Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries

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The study examines the effects of AI adoption on labour productivity and sales growth in selected sub-Saharan African countries using a firm-level dataset from the World Bank Enterprises from 2007 to 2024. The study employs feasible generalised least squares (FGLS), robust ordinary least squares (OLS), and high-dimensional fixed effects (HDFE) linear regression techniques. The results show that AI has a significant positive relationship with firm labour productivity and sales growth in the selected sub-Saharan African countries. However, nuances differ across countries due to varying industrial structures. Results vary across the 10 selected countries due to differences in technological readiness. These results underscore the importance of targeted policy interventions, such as upskilling initiatives and supportive regulatory frameworks, to harness AI’s benefits while mitigating adverse impacts on workers. This research contributes to the growing body of literature on technology adoption in developing economies, offering policymakers and business leaders in sub-Saharan Africa valuable insights." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/15-711/v1", "name": "Estimation of Firm Labour Productivity and Sales Growth from Artificial..." } } ] } Home Browse Estimation of Firm Labour Productivity and Sales Growth from Artificial... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Adewole Adediran O. Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :711 ( https://doi.org/10.12688/f1000research.179912.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries [version 1; peer review: awaiting peer review] Olanrewaju Adewole Adediran https://orcid.org/0000-0003-2978-3523 Olanrewaju Adewole Adediran https://orcid.org/0000-0003-2978-3523 PUBLISHED 12 May 2026 Author details Author details Sustainable Livelihoods, University of South Africa Graduate School of Business Leadership, Johannesburg, Gauteng, 1686, South Africa Olanrewaju Adewole Adediran Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract The fast integration of artificial intelligence (AI) into business operations and labour processes is reshaping global economic landscapes. The study examines the effects of AI adoption on labour productivity and sales growth in selected sub-Saharan African countries using a firm-level dataset from the World Bank Enterprises from 2007 to 2024. The study employs feasible generalised least squares (FGLS), robust ordinary least squares (OLS), and high-dimensional fixed effects (HDFE) linear regression techniques. The results show that AI has a significant positive relationship with firm labour productivity and sales growth in the selected sub-Saharan African countries. However, nuances differ across countries due to varying industrial structures. Results vary across the 10 selected countries due to differences in technological readiness. These results underscore the importance of targeted policy interventions, such as upskilling initiatives and supportive regulatory frameworks, to harness AI’s benefits while mitigating adverse impacts on workers. This research contributes to the growing body of literature on technology adoption in developing economies, offering policymakers and business leaders in sub-Saharan Africa valuable insights. READ ALL READ LESS Keywords Artificial intelligence, labour productivity growth, sales growth, high-dimensional fixed effects (HDFE) linear regression Corresponding Author(s) Olanrewaju Adewole Adediran ( [email protected] ) Close Corresponding author: Olanrewaju Adewole Adediran Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Adewole Adediran O. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Adewole Adediran O. Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :711 ( https://doi.org/10.12688/f1000research.179912.1 ) First published: 12 May 2026, 15 :711 ( https://doi.org/10.12688/f1000research.179912.1 ) Latest published: 12 May 2026, 15 :711 ( https://doi.org/10.12688/f1000research.179912.1 ) 1. Introduction Artificial intelligence (AI) refers to the development, application, and implementation of new technology (or computer programmes) to solve or simplify tasks that require human ability, 1–3 as well as to produce new goods and services or improve their production. It is challenging to define AI precisely. Existing studies have revealed that AI could be influenced by factors such as organisational administration, government policy, 4–7 globalisation, among others. Despite the enormous benefit of adopting AI, studies have argued that it could create skill mismatches and labour displacement, 8 , 9 which might affect productivity and sales. 10 , 11 Other limitations to the adoption of AI may include limited infrastructure, a lack of know-how or expertise, limited access to affordable AI technology, poor data ecosystems, and a lack of government policy that supports AI, 12 , 13 especially in sub-Saharan African countries. AI is known as a catalyst for increased firm labour productivity. 14 , 15 However, asymmetric information within businesses 16 , 17 may limit the adoption of AI. In such cases, the firm’s productivity slows down for firms without AI technologies and speeds up for firms with technological expertise. As the AI adoption may improve labour productivity, it could also enhance sales. AI adoption provides a digital platform for competitive advantage, influencing marketing, increasing customer awareness, and driving sales. 