Impact of Adopting Artificial Intelligence on the Profitability of Manufacturing Industries in Zimbabwe: A Systematic analysis

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However, in developing economies such as Zimbabwe, the extent to which AI adoption contributes to profitability remains inadequately explored. This study examines the impact of AI adoption on profitability in Zimbabwe’s manufacturing sector using a systematic review of secondary data from sources published between 2000 and 2026. The analysis is anchored in the Technology Acceptance Model (Venkatesh and Bala, 2008), the Resource-Based View (Barney, 1991), and Schumpeter’s Innovation Theory (Schumpeter, 1934). The findings suggest that AI adoption enhances firm performance by streamlining production processes, lowering operational costs, and enabling data-driven decision-making (Brynjolfsson and McAfee, 2014; McKinsey Global Institute, 2018). However, adoption remains constrained by high implementation costs, inadequate infrastructure, limited technical expertise, and regulatory uncertainty (Acemoglu and Restrepo, 2018; Ndung’u and Signé, 2020). The study recommends investment in digital infrastructure, skills development, and policy frameworks to facilitate AI integration. These insights are valuable for policymakers and industry stakeholders seeking to improve competitiveness and profitability in Zimbabwe’s manufacturing sector. Artificial Intelligence barriers to adoption manufacturing industries profitability Zimbabwe 1. Introduction and Background The whole landscape of manufacturing and production globally is facing a transformation under the 4IR, which is the blurring of digital technologies, robotics and AI. As Schwab (2016) stated, the 4IR is a merging of technologies that converge the physical and digital spheres and even the biology, which lead to the reform of the way of how the industries create, handle and deliver the value. The AI, which is one of the most disruptive technologies of this era, has proven to be highly potential to improving operation effectiveness and efficiency, energy conservation and cost saving in manufacturing industries, such as Brynjolfsson and McAfee (2014) indicated.). Meanwhile manufacturers throughout Africa, and in Zimbabwe's case particularly, have long been afflicted by low levels of productivity, aging machinery and little recourse to capital or technology (African Development Bank, 2022). Zimbabwe's once vibrant manufacturing sector that used to contribute immensely to the country's GDP has been on a long-drawn decline attributed to hyperinflation, policy chaos and crumbling infrastructures (Reserve Bank of Zimbabwe, 2023). The contribution of the sector to GDP, which was around 25 per cent in the 1990s, had fallen to under 10 per cent by 2020 owing to deep-seated structural problems (Zimbabwe National Statistics Agency, 2022). In this context, adoption of AI opens up opportunity for reviving Zimbabwe's manufacturing industries and hence their contribution to the national economy growth. Such applications worldwide span predictive maintenance, quality control, supply chain optimization, demand forecasting, and robotic process automation in manufacturing (McKinsey Global Institute, 2018).It has also been demonstrated, that these are applications of Industry 4.0 to decrease operational costs by up to 20% and increase production efficiency by up to 30% in developed countries (PwC, 2017).In the meantime, the take-up path in developing countries such as Zimbabwe -is–distinctly- cleaved by structural, financial and institutional barriers (Ndung’u and Signé, 2020). These are the dynamics that policymakers and industry actors will have to come to grips with if they want to use AI as an engine of industrial development. The government of Zimbabwe through the National Development Strategy 1 (NDS1) 2021–2025 has recognized the need for digital transformation and technology take-up as key drivers for reviving the economy (Government of Zimbabwe, 2020). Also When Zimbabwe Digital Economy Strategy 2023–2027 Lists AI and Automation as Investment, Policy Support and Priority Areas (Ministry of Information Communication Technology, 2023). While these policy statements exist, there is paucity of empirical research on the influence of AI investment on profitability of cane sugar manufacturing firms in Zimbabwe and hence there is a gap of knowledge which this study intends to fill. Problem Statement Zimbabwe’s manufacturing sector continues to experience structural challenges, including outdated production technologies, high operating costs, and limited integration of advanced digital systems, which collectively undermine productivity and competitiveness (Confederation of Zimbabwe Industries, 2023; Zimbabwe National Statistics Agency, 2022). While Artificial Intelligence has been associated with improved efficiency, cost reduction, and enhanced decision-making in developed economies (Brynjolfsson and McAfee, 2014; McKinsey Global Institute, 2018), its adoption and measurable impact within Zimbabwe’s manufacturing industries remain limited and insufficiently documented. Existing literature indicates that AI-driven innovations can significantly enhance firm performance when supported by appropriate resources and capabilities (Barney, 1991; Acemoglu and Restrepo, 2018). However, in developing economies, structural constraints such as inadequate infrastructure, limited access to finance, and shortages of skilled human capital restrict the effective adoption of advanced technologies (Ndung’u and Signé, 2020; Mudzonga, Hlatshwayo and Ncube, 2023). In Zimbabwe, these challenges are further compounded by macroeconomic instability and policy uncertainty, which discourage long-term technological investments. Despite increasing global emphasis on digital transformation, there is limited consolidated evidence examining how AI adoption influences profitability within Zimbabwe’s manufacturing sector and how existing barriers shape this relationship. This gap limits the ability of policymakers and industry stakeholders to formulate effective strategies. This study therefore evaluates the impact of AI adoption on profitability and examines the barriers affecting its implementation in Zimbabwe’s manufacturing industries. 1.2 Research Objectives The study is guided by the following research objectives: To assess the impact of Artificial Intelligence adoption on profitability in Zimbabwe's manufacturing industries. To identify barriers to Artificial Intelligence adoption and their effects on profitability in Zimbabwe's manufacturing industries. To recommend policies that could enhance Artificial Intelligence adoption and boost profitability in Zimbabwe's manufacturing industries 2. Literature Review 2.1 Theoretical Framework This study is anchored in three theoretical frameworks that collectively explain the adoption of AI technologies and their relationship with organisational performance and profitability. 2.1.1 Technology Acceptance Model (TAM) Originated in the field of information systems, the Technology Acceptance Model (TAM) is a widely used model for investigating user acceptance of new technology and was initially introduced by Davis (1989), it argues that users decisions to accept or reject new technology can be explained mainly by two factors; Perceived usefulness and perceived ease of use. When it comes to AI application in manufacturing, TAM provides insight into the question of why manufacturing organizations in Zimbabwe would wish to adopt or not adopt such technologies. Venkatesh and Bala (2008) adapted TAM to add additional factors including subjective norms and facilitating conditions, which can be considered very relevant in Zimbabwe as social and institutional influence are high in the decision to adopt technology. TAM has been used in various studies to explore the adoption of AI in the industry and has generally confirmed that perceived usefulness continues to be the most influential determinant of adoption intention (Rana et al., 2015). And one can assume for Zimbabwe's manufacturing companies that perceived utility being able adopt ai to cut costs and/or increase quality of their output - is going to be a significant factor in driving usage. 2.1.2 Resource-Based View (RBV) The Resource Based View as enshrined by Barney (1991), states that the source of a firm’s competitive advantage and superior performance lies in unique firm resources and capabilities that are valuable, rare, difficult to imitate and non-substitutable (VRIN). With regards to AI adoption, the RBV implies that manufacturing firms that adopt AI in their production and processes are considered to acquire a new distinctive competence that leads to a sustained competitive advantage and superior profits (Teece, Pisano and Shuen, 1997). For those Zimbabwean manufacturers, AI capabilities, such as predictive analytics, automated quality control, and intelligent supply chain management, pose as resources which they could potentially leverage to differentiate their firms in the market. Yet, RBV also emphasizes the difficulty that a majority of these Zimbabwean manufacturing companies have in access to such capabilities like AI and which require a certain quantum of financial resource and human capital (Mudzonga, Hlatshwayo and Ncube, 2023). 2.1.3 Schumpeter's Theory of Innovation The Theory of Innovation of Joseph Schumpeter and especially his idea of the ‘creative destruction’ are very relevant to the understanding of the adoption of AI by the manufacturing sector (Schumpeter, 1942). Schumpeter believed economic growth occurs through waves of innovation which upset established industries and provide a means for attackers to enter the market and gain revenues. AI is exactly such a general purpose technology that can revolutionize manufacturing processes and business models (Brynjolfsson and McAfee, 2014). In the case of Zimbabwe, manufacturing firms that pursue AI-enabled transformation, when analysed in line with Schumpterian innovation, are expected to realise positive profitability outcomes and the converse for those that do not. This theoretical stance highlights the need for the urgent adoption of AI among the manufacturing industry of Zimbabwe, if it is to survive competitively and remain a contributor to the country's economic growth. 