← Papers

AI adoption helps SME founders spot market opportunities and speeds product innovation while automating routine work, but benefits are uneven and accompanied by displacement and access concerns.

Entrepreneurship in the Era of Artificial Intelligence: Redefining Opportunity Recognition, Labour Substitution, and Innovation Processes

Muhammad Sarfraz Latif, Mansoor Ahmed Soomro, Asif Ahmad, Muhammad Irfan Syed · Fetched March 17, 2026 · Inverge Journal of Social Sciences

semantic_scholar correlational low evidence 7/10 relevance DOI Source PDF

A survey of 350 SME entrepreneurs and managers finds that AI adoption is associated with improved opportunity recognition, automation of routine tasks that reallocates human effort toward creative and analytical roles, and accelerated innovation, though adoption is constrained by workforce displacement risks, ethical concerns, and limited access.

This study examined the transformative role of artificial intelligence (AI) in entrepreneurship, focusing on opportunity recognition, labour substitution, and innovation processes. Rapid advancements in AI technologies altered traditional entrepreneurial practices by enabling data-driven decision-making, predictive analytics, and automation of routine tasks. Using a quantitative research design, data were collected from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations. Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes. The results revealed that AI adoption significantly enhanced opportunity recognition by enabling entrepreneurs to identify emerging market trends, assess risks, and make informed strategic decisions. AI also facilitated labour substitution by automating repetitive tasks, allowing human resources to focus on creative and analytical roles. Moreover, AI-driven innovation processes accelerated product development, improved operational efficiency, and supported experimentation, thereby strengthening entrepreneurial performance. Despite these positive outcomes, challenges such as workforce displacement, ethical concerns, and limited access to AI technologies were identified as barriers to full adoption. The study concluded that AI functions as a strategic enabler that reshapes entrepreneurial practices, labour dynamics, and innovation strategies. The findings provide valuable insights for entrepreneurs, policymakers, and academic institutions to implement adaptive strategies for sustainable and inclusive entrepreneurial growth in the era of artificial intelligence. References Ali, A., & Rafiq-uz-Zaman, M. (2025). Institutional inertia vs. ethical innovation: A comparative analysis of AI governance at The Islamia University of Bahawalpur and Cambridge University Press. Contemporary Journal of Social Science Review, 3(4), 91–102. https://doi.org/10.63878/cjssr.v3i4.1695 Asif, M., Shahid, S., & Rafiq-uz-Zaman, M. (2025). Immersive technologies, awe, and the evolution of retail in the metaverse. International Premier Journal of Languages & Literature, 3(4), 713–748. https://ipjll.com/ipjll/index.php/journal/article/view/295 Audretsch, D. B., & Belitski, M. (2023). Artificial intelligence, entrepreneurship, and economic growth. Small Business Economics, 61(3), 1017–1035. https://doi.org/10.1007/s11187-022-00673-4 Badghish, M., & Soomro, T. R. (2024). Artificial intelligence and entrepreneurial innovation: Evidence from emerging markets. Technological Forecasting and Social Change, 191, 122450. https://doi.org/10.1016/j.techfore.2023.122450 Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. https://doi.org/10.1257/mac.20180345 Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute. https://doi.org/10.2139/ssrn.3213930 Bui, T. H., & Duong, P. T. (2024). AI-enabled opportunity recognition: A study on startup decision-making. Journal of Business Research, 161, 113556. https://doi.org/10.1016/j.jbusres.2023.113556 Cockburn, I. M., Henderson, R., & Stern, S. (2019). The impact of artificial intelligence on innovation. NBER Working Paper, 24449. https://doi.org/10.3386/w24449 Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Fossen, F. M., McLemore, T., & Sorgner, A. (2024). Artificial intelligence and entrepreneurship. Foundations and Trends® in Entrepreneurship, 20(8), 781–904. https://doi.org/10.1561/0300000130 Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392 Jamil, R., Zhang, Y., Anwar, M., & Mustafa, S. (2025). Inclusive artificial intelligence in entrepreneurship: Access, adoption, and equity implications. Small Business Economics, 65(2), 1105–1124. https://doi.org/10.1007/s11187-024-00850-7 Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2022). Digital transformation: An overview of the current state of the art of research. Journal