18 , 19 Despite increasing adoption of artificial intelligence among firms, its actual impact on labour productivity and sales growth remains ambiguous, particularly in emerging economies where digital infrastructure, workforce skills, and organisational capacity differ significantly. The study provides answers to the following questions: Does AI adoption increase firm labour productivity and translate into higher sales growth? Are AI effects heterogeneous across firm size, sector, and countries? The theoretical understanding of AI and labour outcomes relied on neoclassical growth theory. The growth theory started with “The Wealth of Nations”, which was first postulated by Adam Smith in the 1950s. 20 When there is steady, constant growth, national savings are likely to equal the capital-output ratio and the growth rate of the active labour force (or workers). However, as the capital-output ratio (which implies technology, including AI) increases, there is a tendency for the employment of skilled workers (professionals or those in information and communication technology (ICT)) to rise, potentially leaving unskilled workers unemployed. The Neoclassical school of thought holds that the introduction of technology (changes in technology and increases in aggregate production) is likely to contribute to steady growth. On the other hand, improvements in the production of goods and services (driven by technologies such as AI) are likely to increase national sales and improve life satisfaction. Empirically, existing studies have found a positive association between AI and labour productivity. 2 , 13 , 21 While studies have shown that AI may affect labour productivity, it may also lead to a decline in labour income due to the uniform displacement of repetitive tasks. 3 Also, the introduction of Artificial Intelligence (AI) into business activities is likely to drive sales growth. AI mechanisms enhance operational efficiency and sales strategies. 22 , 23 AI tends to influence the dynamics of marketing capacity. 24–26 Firms are relying on AI to plan sales strategies by understanding customer choice, conducting internet marketing, forecasting, 27 , 28 and increasing turnover. Despite the enormous benefits of AI for sales growth, there are hindrances to its adoption, including the high cost of access and a lack of manpower and technology know-how. 29 , 30 In addition, a strand of the literature uses primary data collected from 697 people in small and medium enterprises, and a reliability test was conducted on the data. 31 Factor analysis was employed to construct an index of key variables used. It employed the SEM technique to investigate the relationships among AI adoption, workforce management, and marketing and sales. The study revealed that AI helps in labour force and business HR decision-making capacity. AI has a significant positive relationship with business and marketing. However, when small and medium enterprises have limited resources and innovative initiative, it would lead to a lack of technical knowledge and skilled personnel, 31 especially when operating in areas where institutions are unwilling to improve. Pointing out that small and medium enterprises have challenges that may prevent them from adopting AI, 32 such as a lack of a digital framework. It is interesting to note that there is a paucity of empirical studies on how AI enhance labour productivity 21 and sales growth. 33 As mentioned earlier, the study was based on neoclassical theory and hypothesised that AI has the potential to improve labour productivity and sales growth. Additionally, the current study employed a high-dimensional fixed effects (HDFE) estimator to address a unique question: whether AI adoption is likely to influence labour productivity growth in large labour-intensive economies and sales growth, with evidence from selected countries in sub-Saharan Africa. The remaining sections of the study include Section 2, which presents the data and methods; Section 3, which presents the empirical results; and Section 4, which concludes the study. 2. Data and methods The current study utilises a dataset from the World Bank’s Enterprise Surveys, covering various countries and years from 2007 to 2024. The World Bank Enterprise Surveys (WBES) are conducted across many countries and provide diverse firm-level data on the business environment, including conditions that affect firms. The data underlying this study are publicly available from the original data provider, the World Bank Enterprise Surveys database, at. 47 The study uses a panel dataset from 10 selected countries, including the Central African Republic, Ethiopia, Ghana, Kenya, Lesotho, Nigeria, Rwanda, Sierra Leone, South Africa, and Tanzania, covering 2007 to 2024. Variables were sorted for each country before setting a data panel. It is important to note that the question collects both subjective and objective data on firm business conditions. 34 , 35 The survey uses standardised questionnaires and uniform sampling methods. They produce rich data concerning a firm’s characteristics, as well as information on perceived constraints, including labour productivity, sales growth, innovation-related questions, 36 firm age, and international certification, among others. Data collection was initiated in 2006 in a standardised manner, allowing consistent overtime and comparative analysis across countries. 