INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS | Volume X Issue X October 2025  
The Role ofArtificial Intelligence in Enhancing Business Efficiency and  
Competitiveness of SMEs inAba,Abia State, Nigeria  
Anthony Alaegor  
Bank of Industry Limited/PhD Student, Unicaf University, Nigeria  
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.10100000186  
Received: 06 November 2025; Accepted: 12 November 2025; Published: 22 November 2025  
ABSTRACT  
The research design used quantitative methods to study how AI awareness and adoption practices affect  
business efficiency for SMEs operating in Aba's Abia State Nigerian community. The structured survey  
questionnaire reached 370 randomly chosen SME owners and managers through stratified random sampling  
procedures that included manufacturing, retail, and services sectors. Researchers evaluated how familiar  
businesses were with AI along with the degree of implementation and the business effects resulting from AI  
solutions in efficiency and market competitiveness. Researchers performed statistical analyses through the  
combination of descriptive elements alongside inferential methods which incorporated multiple regression  
ANOVA and correlation coefficient testing. AI awareness creates positive effects on individuals' AI adoption  
decisions which in turn produces significant enhancements both in business efficiency and competitiveness.  
The evaluation reveals that business efficiency variations of 26.2% (R² = 0.262, p = 0.000) stem from AI  
awareness and adoption rates. Extensive adoption of AI remains restricted because of elevated implementation  
expenses and inadequate technical capabilities along with privacy-related obstacles. CEO of Trello highlighted  
the necessity for specific government policies along with educational initiatives and financial programs that  
would support SMEs' AI adoption to obtain sustainable business advantages.  
Keywords: Aba, AI Adoption, Artificial Intelligence, Business Efficiency, Competitiveness Nigeria, SMEs.  
INTRODUCTION  
Aba in Abia State represents a city where the continuous noise of industrial sewing machines and traders in  
Ariaria International Market gives space to a developing technological transformation. The commercial core of  
Aba Kick-starts its Small and Medium Enterprises (SMEs) to embrace Artificial Intelligence (AI) which has  
already revolutionized global industrial prospects (Iroka et al., 2021).  
This city has maintained its position of innovation together with its capability to endure for the last fifty years.  
The trading community together with the manufacturing sector and artisan groups in Aba have secured a  
global brand for their high-quality leather footwear alongside complex fabrics despite operating with minimal  
resources and outmoded production methods (Chika & Wale, 2020). Traditional methods that have sustained  
the city no longer provide the foundation required for post-data business success because the city depends  
heavily on both creativity and hustle. Business organizations worldwide are experiencing an AI revolution  
which includes consumer behavior forecasting as well as automation of manufacturing processes personalized  
customer interactions and supply chain optimization. The fundamental question becomes whether AI  
technology can assist Aba's small and medium enterprises to break beyond their current boundaries for digital  
market competition.  
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The promise of AI is vast. AI will drive up to fifteen trillion-dollar worth of value for the global economy by  
2030 and emerging markets such as Nigeria will obtain substantial benefits according to PwC’s 2017 global AI  
impact report (Banerjee et al., 2023). A local shoe producer in Aba would gain market trend prediction through  
the implementation of AI-powered demand forecasting software replacing their traditional instinct-based  
decision-making. The manufacturer can use their AI system to adjust production levels accurately according to  
specific customer needs thus reducing production waste and enhancing profitability. An AI chatbot introduced  
by textile traders enables them to manage online customer queries through automated responses that  
immediately address inquiries from Lagos, Accra, and London prospective clients while eliminating physical  
barriers. A small-scale food processor should implement AI-driven quality control systems to avoid product  
defects in consumer delivery which will protect their reputation and product quality standards. Industry leaders  
in various parts of the globe are already using these opportunities which surpass the realm of science fiction.  
