sustainable
problem-solving. Consequently, fintech dominates Nigeria’s innovation
landscape, as it offers the clearest and fastest path to profitability, while
AI applications in sectors such as agriculture, healthcare, and education,
where market failures are most profound but social value is immense, remain
chronically underfunded. This distortion has led to an innovation landscape
that serves the banked urban elite rather than the wider population.
Complementing
this economic critique, Eze (2023) examines the sociological impact of
capacity-building programmes. . While acknowledging their role in skill
development, she argues that the bootcamp model, with its intensive, short-term
focus, produces what she calls “API technicians” rather than holistic
innovators. These individuals are highly skilled at applying pre-existing
technologies (often built on foreign APIs) but lack the theoretical grounding
required for fundamental innovation or critical interrogation of the
technologies they deploy. This creates a technically competent workforce that
is efficient in implementation but may be ill-equipped to question the ethical
and societal implications of their work.
The literature
on the integration of AI into public sector governance is equally subject to
critical scrutiny, particularly concerning issues of surveillance, ethics, and
institutional control. Mustapha (2021) applies a Foucauldian lens to analyse
the adoption of AI in Nigeria’s security apparatus, arguing that technologies
such as facial recognition and predictive policing are not neutral tools for
efficiency but instruments of a new “digital panopticon” that amplifies the
state’s capacity to surveil and control citizens. He warns that, in a context
marked by weak institutional safeguards and a history of authoritarian rule,
deploying such technologies without robust legal oversight presents grave risks
to democracy and human rights.
Beyond the
security sector, Chikwe (2022) critiques the notion of AI as a universal
solution for public sector inefficiency. Her research on attempts to integrate
AI into Lagos’ service delivery systems reveals a pattern of
“techno-solutionism”, where deeply rooted political and bureaucratic issues,
such as corruption, inertia, and lack of accountability, are reframed as
technical problems awaiting a software fix. She found that many AI systems
failed because they could not navigate the complex realities of governance or
were co-opted by existing power structures, thus perpetuating bias and
inefficiency under a veneer of digital modernisation.
According to
Okonkwo (2021), investment in Nigerian tech startups grew exponentially during
the decade, with a growing share directed toward companies leveraging AI and
machine learning in their core products. Startups such as Ubenwa (AI for infant
health diagnostics via cry analysis), Crop2Cash (AI for agricultural finance
and credit scoring), and Kudi (an AI-powered payment and chatbot platform) are
often cited as examples of how AI has been applied to solve pressing local
problems in healthcare, agriculture, and fintech (Ezeife, 2020).
However,
significant challenges persist. A major theme across the literature is the
“brain drain” of AI talent, as top researchers and engineers are recruited by
multinational corporations (MNCs) or migrate abroad, leaving local startups
struggling in a “talent war” they are often ill-equipped to win (Bala, 2020).
Another consistent critique concerns the concentration of innovation in
fintech, driven by clearer profit incentives at the expense of high-impact
social sectors such as education, climate change, and public service, which
promise slower financial returns but higher social value (Chiedu, 2022).
Limited access to clean, well-annotated datasets, poor data governance, and the
high cost of computing power further constrain innovation (Momoh, 2021).
The literature
on AI integration into public governance during this decade characterises the
period as one of pilot projects, rhetorical ambition, and systemic inertia,
where full-scale transformation remained more aspirational than real. Shehu
(2022) notes that while the potential benefits of AI in governance were widely
discussed – enhanced service delivery, data-driven policymaking, fraud
detection, and improved surveillance – their translation into tangible,
nationwide implementations was minimal. Nwosu (2021) reports that most
Ministries, Departments, and Agencies (MDAs) continued to rely on legacy
systems and manual processes, hampered by resistance to change and a
significant digital literacy gap within the civil service.
In selective
cases, however, the financial sector demonstrated the most notable progress,
with the Central Bank of Nigeria (CBN) and commercial banks deploying AI and
machine learning for fraud detection, credit scoring, and regulatory compliance
(Ojukwu & Danladi, 2020). In contrast, the use of AI in the security sector
raised serious ethical concerns. Agencies such as the Nigeria Police Force and
the Department of State Services (DSS) experimented with facial recognition and
predictive analytics for crime prevention and surveillance, but these
initiatives often lacked transparency and accountability, potentially leading
to violations of civil liberties (Ibrahim, 2023).
Scholars such
as Mohammed (2022) identify poor data infrastructure as a major impediment to
effective AI integration. Nigeria’s public sector is plagued by data silos and
weak inter-agency cooperation, making it difficult to build interoperable
systems for AI-driven governance. The absence of a robust data protection law
for most of the decade created both ethical and legal vacuums, deterring
citizens and institutions from sharing data for public projects (Obi, 2021).
The passage of the Nigeria Data Protection Act in 2023, however, is viewed as a
potential turning point (Bello, 2023).
The scholarly
discourse from 2013 to 2023 consistently draws connections between four major
themes – government investment, startup development, global participation, and
public sector integration – presenting them as interdependent components of a
single national ecosystem. Talabi (2019) argues that the limited scale of
government investment in AI research and development (R&D) created a
foundational weakness that constrained homegrown innovation, forcing Nigeria to
depend heavily on foreign technologies. This dependency limited Nigeria’s
influence in global AI governance and weakened its bargaining position on
international standards (Ismaila, 2023).
