Project – The Influence of Artificial Intelligence on Managerial Decision-Making in Small and Medium-Sized Enterprises (SMEs): A Study of Selected SMEs in Ikeja Local Government Area, Lagos State.

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Artificial intelligence (AI) has emerged as an important technological development with the potential to transform how organisations collect information, analyse business conditions, allocate resources, and make managerial decisions. AI refers broadly to computer-based systems capable of performing tasks associated with human intelligence, including prediction, pattern recognition, language processing, classification, and the generation of recommendations. In business management, these capabilities can help managers interpret large volumes of information, identify emerging trends, evaluate alternatives, and make decisions more quickly. Shrestha, Ben-Menahem, and von Krogh (2019) explain that AI can change organisational decision-making by influencing how alternatives are explored, how quickly decisions are reached, and how human judgement is combined with algorithmic recommendations. Therefore, AI is increasingly relevant to managerial functions such as planning, organising, marketing, financial management, inventory control, and customer relationship management. Its influence, however, depends on how appropriately the technology is selected, implemented, and integrated into existing organisational processes.

Managerial decision-making is the process through which managers identify problems or opportunities, gather and interpret relevant information, evaluate possible courses of action, and select options intended to achieve organisational objectives. Decisions may concern pricing, recruitment, investment, customer service, production, procurement, marketing strategies, and business expansion. Traditionally, managers have relied on experience, professional judgement, consultation, and available accounting or market information when making such decisions. However, rapidly changing markets and increasing volumes of digital information can make purely manual approaches slow or insufficient for some tasks. AI-supported systems can assist managers by identifying patterns in sales data, forecasting demand, segmenting customers, estimating financial risks, and generating insights from unstructured information. Shrestha et al. (2019) argue that the relationship between human and AI-based decision-making can take different forms, including human delegation to AI, sequential collaboration, and the combination of human and machine recommendations. This suggests that AI does not necessarily replace managerial judgement; rather, it can change the information and analytical support available to managers.

One important way AI can influence managerial decision-making is through improved data analysis and forecasting. AI-enabled tools can analyse historical sales, customer behaviour, market trends, stock levels, and operating costs to generate predictions that managers can use when planning business activities. For example, a retailer may use predictive analytics to estimate demand for particular products, while a service business may analyse customer interactions to identify recurring complaints or anticipate changing customer preferences. These applications can potentially reduce the time required to process information and enable decisions to be based on a wider range of evidence. The Organisation for Economic Co-operation and Development (OECD, 2021) identifies opportunities for AI to improve business efficiency and support activities across the value chain of small and medium-sized enterprises. Nevertheless, predictions depend on the quality and relevance of the data supplied to a system, and inaccurate or incomplete data may produce misleading recommendations. Consequently, the value of AI-assisted decision-making depends not only on technological capability but also on the manager’s ability to interpret outputs critically and connect them with actual business conditions.

The potential benefits of AI are particularly important for small and medium-sized enterprises, which often operate with limited financial resources, smaller workforces, and less specialised managerial or technical capacity than large organisations. SMEs must make decisions about pricing, cash flow, procurement, staffing, customer retention, and expansion while managing uncertainty and competing demands on limited resources. AI tools may offer practical support through automated customer-service systems, sales forecasting, inventory recommendations, document processing, marketing analytics, and financial planning assistance. The OECD (2021) notes that AI can support SME activities but also identifies barriers such as limited awareness, skills shortages, implementation costs, uncertainty about returns, and the need for complementary investments. These challenges are relevant because the availability of an AI tool does not automatically mean that an SME can use it effectively. Managers must assess whether the technology addresses a genuine business need, whether the cost is justified, and whether employees have the skills to use its outputs. Therefore, AI may provide decision-making advantages to SMEs, but the extent of those advantages is likely to vary according to organisational resources, readiness, and implementation practices.