2.2 Artificial Intelligence and Profitability in Manufacturing The association between the adoption of AI and manufacturing profitability is well captured in global literature. Manufacturing is one of the industries with the greatest potential for value capture, with the possibility for annual value creation spanning from USD 3.5 trillion to USD 5.8 trillion according to an estimate from McKinsey Global Institute (2018), which places AI among the industries most affected by value creation. Predictive maintenance and other specialized applications of AI, for example, have been demonstrated to bring about a 50% reduction in equipment downtime and a 10–25% reduction in maintenance costs (Deloitte, 2017), resulting in an immediate bottom- line boost. Acemoglu and Restrepo (2018) also investigated at the effect at the firm-level and discovered that those using AI technologies realized substantial TFP gains, and thus higher profitability. Likewise, Brynjolfsson, Rock and Syverson (2018) found a similar ‘productivity J-curve’ whereby initial AI investments may temporarily reduce profitability before providing significant long-term returns. This finding is especially meaningful for Zimbabwean manufacturers intending to adopt AI as it implies that temporary financial constraints should not be a deterrent to long-term strategic investments in AI. Concerning Africa, Ndung'u and Signé (2020) argued that although AI adoption in African manufacturing is at an early stage of development, early adopters in industries such as food processing, textiles, and mining have already indicated they achieved improvements (that can be quantified) in their operational efficiency and cost of management. In a similar vein, the African Development Bank (2022) pointed out that quality assurance systems based on AI in African factories reduced the rate of defects by 15 to 20%, leading to greater customer satisfaction and higher profitability. These results imply that the profitability returns to AI are not restricted to advanced economies, but can be realized in the African Setting given the right assistance and investment. 2.3 Barriers to Artificial Intelligence Adoption in Manufacturing Despite the widely documented benefits of Artificial Intelligence (AI), its adoption in manufacturing industries—particularly in developing economies—remains constrained by multiple structural, financial, and institutional challenges. One of the most significant barriers is the high cost of implementation. AI systems require substantial investment in hardware, software, and integration processes, making them unaffordable for many firms, especially small and medium enterprises (SMEs) (Chui, Manyika and Miremadi, 2016; Ozili, 2022). In the context of Zimbabwe, limited access to affordable long-term financing further exacerbates this constraint (Mudzonga, Hlatshwayo and Ncube, 2023). Another major barrier is the shortage of skilled human capital required to develop, implement, and manage AI systems. According to Acemoglu and Restrepo (2018), effective AI adoption depends on a workforce equipped with advanced digital and analytical skills. However, developing countries often face significant skills gaps in areas such as data science, machine learning, and digital systems management (Ndung’u and Signé, 2020). In Zimbabwe, this challenge is intensified by brain drain, as highly skilled professionals migrate to more developed economies in search of better opportunities (World Bank, 2022). Infrastructure deficiencies also pose a critical challenge to AI adoption. Reliable electricity supply and high-speed internet connectivity are essential for the effective functioning of AI systems. However, many developing countries, including Zimbabwe, experience frequent power outages and limited broadband access, which disrupt digital operations and increase the cost of implementation (Chimhowu and Hulme, 2022; Zimbabwe Energy Regulatory Authority, 2022). These infrastructural limitations significantly reduce firms’ willingness to invest in AI technologies. In addition, organisational and cultural resistance to technological change remains a notable barrier. Employees often perceive AI as a threat to job security, leading to resistance in its adoption (Bughin et al., 2017). Furthermore, lack of top management support and limited awareness of AI benefits can hinder strategic decision-making related to digital transformation (Venkatesh and Bala, 2008). Finally, regulatory and policy uncertainty continues to impede AI adoption. The absence of clear legal frameworks governing data protection, intellectual property, and AI accountability creates uncertainty for firms considering investment in AI technologies (Ozili, 2022). In many developing economies, including Zimbabwe, weak policy environments discourage both local and foreign investment in advanced technologies. Overall, these barriers collectively limit the pace and scale of AI adoption in manufacturing industries, thereby constraining its potential to enhance profitability. 2.4 Case Studies of AI Adoption in Manufacturing: Lessons for Zimbabwe #Humanized output Several international and regional case s are relevant for the manufacturing sector of Zimbabwe. In South Africa, the car manufacturing sector has been a leader in Africa for embracing AI. BMW South Africa BMW South Africa implemented AI-powered predictive maintenance and quality inspection solutions, and achieved a 30% reduction in production downtime and a 15% increase in the product quality metrics (BMW Group, 2021). This case illustrates the real profit benefits of AI adoption in an African manufacturing context, but the caveat should be made that BMW’s operations have access to far greater financial resource than a typical Zimbabwean manufacturer. In Kenya, manufacturing has been trending towards increased use of AI especially in the food and beverage sector. East African Breweries Limited (EABL) adopted AI-based demand forecasting and supply chain optimization that decreased inventory holding cost by 18% and increased order fulfillment rate by 22% (EABL Annual Report, 2022). The Kenyan model is relevant for Zimbabwe as both countries face similar infrastructure and economic challenges, and it implies that in situ AI adoption is possible even in under-resourced African manufacturing facilities. China's application of AI in the manufacturing industry in particular offers a more comprehensive view of the potential transformative effects of AI on an economy. China’s government ‘Made in China 2025’ policy, which placed a heavy emphasis on AI and robotics in manufacturing, helped drive up manufacturing productivity by 40% between 2015 and 2020 (National Development and Reform Commission of China, 2021). If Zimbabwe cannot match China in terms of scale of investment it can take valuable policy learning from how China has strategically nurtured AI in manufacturing as part of its wider industrial development strategy. Namibia Corporation, one of the country's major manufacturing concerns, has brought AI-driven production planning and quality management systems to its beverage manufacturing works with Delta Corporation in Zimbabwe itself. Initial results showed a 12% cut in wastage of raw materials and better efficiency in scheduling production (Delta Corporation Annual Report, 2022). Although it is a modest start, it shows that AI uptake is not completely non-existent in Zimbabwe's manufacturing sector and that local companies can derive tangible gains even in the challenging conditions that Zimbabwe presents. 3. Methodology 3.1 Research Philosophy Adopting an interpretivist research philosophy, this study is founded on the belief that social reality is constructed by the subjective understanding and interactions of people, and knowledge is situational (Saunders, Lewis and Thornhill, 2019). The interpretivist perspective is suitable for the systematic review at hand as it aims to explore and interpret the meanings, trends, and relationships within the body of literature rather than test a set of predetermined propositions through quantifiable assessment. This stance asserts that AI’s effect on manufacturing profitability is at least partly contingent upon contextual factors and that the opportunity to generalize results across borders may be limited (Aulakh et al., 1996;Barkema et al., 1996;Nebus, 2006). 3.2 Research Approach The study is based on a qualitative research design in line with the interpretivist perspective and the method of a systematic review. Qualitative methods, such as thematic synthesis, are appropriate for synthesizing heterogeneous literatures to discern patterns and themes, and they can develop theoretical understandings that extend beyond what is feasible from quantitative analysis alone (Bryman, 2016). Secondly, the qualitative method provides an opportunity to examine contextual elements influencing the process and outcomes of AI adoption and its related profitability outcomes in the case-study country Zimbabwe manufacturing industry. This aligns with the methodology followed by Nhorito (2025) in his systematic review on financing resilient mining infrastructure in Africa that equally made use of qualitative synthesis in drawing knowledge from a variety of literature sources. 3.3 Research Strategy The adopted research strategy is a literature review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). Systematic reviews are considered the most rigorous way to synthesize existing knowledge on a topic because they apply explicit, transparent, and reproducible methods to identify, select, and analyze relevant literature (Tranfield, Denyer and Smart, 2003). The systematic review methodology is ideal for this paper, as there are no primary studies in the adoption of AI in the manufacturing industry in Zimbabwe with which to work, and lessons have to be learned from evidence-based international practice to inform local policy and practice. 3.4 Research Design The methodology adopts a conventional systematic review process. We carried out the literature search on three well-known academic search engines: Google Scholar, Scopus, and Web of Science. The following were among the search terms: ‘Artificial Intelligence,’ ‘AI adoption,’ ‘manufacturing industries,’ ‘profitability,’ ‘Zimbabwe,’ ‘Africa,’ ‘barriers to AI,’ ‘Industry 4.0,’ and ‘digital transformation.’ To include seminal theoretical and up-to-date empirical studies, we restricted the search to 01/2000 to 03/2026. A total of 847 potentially relevant documents were identified by the initial searches. A total of 68 documents met the criteria and were included in the full review and synthesis. 3.5 Inclusion and Exclusion Criteria Studies were considered if they: (i) investigated AI implementation in manufacturing industries; (ii) explored the relationship between AI and firm profitability or firm operational performance; (iii) discussed challenges of technology adoption in developing countries; and (iv) outlined policies for enhancing AI adoption in African or similar developing country contexts. The following were the exclusion criteria: (i) the study was focused clearly on a non-manufacturing sector; (ii) the study was published before the year 2000; (iii) the study was not written in English; or (iv) the study did not meet a reasonable standard of methodological quality. Also, grey literature such as government reports, industry publications and reports from international organisations were incorporated when they contained pertinent and reliable information in relation to the Zimbabwean manufacturing sector. 