Conversely,
private sector and NGO-led initiatives acted as compensating forces.
Capacity-building organisations such as Co-Creation Hub (CcHub), Data Science
Nigeria, and AICE (AI Career Essentials) Africa played crucial roles in
training developers, fostering innovation, and building communities of practice
(Sanni & Adeleke, 2021). These hubs offered not only workspaces and
mentorship but also access to funding and industry networks (Oladipo, 2019).
By the latter
part of the decade, the cumulative impact of these initiatives began to filter
into the public sector, introducing much-needed technical expertise and
proof-of-concept for AI solutions (Faleti, 2023). This convergence between
grassroots innovation and top-down policy gradually prompted the Nigerian
government to formalise its engagement with AI through strategies such as the
National Digital Economy Policy and Strategy (NDEPS) and the draft National
Artificial Intelligence Policy (NAIP) (Musa & Bello, 2022).
Nevertheless,
scholars emphasise that Nigeria’s AI ecosystem remains fragmented and
underfunded, with limited synergy between government, private enterprise, and
academia (Shehu, 2022). As a leading voice in Africa, Nigeria needs to
decolonise approaches to AI governance and strengthen civil society capacity
for international advocacy, which is essential for a more balanced and
participatory global AI governance ecosystem.
It is evident
that non-governmental initiatives are playing a critical role in addressing
some of the systemic gaps in Nigeria's AI ecosystem. One of the most impactful
capacity-building efforts in Nigeria has come from non-governmental initiatives
like Data Science Nigeria (DSN). Founded in 2016, DSN has trained over 100,000
Nigerians in data science, machine learning, and AI through bootcamps, online
courses, and high-school outreach programmes (DSN, 2023). Its "AI for
Good" framework emphasises local relevance and ethical standards, with
projects ranging from fraud detection in microfinance to malaria diagnosis
using image recognition. These programmes are particularly important in the
Nigerian context, where formal higher education institutions struggle with
outdated curricula and limited access to computational resources. DSN's use of
open-source platforms and community mentoring has allowed thousands of young
Nigerians—many without formal STEM degrees—to acquire industry-relevant skills.
However, these grassroots efforts operate in silos, often without long-term
government integration or funding support. While DSN's model has attracted
attention from international donors such as Microsoft and the Bill &
Melinda Gates Foundation, it has not been institutionalised into national
educational and industrial policy. As such, its success, though remarkable,
highlights the gap between civil society-led innovation and state-led capacity
building in the AI sector.
The literature
review also includes a "Regional Comparison and Implications for Nigeria's
AI Strategy", which provides a critical perspective on Nigeria's slow
progress. Several African countries have already begun to assert themselves in
regional and global AI conversations, drawing attention to Nigeria's
comparative lag. For instance, Egypt and Tunisia have released detailed AI
strategies that emphasise ethical use, investment attraction, and education,
while South Africa's national AI policy focuses on social inclusion and
economic competitiveness (AUDA-NEPAD, 2022). These countries have gained
recognition and collaboration from partners such as the United Nations
Educational, Scientific and Cultural Organization (UNESCO), the Organisation
for Economic Co-operation and Development (OECD), and the European Commission,
cementing their presence in shaping global technology governance frameworks.
Despite being Africa's largest economy, Nigeria has yet to take a leadership
role in AI governance, either regionally or globally. Its absence from key
platforms such as the UNESCO AI Ethics Committee or Global Partnership on
Artificial Intelligence (GPAI) raises concerns about missed opportunities to
advocate for Global South perspectives on algorithmic transparency, data
sovereignty, and digital inclusion (Floridi, 2021). As other African nations
build institutional frameworks for AI, Nigeria risks losing its potential to
influence continental AI norms under the African Union's Digital Transformation
Strategy. The lack of urgency in formulating and implementing an AI governance
model may therefore further marginalise Nigeria in a rapidly evolving
international policy arena. The review suggests that Nigeria's current policy
vacuum directly undermines its capacity to shape or even respond to emerging
global norms around AI development, deployment, and regulation. This is further
exacerbated by the fragmented public-private AI innovation ecosystem in
Nigeria. According to the review, Nigeria has a growing number of public and
private players in its AI ecosystem, but the landscape remains fragmented and
lacks national coordination. State-level initiatives like the Lagos State
Innovation Council have promoted innovation clusters and sponsored hackathons,
while private tech hubs like Andela and CcHUB have contributed to AI awareness
through bootcamps and startup incubation. However, these efforts are often
disconnected from national policy or governance frameworks (NITDA, 2022). The
absence of a centralised AI coordination body has led to redundant programmes
and missed opportunities for synergy. Unlike Rwanda's Centre for the Fourth
Industrial Revolution, which serves as a national AI policy hub in partnership
with the World Economic Forum, Nigeria has no singular institutional home for
AI leadership. This fragmentation undermines the country's ability to develop
national benchmarks and effective public-private partnerships for AI
governance. The review notes that the role of national institutions like NCAIR
is vital, but their impact remains limited due to underfunding, lack of
strategic focus, and minimal engagement with private sector actors. As such,
NCAIR functions more as a symbolic institution rather than a functional driver
of AI commercialisation or policy development. The literature suggests that a
concerted effort across all sectors, guided by a unified national strategy, is
essential for Nigeria to overcome its current challenges and fully participate
in global AI governance.