Another important consideration is the distinction between using AI to automate decisions and using it to support human judgement. Automation occurs when a system performs a task or makes a decision with limited direct human involvement, whereas augmentation involves managers and AI systems working together so that technology provides analytical support while people retain appropriate judgement and responsibility. Raisch and Krakowski (2021) describe the relationship between automation and augmentation as a paradox, arguing that organisations need to understand their interdependence rather than treating them as completely separate choices. In an SME, an AI tool might automatically organise customer enquiries while a manager handles sensitive complaints, or it might recommend a marketing budget while the owner-manager considers cash-flow requirements and local market knowledge before approving the decision. Human oversight is especially important where decisions affect employees, customers, financial commitments, or business reputation. Accordingly, the influence of AI on managerial decision-making should be assessed not only by whether businesses use AI, but also by how they combine technological recommendations with experience, accountability, and contextual understanding.

Despite its potential benefits, AI adoption presents risks and practical difficulties that may limit its usefulness in managerial decision-making. These include inaccurate outputs, algorithmic bias, privacy concerns, cybersecurity threats, insufficient staff competence, dependence on external technology providers, and difficulty explaining how a system reached a recommendation. Managers may also over-rely on automated outputs or accept recommendations without verifying their accuracy and relevance. The National Institute of Standards and Technology (NIST), through its AI Risk Management Framework, emphasises the importance of managing AI-related risks and promoting systems that are valid, reliable, secure, accountable, transparent, and fair (Tabassi, 2023). For SMEs, these considerations are important because limited technical expertise may make it difficult to evaluate the reliability of AI tools or establish adequate safeguards. Furthermore, managers must consider the confidentiality of business and customer data when using external AI platforms. These challenges indicate that the influence of AI on decision-making may be positive in some circumstances but limited or potentially harmful in others if appropriate governance, staff training, and human review are absent.

In Nigeria, the growing relevance of digital technologies creates opportunities for SMEs to improve business processes and strengthen the evidence available for managerial decisions. SMEs operate across sectors such as retail, hospitality, professional services, logistics, manufacturing, education, and information technology, where decisions about customers, costs, stock, employees, and competition are central to business performance. AI-enabled services may help some enterprises analyse transactions, improve customer communication, identify sales patterns, and plan operations. However, the extent of adoption may be shaped by the affordability of digital tools, access to reliable internet and electricity, staff digital skills, the availability of suitable business data, and managers’ understanding of AI’s capabilities and limitations. The OECD (2021) identifies awareness, skills, costs, and uncertainty about returns as important barriers to AI adoption among smaller businesses, while Tabassi (2023) emphasises the need to manage risks throughout the use of AI systems. These considerations provide a useful basis for examining AI in Nigerian SMEs, while recognising that international evidence does not by itself establish the level of adoption or its effects among businesses in a particular Nigerian locality.

Ikeja Local Government Area in Lagos State provides a relevant setting for investigating the influence of AI on managerial decision-making among selected SMEs. As a commercial and business location within Lagos, Ikeja includes enterprises operating in varied sectors and facing different operational and competitive demands. Some businesses may use AI-enabled applications for marketing, customer communication, financial analysis, demand forecasting, or administrative work, while others may depend mainly on conventional managerial practices. Differences in financial capacity, digital skills, management attitudes, infrastructure, and awareness may influence both adoption and the practical value derived from AI. Although existing literature explains how AI can reshape organisational decisions and identifies barriers to adoption among SMEs, it does not establish how selected SMEs in Ikeja use AI, which managerial decisions are affected, or what challenges they experience. A local study is therefore necessary to examine the extent of AI use, assess its perceived influence on the speed and quality of managerial decisions, and identify the constraints affecting effective use. This study seeks to generate evidence that may help SME owners, managers, and relevant business-support organisations make informed decisions about the appropriate adoption and use of AI technologies.

1.2 Statement of the Problem

Managerial decision-making is essential to the survival, competitiveness, and growth of small and medium-sized enterprises. Managers must make decisions about pricing, inventory, marketing, customer service, recruitment, cash flow, and business expansion, often under conditions of limited resources and uncertainty. Artificial intelligence offers opportunities to improve these decisions by processing information, identifying patterns, forecasting outcomes, and generating recommendations. Shrestha et al. (2019) explain that AI can influence organisational decision-making by changing the speed of analysis, the range of alternatives considered, and the relationship between human judgement and algorithmic recommendations. However, the availability of AI technologies does not guarantee that SMEs will use them effectively or achieve better managerial outcomes. Some managers may lack awareness of suitable applications, while others may be uncertain about the reliability, affordability, or practical relevance of AI-generated recommendations. This creates a need to investigate how AI influences managerial decision-making among selected SMEs in Ikeja Local Government Area, Lagos State.

