Artificial Intelligence and Public Policy Formulation in Nigeria: Evidence from Rivers State MDAs

Background to the Study

Policy formulation is the stage where government decides what to actually do about a problem — weighing options, estimating their costs and consequences, and picking a course of action. How good that process is depends heavily on the quality of the information and analysis behind it, and Nigerian policymaking has long been criticised as reactive and thinly researched, shaped more by political expediency than rigorous evidence. Artificial intelligence, in principle, offers a way to change that: tools that can process enormous volumes of structured and unstructured data — survey results, administrative records, even social media sentiment — to spot patterns, forecast the likely effects of policy options, and simulate interventions before they're rolled out.

OECD countries have moved fastest on this front, but even there the gap between potential and practice is wide: the OECD's own research on AI in government finds that while 70% of countries use AI to improve internal government processes, only around a third actually apply it to policy design and implementation itself — the harder, more analytically demanding use case. Nigeria's national AI ambitions have been formalising quickly at the federal level, with the Federal Ministry of Communications, Innovation and Digital Economy's National AI Strategy setting out a five-year vision through 2029. What that federal-level ambition looks like in practice at the sub-national level, inside the ministries and agencies actually responsible for day-to-day policy and planning, is exactly what this Rivers State study set out to measure.

Statement of the Problem

Nigerian public policy formulation, including at the Rivers State level, continues to run on weak data infrastructure, limited analytical capacity, and a process shaped more by intuition and political considerations than by rigorous, data-driven analysis — with policies often formulated without properly forecasting their likely socio-economic effects, leading to implementation failures and resource misallocation. International literature has documented AI's value for evidence-based policymaking extensively, but there has been little empirical work examining how much AI is actually used in Nigerian sub-national policy formulation, what effect that use has on policy quality, and what specifically is blocking wider adoption. Existing Nigerian studies on AI and governance have tended to focus on service delivery and revenue collection rather than the upstream work of policy formulation itself, leaving policymakers without the evidence base to design targeted interventions. This study addresses that gap directly, through a structured survey of policy, planning, and research staff across five Rivers State MDAs.

Aim and Objectives of the Study

The aim of this study is to examine the relationship between Artificial Intelligence utilization and public policy formulation in selected Ministries, Departments and Agencies in Rivers State. The specific objectives are to:

●      determine the level of Artificial Intelligence utilization in the policy formulation processes of selected MDAs in Rivers State

●      examine the effect of Artificial Intelligence utilization on the quality of evidence-based policy formulation in the selected MDAs

●      ascertain the relationship between staff exposure to AI-related training and the quality of policy formulation

●      identify the challenges militating against the integration of Artificial Intelligence into public policy formulation in Rivers State

Research Questions

●      What is the level of Artificial Intelligence utilization in the policy formulation processes of selected MDAs in Rivers State?

●      What effect does Artificial Intelligence utilization have on the quality of evidence-based policy formulation in the selected MDAs?

●      What is the relationship between staff exposure to AI-related training and the quality of policy formulation?

●      What challenges militate against the integration of Artificial Intelligence into public policy formulation in Rivers State?

Significance of the Study

Theoretically, this study extends the Rational-Comprehensive Model, Incrementalism, and the Technology Acceptance Model into the specific context of AI-enabled policy formulation within Nigerian sub-national government — an area that has had limited scholarly attention until now. Practically, it gives the Rivers State Government, and other sub-national governments facing similar conditions, empirical evidence on where AI utilization currently stands, what it's already doing for policy quality, and precisely which barriers need addressing to move further. That evidence can directly inform decisions on where to prioritise investment: data infrastructure, staff analytical capacity, or institutional policy frameworks for AI use. It also benefits policy analysts and planning officers directly, by clarifying the specific competencies that actually make a difference in using AI tools effectively, and it adds a locally grounded reference point for other researchers working in public administration, policy analysis, and digital governance. For students designing a comparable survey-based governance study, one-on-one research coaching can help sharpen the sampling design and statistical analysis chapters without doing the fieldwork for you.

Scope of the Study

This study is conceptually limited to examining Artificial Intelligence utilization and its effect on public policy formulation. Geographically, it is delimited to Rivers State, Nigeria, focusing on the planning, research, statistics, and policy units of five purposively selected MDAs: the Ministry of Budget and Economic Planning, the Bureau of Public Service Reforms, the Rivers State Sustainable Development Agency, the Ministry of Economic Development, and the Directorate of Planning, Research and Statistics in the Office of the Head of Service. These units were selected specifically for their direct involvement in policy formulation and planning activities within the state public service, with data collection carried out over 2026.