Finally, a
synthesising body of literature critiques the entire ecosystem through the lens
of data colonialism and epistemic injustice. Scholars like Adamu (2023) and the
collective work of the African Feminist AI Network (AFAIN, 2023) argue that the
current trajectory of AI development in Nigeria risks perpetuating existing
inequalities. Adamu (2023) contends that the datafication of Nigerian society
primarily serves to create a new resource for extraction by global tech
corporations, a modern form of colonialism where data is the raw material. He
points to the lack of robust local data governance as a critical enabler of
this extraction. Meanwhile, AFAIN (2023) argues that the AI ecosystem, from its
investors to its developers, is dominated by a male, urban, technocratic
worldview. This results in AI systems that are blind to the needs and
experiences of women, rural populations, and other marginalized groups, thereby
encoding existing social biases into automated decision-making systems. Their
work calls for a feminist and decolonial approach to AI that centres situated
knowledge, participatory design, and equity from the outset, rather than as an
afterthought. This concluding critical perspective fundamentally challenges the
dominant narratives of progress and argues that without a conscious and radical
shift in approach, AI in Nigeria will not only fail to deliver on its promises
but will actively reinforce the very problems it claims to solve.
Theoretical Framework
This study
adopts neo-institutionalism theory, particularly historical and sociological
institutionalism, as its theoretical framework. Neo-institutionalism theory
emerged as a response to behavioural and rational-choice models that ignored
the deep influence of institutions on social and political life. It views
institutions not only as formal rules but also as enduring systems of norms,
routines, and symbols that shape behaviour. March and Olsen (1984) and Hall and
Taylor (1996) emphasised that institutions create stable patterns of
interaction, influencing how actors define interests and make decisions. Thus,
neo-institutionalism highlights the reciprocal relationship between
institutional structures and human agency within political and organisational contexts.
The three main branches are rational choice institutionalism, historical
institutionalism, and sociological institutionalism.
The fundamental
concept of Historical Institutionalism (HI) is that institutions are historical
constructs arising from particular contexts, which, once formed, engender
"path dependence," limiting future political options and unevenly
distributing power (Pierson, 2000; Thelen, 1999). In contrast, sociological
institutionalism (SI) asserts that institutions are cultural-cognitive
frameworks that delineate what is deemed appropriate, meaningful, and
"rational" (March & Olsen, 1989; Powell & DiMaggio, 1991).
Key scholars in
sociological institutionalism are John W. Meyer, who posited that the
institutions will act on a global cultural stage (Meyer 1977). The notion of
institutional isomorphism was later extended by Paul DiMaggio and Walter Powell
to account for why organisations resemble each other more and more as cultural
forces push them toward greater homogeneity (DiMaggio & Powell, 1983). W.
Richard Scott also contributed by drawing our attention to the fact that
institutions are based on cultural-cognitive, normative and regulatory pillars
(Scott 1995).
In historical
institutionalism, scholars focus on how past institutional choices constrain
present political outcomes. Key proponents include Theda Skocpol, known for
analysing how state structures shape policy and revolutions (Skocpol, 1979).
Kathleen Thelen and Sven Steinmo expanded the approach by explaining how
institutions evolve gradually through processes like layering and drift
(Thelen, 1999; Steinmo, 2008). Paul Pierson emphasised path dependence and
increasing returns, showing how early policy decisions can lock political
systems into long-term trajectories (Pierson, 2000).
Neo-institutionalism
entered political science as scholars sought frameworks capable of explaining
persistent governance patterns, state behaviour, and policy continuity in ways
that behaviouralist models could not (Peters, 2012). Historical institutionalism
became central to comparative politics because it explained why countries
follow different developmental paths based on earlier choices (Pierson, 2000).
Sociological institutionalism gained influence in political science for showing
how public institutions transmit norms and shape identities, helping scholars
understand legitimacy, bureaucratic behaviour, and governance cultures
(DiMaggio & Powell, 1991). By the 1990s, both strands had become
foundational in explaining policy processes, state evolution, and global
governance.
Sociological
and historical institutionalism both agree that institutions affect how people
act, what they like, and how they understand things. Sociological
institutionalism underscores that norms, values, and collective meanings confer
legitimacy to institutions, whereas historical institutionalism concentrates on
their temporal resilience and the cumulative impact of antecedent
decisions. Combining both gives us a better idea of how cultural
legitimacy and historical structures work together to affect institutional
stability and change (Hall & Taylor, 1996; Peters, 2019).
Basic Assumptions of Neo-Institutionalism:
1. Institutions
consist of both formal rules and informal social norms that regulate behavior.
2. Human
actions are guided by institutional contexts rather than purely rational
self-interest.
3. Institutions
provide stability, predictability, and shared meaning to social and political
life.