A further problem concerns the capacity of SMEs to adopt and integrate AI into their management processes. Compared with larger organisations, some SMEs may have limited financial resources, inadequate digital infrastructure, insufficient technical expertise, and restricted access to specialised personnel. These constraints can make it difficult to acquire appropriate tools, train employees, maintain systems, and evaluate whether AI investments produce worthwhile benefits. The OECD (2021) identifies limited awareness, skills shortages, high implementation costs, and uncertainty about returns as important barriers to AI adoption among SMEs. In practical terms, a business owner may recognise the potential benefits of AI-assisted sales forecasting or customer analysis but lack the resources or expertise to implement the technology appropriately. Consequently, even where AI tools are available, their influence on managerial decisions may remain limited. It is therefore important to determine the extent to which selected SMEs in Ikeja use AI and how organisational resources and managerial preparedness affect its contribution to decision-making.

Another concern is the reliability and appropriateness of AI-generated recommendations. Although AI can process information rapidly, its outputs may be affected by incomplete data, biased patterns, technical errors, or limitations in the way a particular tool interprets a business problem. Managers who depend excessively on automated recommendations without adequate verification may make decisions that are unsuitable for their organisations. Raisch and Krakowski (2021) highlight the tension between automation and human augmentation in management, suggesting that organisations need to consider how human judgement and AI capabilities can complement one another. Similarly, Tabassi (2023) emphasises the importance of managing risks associated with AI systems, including concerns about reliability, transparency, security, and fairness. For SMEs, these issues may be particularly significant because limited technical expertise can make it difficult to evaluate AI outputs or establish appropriate safeguards. The extent to which managers in the selected Ikeja SMEs verify AI recommendations, retain decision-making responsibility, and address technological risks therefore requires investigation.

Furthermore, although international research explains the potential contribution of AI to organisational decision-making and identifies barriers to its adoption by SMEs, these findings do not establish the specific experience of businesses operating in Ikeja Local Government Area. SMEs differ in their sectors, management structures, financial capacity, digital readiness, and access to technology, meaning that the influence of AI may vary from one business to another. It remains necessary to establish whether AI use is associated with perceived improvements in decision speed, information analysis, decision quality, and managerial effectiveness among the selected enterprises. Without such local evidence, SME owners, managers, and business-support organisations may find it difficult to identify the most useful applications and the challenges that require priority attention. This study therefore seeks to examine the influence of artificial intelligence on managerial decision-making in selected SMEs in Ikeja Local Government Area, Lagos State.

1.3 Objectives of the Study

The main objective of this study is to examine the influence of artificial intelligence on managerial decision-making in selected small and medium-sized enterprises in Ikeja Local Government Area, Lagos State.

The specific objectives are to:

1. Assess the extent to which selected SMEs in Ikeja Local Government Area adopt and use artificial intelligence in their business operations.

2. Examine the influence of artificial intelligence on the speed and efficiency of managerial decision-making in the selected SMEs.

3. Determine the influence of artificial intelligence on the quality and accuracy of managerial decisions in the selected SMEs.

4. Identify the major challenges affecting the effective use of artificial intelligence in managerial decision-making among the selected SMEs.

1.4 Research Questions

The following research questions will guide the study:

1. To what extent do selected SMEs in Ikeja Local Government Area adopt and use artificial intelligence in their business operations?

2. How does artificial intelligence influence the speed and efficiency of managerial decision-making in the selected SMEs?

3. How does artificial intelligence influence the quality and accuracy of managerial decisions in the selected SMEs?

4. What major challenges affect the effective use of artificial intelligence in managerial decision-making among the selected SMEs?

1.5 Research Hypothesis

The following null hypothesis will be tested at the 0.05 level of significance:

H₀: There is no statistically significant relationship between the use of artificial intelligence and managerial decision-making effectiveness among selected SMEs in Ikeja Local Government Area, Lagos State.

1.6 Significance of the Study

The findings of this study may be beneficial to SME owners, managers, employees, business-support organisations, policymakers, researchers, and students.