37 , 38 Access the dataset used for the analysis via. 47 The outcome variables are labour productivity growth and sales growth. The study computes labour productivity and sales growth following the methods of. 2 , 39 First, the labour productivity growth variable was generated by subtracting the number of permanent full-time workers three financial years ago (labour 3 years ago) from the number of permanent full-time workers at the end of the last financial year (size of employment) as reported in the survey questionnaire. The study generated the previous labour value by dividing past sales dollar conversion by labour in 3 years ago, and the current labour value by dividing current sales dollar conversion by current labour full-time or the size of employment. The study calculates labour productivity growth by generating the current labour value divided by the previous labour value, multiplied by one divided by the current year minus the previous year, minus one. Sales growth is computed the same way as labour productivity growth. The sales-related question is: what are this establishment’s total annual sales for all products and services (current sales), and what were its sales three years ago (previous sales). The study generated sales-dollar conversion for both current and prior sales. Sales growth was calculated by dividing the current year’s sales by the previous year’s sales, minus the difference between the two years. The current study is unique in that it focuses on selected African countries, rather than a cross-sectional study like, 39 which examines the association between innovation and firm performance in India. Uses WBES 2013–14 and the probit model. The study found that long years spent in informal and unregistered firms lead to faster innovation than in other firms. 2 employed a dataset from the Community Innovation Survey (CIS) 2018–2019 and the 2SLS technique (with an instrumental variable, IV) to investigate AI adoption and firm productivity in Germany. The study remarks that AI improves firm productivity. Furthermore, the AI was derived by generating an index of various innovation questions, which comprises New or significantly improved products introduced in the last three years. An improved organisational structure has been introduced in the last 3 years. Over the last three years, has it provided employees with sufficient time to develop new ideas, and has it allocated funds for formal research and development activities? The study uses multiple correspondence analyses to generate the AI index. Additionally, the study creates information asymmetry through mechanisms of international certification, including a firm being Publicly Listed on a stock exchange (with or without shareholders) and undergoing an external audit. It is interesting to note that variables for artificial intelligence and information asymmetry are dummy variables. In the computation, the study uses the MCA command in Stata version 15 to predict the index, then multiplies it by −1 to account for possible errors in the coordinate. Other variables include firm size (micro, small, medium, and large firms), firm age, and industry (a dummy variable). Table 1 presents a summary of the dependent variable, labour productivity growth, with a mean of 0.102 and a range of −0.927 to 195.32. Sales growth ranges from −1 to 36. The average age of the firm is 17 years, ranging from 0 to 220 years. The average of the IA index is −0.04, and the standard deviation is approximately 1. It ranges between −1.62 and 5.12. Generally, the standard deviation measures the dispersion of a dataset from its mean (average), indicating that the data is likely to be consistent for all stages of the analysis. Table 1. Descriptive Statistics. Variable Obs Mean Std. Dev. Min Max Labour Productivity Growth 11837 0.102 2.731 −0.927 195.32 AI index 12578 −0.089 0.807 −1.441 7.087 Firm age 12578 17.014 14.397 0 220 Firm size Micro 11968 0.153 0.36 0 1 Small 11968 0.53 0.499 0 1 Medium 11968 0.235 0.424 0 1 Large 11968 0.082 0.275 0 1 IA index 12579 −0.039 0.951 −1.622 5.124 Sales growth 11889 −0.048 0.998 −1 36.283 Foreign own 12579 0.071 0.257 0 1 Industries Fabricated metal 8842 0.038 0.191 0 1 Food and beverages 11749 0.086 0.281 0 1 Furniture 7034 0.015 0.12 0 1 Garments 10822 0.055 0.228 0 1 Nonmetallic 9014 0.032 0.176 0 1 Publishing 7034 0.015 0.12 0 1 Other manufacturing 12579 0.109 0.312 0 1 Other services 12579 0.103 0.304 0 1 Repair sales of motor vehicles 7034 0.011 0.102 0 1 Wholesales 7034 0.016 0.127 0 1 Hotel and restaurant 10019 0.043 0.204 0 1 Retails 12049 0.09 0.287 0 1 Transportation 8842 0.025 0.156 0 1 Manufacturing panel 8842 0.036 0.187 0 1 Retail panel 7034 0.023 0.149 0 1 Other service panel 7034 0.018 0.132 0 1 2.1 Methods The study first employed robust ordinary least squares (OLS), which examines the relationship between the dependent and independent variables and controls for model stability and estimation despite uncertainty, disturbances, or outliers. Additionally, the employed high-dimensional fixed-effect estimator accounts for unobserved heterogeneity that may otherwise bias the results and is nested within a robust standard error, rather than absorbing cluster identification. The model is as follows; Labour productvity growth it = β 0 + β 1 AI it + β 2 Firm age it + β 3 IA it + β 4 X it + ε it Where AI it denote artificial intelligence; IA it implies information asymmetry; X it denote other independent variables (such as foreign ownership; legal status included in country-specific estimation). β 0 is an intercept or a constant. β 1 , β 2 , β 3 , and β 4 are the coefficients of the regressor and control variables. 3. Results and interpretation Table 2 presents a correlation between the dependent and independent variables. The correlation suggests that labour production growth and the AI index are likely to be positively correlated (r = 0.019). Labour productivity and information asymmetry are positively correlated (r = 0.031). Labour productivity and foreign ownership are positively correlated (r = 0.018). Labour productivity and firm age are negatively correlated (r = −0.020). Existing studies have found that a firm’s age is negatively associated with productivity. 