4. Cultural
legitimacy and societal expectations sustain institutions over time.
5. Historical
sequences and early decisions constrain future institutional possibilities
(path dependence).
6. Institutional
change is gradual, often occurring through adaptation rather than replacement.
7. Institutions
simultaneously constrain and enable actors within political and organizational
settings.
In governance studies,
neo-institutionalism elucidates the influence of institutional legacies and
cultural frameworks on reform outcomes.
Lowndes and Roberts (2013) say that institutions include not only formal
structures but also informal beliefs, routines, and practices that affect how
policies are carried out. This viewpoint
elucidates the reasons behind the resistance to governance reforms, especially
in developing contexts, or their gradual evolution. Neo-institutionalism shows that for
institutional change to last, both structural and cultural changes must happen.
It does this by connecting historical continuity with sociocultural legitimacy.
Neo-institutionalism
also deepens our understanding of how organizations respond to social pressures
and international norms by implementing new technologies or policies. According to DiMaggio and Powell (1991), institutional
isomorphism is the process by which organizations frequently adopt external
norms in order to seem legitimate rather than effective. This makes it clearer why innovations or
reforms propagate throughout systems despite disparate local contexts. It emphasizes that social expectations,
professional models, and international norms rather than just logical planning
are frequently the driving forces behind institutional adaptation.
The theory also
stresses the importance of looking at how current institutional arrangements
work with new ideas in governance.
Peters (2019) and Hall and Taylor (1996) contend that institutional
change is contingent upon the equilibrium between persistence and
adaptability. This balance decides how
well institutions can take in new ideas without losing their legitimacy. Neo-institutionalism offers a framework for
comprehending the evolution of governance practices, demonstrating that
enduring reform necessitates the alignment of new institutional logics with
established norms, historical trajectories, and culturally accepted frameworks.
Methodology
Research Design and Methods of Data Collection
The study is the ex
post facto research design. According to Kerlinger (1973), ex post facto
research involves the study of "causal relationships between independent
and dependent variables when manipulation of the independent variable is not
possible." Furthermore, the study adopted a documentary method of data
collection. The documentary approach is classified as an indirect methodology,
relying solely on secondary sources for information.
Methods of Data Analysis
The study relied on
content analysis for its method of data analysis. Content analysis is a method
of data analysis widely used in various research fields, including sociology,
psychology, media studies, marketing, and political science. It involves systematically
examining qualitative data to identify patterns, themes, or trends within the
content. The process typically begins with clearly defining research
objectives, which serve as the foundation for subsequent steps. Researchers
then select the type of content they want to analyze, which can range from text
and audio to video and images.
DEVELOPMENT OF AI STARTUPS AND CAPACITY
BUILDING PROGRAMS AND THE INTEGRATION OF AI INTO PUBLIC SECTOR GOVERNANCE
Between 2013 and 2023,
Nigeria experienced one of the most paradoxical trajectories in Africa’s
emerging artificial intelligence (AI) landscape: while the private sector and
non-state actors recorded notable advances in AI entrepreneurship, capacity
building, and innovation-driven research, federal ministries, departments, and
agencies (MDAs) continued to demonstrate limited absorption, minimal
institutional adoption, and low integration of AI into governance systems. This
chapter evaluates this persistent gap, providing an evidence-based analysis
grounded in Neo- institutionalism theory which helps explain why state
structures remain slow to internalize technological innovations despite
external pressures and domestic ecosystem growth.
Drawing on the four
strands of evidence provided AI startup proliferation, training programs,
academic capacity production, and innovation hubs this chapter demonstrates
that Nigeria’s state institutions largely remain locked into traditional
bureaucratic routines, paper-based workflows, and static administrative norms
that hinder their ability to adopt AI tools at scale. Meanwhile, non-state
actors have rapidly expanded the national AI capabilities, creating a widening
mismatch between Nigeria’s growing AI potential and the government’s limited
institutional utilization.
The analysis also
aligns with the broader research hypotheses that Development of AI Start Ups
and Capacity building Programs has not influenced the Integration of AI into
Public Sector Governance in Nigeria. Instead, non-state actors have
disproportionately driven Nigeria’s AI progress without corresponding state
capacity to translate such strides into institutional transformation or global
influence.
5.1. Nigeria had over 25 Operating AI-driven
Start-ups Reflecting Growth in Private Sector Innovation Independent of
Government Integration and E-governance Assessment showed that Federal
Institutions still Relied Predominantly on Paper-based Processes
Nigeria’s private
sector has emerged as a major engine of AI innovation despite limited
government intervention. In 2023 alone, the country hosted over 25 operational
AI-driven start-ups (NITDA, 2023), spanning fields such as predictive finance,
health diagnostics, retail automation, agricultural analytics, and logistics
optimization. Many of these firms leveraged machine learning, natural language
processing, and deep learning technologies to solve localized challenges,
demonstrating the dynamism of Nigeria’s tech community.
However, this growth
occurred largely independent of government adoption, revealing a structural
disconnect between ecosystem innovation and public-sector implementation. While
private firms advanced rapidly, federal institutions continued operating with
manual administrative routines, paper-based workflows, and minimal
analytics-driven processes—conditions highlighted in the World Bank’s 2021
e-government report, which described Nigerian MDAs as operating with
“negligible use of predictive analytics” (World Bank, 2021).