SME owners and managers: The study may help owners and managers understand the potential benefits and limitations of AI in business decision-making. Findings on decision speed, information analysis, and decision quality may assist them in identifying applications that are relevant to their operations and making better-informed decisions about technology adoption.

Employees: The study may improve employees’ understanding of how AI tools can support business processes and managerial decisions. It may also highlight the importance of digital skills, appropriate training, data protection, and human oversight when using AI-enabled applications.

Business-support organisations: Business consultants, technology providers, entrepreneurship development centres, and SME support agencies may use the findings to identify common barriers to AI adoption. The results may inform training programmes and advisory services that help smaller businesses evaluate and implement suitable technologies.

Government and policymakers: The study may provide local evidence to support policies and programmes intended to improve digital capability among SMEs. Its findings may help identify areas where support for digital skills, infrastructure, technology awareness, or responsible AI use may be beneficial.

Technology developers and service providers: The findings may offer insights into the practical needs and constraints of SMEs. Such information may help technology providers develop more accessible, affordable, understandable, and context-appropriate AI solutions for smaller businesses.

Researchers and students: The study may contribute to academic discussion on artificial intelligence, managerial decision-making, and SME development in Nigeria. It may also serve as a reference for further research on AI adoption and business performance in other locations or sectors.

1.7 Scope of the Study

The study focuses on the influence of artificial intelligence on managerial decision-making among selected SMEs in Ikeja Local Government Area, Lagos State.

The content scope covers the extent of AI adoption and use, its perceived influence on the speed and efficiency of managerial decisions, its relationship with decision quality and accuracy, and the challenges associated with its implementation. AI applications considered may include predictive analytics, AI-assisted customer service, automated reporting, marketing analysis, demand forecasting, and generative AI tools used for business planning or information processing.

The geographical scope is limited to selected SMEs operating in Ikeja Local Government Area. The population may include business owners, managers, supervisors, and other personnel directly involved in managerial decision-making or the use of AI-enabled business tools. The particular enterprises, sectors, sample size, and period of data collection should be specified in the research methodology.

1.8 Limitations of the Study

The study may encounter limitations relating to access to participating businesses, respondents’ willingness to disclose information about their technology use, time, and financial resources. Some SMEs may not formally classify the digital tools they use as artificial intelligence, while others may be reluctant to disclose details of their internal decision-making processes. Differences in respondents’ understanding of AI may also affect the consistency of responses.

If the study relies on questionnaires or interviews, findings about decision-making effectiveness may reflect respondents’ perceptions rather than independently measured business outcomes. In addition, a cross-sectional survey can identify associations between AI use and managerial decision-making but cannot conclusively establish causation. To minimise these limitations, the researcher should provide a clear explanation of what qualifies as AI for the study, use understandable questionnaire items, protect respondents’ confidentiality, and interpret the findings within the limits of the research design.

1.9 Operational Definition of Terms

Artificial intelligence (AI): Computer-based technologies that perform tasks such as prediction, pattern recognition, language processing, classification, and recommendation generation to support or automate aspects of business activities.

Managerial decision-making: The process through which managers identify business problems or opportunities, evaluate information and alternatives, and select courses of action to achieve organisational objectives.

Small and medium-sized enterprises (SMEs): Businesses classified as small or medium-sized according to the applicable Nigerian classification criteria used by the researcher.

AI adoption: The process through which an SME introduces or integrates AI-enabled tools into its business operations or managerial activities.

Decision-making effectiveness: The extent to which managerial decisions are timely, relevant, informed by appropriate information, and aligned with organisational objectives.

Decision speed: The amount of time required to gather and analyse information, evaluate alternatives, and reach a managerial decision.

Decision quality: The perceived or assessed suitability, accuracy, and usefulness of a managerial decision in addressing a business problem or achieving an intended objective.

AI-related challenges: Difficulties that may limit the adoption or effective use of AI, including cost, insufficient technical skills, poor data quality, infrastructure limitations, privacy concerns, reliability issues, and uncertainty about returns on investment.

Selected SMEs: The enterprises chosen by the researcher for participation in the study within Ikeja Local Government Area, Lagos State.

Project – The Influence of Artificial Intelligence on Managerial Decision-Making in Small and Medium-Sized Enterprises (SMEs): A Study of Selected SMEs in Ikeja Local Government Area, Lagos State.