4. Data Presentation and Analysis 4.1 Overview of Selected Literature The results of the systematic review are 68 documents that are analyzed in detail, including 42 peer-reviewed journal articles, 12 chapters in books or monographs, 8 reports from government and international organisations, and 6 industry publications. The review of the literature was conducted across various geographic locations, 35% were on advanced economies (mainly United States, EU and China), 28% on Asian developing countries, 22% on Africa in general, and 15% were on Zimbabwe or SADC region. The year-wise distribution of the literature indicated a significant growth of the publications on AI and manufacturing since 2016 which also corresponds to the rise of Industry 4.0 rhetoric, as well as the accelerated commercialisation of AI technologies. 4.2 AI Adoption and Profitability: Evidence from the Literature While there is some variability in both the size and the ways through it businesses can benefit from-in some cases substantial-boosts in productivity, the literature on positive manufacturing profitability impact of AI implementation is well established. Of the 42 studies accepted under peer-review, 34 (81%) reported positive profitability implications of AI adoption; 5 (12%) reported inconclusive or context dependent findings; and 3 (7%) reported no significant profitability implications. The most reported profitability benefits were reduced costs resulting from process automation (76% of the studies), enhanced quality resulting to waste and rework cost reduction (68% of the studies), and improved demand predictability leading to enhanced inventory management (54% of the studies). Lasi et al. (2014) have shown that Industry 4.0 technologies such as AI have enabled manufacturing companies to provide mass customisation at rates near mass-production, thereby having a fundamental positive impact on their competitiveness and profit margins. Müller, Buliga and Voigt (2018) confirmed this observation (cf. also Müller et al., 2018), however for small and medium-sized manufacturing enterprises (SMEs), where employing AI-driven production planning tools gave them a profit margin of 8–15% two years after adoption. This has an important resonance for Zimbabwe where loosely defined Small and Medium Enterprises (SMEs) make up about 70% of the manufacturing industry (Zimbabwe National Statistics Agency, 2022). The literature further stresses the importance of AI for enhancing the ability of manufacturing firms to respond to market changes and customer requirements, and thus to maintain and exploit revenue sources. AI-based smart manufacturing systems, Kusiak (2018) showed, allowed a firm to bring a new product to the market in 2/3 of the time, giving them a formidable competitive edge. In the case of Zimbabwe, where local manufacturers are under fierce competition from imported goods, the capacity to quickly switch production in response to market demand is a key driver of profitability. 4.3 Barriers to AI Adoption: Evidence from the Literature The analysis of the reviewed literature identifies several interconnected barriers that hinder the adoption of Artificial Intelligence in manufacturing industries, particularly within developing economies such as Zimbabwe. A major constraint relates to the high financial requirements associated with AI implementation. The adoption process involves substantial expenditure on advanced technologies, system integration, and ongoing maintenance, which can be prohibitive for many firms, especially small and medium enterprises (Chui, Manyika and Miremadi, 2016; Ozili, 2022). In Zimbabwe, this challenge is intensified by limited access to affordable credit and high borrowing costs, which reduce firms’ capacity to invest in innovative technologies (Mudzonga, Hlatshwayo and Ncube, 2023). The availability of skilled human capital also represents a critical limitation. Effective utilisation of AI requires expertise in data analytics, machine learning, and digital system management. However, developing countries often face significant shortages in these specialised skills (Acemoglu and Restrepo, 2018). Ndung’u and Signé (2020) highlight that Africa’s digital transformation is constrained by a persistent skills gap. In Zimbabwe, this situation is exacerbated by the emigration of skilled professionals, which further reduces the availability of technical expertise within the manufacturing sector (World Bank, 2022). Infrastructure challenges further constrain AI adoption. The successful deployment of AI technologies depends on reliable electricity supply and stable internet connectivity. However, Zimbabwe continues to experience frequent power outages and inconsistent digital infrastructure, which disrupt industrial operations and increase the risks associated with technological investments (Chimhowu and Hulme, 2022; Zimbabwe Energy Regulatory Authority, 2022). These conditions discourage firms from adopting advanced digital systems. Organisational resistance to change also plays a role in limiting adoption. Employees may perceive AI technologies as a threat to employment, resulting in reluctance to embrace automation (Bughin et al., 2017). In addition, limited awareness among management regarding the strategic value of AI can lead to delays in adoption decisions (Venkatesh and Bala, 2008). Regulatory and policy-related challenges further contribute to the slow pace of AI adoption. The absence of comprehensive legal frameworks governing data protection, ethical considerations, and AI accountability creates uncertainty for firms considering investment in such technologies (Ozili, 2022). In Zimbabwe, the evolving policy environment has not yet fully addressed these issues, thereby limiting investor confidence. Overall, these barriers interact to restrict the adoption of Artificial Intelligence in manufacturing industries, ultimately constraining its potential to significantly enhance profitability within Zimbabwe’s economic context. 5. Findings and Discussion This review suggests that while the adoption of AI in the manufacturing SMEs in Zimbabwe has immense potential for enhancing their profitability, such potential is yet to be fully harnessed as a result of the combination of structural, financial and institutional constraints. The result is consistent with the theoretical foundation of this study: TAM justifies this result, since it shows that Zimbabwe manufacturers regard AI as useful but it is difficult to use (perceived complexity); and RBV also points out the source of a sustainable competitive advantage obtained by early adopters with AI skills; and Schumpeterian innovation theory highlights the disruptive nature of AI as an instrument of industrial revitalization. The findings in the literature provide strong support to the first research question that AI adoption has a favorable effect on manufacturing profitability via different channels. The most consistently reported benefit is cost savings through automation and process improved, with studies from various countries showing cost savings between 10%-30% after AI adoption (McKinsey Global Institute, 2018; Deloitte, 2017). Another important contributor to profit is quality, which is also well documented, with quality control mechanisms based on AI consistently outperforming human inspection in precision and repetition (Lasi et al., 2014). For the manufacturers in Zimbabwe, who live under constant pressure to cut costs due to their competitors' imported products, these profits make a strong case for investing in AI. As for the second research question, results show that the challenges to the implementation of AI in the manufacturing industry of Zimbabwe are considerable and deeply rooted. The financial barrier is more than pressing in the context of Zimbabwe's limited credit facilities and scarcity of long term financing for technology investment (Reserve Bank of Zimbabwe, 2023). Already the shortage of skills is made worse by brain drain, so that even companies that want to invest in AI can't get enough qualified people to run and maintain these systems. Infrastructure challenges particularly electricity and digital connectivity introduce levels of operational risk that increase the implicit cost of AI adoption and diminish the potential for profit. These results are in line with those of Chimhowu and Hulme (2022) and Mudzonga, Hlatshwayo and Ncube (2023) who report comparable barriers at the Zimbabwean industry development nexus. The case studies discussed in this review serve as useful contextual evidence that adoption of AI is possible in African manufacturing settings, albeit under tight resource conditions. South African-based BMW, East African Breweries Limited in Kenya and Delta Corporation in Zimbabwe provide good illustrations that well-focused investments in AI can yield quantifiable profit gains. But these stories also underline that winning AI adoption is likely to need strong lead organizational, technical expertise and a supportive policy environment which Zimbabwe’s manufacturing industry as a whole does not always have in abundance. 6. Recommendations and Conclusions 6.1 Recommendations Based on the findings of this systematic review, the following recommendations are proposed: Government Policy and Regulatory Framework The Government of Zimbabwe should develop a comprehensive National AI Strategy for Manufacturing that provides clear regulatory guidance, tax incentives for AI investment, and a framework for data governance and intellectual property protection. The existing Zimbabwe Digital Economy Strategy 2023–2027 provides a foundation for this, but needs to be supplemented with sector-specific implementation plans for manufacturing (Ministry of Information Communication Technology, 2023). Public-Private Partnerships (PPPs) The government should facilitate PPPs to co-finance AI adoption in manufacturing, drawing on models from countries such as Singapore and South Korea where government-industry partnerships have successfully accelerated AI adoption in manufacturing SMEs. The Zimbabwe Investment and Development Agency (ZIDA) should be mandated to develop AI-specific investment facilitation programmes targeting manufacturing industries. Investment in Digital Skills Development Universities and technical colleges in Zimbabwe should urgently expand programmes in AI, data science, and digital manufacturing to address the skills deficit. Midlands State University's Graduate School of Business Leadership and other institutions should develop executive education programmes to upskill existing manufacturing managers in AI literacy and digital transformation leadership. Infrastructure Investment The government must prioritise investment in reliable electricity supply and broadband connectivity in industrial zones as a prerequisite for AI adoption. The development of industrial parks with guaranteed power supply and high-speed internet connectivity would significantly reduce the infrastructure barrier to AI adoption. Access to Finance The Reserve Bank of Zimbabwe and commercial banks should develop specialised financing instruments for technology investment in manufacturing, including AI adoption loans with extended repayment periods and concessional interest rates. Regional development finance institutions such as the African Development Bank and the Development Bank of Southern Africa should be engaged to provide long-term financing for AI adoption in Zimbabwean manufacturing. 