The potential of AI has gone largely untapped among Nigerian SMEs especially in the city of Aba because  
adoption rates remain very low. Multiple barriers exist to hinder its adoption process. First, awareness is  
limited. According to the National Information Technology Development Agency (NITDA) in 2020 only 32%  
of Nigerian SMEs demonstrated a basic understanding of Artificial Intelligence along with its practical  
applications (Ebuka et al., 2023). Most business operators look at artificial intelligence as an unapproachable  
high-tech technology designed for Silicon Valley enterprises instead of their small businesses. Affordability  
represents one of the main obstacles. The tight profit margins experienced by SMEs make the expensive cost  
of artificial intelligence adoption appear out of reach to them even though major companies can fund extensive  
AI research. The third stumbling block involves a limited understanding of the technology. The exceptional  
challenges with unstable electricity power alongside unreliable internet connectivity alongside restricted cloud  
computing services become major barriers that impede the adoption of AI solutions in Aba. The obstacles to  
AI adoption do not exceed what companies can manage. Worldwide small and medium enterprises (SMEs)  
implement artificial intelligence technologies in limited yet effective applications. Artificial intelligence  
through e-commerce tools supports Chinese small businesses to expand their international customer bases. AI-  
based credit scoring systems in India allow SMEs to obtain financing using mobile payment records as well as  
utility bill information instead of traditional measures. Kenyan businesses maximize their resources by  
allowing AI-powered chatbots to operate their customer service automation which creates space for strategic  
business activities. Nigeria should not overlook its opportunity to thrive since the country shows strong  
entrepreneurial activity (Ikpe, 2024).  
The Nigerian government utilizes the National Digital Economy Policy and Strategy (2020-2030) to drive  
forward technology-based economic growth. The Nigerian economy can fully harness AI when the technology  
moves past Lagos and Abuja tech hubs to enter commercial centers such as Aba. The Nigerian Startup Act  
(2022) established by the government offers tech adoption incentives as small and medium enterprises evolve  
into tomorrow's main commercial innovator force. The necessary environment for AI exploration by SMEs  
requires all stakeholders to build a framework that avoids high implementation costs alongside technical  
complexity (Adelodun & Daibu, 2023).  
During the beginning of the 2000s, numerous individuals excluded mobile banking from their practices  
because they sustained a preference for cash-based transactions (Warchlewska, 2020). The market has adopted  
mobile money as a universally present service. The implementation of AI requires businesses to pursue it as a  
necessity for survival together with increased market competitiveness in modern industry. The study  
investigates how Artificial Intelligence supports SMEs in Aba Abia State to achieve better efficiency levels  
and business competitiveness. The study seeks to fulfill these several objectives:  
1. The research investigates the diversity of artificial intelligence (AI) awareness along with its  
implementation among small and medium enterprises operating within Aba, Abia State.  
2. To evaluate the effect of artificial intelligence (AI) on business efficiency and competitive advantages for  
SMEs.  
3. To examine AI adoption challenges as well as opportunities that exist for SMEs operating in Aba.  
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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS | Volume X Issue X October 2025  
The following have been hypothesized by the researcher:  
H₁: There is a significant relationship between AI awareness and its adoption among SMEs in Aba, Abia State.  
H₂: AI adoption has a positive effect on business efficiency and competitive advantage among SMEs in Aba,  
Abia State.  
H₃: The challenges associated with AI adoption significantly impact the willingness of SMEs in Aba to  
implement AI-driven solutions.  
LITERATURE REVIEW  
Theoretical Foundations of Artificial Intelligence in Business  
Artificial Intelligence (AI) transforms global businesses through its introduction of automation together with  
data-driven decisions and its intelligence in customer management systems. The capacity of AI to analyze vast  
data while determining patterns along with generating predictions positions it as the essential force behind  
operational enhancement and business transformation. Previous business administration depended extensively  
on human choices and manual operations yet this model proved successful to some extent but introduced  
avoidable problems when adapting to market shifts (Bruno, 2024). The fundamental shift brought by AI allows  
companies to obtain specialized automation capabilities and market prediction technologies paired with  
resource management improvements which result in better operational results and competitive advantage.  
The core foundation of AI applications in business originates from its capability to minimize prediction  
expenses. AI delivers its most valuable contribution to business by establishing precise data-based forecasts  
which substantially reduce the indecisiveness of organizational decisions according to Prasanth et al. (2023).  
The analysis conducted by their study indicates businesses that utilize AI for predictive analytics improve their  
operational efficiency by five to fifteen percent against organizations that do not employ AI. The observed  
result demonstrates how businesses benefit from AI because it sharpens their intelligence capabilities to predict  
market needs along with maximizing inventory control and tailoring individual customer interactions. AI  
delivers maximum value to organizations by integrating with business strategy instead of operating as an  
independent technology acquisition.  
The study conducted by Rahman (2024) revealed that business operations that employ AI for process  
automation achieve a 25-30% cost reduction during their second year of AI implementation. Businesses  
implementing AI cognitive insights achieve a 12% rise in their revenue because they make superior market  
predictions and allocate resources effectively. The study demonstrates that customer engagement solutions  
driven by artificial intelligence produce varying results because these outcomes depend on both the sector of  
operation and the quality of AI system implementation. The successful high-speed responses of AI-driven  
chatbots in e-commerce fail to produce similar outcomes in healthcare and legal services particularly due to  
their human interaction requirements.  