This dual reality
suggests a classic case of institutional inertia, where existing bureaucratic
norms prevent the absorption of new technologies. Historical institutionalism
argues that early institutional choices such as adopting manual administrative
systems create “path dependencies” that make change difficult (Pierson, 2000).
Nigerian bureaucracies, built on 20th-century administrative models, have
struggled to transition into data-driven, automated governance frameworks. The
failure of MDAs to adopt AI solutions despite the availability of local startup
innovations indicates a weak state-industry linkage. This undermines:
1. Policy
experimentation, since government agencies rarely pilot or scale innovative
technologies.
2. Technology
diffusion, as innovations remain stuck in private sector domains without
regulatory or institutional support.
3. Global
competitiveness, because countries influencing AI governance, such as the U.S.,
China, U.K., Singapore, and UAE, derive legitimacy from strong domestic
deployment of AI tools.
4. 
Nigeria’s
ability to shape global AI norms remains limited without corresponding domestic
institutional adoption, reinforcing the research hypotheses.
Figure 5.1: Estimated AI Startups in Nigeria (2013–2023)
Compiled by author
Sources: (WIPO,2022; UNESCO,2021; OECD,2019)
Nigeria's private
sector has defied infrastructural and regulatory hurdles to become a vibrant
hub of AI innovation. As indicated in Table 1, the number of operational AI
startups grew from a mere 0.7% in 2013 to over 25% by 2023 (NITDA, 2023). These
enterprises are not mere replicas of Western models; they are developing
context-specific solutions. For example:
·
Health-tech: Start-ups like Ubenwa (using AI
for infant cry diagnosis) and Helium Health (using AI for hospital management
and diagnostics) are addressing critical gaps in the healthcare system.
·
Fintech: Companies such
as Paystack (before its acquisition) and Flutterwave leverage AI for fraud
detection and risk assessment, crucial for building trust in digital financial
ecosystems.
·
Agriculture: Hello
Tractor uses AI and IoT to connect tractor owners with smallholder farmers,
optimizing agricultural productivity.
This growth, however,
has occurred in a parallel universe to the public sector. The World Bank's
(2021) e-government assessment confirmed that Nigerian MDAs predominantly rely
on manual, paper-based processes with "negligible use of predictive analytics."
This duality is a textbook manifestation of historical institutionalism. The
early institutional choice to build a paper-based bureaucracy has created a
powerful path dependency. Transitioning to a data-driven model would require
not just new software, but a fundamental overhaul of administrative law,
record-keeping, and workflow design, a change that is prohibitively costly and
politically challenging for incumbent bureaucracies (Adegboye et al., 2021).
Consequences of the Disconnect:
·
Weak State-Industry
Linkage: The government is a major consumer of goods and services. Its failure
to procure local AI solutions stifles the growth of the startup ecosystem and
denies the government access to innovative, cost-effective tools.
·
Stifled Policy
Experimentation: Governments in the U.S. and U.K. routinely run
"regulatory sandboxes" and pilot projects with startups (Mergel,
2019). The absence of such mechanisms in Nigeria means that policy learning is
severely constrained.
5.2. Capacity-Building Programs and Limited
Adoption in Federal MDAs
Between 2016 and 2022,
Data Science Nigeria (DSN) organized some of the most extensive AI
capacity-building programs in Africa, training over 50,000 learners through
boot camps, hackathons, virtual learning tracks, and mentorship programs (Data
Science Nigeria, 2022). These programs built a significant pool of AI-skilled
youth capable of contributing to national development through data science,
machine learning, and big-data analytics.
Yet, evidence shows
that government agencies did not systematically absorb these trained
individuals, reflecting a major institutional-capability bottleneck. By 2023,
fewer than 10 federal MDAs had deployed operational AI tools, such as chatbots
or analytics systems (OECD, 2023). This illustrates that while talent
production increased, state structures lacked the systems, policies, and
incentives to absorb this talent into civil service roles.
Structural barriers to workforce absorption
1. Rigid recruitment
procedures
The Nigerian civil
service recruitment process emphasizes administrative qualifications rather
than technical competencies. This makes it difficult for AI-skilled graduates
to enter MDAs.
2. Lack of AI-specific
job roles
Until the drafting of
the 2023 National AI Strategy, MDAs had no defined AI career pathways—only
generic ICT roles that did not support analytics or automation tasks.
3. Budgetary
constraints
Personnel budget
ceilings and limited R&D allocations prevented agencies from hiring
specialized technical talents.
4. Absence of political
prioritization
Without top-level
directives mandating AI integration, agencies lacked incentives to redesign
their workforce structures.
Consequences for governance modernization
The low uptake of AI
tools and talent has profound consequences:
1. Low
analytical capacity in MDAs, limiting forecasting and evidence-based policy
design.
2. Limited
automation, resulting in inefficiency, corruption vulnerabilities, and delays.
3. Underutilization
of nationally available AI talent, reinforcing youth unemployment and brain
drain.