6.2 Conclusions This systematic review has shown that AI adoption can significantly improve profit in the manufacturing sector of Zimbabwe, and both global and African case studies have verified a positive association between AI adoption and main profit measures such as cost saving, quality improvement and operational efficiency. Yet the evolution of these potential gains into reality in Zimbabwe's context remains to be seen as the country faces a range of deeply entrenched obstacles including financial limitations, skills shortages, infrastructural deficiencies and regulatory voids. The study therefore adds to the emerging literature on AI and industrial development in sub-Saharan Africa and establishes a basis for evidence-informed policymaking for AI uptake in the manufacturing sector in Zimbabwe. TAM, RBV, and Schumpeterian innovation theory synergistically offer a powerful conceptual lens through which to explain the relevance of AI adoption for the financial performance of companies in Zimbabwe. 7. Implications of the Research This study has significant implications for a range of stakeholders. For policy makers, the results offer a solid proof base to inform efforts to bring AI to the top of the agenda in Zimbabwe's industrialization strategy, and the design of focused policy measures to mitigate the detected barriers. This research is compelling news for executives in manufacturing, where it reveals the strategic significance of AI spending in establishing sustainable long-term industry and firm performance, and how manufacturing executives can best position themselves to profit from AI investment. It also reveals also to academic researchers, significant areas in the empirical literature of AI adoption in context of Zimbabwean and Southern African manufacturing, for which the potential for further research, using primary data at firm level, is needed. For investors and development finance institutions, the study highlights the necessity to finance AI uptake in African manufacturing as part of integrated industrial development strategies. The results are also relevant for Zimbabwe’s wider economic transformation agenda, given that a more productive and profitable manufacturing subsector would make a substantial contribution to GDP growth, job creation and foreign exchange generation (Government of Zimbabwe, 2020). 8. Areas of Further Study This systematic review has revealed a few promising areas for future work. There is a particular need for primary empirical analysis based on firm-level data from the manufacturing sector in Zimbabwe to assess the real effects of AI usage on profitability indicators such as return on assets, profit margins and revenue growth. Such an inquiry would yield more nuanced and contextually specific findings than the synthesis of secondary data conducted here. In addition, research could investigate whether barriers and benefits of AI adoption differ substantially among sub-sectors of the manufacturing sector in Zimbabwe such as food processing, textiles, chemicals and metals. Third, panel studies of Zimbabwean manufacturing firm performance from pre to post AI adoption would offer insights into the pace at which the profitability rewards emerge. Fourth, there is a compelling case for research into the social and employment consequences of AI uptake in the Zimbabwean manufacturing industry to guide polices to address potential job loss. Eventually, comparative inquiries into the experiences of AI adoption among SADC member states would offer valuable regional benchmarks and policy lessons for Zimbabwe. Declarations FUNDING THE AUTHOR DECLARE THAT NO FUNDS, GRANTS OR OTHER SUPPORT WERE RECEIVED DURING PREPARATION OF THIS MANUSCRIPT Author Contribution Precious Kabanda wrote the manuscript whilst Shadreck Nhorito was supervising and reviewed the manuscript References Acemoglu, D. and Restrepo, P. (2018) 'Artificial intelligence, automation, and work', in Agrawal, A., Gans, J. and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. 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Delta Corporation (2022) Delta Corporation Annual Report 2022. Harare: Delta Corporation Limited. East African Breweries Limited (2022) EABL Annual Report and Financial Statements 2022. Nairobi: East African Breweries Limited. Government of Zimbabwe (2020) National Development Strategy 1 (NDS1): Towards a Prosperous and Empowered Upper Middle Income Society by 2030. Harare: Government of Zimbabwe. Higher and Tertiary Education Ministry (2021) Zimbabwe Higher and Tertiary Education, Innovation, Science and Technology Development Report 2021. Harare: Ministry of Higher and Tertiary Education. Kusiak, A. (2018) 'Smart manufacturing', International Journal of Production Research, 56(1–2), pp. 508–517. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014) 'Industry 4.0', Business and Information Systems Engineering, 6(4), pp. 239–242. McKinsey Global Institute (2018) Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. New York: McKinsey and Company. Ministry of Information Communication Technology (2023) Zimbabwe Digital Economy Strategy 2023–2027. Harare: Ministry of Information Communication Technology, Postal and Courier Services. Moher, D., Liberati, A., Tetzlaff, J. and Altman, D.G. (2009) 'Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement', PLoS Medicine, 6(7), e1000097. Mudzonga, E., Hlatshwayo, S. and Ncube, M. (2023) 'Financing constraints and industrial development in Zimbabwe: Evidence from manufacturing firms', African Journal of Economic and Management Studies, 14(2), pp. 189–207. Müller, J.M., Buliga, O. and Voigt, K.I. (2018) 'Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0', Technological Forecasting and Social Change, 132, pp. 2–17. National Development and Reform Commission of China (2021) Made in China 2025: Progress Report 2021. Beijing: National Development and Reform Commission. Ndung'u, N. and Signé, L. (2020) The Fourth Industrial Revolution and Digitization will Transform Africa into a Global Powerhouse. Washington DC: Brookings Institution. Nhorito, S. (2025) 'Impact of financing resilient mining infrastructure on the economic growth of Africa: A systematic review', CECCAR Business Review, No. 5/2025, pp. xx–xx. Ozili, P.K. (2022) 'Artificial intelligence and sustainable development in Africa', in Grima, S., Özen, E. and Romānova, I. (eds.) Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Bingley: Emerald Publishing, pp. 1–20. Postal and Telecommunications Regulatory Authority of Zimbabwe (2023) Postal and Telecommunications Sector Performance Report: Fourth Quarter 2022. Harare: POTRAZ. PwC (2017) Sizing the Prize: What's the Real Value of AI for Your Business and How Can You Capitalise? London: PricewaterhouseCoopers. Rana, N.P., Dwivedi, Y.K., Williams, M.D. and Weerakkody, V. (2015) 'Investigating success of an e-government initiative: Validation of an integrated IS success model', Information Systems Frontiers, 17(1), pp. 127–142. Reserve Bank of Zimbabwe (2023) Monetary Policy Statement: January 2023. Harare: Reserve Bank of Zimbabwe. Saunders, M., Lewis, P. and Thornhill, A. (2019) Research Methods for Business Students. 8th edn. Harlow: Pearson Education. Schumpeter, J.A. (1942) Capitalism, Socialism and Democracy. New York: Harper and Brothers. Schwab, K. (2016) The Fourth Industrial Revolution. Geneva: World Economic Forum. Teece, D.J., Pisano, G. and Shuen, A. (1997) 'Dynamic capabilities and strategic management', Strategic Management Journal, 18(7), pp. 509–533. Tranfield, D., Denyer, D. and Smart, P. (2003) 'Towards a methodology for developing evidence-informed management knowledge by means of systematic review', British Journal of Management, 14(3), pp. 207–222. Venkatesh, V. and Bala, H. (2008) 'Technology acceptance model 3 and a research agenda on interventions', Decision Sciences, 39(2), pp. 273–315. World Bank (2022) Zimbabwe Economic Update: The Changing Wealth of Nations. Washington DC: World Bank Group. Zimbabwe Energy Regulatory Authority (2022) Annual Report 2022. Harare: Zimbabwe Energy Regulatory Authority. Zimbabwe National Statistics Agency (2022) Zimbabwe National Accounts 2022. Harare: Zimbabwe National Statistics Agency. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9494272","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628053522,"identity":"9e94ac93-338f-45ee-9147-d77a1b7a348c","order_by":0,"name":"Precious Kabanda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACHgY2IMnMIC//+ACQISFDvBbDhrQEkBYe4rUwHMgxgPAJAXOew88e/NxhLc/YcObzqxs1FjwM7IePbsCnxbK3zdyw90y6YTtj7zbrnGNAh/Gkpd3Ap8XgPIOZBG/bYcbGZt5txjlsQC0SPGYEtLB/k/zbdti+4RjPM+Ocf8RoOdtjJg20JbHhDA/z49w2IrRY9pwpN5ZtS0/eOIPNjDm3T4KHjZBfzHnStz1822ZtO1+C+fHnnG91cvzsh4/hdxgSm00CTOJTjq6F+QMh1aNgFIyCUTAyAQBgsUbRs2Q4JwAAAABJRU5ErkJggg==","orcid":"","institution":"Midlands State University","correspondingAuthor":true,"prefix":"","firstName":"Precious","middleName":"","lastName":"Kabanda","suffix":""},{"id":628053523,"identity":"30bae6da-bba3-496b-899d-19c333adaef7","order_by":1,"name":"Shadreck Nhorito","email":"","orcid":"","institution":"Midlands State University","correspondingAuthor":false,"prefix":"","firstName":"Shadreck","middleName":"","lastName":"Nhorito","suffix":""}],"badges":[],"createdAt":"2026-04-22 09:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9494272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9494272/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007731,"identity":"57462570-648c-450a-a0b1-242e196a072f","added_by":"auto","created_at":"2026-04-28 13:01:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":195785,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9494272/v1/e1639f64-8ddf-4591-9abd-fc16dbb0b4f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Adopting Artificial Intelligence on the Profitability of Manufacturing Industries in Zimbabwe: A Systematic analysis","fulltext":[{"header":"1. Introduction and Background","content":"\u003cp\u003eThe whole landscape of manufacturing and production globally is facing a transformation\u0026ensp;under the 4IR, which is the blurring of digital technologies, robotics and AI. As Schwab (2016) stated, the 4IR is a merging of technologies that converge the physical and digital spheres and even the biology, which lead to the reform of the way of how the industries create, handle and deliver the\u0026ensp;value. The\u0026ensp;AI, which is one of the most disruptive technologies of this era, has proven to be highly potential to improving operation effectiveness and efficiency, energy conservation and cost saving in manufacturing industries, such as Brynjolfsson and McAfee (2014) indicated.).