AI provides small and medium-sized enterprises (SMEs) the chance to enhance operations efficiency which  
enables them to achieve better market competition with larger corporations. Smaller businesses in Aba, Nigeria  
can effectively use AI to boost their operational efficiency since they are key economic drivers in the region.  
SMEs gain operational streamlining capabilities and cost reductions through their implementation of AI-  
powered customer tools predictive analytics systems and automated financial solutions (Andayani et al., 2024).  
AI delivers successful business outcomes based on the extent the system integrates into current business  
operational frameworks according to researchers Brynjolfsson and McAfee. Precise evaluation of specific  
needs and capabilities becomes essential for SMEs to make perceptible gains from AI solutions during their  
investment process.  
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Challenges to AI Adoption in SMEs  
The adoption rate of AI in small and medium enterprises stands at a much lower level than that exhibited by  
large corporations. Several obstacles maintain this difference between SMEs and big corporations since they  
face restrictions from finances along with technical limitations insufficient infrastructure and reluctance to  
transform.  
The exorbitant expenses required to deploy AI systems represent a major obstacle for SMEs trying to  
implement this technology. Advanced machine learning models together with automation tools demand  
substantial expenses on hardware platforms and software systems and a trained workforce. A survey conducted  
by Govori and Sejdija (2023) examined 500 SMEs from emerging markets to determine whether financial  
limitations stood as the main obstacle to AI adoption by SMEs while influencing 64% of businesses. The tight  
operational budgets of SMEs make it complicated for these businesses to find sufficient funds for  
implementing Artificial Intelligence systems. The cost of installing AI infrastructure together with cloud-based  
services and data facilities makes this challenge even more intense. Small to medium-sized enterprises fail to  
obtain inexpensive funding sources needed to purchase advanced technologies because these options remain  
beyond their financial reach.  
The main hurdle for SMEs rests in their limited digital competencies and technical capabilities. Small and  
medium-sized enterprises encounter difficulties with AI implementation because they lack skilled resources in  
data science and machine learning together with algorithm development expertise. According to Govori and  
Sejdija’s research findings, sixty-eight percent of SMEs identified their insufficient internal expertise as their  
primary barrier to AI adoption.  
The limitations of infrastructure networks become major obstacles to AI adoption by SMEs within developing  
economic regions. The necessary components for AI technologies including reliable internet access alongside  
robust data storage systems and high computing power often fail to exist in many parts of the world. The  
unstable electricity and sparse broadband capabilities across Nigeria confront SMEs with obstacles as they  
seek to incorporate AI solutions inside their business operations. Businesses working with insufficient  
infrastructure face delayed system speed and system shutdowns along with higher maintenance costs that  
reduce their AI implementation appeal (Mdladla et al., 2024).  
Social resistance to AI adoption emerges primarily from wrong ideas about the technology because individuals  
fear computers will entirely replace workers instead of helping them in their work. According to Murire  
(2024), businesses need to implement organizational change management methods that train all stakeholders  
about AI benefits while developing flexible technology-adopting cultures.  
A solution to overcome these difficulties needs to combine multiple strategies. SMEs can adopt AI through  
efforts made by governments and industry stakeholders who support the implementation with financial  
backing training programs and digital infrastructure development. Government-subsidized AI education  
programs for SME workers combined with AI project funding grants substantially minimize AI adoption  
roadblocks (Nurlia et al., 2023).  
The adoption of affordable user-friendly AI solutions becomes feasible when technology providers establish  
partnerships with SMEs to provide small business-oriented solutions. The complete exploitation of AI in  
business by SMEs in Aba, Nigeria depends on their ability to manage these problems (Nnadozie, 2024).  