40 , 41 Table 2. Matrix of correlations. Variables (1) (2) (3) (4) (5) (1) Labour Productivity G 1.000 (2) AI 0.019 1.000 (3) Firm age −0.020 0.080 1.000 (4) IA index 0.031 0.245 0.187 1.000 (5) Foreign-owned 0.018 0.124 0.115 0.238 1.000 Table 3 presents the VIFs of the independent variables, which are moderately correlated as they satisfy the rule of thumb. The rule of thumb for the variance inflation factor (VIF) states that VIFs should be less than 10. If it is less than 10, it suggests the possibility of multicollinearity in the analysis. Table 3. Variance inflation factor. VIF 1/VIF IA index 1.143 0.875 foreign own 1.071 0.934 AI index 1.07 0.935 Firm age 1.043 0.959 Mean VIF 1.082 In Table 4 (1), the study employs feasible generalised least squares (FGLS) to control for autocorrelation in a model with no precise error structure. The study performed ordinary least squares (OLS) regression, obtained residuals, estimated the variance, assessed model heteroscedasticity, predicted the variance, generated weights, and plugged them into the model. Other columns (2–5) applied OLS robust. Table 4 column (1) presents that there is a significant positive relationship between labour productivity growth and AI. 2 Also, information asymmetry has a significant positive relationship with labour productivity growth. However, firm age and foreign ownership of business have a significant negative effect on labour productivity growth. Column (2) presents that the information asymmetry has a significant positive relationship with labour productivity growth for micro-scale enterprises. Sales growth has a significant positive relationship with labour productivity. However, firm age has a significant negative relationship with labour productivity growth. This implies that, as firms age, they tend to be unable to keep pace with technological advancements in sub-Saharan Africa. There is a positive relationship between labour productivity and sales growth. Sales growth in micro-scale enterprises is higher than in other types of enterprises, such as small-scale enterprises. Table 4. Labour productivity growth and AI: robust OLS. (1) (2) (3) (4) (5) Panel Micro Small Medium Large Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.00702 * −0.0163 0.0235 −0.0170 0.0261 (0.00427) (0.0784) (0.0285) (0.0208) (0.0280) Firm age −0.000557 *** −0.00757 0.000285 −0.00323 *** −0.00173 *** (0.000198) (0.00486) (0.00147) (0.000973) (0.000390) IA index 0.00888 *** 0.416 *** 0.0166 −0.00339 0.0203 (0.00332) (0.149) (0.0110) (0.0150) (0.0124) Foreign ownership −0.0242 * (0.0146) Sales Growth 6.265 *** 1.145 *** 0.803 *** 0.790 *** (0.891) (0.207) (0.0325) (0.0661) Constant −0.0581 *** 2.931 *** 0.0726 *** 0.126 *** 0.138 *** (0.00713) (0.424) (0.0227) (0.0405) (0.0258) Year dummy Yes Observations 11,837 1,737 6,311 2,805 984 R-squared 0.253 0.732 0.430 0.385 0.716 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetry index; AI implies artificial intelligence; Source: computed by the Author 2026 Column (3) presents that the firm age has a significantly negative relationship with labour productivity growth for small-scale enterprises. Sales growth has a significant positive relationship with labour productivity. This is consistent with the literature. 42 A previous study remarked that innovation (AI technology) is likely to enhance productivity, and an increase in sales growth is likely to increase labour productivity. 42 Sales growth in small-scale enterprises follows that of micro-scale enterprises. It is higher than medium and large enterprises. This indicates that micro and small enterprises are growing faster than medium and large enterprises in sales and labour productivity. Column (4) presents that information asymmetry has an insignificant negative relationship with labour productivity growth for medium-scale enterprises. Sales growth has a significant positive relationship with labour productivity. While firm age has a significant negative relationship with labour productivity growth. Column (5) presents that artificial intelligence has an insignificant positive relationship with labour productivity growth for large-scale enterprises. This is expected because large-scale enterprises may be able to expand their production capacity using capital-intensive technology. Also, sales growth has a significant positive relationship with labour productivity. Sales growth in large-scale enterprises is the lowest among all types, including micro, small, and medium enterprises. Table 5 column (1) presents that there is a significant positive relationship between AI and sales growth. 2 However, firm age has a significant negative relationship with sales growth. Column (2) presents that AI has an insignificant negative relationship with sales growth for micro-scale enterprises. Column (3) presents that the information asymmetry has a significant positive relationship with sales growth for small-scale enterprises. The firm age has a significant negative relationship with sales growth among micro-scale enterprises. Column (4) presents that firm age has a significant negative relationship with sales growth for medium-scale enterprises. Column (5) presents that firm age has a significant negative relationship with sales growth for large-scale enterprises. Table 5. Sales growth and AI: robust OLS. (1) (2) (3) (4) (5) Panel Micro Small Medium Large Variables Sales Growth Sales Growth Sales Growth Sales Growth Sales Growth AI 0.0420 *** −0.0101 0.0769 *** −0.0290 0.0323 (0.0125) (0.0278) (0.0213) (0.0268) (0.0331) Firm age −0.00116 ** 0.000292 −0.00385 *** −0.00271 *** 0.00138 * (0.000470) (0.00170) (0.000689) (0.000907) (0.000752) IA index 0.0172 0.0624 −0.00120 −0.00240 0.0938 (0.0151) (0.0434) (0.0190) (0.0209) (0.0593) Foreign ownership 0.0717 (0.0503) Constant −0.0150 * −0.363 *** 0.0991 *** 0.0567 ** −0.216 *** (0.00784) (0.0510) (0.0201) (0.0285) (0.0473) Year dummy Yes Observations 11,889 1,761 6,332 2,810 986 R-squared 0.141 0.002 0.006 0.003 0.014 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Source: computed by the Author 2026 High-dimensional fixed effects (HDFE) Linear regression. To conduct a robustness check of robust OLS, the study employs high-dimensional fixed effects (HDFE) linear regression, with standard errors corrected following. 