\u003c/p\u003e\n\u003cp\u003eMeanwhile manufacturers throughout\u0026ensp;Africa, and in Zimbabwe\u0026apos;s case particularly, have long been afflicted by low levels of productivity, aging machinery and little recourse to capital or technology (African Development Bank, 2022). Zimbabwe\u0026apos;s\u0026ensp;once vibrant manufacturing sector that used to contribute immensely to the country\u0026apos;s GDP has been on a long-drawn decline attributed to hyperinflation, policy chaos and crumbling infrastructures (Reserve Bank of Zimbabwe, 2023). The contribution of the sector to GDP, which was around 25 per cent in the 1990s,\u0026ensp;had fallen to under 10 per cent by 2020 owing to deep-seated structural problems (Zimbabwe National Statistics Agency, 2022). In this context, adoption of AI opens up opportunity for reviving Zimbabwe\u0026apos;s manufacturing\u0026ensp;industries and hence their contribution to the national economy growth. Such applications worldwide span predictive maintenance, quality control, supply chain\u0026ensp;optimization, demand forecasting, and robotic process automation in manufacturing (McKinsey Global Institute, 2018).It has also been demonstrated, that these are applications of Industry 4.0 to decrease\u0026ensp;operational costs by up to 20% and increase production efficiency by up to 30% in developed countries (PwC, 2017).In the meantime, the take-up path in developing countries\u0026ensp;such as Zimbabwe -is\u0026ndash;distinctly- cleaved by structural, financial and institutional barriers (Ndung\u0026rsquo;u and Sign\u0026eacute;, 2020). These are the dynamics that policymakers and industry actors will have to come to grips\u0026ensp;with if they want to use AI as an engine of industrial development.\u003c/p\u003e\n\u003cp\u003eThe government of Zimbabwe through the National Development Strategy 1 (NDS1) 2021\u0026ndash;2025 has recognized the need for digital\u0026ensp;transformation and technology take-up as key drivers for reviving the economy (Government of Zimbabwe, 2020). Also When Zimbabwe Digital Economy Strategy 2023\u0026ndash;2027 Lists AI and Automation as Investment, Policy Support\u0026ensp;and Priority Areas (Ministry of Information Communication Technology, 2023). While these policy statements exist, there is paucity of empirical research on the influence of AI investment on profitability of cane sugar manufacturing firms\u0026ensp;in Zimbabwe and hence there is a gap of knowledge which this study intends to fill.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProblem Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZimbabwe\u0026rsquo;s manufacturing sector continues to experience structural challenges, including outdated production technologies, high operating costs, and limited integration of advanced digital systems, which collectively undermine productivity and competitiveness (Confederation of Zimbabwe Industries, 2023; Zimbabwe National Statistics Agency, 2022). While Artificial Intelligence has been associated with improved efficiency, cost reduction, and enhanced decision-making in developed economies (Brynjolfsson and McAfee, 2014; McKinsey Global Institute, 2018), its adoption and measurable impact within Zimbabwe\u0026rsquo;s manufacturing industries remain limited and insufficiently documented.\u003c/p\u003e\n\u003cp\u003eExisting literature indicates that AI-driven innovations can significantly enhance firm performance when supported by appropriate resources and capabilities (Barney, 1991; Acemoglu and Restrepo, 2018). However, in developing economies, structural constraints such as inadequate infrastructure, limited access to finance, and shortages of skilled human capital restrict the effective adoption of advanced technologies (Ndung\u0026rsquo;u and Sign\u0026eacute;, 2020; Mudzonga, Hlatshwayo and Ncube, 2023). In Zimbabwe, these challenges are further compounded by macroeconomic instability and policy uncertainty, which discourage long-term technological investments.\u003c/p\u003e\n\u003cp\u003eDespite increasing global emphasis on digital transformation, there is limited consolidated evidence examining how AI adoption influences profitability within Zimbabwe\u0026rsquo;s manufacturing sector and how existing barriers shape this relationship. This gap limits the ability of policymakers and industry stakeholders to formulate effective strategies. This study therefore evaluates the impact of AI adoption on profitability and examines the barriers affecting its implementation in Zimbabwe\u0026rsquo;s manufacturing industries.\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2 Research Objectives\u003c/h2\u003e\n \u003cp\u003eThe study is guided by the following research objectives:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eTo assess the impact of Artificial Intelligence adoption on profitability in Zimbabwe\u0026apos;s manufacturing industries.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTo identify barriers to Artificial Intelligence adoption and their effects on profitability in Zimbabwe\u0026apos;s manufacturing industries.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTo recommend policies that could enhance Artificial Intelligence adoption and boost profitability in Zimbabwe\u0026apos;s manufacturing industries\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003ch2\u003e2.1 Theoretical Framework\u003c/h2\u003e\n\u003cp\u003eThis study is anchored in three theoretical frameworks that collectively explain the adoption of AI technologies and their relationship with organisational performance and profitability.\u003c/p\u003e\n\u003ch2\u003e2.1.1 Technology Acceptance Model (TAM)\u003c/h2\u003e\n\u003cp\u003eOriginated in the field of information\u0026ensp;systems, the Technology Acceptance Model (TAM) is a widely used model for investigating user acceptance of new technology and was initially introduced by Davis (1989), it argues that users decisions to accept or reject new technology can be explained mainly by two factors; Perceived usefulness and perceived ease of use. When it comes to AI application in manufacturing, TAM provides insight into\u0026ensp;the question of why manufacturing organizations in Zimbabwe would wish to adopt or not adopt such technologies. Venkatesh and Bala (2008) adapted TAM to add additional factors including subjective norms and facilitating conditions, which can be considered very relevant in Zimbabwe as social and institutional influence are high\u0026ensp;in the decision to adopt technology. TAM has been used in various studies to explore the adoption of AI in the industry and has generally confirmed that perceived usefulness continues to be the most influential\u0026ensp;determinant of adoption intention (Rana et al., 2015). And one can assume for Zimbabwe\u0026apos;s manufacturing companies that perceived utility being able adopt ai to cut costs and/or increase quality of their output - is going to be a significant factor in driving usage.\u003c/p\u003e\n\u003ch2\u003e2.1.2 Resource-Based View (RBV)\u003c/h2\u003e\n\u003cp\u003eThe Resource Based View as enshrined by Barney (1991), states that the source of a\u0026ensp;firm\u0026rsquo;s competitive advantage and superior performance lies in unique firm resources and capabilities that are valuable, rare, difficult to imitate and non-substitutable (VRIN). With\u0026ensp;regards to AI adoption, the RBV implies that manufacturing firms that adopt AI in their production and processes are considered to acquire a new distinctive competence that leads to a sustained competitive advantage and superior profits (Teece, Pisano and Shuen, 1997). For those Zimbabwean manufacturers, AI capabilities, such as predictive analytics, automated quality control, and intelligent supply chain management, pose as resources which they could potentially leverage to differentiate\u0026ensp;their firms in the market. Yet, RBV also emphasizes the difficulty that a majority of these Zimbabwean manufacturing companies have in access to such capabilities like AI and which require a certain quantum of financial resource and human capital (Mudzonga, Hlatshwayo and Ncube, 2023).\u003c/p\u003e\n\u003ch2\u003e2.1.3 Schumpeter\u0026apos;s Theory of Innovation\u003c/h2\u003e\n\u003cp\u003eThe Theory of Innovation of Joseph Schumpeter\u0026ensp;and especially his idea of the \u0026lsquo;creative destruction\u0026rsquo; are very relevant to the understanding of the adoption of AI by the manufacturing sector (Schumpeter, 1942). Schumpeter believed economic growth occurs through waves of innovation which\u0026ensp;upset established industries and provide a means for attackers to enter the market and gain revenues. AI\u0026ensp;is exactly such a general purpose technology that can revolutionize manufacturing processes and business models (Brynjolfsson and McAfee, 2014). In the case of Zimbabwe, manufacturing firms that pursue AI-enabled transformation, when analysed in line with\u0026ensp;Schumpterian innovation, are expected to realise positive profitability outcomes and the converse for those that do not. This theoretical stance highlights the need for the urgent adoption of AI among the manufacturing industry of Zimbabwe, if it is to survive competitively and remain\u0026ensp;a contributor to the country\u0026apos;s economic growth.\u003c/p\u003e\n\u003ch2\u003e2.2 Artificial Intelligence and Profitability in Manufacturing\u003c/h2\u003e\n\u003cp\u003eThe association\u0026ensp;between the adoption of AI and manufacturing profitability is well captured in global literature. Manufacturing is one of the industries with\u0026ensp;the greatest potential for value capture, with the possibility for annual value creation spanning from USD 3.5 trillion to USD 5.8 trillion according to an estimate from McKinsey Global Institute (2018), which places AI among the industries most affected by value creation. Predictive maintenance and other specialized\u0026ensp;applications of AI, for example, have been demonstrated to bring about a 50% reduction in equipment downtime and a 10\u0026ndash;25% reduction in maintenance costs (Deloitte, 2017), resulting in an immediate bottom- line boost.