of Small Business Management, 60(1), 1–25. https://doi.org/10.1080/00472778.2020.1766690 Li, X., Wang, J., & Liu, X. (2022). Artificial intelligence capability and entrepreneurial opportunity recognition. Journal of Business Venturing Insights, 18, e00313. https://doi.org/10.1016/j.jbvi.2022.e00313 Machucho, J., & Ortiz, P. (2025). The role of artificial intelligence in entrepreneurial opportunity identification. Journal of Small Business Management, 63(1), 67–88. https://doi.org/10.1080/00472778.2023.2198765 Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2023). Artificial intelligence and innovation: A systematic review and research agenda. Technovation, 120, 102528. https://doi.org/10.1016/j.technovation.2022.102528 Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2024). Artificial intelligence-enabled dynamic capabilities and firm performance. Journal of Business Research, 168, 114101. https://doi.org/10.1016/j.jbusres.2023.114101 Obschonka, M., Audretsch, D. B., & Volkmann, C. (2020). Artificial intelligence and big data in entrepreneurship. Small Business Economics, 55(2), 339–351. https://doi.org/10.1007/s11187-019-00186-0 Omidmand, P., Dorri, R., Mozaffari, A., & Ataei, S. (2025). Artificial intelligence applications in lean startup methodology: A bibliometric analysis of research trends and future directions. Journal of Small Business and Entrepreneurship Development, 13(1), 45-67. Park, J.-H., Kim, S.-J., & Lee, S.-T. (2025). AI and creativity in entrepreneurship education: A systematic review of LLM applications. AI, 6(5), 100. https://doi.org/10.3390/ai6050100 Rafiq-uz-Zaman, M. (2022). Strategic upskilling: Fusing technical expertise with human capabilities. Journal of Business Insight and Innovation, 1(1), 76–86. https://doi.org/10.5281/zenodo.17766381 Rafiq-uz-Zaman, M. (2023). Bridging CPEC-driven industrial growth and skill-based education in Pakistan: A systematic review. Journal of Business Insight and Innovation, 2(1), 55–78. https://insightfuljournals.com/index.php/JBII/article/view/57 Rafiq-uz-Zaman, M. (2023). Redesign for 21st-century skills: Preparing learners for a rapidly changing workforce. Journal of Business Insight and Innovation, 1(2), 89–102. https://insightfuljournals.com/index.php/JBII/article/view/58 Rafiq-uz-Zaman, M. (2024). Leveraging skill development and STEAM innovation for business growth: A strategic framework for enhancing workforce performance in emerging markets. Journal of Business Insight and Innovation, 3(1), 48–63. https://insightfuljournals.com/index.php/JBII/article/view/55 Rafiq-uz-Zaman, M. (2025). Beyond the blackboards: Building a micro-edtech economy through teacher-led innovation in low-income schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M. (2025). From chalkboards to competence: Rethinking skill-based education in Pakistan for a business-led innovation economy. International Journal of Academic Research for Humanities, 5(3), 1–13. https://doi.org/10.5281/zenodo.17112058 Rafiq-uz-Zaman, M. (2025). Use of artificial intelligence in school management: A contemporary need of school education system in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984–2009. https://doi.org/10.62345/jads.2025.14.2.56 Rafiq-uz-Zaman, M. (2025). Between adoption and ambiguity: Navigating the AI policy vacuum in Pakistani higher education. Research Journal for Social Affairs, 3(6), 877–885. https://doi.org/10.71317/RJSA.003.06.0523 Rafiq-uz-Zaman, M., Malik, N., & Bano, S. (2025). Learning to innovate: WhatsApp groups as grassroots innovation ecosystems among micro-entrepreneurs in emerging markets. Journal of Asian Development Studies, 14(1), 1854–1862. https://doi.org/10.62345/jads.2025.14.1.47 Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 Review of Managerial Science. (2026). Artificial intelligence technologies and entrepreneurship: A hybrid literature review. Advance online publication. https://doi.org/10.1007/s11846-025-00839-4 Siddiqui, D., Mumtaz, U., & Ahmad, N. (2024). Artificial intelligence in entrepreneurship: A bibliometric analysis of the literature. Journal of Global Entrepreneurship Research, 14(1), 1–27. https://doi.org/10.1007/s40497-024-00385-5 Sirait, M., Hidayat, R., & Nugroho, Y. (2025). Artificial intelligence, labor substitution, and human capital development in startups. Journal of Innovation & Knowledge, 10(2), 101430. https://doi.org/10.1016/j.jik.2024.101430 Twabu, J. (2025). The impact of artificial intelligence on product innovation in SMEs. Technovation, 129, 102874. https://doi.org/10.1016/j.technovation.2024.102874 Yesuf, Y., & Fields, Z. (2025). Artificial intelligence adoption as a driver of innovation and competitiveness in SMEs: A bibliometric and systematic revi