43 Table 6 column (1) presents that AI has an insignificant positive impact on labour productivity growth. The information asymmetry has a significant positive impact on labour productivity growth. While firm age has a significant negative effect on labour productivity growth. Column (2) presents that information asymmetry has a significant positive impact on labour productivity growth for micro-scale enterprises. Sales growth has a significant positive impact on labour productivity. However, the AI has a significant negative impact on labour productivity growth for micro-business enterprises. Column (3) presents that the information asymmetry has an insignificant positive impact on labour productivity growth for small-scale enterprises. Sales growth has a significant positive impact on labour productivity. Column (4) presents that firm age has a significant negative effect on labour productivity growth for medium-scale enterprises. Sales growth has a significant positive impact on labour productivity. Column (5) presents that sales growth has a significant positive impact on labour productivity for large-scale enterprises. Table 6. Labour productivity and AI using the HDFE estimator. (1) (2) (3) (4) (5) Panel Micro Small Medium Large Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.0245 −0.151 * −0.0140 −0.0323 0.00689 (0.0316) (0.0804) (0.0205) (0.0210) (0.0196) Firm age −0.00352 * 0.00296 1.41e-05 −0.00216 * −0.00111 (0.00181) (0.00571) (0.00126) (0.00122) (0.000846) IA index 0.0892 *** 0.403 *** 0.00809 −0.00502 0.0126 (0.0280) (0.0838) (0.0206) (0.0172) (0.0166) Foreign ownership 0.0282 (0.0989) Sales growth 6.626 *** 1.246 *** 0.851 *** 0.837 *** (0.0838) (0.0176) (0.0203) (0.0162) Constant 0.164 *** 2.878 *** 0.0670 *** 0.106 *** 0.136 *** (0.0407) (0.120) (0.0246) (0.0301) (0.0311) Observations 11,837 1,737 6,311 2,805 984 R-squared 0.020 0.790 0.466 0.426 0.755 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Source: computed by the Author 2026 Table 7 column (1) presents that AI has a significant positive impact on sales growth. Information asymmetry has a significant positive impact on sales growth. Foreign ownership has a significant positive impact on sales growth. However, firm age has a significant negative effect on sales growth. It is worth noting that labour efficiency is likely to lead to higher output 42 and increased sales. Also, when a firm adopts AI, the production process is likely to be enhanced, leading to improvement in products and better sales performance. Table 7. Sales growth and AI using the HDFE estimator. (1) (2) (3) (4) (5) Micro Small Medium Large Variables Sales Growth Sales Growth Sales Growth Sales Growth Sales Growth AI 0.0420 *** −0.00694 0.0916 *** −0.0124 0.0684 * (0.0108) (0.0335) (0.0146) (0.0196) (0.0388) Firm age −0.00116 * −0.000616 −0.00203 ** −0.000363 0.000232 (0.000620) (0.00238) (0.000902) (0.00114) (0.00168) IA index 0.0172 * 0.0579 * 0.0172 −0.0150 0.0544 * (0.00956) (0.0348) (0.0147) (0.0160) (0.0327) Foreign ownership 0.0717 ** (0.0337) Constant −0.0301 ** −0.350 *** 0.0766 *** 0.0118 −0.165 *** (0.0139) (0.0485) (0.0176) (0.0281) (0.0613) Observations 11,889 1,761 6,332 2,810 986 R-squared 0.141 0.029 0.166 0.153 0.153 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Column (2) presents that information asymmetry has a significant positive impact on sales growth for micro-scale enterprises. Column (3) presents that AI has a significantly positive influence on sales growth for small-scale enterprises. Firm age has a significant negative impact on sales growth for small-scale enterprises. Column (4) presents that AI has an insignificant negative impact on sales growth for medium-scale enterprises. Column (5) presents that artificial intelligence has a significant positive effect on sales growth for large-scale enterprises. Also, information asymmetry has a significant positive impact on sales growth. It is plausible that small enterprises experience higher sales growth than large enterprises because they are better able to adapt to market changes and rely less on management decision-making. Large firms are likely to have administrative procedures that may slow decision-making in the marketplace. Micro and small enterprises have faster decision-making processes. Table 8 column (1) presents that sales growth has a significant negative relationship with labour productivity growth for the Central African Republic (CAR). Having a legal status did not increase the labour productivity growth in CAR. Notwithstanding, IA IA index denotes information asymmetric index; AI implies artificial intelligence; Legal status means company with shareholders and without shareholders Source: computed by the Author 2026 is likely to increase labour productivity growth in the CAR. Column (2) presents that sales growth and firm age have a significant positive relationship with labour productivity growth for Ethiopia. However, AI is unlikely to increase labour productivity in Ethiopia. Column (3) displays that sales growth has a significant positive relationship with labour productivity growth for Ghana. Column (4) presents that the AI has a significant negative relationship with labour productivity growth for Kenya. Firm age and sales growth are positively and significantly related to labour productivity within the same country. Column (5) presents that artificial intelligence has a significant positive relationship with labour productivity growth for Lesotho. While sales growth has a negative relationship with labour productivity growth. The reasons for negative sales growth in Lesotho may include institutional factors, firm-level challenges, infrastructural constraints, and external market competition, among others. Neighbouring countries such as South Africa may be strong competitors to Lesotho in the external market. Hence, their sales may eventually decline. Table 8. Labour productivity and AI by country. (1) (2) (3) (4) (5) Central African Republic Ethiopia Ghana Kenya Lesotho Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI −7.04e-07 −0.00981 *** −0.0883 −0.00189 *** 4.36e-05 ** (3.64e-05) (0.00271) (0.111) (0.000580) (2.03e-05) Firm age −5.61e-06 0.000955 *** 0.0341 0.000199 *** −8.16e-07 (4.01e-06) (0.000177) (0.0242) (3.28e-05) (1.60e-06) IA index 0.00106 ** 0.00431 0.411 0.00125 −3.75e-05 (0.000512) (0.00341) (0.300) (0.000846) (2.28e-05) Legal status −0.000261 * −0.000139 −0.463 0.00161 7.37e-05 (0.000149) (0.00841) (0.484) (0.00182) (6.54e-05) Sales growth −0.992 *** 1.718 *** 7.555 *** 0.903 *** −0.937 *** (0.0629) (0.0605) (0.332) (0.0143) (0.0754) Constant 0.000613 *** 0.789 *** 6.598 *** −0.0474 *** 2.81e-05 (0.000230) (0.0371) (0.279) (0.00518) (4.22e-05) Observations 78 901 479 1,901 124 R-squared 0.749 0.755 0.944 0.854 0.823 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Legal status means company with shareholders and without shareholders Source: computed by the Author 2026 Table 9 column (1) presents that AI has a significant positive relationship with labour productivity growth in Nigeria. Similarly, sales growth has a significant positive relationship with labour productivity growth. Column (2) presents that sales growth and firm age have a significant positive relationship with labour productivity growth for Rwanda. Column (3) displays that sales growth has a significant positive relationship with labour productivity growth for Sierra Leone. Informatin asymmetry may likely reduce labour productivity growth in Sierra Leone. Column (4) presents that AI has a significant positive relationship with labour productivity growth in South Africa. Also, firm age has a significant positive relationship with labour productivity growth in South Africa. Similarly, sales growth has a significant positive relationship with labour productivity growth. Legal status (shareholder involvement) may likely reduce labour productivity growth in South Africa. Column (5) presents that firm age has a significant negative relationship with labour productivity growth for Tanzania. Also, legal status and sales growth have a significant negative relationship with labour productivity growth. Table 9. Labour productivity and AI by country. (1) (2) (3) (4) (5) Nigeria Rwanda Sierra Leone South Africa Tanzania Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.0146 *** −0.0955 0.000627 0.00194 *** 1.20e-05 (0.00558) (0.104) (0.00196) (0.000526) (8.36e-06) Firm age 0.000294 −0.00669 5.91e-05 0.000113 *** −1.01e-06 * (0.000239) (0.00412) (0.000218) (2.20e-05) (5.17e-07) IA index −0.00326 −0.00259 −0.00479 ** −0.000616 4.33e-06 (0.00438) (0.138) (0.00219) (0.000596) (9.62e-06) Legal status 0.0268 0.0542 0.0142 −0.00443 * −8.04e-05 * (0.0325) (0.337) (0.0137) (0.00251) (4.62e-05) Sales growth 0.935 *** 529.8 *** 0.904 *** 0.862 *** −0.925 *** (0.0164) (176.4) (0.0473) (0.0237) (0.0345) Constant −0.0329 *** 529.5 *** −0.0207 *** −0.0186 *** 8.97e-05 *** (0.00525) (176.2) (0.00582) (0.00142) (1.49e-05) Observations 5,500 299 326 1,807 422 R-squared 0.981 0.390 0.628 0.786 0.738 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Legal status means company with shareholders and without shareholders Source: computed by the Author 2026 Table 10 column (1) presents that AI has an insignificant negative relationship with sales growth for the Central African Republic. Column (2) presents that AI has a significant positive relationship with sales growth for Ethiopia. Shareholders’ involvement is likely to increase sales growth in Ethiopia. Firm age has a significant negative relationship with labour productivity. It is surprising that IA may reduce sales growth in Ethiopia. Column (3) displays that information asymmetry has a significant positive relationship with sales growth for Ghana. Column (4) presents that firm age has a significant negative relationship with sales growth for Kenya. Column (5) presents that AI is likely to increase the sales growth in Lesotho. Whereas artificial intelligence may reduce the sales growth in the same country, Lesotho. Table 10. Sales growth and AI by country. (1) (2) (3) (4) (5) Central African Republic Ethiopia Ghana Kenya Lesotho Variables Sales growth Sales growth Sales growth Sales growth Sales growth AI index −3.95e-05 0.0105 *** −0.0721 0.00111 5.84e-05 * (6.54e-05) (0.00272) (0.0922) (0.00149) (3.49e-05) Firm age −1.44e-06 −0.000570 *** 0.00490 −0.000339 *** 3.62e-06 (1.04e-05) (0.000147) (0.00966) (9.37e-05) (4.89e-06) IA index 0.000467 −0.00528 * 0.159 * −0.00183 −8.98e-05 * (0.00105) (0.00282) (0.0926) (0.00224) (5.03e-05) Legal status −5.10e-05 0.0198 ** 0.210 −0.00565 7.70e-05 (0.000402) (0.00846) (0.338) (0.00447) (0.000114) Constant 0.000530 −0.594 *** −0.696 *** −0.349 *** 0.000156 * (0.000481) (0.00364) (0.155) (0.00266) (8.80e-05) Observations 78 902 504 1,902 125 R-squared 0.011 0.036 0.006 0.014 0.036 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Legal status means company with shareholders and without shareholders Source: computed by the Author 2026 Table 11 column (1) presents that AI has a significant positive relationship with sales growth in Nigeria. Having legal status (shareholder involvement) may likely increase sales growth in the Nigerian case. Column (2) presents that firm age has a significant negative relationship with sales growth for Rwanda. Column (3) displays that legal status has a significant negative relationship with sales growth for Sierra Leone. Column (4) presents that AI has a significant negative relationship with sales growth in South Africa. Similarly, firm age has a significant positive relationship with sales growth. Information asymmetry has a significant positive relationship with sales growth in South Africa. Column (5) presents that AI has an insignificant negative relationship with sales growth for Tanzania. Table 11. Sales growth and AI by country. (1) (2) (3) (4) (5) Nigeria Rwanda Sierra Leone South Africa Tanzania Variables Sales growth Sales growth Sales growth Sales growth Sales growth AI 0.218 *** −0.000151 −0.00270 −0.00264 ** 9.42e-06 (0.0437) (0.000187) (0.00267) (0.00108) (1.19e-05) Firm age 6.16e-06 −2.26e-05 ** 0.000227 −0.000253 *** 9.80e-07 (0.00170) (1.07e-05) (0.000277) (4.24e-05) (7.30e-07) IA index −0.0203 −8.39e-05 −0.000277 0.0129 *** 1.27e-05 (0.0260) (0.000200) (0.00311) (0.00121) (1.88e-05) Legal status 0.553 ** 0.000255 −0.0486 * 0.00160 −5.39e-05 (0.261) (0.000612) (0.0278) (0.00953) (6.95e-05) Constant 0.258 *** −0.998 *** −0.0839 *** −0.0511 *** 0.000130 *** (0.0279) (0.000292) (0.00479) (0.00148) (1.88e-05) Observations 5,520 300 327 1,808 423 R-squared 0.018 0.008 0.052 0.090 0.008 *** p < 0.01, ** p < 0.05, * p < 0.1; IA index denotes information asymmetric index; AI implies artificial intelligence; Legal status means company with shareholders and without shareholders Source: computed by the Author 2026 Furthermore, the study examines the industry to determine whether contributions from specific industries drive labour productivity growth. Table 12 column (1) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, but fabricated metal has an insignificant negative relationship with labour productivity growth. Column (2) revealed that information asymmetry has a significant positive relationship with labour productivity growth, but Food and beverages have an insignificant positive relationship with labour productivity growth. Column (3) displays that AI and information asymmetry have a significant positive relationship with labour productivity growth, and furniture has a significant positive relationship with labour productivity growth. Column (4) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, but garment has a significant negative relationship with labour productivity growth. Column (5) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, whereas non-metallic minerals have an insignificant negative relationship. Column (6) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, whereas publishing has an insignificant positive relationship. Table 12. Labour Productivity Growth and AI by industry. (1) (2) (3) (4) (5) (6) Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.102 *** 0.0559 0.139 *** 0.0626 *** 0.0782 *** 0.137 *** (0.0206) (0.0389) (0.0292) (0.0142) (0.0181) (0.0291) Firm age −0.00175 *** −0.00479 *** 0.000388 −0.00322 *** −0.00461 *** 0.000281 (0.000549) (0.00171) (0.00129) (0.000425) (0.000678) (0.00128) IA index 0.0350 * 0.0968 ** 0.0294 0.0298 * 0.0363 * 0.0315 (0.0200) (0.0420) (0.0254) (0.0163) (0.0207) (0.0262) Fabricated metal −0.0354 (0.0598) Food & beverages 0.0369 (0.138) Furniture 0.552 ** (0.238) Garment −0.0932 *** (0.0295) Nonmetallic mineral −0.0793 (0.0651) Publishing 0.183 (0.186) Constant 0.139 *** 0.181 *** 0.142 *** 0.0781 *** 0.110 *** 0.149 *** (0.0154) (0.0461) (0.0226) (0.0132) (0.0164) (0.0225) Observations 8,208 11,010 6,401 10,109 8,302 6,401 R-squared 0.009 0.002 0.015 0.008 0.009 0.011 *** p < 0.01, ** p < 0.05, * p < 0.1; Source: computed by the Author 2026 Table 13 column (1) shows that information asymmetry has a significant positive relationship with labour productivity growth, but other manufacturing has an insignificant positive relationship with labour productivity growth. Column (2) revealed that information asymmetry has a significant positive relationship with labour productivity growth, but other services have an insignificant negative relationship with labour productivity growth. Column (3) shows that AI and information asymmetry are positively and significantly associated with labour productivity growth. Also, motor vehicle repair sales have a significant positive relationship with labour productivity growth. Column (4) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, but wholesales have a significant negative relationship with labour productivity growth. Column (5) shows that information asymmetry has a significant positive relationship with labour productivity growth, whereas hotels and restaurants show an insignificant positive relationship. Table 13. Labour Productivity Growth and AI by Industry. (1) (2) (3) (4) (5) Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.0418 0.0453 0.136 *** 0.137 *** 0.0581 (0.0363) (0.0353) (0.0290) (0.0292) (0.0474) Firm age −0.00586 *** −0.00504 *** 0.000349 0.000356 −0.00612 ** (0.00182) (0.00154) (0.00129) (0.00129) (0.00249) IA index 0.0915 ** 0.0928 ** 0.0322 0.0326 0.121 ** (0.0402) (0.0403) (0.0262) (0.0262) (0.0540) Other manufacturing 0.287 (0.186) Other services −0.0674 (0.0477) Repair sales of motor 0.725 ** (0.341) Wholesales −0.182 * (0.103) Hotel & restaurant −0.0122 (0.0722) Constant 0.176 *** 0.202 *** 0.143 *** 0.154 *** 0.231 *** (0.0404) (0.0490) (0.0219) (0.0222) (0.0646) Observations 11,837 11,837 6,401 6,401 9,281 R-squared 0.003 0.002 0.016 0.011 0.002 *** p < 0.01, ** p < 0.05, * p < 0.1; Source: computed by the Author 2026 Table 14 column (1) shows that information asymmetry has a significant positive relationship with labour productivity growth, but retail has a significant negative relationship with labour productivity growth (LPG). Column (2) revealed that information asymmetry has a significant positive relationship with labour productivity growth, while transportation has a significant negative relationship with labour productivity growth. Column (3) displays that AI and information asymmetry have a significant positive relationship with labour productivity growth, and the manufacturing panel has a significant positive relationship with labour productivity growth. Column (4) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, and the retail panel has a significant positive relationship with labour productivity growth. Column (5) shows that AI and information asymmetry have a significant positive relationship with labour productivity growth, while the other service panel also exhibits a significant positive relationship with labour productivity growth. Table 14. Labour Productivity Growth and AI by