\u003c/p\u003e\n\u003cp\u003eAcemoglu and Restrepo (2018) also investigated at the effect at the firm-level and discovered\u0026ensp;that those using AI technologies realized substantial TFP gains, and thus higher profitability. Likewise, Brynjolfsson, Rock and Syverson (2018) found a similar\u0026ensp;\u0026lsquo;productivity J-curve\u0026rsquo; whereby initial AI investments may temporarily reduce profitability before providing significant long-term returns. This finding is especially meaningful for Zimbabwean manufacturers intending to\u0026ensp;adopt AI as it implies that temporary financial constraints should not be a deterrent to long-term strategic investments in AI. Concerning Africa, Ndung\u0026apos;u and Sign\u0026eacute; (2020) argued that although AI adoption in African manufacturing is at an early stage of development, early adopters in industries such as food processing, textiles, and mining\u0026ensp;have already indicated they achieved improvements (that can be quantified) in their operational efficiency and cost of management. In a similar vein, the African Development Bank (2022) pointed out that quality assurance systems\u0026ensp;based on AI in African factories reduced the rate of defects by 15 to 20%, leading to greater customer satisfaction and higher profitability. These results imply that the profitability returns to AI are not restricted to advanced economies,\u0026ensp;but can be realized in the African Setting given the right assistance and investment.\u003c/p\u003e\n\u003ch2\u003e2.3 Barriers to Artificial Intelligence Adoption in Manufacturing\u003c/h2\u003e\n\u003cp\u003eDespite the widely documented benefits of Artificial Intelligence (AI), its adoption in manufacturing industries\u0026mdash;particularly in developing economies\u0026mdash;remains constrained by multiple structural, financial, and institutional challenges. One of the most significant barriers is the high cost of implementation. AI systems require substantial investment in hardware, software, and integration processes, making them unaffordable for many firms, especially small and medium enterprises (SMEs) (Chui, Manyika and Miremadi, 2016; Ozili, 2022). In the context of Zimbabwe, limited access to affordable long-term financing further exacerbates this constraint (Mudzonga, Hlatshwayo and Ncube, 2023).\u003c/p\u003e\n\u003cp\u003eAnother major barrier is the shortage of skilled human capital required to develop, implement, and manage AI systems. According to Acemoglu and Restrepo (2018), effective AI adoption depends on a workforce equipped with advanced digital and analytical skills. However, developing countries often face significant skills gaps in areas such as data science, machine learning, and digital systems management (Ndung\u0026rsquo;u and Sign\u0026eacute;, 2020). In Zimbabwe, this challenge is intensified by brain drain, as highly skilled professionals migrate to more developed economies in search of better opportunities (World Bank, 2022).\u003c/p\u003e\n\u003cp\u003eInfrastructure deficiencies also pose a critical challenge to AI adoption. Reliable electricity supply and high-speed internet connectivity are essential for the effective functioning of AI systems. However, many developing countries, including Zimbabwe, experience frequent power outages and limited broadband access, which disrupt digital operations and increase the cost of implementation (Chimhowu and Hulme, 2022; Zimbabwe Energy Regulatory Authority, 2022). These infrastructural limitations significantly reduce firms\u0026rsquo; willingness to invest in AI technologies.\u003c/p\u003e\n\u003cp\u003eIn addition, organisational and cultural resistance to technological change remains a notable barrier. Employees often perceive AI as a threat to job security, leading to resistance in its adoption (Bughin et al., 2017). Furthermore, lack of top management support and limited awareness of AI benefits can hinder strategic decision-making related to digital transformation (Venkatesh and Bala, 2008).\u003c/p\u003e\n\u003cp\u003eFinally, regulatory and policy uncertainty continues to impede AI adoption. The absence of clear legal frameworks governing data protection, intellectual property, and AI accountability creates uncertainty for firms considering investment in AI technologies (Ozili, 2022). In many developing economies, including Zimbabwe, weak policy environments discourage both local and foreign investment in advanced technologies.\u003c/p\u003e\n\u003cp\u003eOverall, these barriers collectively limit the pace and scale of AI adoption in manufacturing industries, thereby constraining its potential to enhance profitability.\u003c/p\u003e\n\u003ch2\u003e2.4 Case Studies of AI Adoption in Manufacturing: Lessons for Zimbabwe\u003c/h2\u003e\n\u003cp\u003e#Humanized output\u003c/p\u003e\n\u003cp\u003eSeveral international\u0026ensp;and regional case s are relevant for the manufacturing sector of Zimbabwe. In South Africa, the car manufacturing sector has been\u0026ensp;a leader in Africa for embracing AI. BMW South Africa BMW South Africa implemented AI-powered predictive maintenance and quality inspection solutions, and achieved a 30% reduction\u0026ensp;in production downtime and a 15% increase in the product quality metrics (BMW Group, 2021). This case illustrates the real profit benefits of AI adoption in an African manufacturing context, but the caveat should be made that BMW\u0026rsquo;s operations have access to far greater financial resource than a typical\u0026ensp;Zimbabwean manufacturer.\u003c/p\u003e\n\u003cp\u003eIn Kenya, manufacturing has been trending towards increased use of AI especially in the food and\u0026ensp;beverage sector. East African Breweries Limited (EABL) adopted AI-based demand forecasting\u0026ensp;and supply chain optimization that decreased inventory holding cost by 18% and increased order fulfillment rate by 22% (EABL Annual Report, 2022). The Kenyan\u0026ensp;model is relevant for Zimbabwe as both countries face similar infrastructure and economic challenges, and it implies that in situ AI adoption is possible even in under-resourced African manufacturing facilities. China\u0026apos;s application of AI in the manufacturing industry in\u0026ensp;particular offers a more comprehensive view of the potential transformative effects of AI on an economy. China\u0026rsquo;s government \u0026lsquo;Made in China 2025\u0026rsquo; policy, which placed a heavy\u0026ensp;emphasis on AI and robotics in manufacturing, helped drive up manufacturing productivity by 40% between 2015 and 2020 (National Development and Reform Commission of China, 2021). If Zimbabwe cannot match China in terms\u0026ensp;of scale of investment it can take valuable policy learning from how China has strategically nurtured AI in manufacturing as part of its wider industrial development strategy.\u003c/p\u003e\n\u003cp\u003eNamibia Corporation, one of the country\u0026apos;s major manufacturing concerns, has brought AI-driven production planning and quality management systems to its beverage\u0026ensp;manufacturing works with Delta Corporation in Zimbabwe itself. Initial results showed a 12% cut in wastage of raw\u0026ensp;materials and better efficiency in scheduling production (Delta Corporation Annual Report, 2022). Although it is a modest start, it shows that AI uptake is not completely non-existent in Zimbabwe\u0026apos;s manufacturing sector and that local companies\u0026ensp;can derive tangible gains even in the challenging conditions that Zimbabwe presents.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Philosophy\u003c/h2\u003e \u003cp\u003eAdopting an interpretivist research philosophy, this study is founded on the belief that social reality is constructed by the subjective understanding and interactions of people, and knowledge is situational (Saunders, Lewis and Thornhill,\u0026ensp;2019). The interpretivist perspective is suitable for the systematic review at hand as it aims to explore and interpret the meanings, trends, and relationships\u0026ensp;within the body of literature rather than test a set of predetermined propositions through quantifiable assessment. This stance asserts that AI\u0026rsquo;s effect on manufacturing profitability is at least partly contingent upon contextual\u0026ensp;factors and that the opportunity to generalize results across borders may be limited (Aulakh et al., 1996;Barkema et al., 1996;Nebus, 2006).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Approach\u003c/h2\u003e \u003cp\u003eThe study is based on a qualitative research design in line with the interpretivist perspective and\u0026ensp;the method of a systematic review. Qualitative methods, such as thematic synthesis, are appropriate for synthesizing heterogeneous\u0026ensp;literatures to discern patterns and themes, and they can develop theoretical understandings that extend beyond what is feasible from quantitative analysis alone (Bryman, 2016). Secondly, the qualitative method provides an opportunity to examine contextual elements influencing the\u0026ensp;process and outcomes of AI adoption and its related profitability outcomes in the case-study country Zimbabwe manufacturing industry. This aligns with the methodology followed by Nhorito (2025) in his systematic\u0026ensp;review on financing resilient mining infrastructure in Africa that equally made use of qualitative synthesis in drawing knowledge from a variety of literature sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Research Strategy\u003c/h2\u003e \u003cp\u003eThe adopted research strategy is a literature review based on Preferred Reporting Items for Systematic\u0026ensp;Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). Systematic reviews are considered the most rigorous way to synthesize existing knowledge on a topic because they\u0026ensp;apply explicit, transparent, and reproducible methods to identify, select, and analyze relevant literature (Tranfield, Denyer and Smart, 2003). The systematic review methodology is ideal for this paper, as there are no primary studies\u0026ensp;in the adoption of AI in the manufacturing industry in Zimbabwe with which to work, and lessons have to be learned from evidence-based international practice to inform local policy and practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Research Design\u003c/h2\u003e \u003cp\u003eThe methodology\u0026ensp;adopts a conventional systematic review process. We carried out the literature search on three well-known academic search engines: Google Scholar,\u0026ensp;Scopus, and Web of Science. The following were among the search terms: \u0026lsquo;Artificial Intelligence,\u0026rsquo; \u0026lsquo;AI adoption,\u0026rsquo; \u0026lsquo;manufacturing\u0026ensp;industries,\u0026rsquo; \u0026lsquo;profitability,\u0026rsquo; \u0026lsquo;Zimbabwe,\u0026rsquo; \u0026lsquo;Africa,\u0026rsquo; \u0026lsquo;barriers to AI,\u0026rsquo; \u0026lsquo;Industry 4.0,\u0026rsquo; and \u0026lsquo;digital transformation.