Weak state presence in
global AI diplomacy, as countries with strong domestic AI ecosystems are better
positioned to shape international norms (UNESCO, 2022).
Thus, even though
training initiatives expanded Nigeria’s human capital, the public sector lacked
the institutional absorption capacity needed to convert such talent into
governance transformation.
Table 5.2: Major AI Capacity-Building Programs
in Nigeria (2016–2023)
|
Programs |
Institution |
Years Active |
Estimated Trainees |
|
AI Boot camps |
Data Science Nigeria |
2016-2023 |
50,000+ |
|
AI career readiness |
Google AI Africa |
2018-2023 |
20,000+ |
|
Digital Academy |
NITDA |
2020-2023 |
15,000+ |
The period from 2016
saw an explosion in AI and data science capacity-building initiatives. As shown
in Table 2, organizations like Data Science Nigeria (DSN) and Google AI Africa
trained tens of thousands of Nigerians, creating a substantial pipeline of
AI-literate talent (Data Science Nigeria, 2022; Google, 2023). These programs
were crucial for democratizing access to AI skills and fostering a community of
practice.
Yet, this human capital
boom did not translate into a transformed public sector workforce. By 2023,
fewer than 10 federal MDAs had deployed operational AI tools like chatbots or
analytics dashboards (OECD, 2023). This failure of absorption can be attributed
to several structural barriers rooted in the institutional fabric of the civil
service:
1. Anachronistic
Recruitment and Career Structures: The civil service commission's recruitment
templates are frozen in time, emphasizing credentials in administration, law,
and the social sciences, with little room for specialized technical roles like "Machine
Learning Engineer" or "Data Scientist" (NITDA, 2022).
2. Budgetary
and Compensation Disincentives: Personnel budget ceilings and rigid salary
structures (the Integrated Personnel and Payroll Information System - IPPIS)
make it impossible for MDAs to offer competitive salaries to attract and retain
AI talent, who are highly sought after by the private sector and international
organizations (World Bank, 2021).
3. Absence
of a Mandate for Innovation: Without a clear, top-down political
directive—backed by budgetary allocation and performance metrics—mandating the
adoption of AI and data analytics, individual agencies have no incentive to
take the risks associated with innovation.
From a sociological
institutionalist perspective, the civil service's
"cultural-cognitive" framework does not yet value a data scientist.
The legitimate and recognized roles are the Permanent Secretary, the Director,
and the Administrative Officer, roles defined by hierarchical authority, not
technical expertise (Ojo, 2022). Integrating AI talent would challenge this
established social order.
5.3. Academic AI Training Output and Minimal
Transition into Public Sector
Nigerian universities
and academic institutions have been central to the country’s emerging AI
ecosystem. By 2021, partnerships such as the UNILAG AI Hub, the African
Institute for Mathematical Sciences (AIMS), and various research clusters
across tertiary institutions collectively produced hundreds of AI-trained
graduates annually (AIMS, 2021).
However, despite this
significant academic output, few AI specialists transitioned into public-sector
AI roles, largely because such roles did not formally exist. Furthermore,
before the Draft National AI Strategy of 2023, Nigeria lacked a comprehensive framework
to guide the use of AI in government ministries and agencies (Federal Ministry
of Communications, 2023).
Nigerian universities
have become critical nodes in the AI ecosystem. Institutions like the
University of Lagos (UNILAG AI Hub), the University of Ibadan, and the African
Institute for Mathematical Sciences (AIMS) now produce hundreds of highly
qualified AI graduates annually (AIMS, 2021; UNILAG, 2022). These graduates
possess the skills to build predictive models, design neural networks, and
manage big data infrastructure.
However, the pathway
for these specialists to enter and modernize the public sector is effectively
blocked. Prior to the 2023 draft strategy, there was no framework to guide the
utilization of AI talent in government. The issue is not a lack of supply, but
a lack of demand institutionalized within the state apparatus.
Institutional Gaps Blocking Knowledge Transfer:
·
The "Two
Communities" Problem: A significant chasm exists between the
academic/research community and the policy world. They operate with different
languages, incentives, and timeframes (Caplan, 1979). While academics are
rewarded for publications, civil servants are rewarded for maintaining
stability and following procedure.
·
Weak Research-Translation Mechanisms: Unlike
in Singapore (with A*STAR) or the United States (with DARPA and the NSF),
Nigeria lacks funded programs specifically designed to translate academic AI
research into pilot projects for public administration.
·
Brain Drain as a Symptom:
The exodus of AI talent to private firms in Lagos, Europe, and North America is
a direct consequence of the state's failure to create attractive, meaningful
career pathways. This represents a massive national loss on the investment made
in their undergraduate and postgraduate education.
This failure to
leverage academic output reinforces Nigeria's position as a consumer in the
global AI knowledge economy. It cannot contribute meaningfully to global AI
standard-setting bodies like the International Telecommunication Union (ITU) or
the Global Partnership on Artificial Intelligence (GPAI) because it lacks the
domestic implementation experience that forms the basis of diplomatic leverage
(Ulnicane et al., 2021).