METHODOLOGY  
Population and Sample  
The population of study is 2406 SMEs operators in Aba registered under Abia state corporate affairs  
commission (CAC). This study employed Cochran’s formula to determine the 370-participant sample which  
achieved statistical representation between manufacturing and retail and services sector participants (Figure 2)  
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within various local governments shown in Figure 1. (Ifraheem et al., 2024). The research used a stratified  
random sampling technique to increase the general validity of results through equal representation of  
participants across different business fields  
Data and Sources of Data  
Researchers utilized standardized survey questionnaires to gather data from SME owners and managers about  
their AI awareness statistics implementation rates and organizational performance effects. The Surveys were  
distributed online together with in-person methods to satisfy the accessibility requirements of respondents. The  
research instrument included a Likert-scale together with multiple-choice questions to collect standardized  
responses that could undergo quantitative analysis. Data were collected through primary sources using  
standardized survey questionnaires to gather data from SME owners and managers about their AI awareness  
statistics implementation rates and organizational performance effects  
THEORETICAL FRAMEWORK  
The Study comprises of independent variable namely AI Awareness, AI Adoption and AI challenges and  
dependent known as business efficiency and competitiveness. Their relationship illustrates how knowledge and  
implementation of artificial intelligence can influence SME performance. Increased AI Awareness among  
SMEs can lead to greater AI Adoption, enabling businesses to leverage technology for improved operational  
processes. However, AI Challenges, such as cost and technical barriers, can hinder adoption. When SMEs  
successfully adopt AI, they often experience enhanced business efficiency and competitive advantage, as  
streamlined operations and innovative solutions allow them to respond better to market demands and improve  
their overall positioning in the industry.  
Statistical Tools and Econometric Model  
Descriptive Statistics  
The study conducted its statistical data evaluation through descriptive and inferential methods which operated  
through SPSS software. The research used multiple regression analysis as part of inferential analysis to  
evaluate hypotheses about the relationships between AI awareness and adoption and business efficiency  
alongside organizational challenges. ANOVA tests determined the statistical relationships between the study  
variables  
Econometric Model  
The functional relationship between the dependent and independent variables is specified as illustrated in  
Figure 1, is derived as follows: Specifying econometrically, we have:  
BEC = αo + α1 AIAW+ α2 AIAD + AICH μt -----------------------(1)  
Where: BEC= Business Efficiency and Competitiveness  
αo = The Intercept  
μt = Stochastic error margin  
AIAW= AI Awareness  
AIAD = AI Adoption  
AICH= AI Challenges  
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While α1, α2, … αn are the coefficients of the variables to be estimated. The a priori or expected signs of the  
coefficients are as follows: α1> 0, α2> 0 and α3> 0 or αi‟s> 0. The functional equation (model of estimation)  
shows that Business Efficiency and Competitiveness (BEC) would depend on AI Awareness (AIAW), AI  
Adoption (AIAD) and AI Challenges (AICH)  
RESULTS AND DISCUSSION  
Demographic Representation of Respondents  
The demography data were analyzed using bar and pie charts as indicated below:  
Figure 1: Participant geographical local government distribution  
Figure 1 revealed that 60% of the participants are based in Aba while the remaining 40% are based in other  
local government areas surrounding Aba suburbs. This indicates that most of the businesses are based in Aba  
city compared to the few based outside the city.  
Figure 2: Participant Industry Distribution on Sectors.  
Figure 2 above showed that more SME businesses in Aba area are primarily focused on retail and trade. This  
indicates that the retail industry in Aba is fraught with increased competition while other industry sectors have  
less competition.  
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Figure 3: Age of Respondents  
Age of Respondents  
Age of Respondents  
18-30 31-40 41-55 56& above  
Figure 3 revealed that most SME owners in Aba area fall within the ages of 31 to 40 years which suggest that  
the young populace are actively involved in the business sectors of the city.  
Years of Business Experience  
150  
100  
50  
0
0 to 5  
6 to 10  
11 to 15  
Series1  
16 to 20  
21& above  
Figure 4: Years of Experience in Business  
Figure 4 revealed that most of the SME owners in Aba area have been in business six to ten years since the  
inception of their businesses. This indicates that most SME businesses in Aba area have been active for the  
past ten years leading to the growth of the private sector and job creation in Aba area.  
Results of Descriptive Statics of Study Variables  
The research hypotheses were analyzed with multiple regression through SPSS software in this section of the  
study. The research method enables the evaluation of how AI awareness adoption and challenges affect  
business performance and competitive strength of SMEs operating in Aba, Abia State. A full statistical  
interpretation exists through the combination of Model Summary with ANOVA and Regression Coefficients.  