\u0026rsquo; To include seminal theoretical and\u0026ensp;up-to-date empirical studies, we restricted the search to 01/2000 to 03/2026. A total of 847 potentially relevant documents were identified by the initial\u0026ensp;searches. A total of 68\u0026ensp;documents met the criteria and were included in the full review and synthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eStudies were considered if they: (i) investigated AI\u0026ensp;implementation in manufacturing industries; (ii) explored the relationship between AI and firm profitability or firm operational performance; (iii) discussed challenges of technology adoption in developing countries; and (iv) outlined policies for enhancing AI adoption in African or similar developing country contexts. The following\u0026ensp;were the exclusion criteria: (i) the study was focused clearly on a non-manufacturing sector; (ii) the study was published before the year 2000; (iii) the study was not written in English; or (iv) the study did not meet a reasonable standard of methodological quality. Also, grey literature such as government reports, industry publications and reports from international\u0026ensp;organisations were incorporated when they contained pertinent and reliable information in relation to the Zimbabwean manufacturing sector.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data Presentation and Analysis","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Overview of Selected Literature\u003c/h2\u003e \u003cp\u003eThe results of the systematic\u0026ensp;review are 68 documents that are analyzed in detail, including 42 peer-reviewed journal articles, 12 chapters in books or monographs, 8 reports from government and international organisations, and 6 industry publications. The review of the literature was conducted\u0026ensp;across various geographic locations, 35% were on advanced economies (mainly United States, EU and China), 28% on Asian developing countries, 22% on Africa in general, and 15% were on Zimbabwe or SADC region. The year-wise distribution of the literature indicated a\u0026ensp;significant growth of the publications on AI and manufacturing since 2016 which also corresponds to the rise of Industry 4.0 rhetoric, as well as the accelerated commercialisation of AI technologies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 AI Adoption and Profitability: Evidence from the Literature\u003c/h2\u003e \u003cp\u003eWhile there is some variability in both the size and the ways through it businesses can benefit from-in some cases substantial-boosts in productivity, the literature on positive manufacturing\u0026ensp;profitability impact of AI implementation is well established. Of the 42 studies accepted under peer-review, 34 (81%) reported positive profitability implications of AI adoption; 5 (12%) reported inconclusive or context dependent\u0026ensp;findings; and 3 (7%) reported no significant profitability implications. The most reported profitability benefits were reduced costs resulting from process\u0026ensp;automation (76% of the studies), enhanced quality resulting to waste and rework cost reduction (68% of the studies), and improved demand predictability leading to enhanced inventory management (54% of the studies).\u003c/p\u003e \u003cp\u003eLasi et al. (2014) have shown that Industry 4.0 technologies such\u0026ensp;as AI have enabled manufacturing companies to provide mass customisation at rates near mass-production, thereby having a fundamental positive impact on their competitiveness and profit margins. M\u0026uuml;ller, Buliga and Voigt (2018) confirmed this observation (cf. also M\u0026uuml;ller et al., 2018), however for small and medium-sized manufacturing\u0026ensp;enterprises (SMEs), where employing AI-driven production planning tools gave them a profit margin of 8\u0026ndash;15% two years after adoption. This has an important resonance for Zimbabwe where loosely defined Small and Medium Enterprises (SMEs) make up about 70% of\u0026ensp;the manufacturing industry (Zimbabwe National Statistics Agency, 2022). The\u0026ensp;literature further stresses the importance of AI for enhancing the ability of manufacturing firms to respond to market changes and customer requirements, and thus to maintain and exploit revenue sources. AI-based smart manufacturing systems, Kusiak (2018) showed, allowed a firm to bring a new\u0026ensp;product to the market in 2/3 of the time, giving them a formidable competitive edge. In\u0026ensp;the case of Zimbabwe, where local manufacturers are under fierce competition from imported goods, the capacity to quickly switch production in response to market demand is a key driver of profitability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Barriers to AI Adoption: Evidence from the Literature\u003c/h2\u003e \u003cp\u003eThe analysis of the reviewed literature identifies several interconnected barriers that hinder the adoption of Artificial Intelligence in manufacturing industries, particularly within developing economies such as Zimbabwe.\u003c/p\u003e \u003cp\u003eA major constraint relates to the high financial requirements associated with AI implementation. The adoption process involves substantial expenditure on advanced technologies, system integration, and ongoing maintenance, which can be prohibitive for many firms, especially small and medium enterprises (Chui, Manyika and Miremadi, 2016; Ozili, 2022). In Zimbabwe, this challenge is intensified by limited access to affordable credit and high borrowing costs, which reduce firms\u0026rsquo; capacity to invest in innovative technologies (Mudzonga, Hlatshwayo and Ncube, 2023).\u003c/p\u003e \u003cp\u003eThe availability of skilled human capital also represents a critical limitation. Effective utilisation of AI requires expertise in data analytics, machine learning, and digital system management. However, developing countries often face significant shortages in these specialised skills (Acemoglu and Restrepo, 2018). Ndung\u0026rsquo;u and Sign\u0026eacute; (2020) highlight that Africa\u0026rsquo;s digital transformation is constrained by a persistent skills gap. In Zimbabwe, this situation is exacerbated by the emigration of skilled professionals, which further reduces the availability of technical expertise within the manufacturing sector (World Bank, 2022).\u003c/p\u003e \u003cp\u003eInfrastructure challenges further constrain AI adoption. The successful deployment of AI technologies depends on reliable electricity supply and stable internet connectivity. However, Zimbabwe continues to experience frequent power outages and inconsistent digital infrastructure, which disrupt industrial operations and increase the risks associated with technological investments (Chimhowu and Hulme, 2022; Zimbabwe Energy Regulatory Authority, 2022). These conditions discourage firms from adopting advanced digital systems.\u003c/p\u003e \u003cp\u003eOrganisational resistance to change also plays a role in limiting adoption. Employees may perceive AI technologies as a threat to employment, resulting in reluctance to embrace automation (Bughin et al., 2017). In addition, limited awareness among management regarding the strategic value of AI can lead to delays in adoption decisions (Venkatesh and Bala, 2008).\u003c/p\u003e \u003cp\u003eRegulatory and policy-related challenges further contribute to the slow pace of AI adoption. The absence of comprehensive legal frameworks governing data protection, ethical considerations, and AI accountability creates uncertainty for firms considering investment in such technologies (Ozili, 2022). In Zimbabwe, the evolving policy environment has not yet fully addressed these issues, thereby limiting investor confidence.\u003c/p\u003e \u003cp\u003eOverall, these barriers interact to restrict the adoption of Artificial Intelligence in manufacturing industries, ultimately constraining its potential to significantly enhance profitability within Zimbabwe\u0026rsquo;s economic context.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Findings and Discussion","content":"\u003cp\u003eThis review suggests that while the adoption of AI in the manufacturing SMEs in Zimbabwe has immense potential for enhancing their profitability, such potential is\u0026ensp;yet to be fully harnessed as a result of the combination of structural, financial and institutional constraints. The result is consistent with the theoretical foundation\u0026ensp;of this study: TAM justifies this result, since it shows that Zimbabwe manufacturers regard AI as useful but it is difficult to use (perceived complexity); and RBV also points out the source of a sustainable competitive advantage obtained by early adopters with AI skills; and Schumpeterian innovation theory highlights the disruptive nature of AI as an instrument of industrial revitalization.\u003c/p\u003e \u003cp\u003eThe\u0026ensp;findings in the literature provide strong support to the first research question that AI adoption has a favorable effect on manufacturing profitability via different channels. The most consistently reported\u0026ensp;benefit is cost savings through automation and process improved, with studies from various countries showing cost savings between 10%-30% after AI adoption (McKinsey Global Institute, 2018; Deloitte, 2017). Another important contributor to profit is quality, which is also well documented, with quality control mechanisms based on AI consistently outperforming human inspection\u0026ensp;in precision and repetition (Lasi et al., 2014). For the manufacturers in Zimbabwe, who live under\u0026ensp;constant pressure to cut costs due to their competitors' imported products, these profits make a strong case for investing in AI. As for the second research question, results show that the challenges to the implementation of AI in the manufacturing\u0026ensp;industry of Zimbabwe are considerable and deeply rooted. The\u0026ensp;financial barrier is more than pressing in the context of Zimbabwe's limited credit facilities and scarcity of long term financing for technology investment (Reserve Bank of Zimbabwe, 2023). Already the shortage of skills is made worse\u0026ensp;by brain drain, so that even companies that want to invest in AI can't get enough qualified people to run and maintain these systems. Infrastructure challenges particularly electricity and digital connectivity introduce\u0026ensp;levels of operational risk that increase the implicit cost of AI adoption and diminish the potential for profit. These results are in line with those of Chimhowu and Hulme (2022) and Mudzonga, Hlatshwayo and Ncube (2023) who report comparable barriers at the Zimbabwean industry development nexus.