The drafting of
Nigeria's National AI Strategy in 2023 (Federal Ministry of Communications,
2023) represents a potential critical juncture. The strategy acknowledges the
existing gaps and outlines ambitions for AI in agriculture, healthcare,
education, and governance. It proposes the establishment of a National AI
Council and the development of AI career pathways in the civil service.
However, from a
historical institutionalist view, strategies are not self-executing. The
"implementation gap" is a well-documented phenomenon in Nigerian
public policy (Adeyemi, 2020). The strategy's success will hinge on its ability
to overcome the very institutional barriers it identifies:
·
Political Will and
Budgetary Backing: Will the strategy be backed by a dedicated and sustained
budgetary allocation, or will it remain a paper document?
·
Institutional Reform: Will it lead to concrete
changes in civil service rules, procurement laws, and performance management
systems to incentivize AI adoption?
·
Coordination: Can it
effectively coordinate the often-siloed activities of MDAs, universities, and
the private sector?
The strategy is a
necessary but insufficient condition for change. Its fate will be determined by
the ongoing battle between the forces of institutional inertia and the
transformative potential of a coordinated reform agenda.
The role of institutional gaps in blocking
academic-to-government pathways
Sociological
institutionalism suggests that institutions adopt practices based on cultural
legitimacy rather than technical efficiency (Meyer & Rowan, 1977). Nigerian
bureaucracies traditionally value administrative continuity over innovation,
resulting in minimal incentives to integrate AI specialists. Key hindrances
include:
·
Absence of AI units or
departments in most MDAs.
·
Lack of policies
mandating data-driven governance.
·
Weak collaboration
between universities and government agencies.
·
Limited funding for
research translation into public administration.
Impacts on national AI leadership and global
participation
Countries with strong
academic-government linkages e.g., the U.S. (through DARPA), China (through
university-government labs), and Singapore (through A*STAR initiatives)
transform academic research into state capacity. Nigeria’s inability to do this
creates a gap where:
·
AI graduates migrate to
private firms or abroad, reinforcing the talent drain.
·
Government remains poorly
equipped to engage in AI standard-setting discussions globally.
·
Nigeria appears as a
consumer not a contributor to global AI governance frameworks.
·
This reinforces the core
research argument: domestic capacity did not translate into international AI
governance participation.
5.4. Innovation Hubs, AI Research Funding and
Low Government Uptake
Innovation hubs such as
CcHub, NCAIR, and iHub have attracted multi-million-naira investments to
support AI research, entrepreneurship, and prototype development (NCAIR, 2021).
These hubs drive experimentation in health-tech, fintech, agriculture, civic automation,
and education technology.
Innovation hubs such as
the Co-Creation Hub (CcHub), the Nigerian Centre for Artificial Intelligence
and Robotics (NCAIR), and iHub Africa have played a pivotal role in catalyzing
the AI ecosystem. They provide funding, mentorship, and collaborative spaces
that have led to the development of promising AI prototypes in areas like civic
tech (e.g., election monitoring), education (personalized learning), and public
health (disease outbreak prediction) (NCAIR, 2021; CcHub, 2023).
Despite this
flourishing of innovation, government uptake remains minimal. The NITDA (2022)
Digital Maturity Assessment identified core infrastructural and governance
deficits that prevent MDAs from being capable consumers of these innovations:
·
Inadequate Data
Infrastructure: Unreliable broadband, lack of cloud migration strategies, and
the absence of interoperable databases mean that AI solutions, which are
inherently data-hungry, cannot function effectively.
·
Weak Data Governance: The
absence of robust data protection laws (prior to the 2023 Act's full
implementation), data classification standards, and ethical guidelines creates
legal and operational uncertainty for deploying AI in the public sector.
·
Cybersecurity Concerns:
MDAs are often vulnerable to cyber-attacks, making them hesitant to adopt new
technologies that could increase their attack surface.
This scenario creates a
vicious cycle: the government's low digital maturity prevents it from absorbing
innovations, which in turn deprives it of the tools needed to improve that very
maturity. The hubs and start-ups, responding to market signals, naturally pivot
to serving the private sector and foreign markets, further widening the gap
between ecosystem output and public sector needs.
Despite this,
government MDAs demonstrated a persistent inability to integrate innovations
originating from these hubs due to infrastructure deficits, weak data
governance, and low ICT skills. According to the NITDA Digital Maturity
Assessment (2022), most MDAs lacked:
·
reliable broadband
infrastructure,
·
functional data
governance systems,
·
interoperable databases,
·
cybersecurity frameworks,
·
skilled personnel for
digital transformation.
·
Innovation without
institutional absorption
The innovation
ecosystem can expand, yet without institutional uptake, its national
development impact remains limited:
·
Start-ups create
solutions MDAs cannot adopt.
·
Research outputs fail to
inform policymaking.
·
Entrepreneurs seek
private-sector markets, not government procurement systems.
·
Innovation becomes
decoupled from governance modernization.
Effects on Nigeria’s AI diplomacy and global
standing
Countries that
influence global AI governance typically demonstrate strong state-led adoption
and experimentation. Nigeria’s inability to deploy innovations from its growing
ecosystem means:
·
It cannot showcase
domestic AI success stories at global forums.
·
It struggles to negotiate
AI standards effectively.