Operational Definition of Terms

Artificial Intelligence (AI): computer-based systems and tools, including data analytics software, predictive modelling, machine learning, and simulation tools, capable of processing data and generating insights to support policy formulation, as utilized within the selected MDAs.

Public Policy Formulation: the process by which government MDAs identify policy problems, generate and analyse policy options, and select preferred courses of action to address identified socio-economic challenges.

Evidence-Based Policy Formulation: a policy formulation approach in which decisions are informed by rigorous analysis of relevant data, research findings, and empirical evidence, rather than by intuition, precedent, or political expediency alone.

Job Cadre: the hierarchical grade-level classification of civil servants within the Rivers State public service, ranging from junior to management cadre.

MDA (Ministry, Department and Agency): any constituent administrative unit of the Rivers State Government responsible for formulating and implementing specific government policies and programmes.

Conclusion

The core tension in this study's findings is worth sitting with: AI utilization in Rivers State MDAs is genuinely low, but where it is used, it moves the needle on policy quality in a real, statistically significant way, accounting for a third of the variance in how good the resulting policy actually is. That's not a marginal effect — it's a strong practical case for investment, not just an academic one. The barriers standing in the way are the familiar ones for public-sector technology adoption anywhere: patchy data, weak infrastructure, thin technical skills, tight budgets, political interference, and no clear framework for how AI should even be used. None of those are exotic problems, and none of them require AI-specific breakthroughs to fix — they require ordinary, sustained investment in data systems and staff capacity, backed by a governance framework that gives policy staff clear permission and guardrails to actually use these tools. Anyone building a similar governance or policy-formulation study will find it useful to look at a few worked survey designs and statistical models before setting up their own questionnaire.

Frequently Asked Questions

1. How much is AI actually used in Nigerian public policy formulation?

Based on this Rivers State study, not much — the overall level of AI utilization across the five surveyed MDAs came out at a grand mean of 2.62, indicating generally low usage, with predictive modelling, machine learning, and simulation tools particularly rare.

2. Does using AI actually improve the quality of government policy?

Yes, according to this study — AI utilization showed a significant positive relationship with the quality of evidence-based policy formulation (r = 0.578, p < 0.05), accounting for 33.4% of the variance in policy formulation quality.

3. What kind of AI tools are Nigerian policy staff actually using?

Data analytics software saw the most use, though still modest, while more advanced tools like predictive modelling, machine learning, and simulation were particularly limited among the MDAs surveyed.

4. Does staff training in AI make a difference to policy quality?

Yes — staff exposure to AI-related training showed a significant positive relationship with the quality of policy formulation (r = 0.501, p < 0.05) in this study.

5. What's stopping Nigerian government agencies from using more AI in policymaking?

The study identified inadequate access to quality data, weak ICT infrastructure, low technical and analytical skills, inadequate funding, political interference, and the absence of a clear AI policy framework as the main barriers.

6. Do senior and junior civil servants see AI use differently?

Yes — the study found a statistically significant difference in perceived AI utilization based on job cadre (F = 3.84, p < 0.05), with management-level staff reporting higher perceived usage than junior staff.

7. Which government agencies were studied?

Five Rivers State MDAs directly involved in policy and planning: the Ministry of Budget and Economic Planning, the Bureau of Public Service Reforms, the Rivers State Sustainable Development Agency, the Ministry of Economic Development, and the Directorate of Planning, Research and Statistics.

8. How was the data for this study collected?

Through a structured, expert-validated questionnaire distributed to a sample of 243 staff (drawn via the Taro Yamane formula from a population of 620), with 220 valid responses returned — a 90.5% response rate.

9. Does Nigeria have a national strategy for AI in government?

Yes — the Federal Ministry of Communications, Innovation and Digital Economy has published a National Artificial Intelligence Strategy setting out a five-year vision, though this study's findings suggest that ambition hasn't yet translated into wide sub-national adoption.

10. Where can I see how a survey-based governance study like this is structured?

You can review comparable social science and public administration studies in the sample research library for reference on structuring objectives, sampling, and statistical analysis.