Table 4.1: Model Summary  
Model R  
R
Adjusted R  
Square  
Std. Error of the  
Estimate  
Durbin-  
Watson  
Square  
1
0.512 0.262  
0.254  
0.318  
1.891  
Source: SPSS v25  
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Table 4.1 indicates that AI awareness and adoption share a moderate 0.512 positive relationship with business  
efficiency. The 26.2% portion of business efficiency and competitiveness modification in SMEs stems from AI  
awareness and adoption while 73.8% comes from unexamined elements in this study. The AI-related variables  
explain 25.4% of SME performance changes, thus producing a slightly lower but stable model fit according to  
the Adjusted R² value of 0.254. The tested Durbin-Watson statistic valued at 1.891 meets the acceptable  
criteria of 1.5–2.5 which proves there is no significant autocorrelation in the model  
Table 4.2: ANOVA Table  
Model  
Sum of Squares  
12.143  
df  
Mean Square  
4.048  
F
Sig.  
3
27.311  
1 Regression  
Residual  
34.087  
366  
369  
0.093  
Total  
46.230  
Source: SPSS v25  
An ANOVA test in Table 4.2 above demonstrated the statistical significance of the overall regression model.  
The ANOVA table establishes the statistical significance of the overall model through the F-statistic value of  
27.311 together with the p-value of 0.000. Business efficiency and competitiveness among SMEs in Aba  
strongly relate to their AI awareness and adoption experiences and technical obstacles. The p-value below 0.05  
leads to null hypothesis rejection thus proving the reliability of the constructed regression model.  
Table 4.3: Regression Coefficients  
Model  
Unstandardized  
Coefficients (B)  
Std.  
Error  
Standardized Coefficients  
(Beta)  
t
Sig.  
(Constant)  
2.114  
0.376  
0.241  
-0.194  
0.203  
0.058  
0.065  
0.071  
10.412 0.000  
AI Awareness  
AI Adoption  
0.491  
0.278  
-0.226  
6.482  
3.708  
-2.732  
0.000  
0.000  
0.007  
AI Challenges  
Source: SPSS v25  
According to Table 4.3, a 0.376 increase in business efficiency and competitiveness results from every unit  
increase in AI awareness (p = 0.000). SMEs achieve better business efficiency and competitiveness levels  
through increased adoption of AI as indicated by the regression coefficient (B = 0.241) with p = 0.000. The  
statistical relationship between AI Challenges and SME efficiency and competitiveness reveals a negative  
correlation based on the coefficient value (B = -0.194, p = 0.007).  
Research findings are strengthened by t-values which confirm the positive effects of AI Awareness (t = 6.482,  
p = 0,000) and AI Adoption (t = 3,708, p = 0,000) on SMEs. The analysis shows AI Challenges as an element  
that negatively impacts business efficiency because their statistical significance reaches p = 0.007 (t = -2.732).  
DISCUSSION  
H₁: There is a significant relationship between AI awareness and its adoption among SMEs in Aba, Abia State.  
The regression analysis demonstrates that SMEs in Aba adopt AI more frequently when managers show  
awareness about AI through their significant relationship leading to a 0.376 coefficient value with zero  
statistical probability. Business managers who gather more information about AI develop an increased  
likelihood to adopt AI-driven solution implementations. Brynjolfsson and McAfee (2018) confirm the same  
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finding through their research that shows more awareness about AI leads to higher adoption rates if businesses  
can correctly identify practical applications of AI. The study results verify that AI awareness measures account  
for a substantial portion of AI acceptance through their moderate R-value of 0.512 which correlates with an R²  
value of 0.262. According to Almashawreh et al, (2024). SMEs possess rapid technology adoption capabilities  
when they have a stronger understanding of AI concepts. The investigation asserts that SMEs in Aba will  
benefit from higher AI adoption rates when they receive targeted training and education about AI.  
H₂: AI adoption has a positive effect on business efficiency and competitive advantage among SMEs in Aba,  
Abia State.  
The analysis shows that AI adoption creates positive statistically significant effects on efficiency and  
competitive advantage because the coefficient stands at 0.241 and the p-value equals 0.000. By integrating AI  
into their systems SMEs achieve better operational efficiency together with cost efficiency and enhanced  
market position. Businesses applying AI systems can run repetitive workloads uninterrupted while enhancing  
decision systems and resource usage as reported by Hızarcı et al. (2024). The analysis of data through ANOVA  
shows that AI-driven solutions lead to better business outcomes since F = 27.311 and p = 0.000. Quispe et al,  
(2023). along with other researchers found that small businesses using AI for data analytics along with  
customer engagement develop substantial market advantages. Phase One of the Aba region has experienced  
operational improvements through AI-based automation systems within retail and manufacturing facilities.  