industry. (1) (2) (3) (4) (5) Variables Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth Labour Productivity Growth AI 0.0462 0.102 *** 0.0960 *** 0.130 *** 0.135 *** (0.0382) (0.0206) (0.0208) (0.0289) (0.0289) Firm age −0.00515 *** −0.00164 *** −0.00191 *** 0.000210 −9.66e-05 (0.00159) (0.000549) (0.000550) (0.00129) (0.00130) IA index 0.0942 ** 0.0333 * 0.0366 * 0.0345 0.0254 (0.0418) (0.0197) (0.0198) (0.0261) (0.0263) Retails −0.127 *** (0.0479) Transportation −0.157 *** (0.0440) Manufacturing panel 0.338 *** (0.105) Retail panel 0.542 *** (0.145) Other service panel 0.706 *** (0.204) Constant 0.216 *** 0.140 *** 0.127 *** 0.139 *** 0.143 *** (0.0497) (0.0153) (0.0150) (0.0218) (0.0218) Observations 11,309 8,208 8,208 6,401 6,401 R-squared 0.002 0.010 0.014 0.017 0.019 *** p < 0.01, ** p < 0.05, * p < 0.1; Source: computed by the Author 2026 4. Conclusion The empirical analysis offers insight into how AI can affect labour productivity and sales growth in sub-Saharan African countries, utilising a WBES dataset. The study selected 10 countries in sub-Saharan Africa, which are growing economies in terms of gross domestic product (GDP). We generated an AI variable from the proxy of innovation-related variables in the enterprise survey dataset. The current study investigated the relationships between artificial intelligence (AI) and labour productivity growth, as well as between AI and firm-level sales growth. The results revealed that AI is an enabler of productivity improvement 1 , 14 through capacity building, professional advancement, and understanding of timely technological advancements. It is noteworthy that harmonising investment in capacity building and enabling innovation in organisations are prerequisites for the AI and labour productivity outcomes. When firms move in tandem with technological advancements and creativity in their production. Firms’ innovation in goods and services is likely to attract customers, leading to a spillover effect on sales growth. However, the effects of AI on labour productivity and sales growth vary by firm size. The findings show that micro-scale enterprises make the highest contributions to labour productivity 44 and sales growth. 14 The results show an upward-downward trend among firms, with large firms making the least contribution to labour productivity in Africa, followed by medium-scale enterprises. The contribution of small-scale enterprises was higher than that of medium and large-scale enterprises. Also, the results show that AI, labour productivity, and sales growth yield diverse outcomes across the selected countries. Counties such as Ethiopia and Nigeria have a significant positive relationship between their AI and labour productivity. 45 , 46 One limitation of this study is that some countries with a large number of observations might determine the outcomes. AI has a significant positive relationship with labour productivity and sales growth; however, the outcome varies across industries. Industries such as furniture, motor vehicle repair sales, manufacturing, retail, and other service sectors influence labour productivity growth. Overall, the results underline the strategic importance of AI not only as a technological upgrade but as a catalyst for firm performance and growth in sub-Saharan African countries. With a large population in Africa, investment in AI is likely to be a lucrative opportunity in a rapidly changing environment. The study suggests government policy recommendations and industry strategies that facilitate a smooth transition of AI-driven initiatives and foster their success. The study could not identify a relevant instrument (IV) to control endogeneity. The study made a significant contribution to the literature by improving our understanding of AI and labour productivity using firm-level datasets and by employing techniques that control for unobserved heterogeneity, eliminate bias from omitted-variable bias within groups, and account for standard errors. Future research should investigate the longitudinal impacts, sectoral differences, and inclusive AI deployment on shaping sustainable firm outcomes. This project contains the following underlying data: Enterprise survey dataset (firm-level data). The study uses a panel dataset from 10 selected countries, including the Central African Republic, Ethiopia, Ghana, Kenya, Lesotho, Nigeria, Rwanda, Sierra Leone, South Africa, and Tanzania, covering 2007 to 2024. Contains information on firm characteristics, labour productivity, sales, and business environment indicators across Sub-Saharan African countries. Processed dataset used for analysis. Variables were sorted for each country before setting a data panel. The data processing includes cleaned and transformed variables used to estimate labour productivity and sales growth. Replication files. Contains the dataset used to compute summary statistics and generate tables and figures presented in the study. The data are publicly available from the World Bank Enterprise Surveys portal and can be accessed upon registration. The processed dataset is available at https://doi.org/10.6084/m9.figshare.32055780 , 47 and the replication material used in this study is available from the corresponding author. The cleaned data is available under the terms of the Figshare.com metadata access policy (compatible with CC BY 4.0 use). Data availability Name of data repository: figshare.com Title of dataset: Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 12 May 2026 ADD YOUR COMMENT Comment Author details Author details Sustainable Livelihoods, University of South Africa Graduate School of Business Leadership, Johannesburg, Gauteng, 1686, South Africa Olanrewaju Adewole Adediran Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 12 May 2026, 15:711 https://doi.org/10.12688/f1000research.179912.1 Copyright © 2026 Adewole Adediran O. 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