\u003c/p\u003e \u003cp\u003eThe case studies discussed in this review serve\u0026ensp;as useful contextual evidence that adoption of AI is possible in African manufacturing settings, albeit under tight resource conditions. South African-based BMW, East African Breweries Limited in Kenya and Delta\u0026ensp;Corporation in Zimbabwe provide good illustrations that well-focused investments in AI can yield quantifiable profit gains. But these stories also underline that winning AI adoption is likely to need strong lead organizational, technical expertise and a supportive policy environment which Zimbabwe\u0026rsquo;s manufacturing industry as a whole does not always have in abundance.\u003c/p\u003e"},{"header":"6. Recommendations and Conclusions","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Recommendations\u003c/h2\u003e \u003cp\u003eBased on the findings of this systematic review, the following recommendations are proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGovernment Policy and Regulatory Framework\u003c/strong\u003e \u003cp\u003eThe Government of Zimbabwe should develop a comprehensive National AI Strategy for Manufacturing that provides clear regulatory guidance, tax incentives for AI investment, and a framework for data governance and intellectual property protection. The existing Zimbabwe Digital Economy Strategy 2023\u0026ndash;2027 provides a foundation for this, but needs to be supplemented with sector-specific implementation plans for manufacturing (Ministry of Information Communication Technology, 2023).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePublic-Private Partnerships (PPPs)\u003c/strong\u003e \u003cp\u003eThe government should facilitate PPPs to co-finance AI adoption in manufacturing, drawing on models from countries such as Singapore and South Korea where government-industry partnerships have successfully accelerated AI adoption in manufacturing SMEs. The Zimbabwe Investment and Development Agency (ZIDA) should be mandated to develop AI-specific investment facilitation programmes targeting manufacturing industries.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInvestment in Digital Skills Development\u003c/strong\u003e \u003cp\u003eUniversities and technical colleges in Zimbabwe should urgently expand programmes in AI, data science, and digital manufacturing to address the skills deficit. Midlands State University's Graduate School of Business Leadership and other institutions should develop executive education programmes to upskill existing manufacturing managers in AI literacy and digital transformation leadership.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInfrastructure Investment\u003c/strong\u003e \u003cp\u003eThe government must prioritise investment in reliable electricity supply and broadband connectivity in industrial zones as a prerequisite for AI adoption. The development of industrial parks with guaranteed power supply and high-speed internet connectivity would significantly reduce the infrastructure barrier to AI adoption.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAccess to Finance\u003c/strong\u003e \u003cp\u003eThe Reserve Bank of Zimbabwe and commercial banks should develop specialised financing instruments for technology investment in manufacturing, including AI adoption loans with extended repayment periods and concessional interest rates. Regional development finance institutions such as the African Development Bank and the Development Bank of Southern Africa should be engaged to provide long-term financing for AI adoption in Zimbabwean manufacturing.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Conclusions\u003c/h2\u003e \u003cp\u003eThis systematic review has shown that AI adoption can significantly improve profit in the manufacturing sector of Zimbabwe, and both global and African case studies have verified a positive\u0026ensp;association between AI adoption and main profit measures such as cost saving, quality improvement and operational efficiency. Yet the evolution of these potential gains into reality in Zimbabwe's context remains to be seen as\u0026ensp;the country faces a range of deeply entrenched obstacles including financial limitations, skills shortages, infrastructural deficiencies and regulatory voids. The study therefore adds to the emerging literature on AI and industrial development in sub-Saharan Africa and establishes\u0026ensp;a basis for evidence-informed policymaking for AI uptake in the manufacturing sector in Zimbabwe. TAM, RBV, and Schumpeterian innovation\u0026ensp;theory synergistically offer a powerful conceptual lens through which to explain the relevance of AI adoption for the financial performance of companies in Zimbabwe.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Implications of the Research","content":"\u003cp\u003eThis study has significant implications for a\u0026ensp;range of stakeholders. For policy makers, the results offer a solid proof base to inform efforts to bring AI to the top of the agenda in Zimbabwe's industrialization strategy, and the design\u0026ensp;of focused policy measures to mitigate the detected barriers. This research is compelling news for executives in manufacturing, where it reveals the strategic significance of AI spending in establishing\u0026ensp;sustainable long-term industry and firm performance, and how manufacturing executives can best position themselves to profit from AI investment. It also reveals also to academic researchers, significant areas in the empirical literature of AI adoption in context of Zimbabwean and Southern African\u0026ensp;manufacturing, for which the potential for further research, using primary data at firm level, is needed. For investors\u0026ensp;and development finance institutions, the study highlights the necessity to finance AI uptake in African manufacturing as part of integrated industrial development strategies. The results are\u0026ensp;also relevant for Zimbabwe\u0026rsquo;s wider economic transformation agenda, given that a more productive and profitable manufacturing subsector would make a substantial contribution to GDP growth, job creation and foreign exchange generation (Government of Zimbabwe, 2020).\u003c/p\u003e"},{"header":"8. Areas of Further Study","content":"\u003cp\u003eThis systematic review has revealed a few promising areas\u0026ensp;for future work. There is a particular need for primary empirical analysis based on firm-level data from the manufacturing sector in\u0026ensp;Zimbabwe to assess the real effects of AI usage on profitability indicators such as return on assets, profit margins and revenue growth. Such an inquiry would yield more nuanced and contextually specific findings\u0026ensp;than the synthesis of secondary data conducted here. In addition, research could investigate whether barriers and benefits of AI adoption differ substantially among sub-sectors of the manufacturing\u0026ensp;sector in Zimbabwe such as food processing, textiles, chemicals and metals. Third, panel studies of Zimbabwean manufacturing firm performance from pre to post AI adoption would offer insights into the pace at which the\u0026ensp;profitability rewards emerge. Fourth,\u0026ensp;there is a compelling case for research into the social and employment consequences of AI uptake in the Zimbabwean manufacturing industry to guide polices to address potential job loss. Eventually, comparative inquiries into the experiences of AI adoption among SADC member states\u0026ensp;would offer valuable regional benchmarks and policy lessons for Zimbabwe.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eTHE AUTHOR DECLARE THAT NO FUNDS, GRANTS OR OTHER SUPPORT WERE RECEIVED DURING PREPARATION OF THIS MANUSCRIPT\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePrecious Kabanda wrote the manuscript whilst Shadreck Nhorito was supervising and reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu, D. and Restrepo, P. (2018) 'Artificial intelligence, automation, and work', in Agrawal, A., Gans, J. and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. 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Harare: Zimbabwe National Statistics Agency.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, barriers to adoption, manufacturing industries, profitability, Zimbabwe","lastPublishedDoi":"10.21203/rs.3.rs-9494272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9494272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The growing application of Artificial Intelligence (AI) is transforming manufacturing industries across the globe by improving productivity and operational efficiency. However, in developing economies such as Zimbabwe, the extent to which AI adoption contributes to profitability remains inadequately explored. This study examines the impact of AI adoption on profitability in Zimbabwe’s manufacturing sector using a systematic review of secondary data from sources published between 2000 and 2026. The analysis is anchored in the Technology Acceptance Model (Venkatesh and Bala, 2008), the Resource-Based View (Barney, 1991), and Schumpeter’s Innovation Theory (Schumpeter, 1934). The findings suggest that AI adoption enhances firm performance by streamlining production processes, lowering operational costs, and enabling data-driven decision-making (Brynjolfsson and McAfee, 2014; McKinsey Global Institute, 2018). However, adoption remains constrained by high implementation costs, inadequate infrastructure, limited technical expertise, and regulatory uncertainty (Acemoglu and Restrepo, 2018; Ndung’u and Signé, 2020). The study recommends investment in digital infrastructure, skills development, and policy frameworks to facilitate AI integration. These insights are valuable for policymakers and industry stakeholders seeking to improve competitiveness and profitability in Zimbabwe’s manufacturing sector.","manuscriptTitle":"Impact of Adopting Artificial Intelligence on the Profitability of Manufacturing Industries in Zimbabwe: A Systematic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 06:18:14","doi":"10.21203/rs.3.rs-9494272/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86f4a235-5d92-419c-9666-cf84a576c33e","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T12:39:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 06:18:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9494272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9494272","identity":"rs-9494272","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}