·
It lacks geopolitical
leverage in emerging AI power blocs.
·
Therefore, despite
vibrant innovation hubs, Nigeria’s weak institutional adoption undermines both
domestic governance transformation and global participation.
This chapter
demonstrates that Nigeria’s AI journey between 2013 and 2023 is characterized
by a significant mismatch between the rapid growth of the private AI ecosystem
and the slow adoption within government institutions. Although start-ups,
training programs, academic hubs, and innovation centers expanded Nigeria’s AI
capabilities, state institutions remained structurally unprepared to absorb,
deploy, or scale AI solutions. As a result, Nigeria’s global AI participation
remained limited, reinforcing the research hypotheses.
For Nigeria to
strengthen its role in global AI governance, it must bridge this institutional
gap by adopting the Draft National AI Strategy, investing more in AI
R&D,reforming bureaucratic structures, and establishing stronger
university–industry–government linkages.
This chapter has
demonstrated that between 2013 and 2023, Nigeria's AI journey was defined by a
profound institutional disconnect. A vibrant and growing ecosystem of startups,
hubs, academies, and universities expanded the nation's AI frontiers, while the
state remained largely entrenched in pre-digital bureaucratic models. Through
the lenses of historical and sociological institutionalism, we see that this
was not an accident but a predictable outcome of path dependencies and
culturally embedded norms that privilege administrative continuity over
technological innovation.
The consequences are
twofold. Domestically, it results in continued governance inefficiency, policy
incapacity, and an inability to leverage homegrown talent for public good.
Internationally, it marginalizes Nigeria in the critical forums where the future
of AI governance is being shaped, limiting its global influence in the digital
age.
Conclusion and Recommendations
The study
concludes that government investment in AI R&D during the review period was
minimal, fragmented, and lacking in strategic direction. This insufficient
investment directly correlated with Nigeria’s peripheral and reactive
participation in global AI governance forums. Without dedicated funding,
research initiatives, and policy-driven R&D agendas, Nigeria’s ability to
contribute to or shape international AI norms remained limited, reinforcing its
status as a rule-taker rather than a rule-maker in the global digital order.
Again, while AI startups and capacity-building programs have flourished and
created a foundation for local AI expertise, their translation into meaningful
public sector governance reforms has been inadequate. Innovations originating
in the private sector have struggled to permeate governmental structures due to
institutional inertia, regulatory vacuums, infrastructural deficits, and a lack
of political will to adopt and adapt AI solutions for public administration.
Consequently, AI’s potential to enhance transparency, efficiency, and
accountability in governance remains largely untapped.
In this light,
the government should foster structured public-private partnerships to
integrate AI startups and capacity-building outcomes into public sector
governance.
To bridge the
gap between private sector innovation and public sector adoption, the
government must create institutionalised pathways for AI startups and
capacity-building initiatives that contribute to governance reform. This
requires moving beyond ad hoc collaborations to structured, sustainable
partnerships. Key measures include creating an AI for the Governance Innovation
Lab: Establish a government-funded but independently managed innovation lab
that brings together AI startups, academia, and civil servants to co-develop AI
solutions for public administration challenges (e.g., revenue collection,
service delivery, fraud detection); implement a Regulatory Sandbox for GovTech:
NITDA, in partnership with the Bureau of Public Service Reforms, should launch
a regulatory sandbox that allows AI startups to pilot governance solutions in a
controlled environment with legal waivers, enabling real-world testing and
iteration without immediate regulatory burdens. Institutionalise AI Capacity
Building in the Civil Service: Mandate AI literacy and technical training for
mid-to-senior-level civil servants through the Administrative Staff College of
Nigeria (ASCON) and other training institutions. Develop certification
programmes in collaboration with organisations like Data Science Nigeria;
develop an AI Procurement Framework: the Bureau of Public Procurement should
issue guidelines for the ethical and transparent procurement of AI solutions by
government agencies, prioritising locally developed technologies and ensuring
fairness, accountability, and socio-economic impact assessments; launch a
National AI Talent Pipeline Programme: Scale up successful grassroots
capacity-building programmes (e.g., those run by Co-Creation Hub) through
government grants and ensure that graduates are channelled into public sector
AI projects through fellowship and internship programmes.
By creating
these structured interfaces, Nigeria can systematically harness the energy and
expertise of its AI startup ecosystem to drive digital transformation in
governance, ensuring that innovation leads to tangible improvements in public
service delivery, transparency, and democratic accountability.
The period from
2013 to 2023 has laid a foundational, albeit uneven, bedrock for artificial
intelligence in Nigeria. The country stands at a crossroads: it can continue on
a path of reactive adaptation and digital dependency, or it can seize the
opportunity to build a sovereign, ethical, and inclusive AI governance model
that serves its people and asserts its voice globally. The recommendations
provided offer a pragmatic roadmap for the latter path. Their implementation
requires political will, cross-sector collaboration, and sustained investment.
If embraced, Nigeria can not only mitigate the risks associated with AI but
also harness its transformative potential to foster sustainable development,
strengthen democratic governance, and secure a position of leadership in
Africa’s digital future. The time for strategic action is now.
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