Research results show that businesses achieve greater operational efficiency and market competitiveness when  
they adopt AI technologies at higher levels thus establishing conditions for long-term business expansion.  
H₃: The challenges associated with AI adoption significantly impact the willingness of SMEs in Aba to  
implement AI-driven solutions.  
The research findings demonstrate that the extent of AI adoption challenges decreases SMEs' propensity for AI  
solution implementation through a statistically significant factor of -0.194 with a p=0.007. The increase in AI  
adoption challenges among SMEs produces a direct negative impact on their likelihood of implementing AI  
technologies. Paunov et al. (2019) support these findings because they determined that financial and  
technological barriers block AI adoption primarily among small businesses operating in developing  
economies. A Durbin-Watson statistic value of 1.891 indicates that severe autocorrelation does not affect the  
validity of the results. SMEs face additional barriers because of their insufficient IT technology infrastructure  
and complicated regulations which reduces their interest in AI adoption. Technical limitations and affordability  
restrictions prevent SMEs in Aba from adopting new solutions. Wider adoption of AI requires government  
support together with professional training and easy access to AI solutions to overcome identified challenges.  
CONCLUSION  
The research conducts quantitative analysis on the relationship between AI awareness and adoption as well as  
business efficiency among small businesses in Aba's Abia State Nigerian market. Businesses that increase  
awareness about AI technology tend to adopt AI solutions quickly and these AI adoption programs lead to  
improved business performance and market competitiveness. The willingness of SMEs to implement AI-based  
solutions faces hurdles due to exorbitant implementation costs together with limited technological skills and  
privacy fears affecting their willingness to adopt AI. The study results demonstrate that awareness of AI  
together with its implementation helps explain 26.2% of business efficiency changes in SME operations.  
Policymakers need to establish strategic actions with financial support staff training and regulatory controls to  
improve and safeguard AI adoption among organizations. The sustainable implementation of AI capabilities by  
SMEs requires successful solutions to these challenges to achieve maximum benefits of innovation and  
decision-making accuracy alongside competitive market advantages within evolving digital markets.  
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Appendix  
Section A: Respondents’ Biodata  
For your responses, please tick the appropriate boxes.  
1. Age of Respondents  
☐ 18-30  
☐ 31-40  
☐ 41-55  
☐ 56 & Above  
2. Years of Business Experience  
☐ 0-5  
☐ 6-10  
☐ 11-15  
☐ 16-20  
☐ 21 & Above  
3. Business Sector  
☐ Manufacturing  
☐ Retail & Trade  
☐ Services  
☐ Technology  
☐ Other (please specify) ___________________  
4. Location (State/LGA)  
☐ Aba North  
☐ Aba South  
☐ Osisioma  
☐ Obingwa  
☐ Other (please specify) ________________  
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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS | Volume X Issue X October 2025  
Section B Research Questionnaire  
Please indicate your level of agreement with the following statements using the key below:  
Key:  
SA – Strongly Agree  
A – Agree  
N - Neutral  
D – Disagree  
SD – Strongly Disagree  
S/N  
RQ1  
1
ITEMS  
SA  
A
N
D
SD  
☐
☐
☐
☐
☐
Awareness and Adoption of AI Technologies Among SMEs  
I am aware of artificial intelligence  
(AI) and its applications in business.  
My business has adopted at least one  
AI-driven tool or solution.  
The use of AI in business operations  
is increasing in Aba.  
I have attended training or workshops  
on AI and its business applications.  
AI adoption in SMEs is mostly driven  
by competition and technological  
trends.  
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
2
3
4
5
RQ2  
Impact of AI on Business Efficiency and Competitiveness  
6
AI has improved the efficiency of my  
business operations.  
AI-driven automation has helped  
reduce operational costs in my  
business.  
☐
☐
☐
☐
☐
☐
☐
☐
7
☐
☐
8
AI tools have enhanced customer  
engagement and satisfaction.  
The use of AI gives businesses in Aba  
a competitive advantage.  
AI-based decision-making has  
improved productivity and  
profitability.  
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
9
10  
RQ3  
Challenges and Opportunities in AI Adoption for SMEs  
11  
The high cost of AI implementation is  
a barrier for SMEs.  
Lack of technical expertise hinders AI  
adoption in SMEs.  
Data privacy and security concerns  
discourage the use of AI.  
Government policies and support can  
enhance AI adoption among SMEs.  
AI adoption will create more  
opportunities for SMEs in the future.  
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
12  
13  
14  
15  
Thank you for participating.sss  
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