Summary

Main Finding

AI adoption in small and medium-sized enterprises significantly reshapes entrepreneurship: it improves opportunity recognition (better market sensing and trend forecasting), facilitates labour substitution of routine tasks (freeing humans for creative/analytical work), and accelerates AI-driven innovation (faster prototyping, higher operational efficiency, and more experimentation), but raises challenges around workforce displacement, ethics, access, and regulation.

Key Points

Core claim: Artificial intelligence functions as a strategic enabler that restructures opportunity discovery, labor allocation, and innovation processes in entrepreneurial ventures.
Opportunity recognition: AI (ML, predictive analytics) expands entrepreneurs’ ability to detect emerging market needs, reduce information asymmetry, and make real-time data-driven decisions.
Labour substitution and reconfiguration: AI automates repetitive tasks, prompting a shift in human roles toward higher-order, creative, and analytical activities; this creates both complementarities (demand for new skills) and displacement risks.
Innovation processes: AI shortens development cycles, enables simulation/prototyping, supports iterative experimentation, and fosters novel business models based on algorithmic capabilities.
Distributional concerns: Adoption is uneven — limited access in some regions/SMEs, potential for increased labour inequality without institutional protections, and ethical/legal gaps hamper responsible uptake.
Conceptual model: AI adoption → (opportunity recognition, labour substitution, innovation) → entrepreneurial performance; moderated by firm size, industry, and managerial experience.

Data & Methods

Design: Cross-sectional quantitative survey with a deductive approach.
Sample: 350 entrepreneurs and SME managers who had adopted AI in operations; respondents selected via purposive (non-probability) sampling.
Instrument: Structured questionnaire built from validated scales measuring AI adoption, opportunity recognition, labour substitution, innovation processes, and entrepreneurial performance (responses measured on Likert-type scales—exact scale not specified).
Controls: Firm size, industry type, and entrepreneur/manager experience included as control variables.
Analyses: Descriptive statistics, reliability testing (e.g., Cronbach’s alpha implied), multiple regression analyses, and structural equation modelling (SEM) to estimate relationships among AI adoption, mediators, and performance.
Limitations (reported/implied): non-probability sampling (limits generalizability), cross-sectional design (limits causal claims), reliance on self-reported measures, and likely geographic/contextual concentration (developing-economy concerns emphasized).

Implications for AI Economics

Labour markets and skill demand: Findings reinforce models where AI substitutes routine tasks while complementing high-skill cognitive work — implying shifting demand toward analytical/creative skills and potential upward wage pressure for those skills, with downward pressure/anxiety in routine occupations.
Productivity and firm performance: AI-induced improvements in sensing, decision-making, and innovation speed point to measurable productivity gains at the firm level; these gains can affect aggregate productivity if adoption diffuses.
Market entry and competition: Democratized analytics and automation lower barriers for resource-constrained startups/SMEs to compete with incumbents, potentially increasing entry rates and altering market concentration dynamics.
Distributional and welfare effects: Uneven access and regulatory gaps risk widening inequality between firms/regions and workers with/without AI-capable skills; policy interventions (training, access subsidies, social safety nets) are important to manage transitional costs.
Policy and regulation: Need for clearer legal frameworks, ethical guidelines, and support mechanisms to promote inclusive, responsible AI adoption among entrepreneurs — particularly in emerging economies and SMEs.
Measurement and empirical strategy for future research: Economics studies should develop granular measures of AI intensity and task-level substitution/complementarity, use longitudinal or quasi-experimental designs to identify causal effects on employment, wages, and firm dynamics, and quantify welfare impacts.
Innovation economics: AI’s role in accelerating experimentation and lowering innovation costs suggests changes in R&D investment patterns, faster creative destruction cycles, and new forms of intangible capital; modeling should account for endogenous adoption spillovers and network effects.
Policy-relevant research priorities: heterogeneity by industry/firm size, long-run labor-share impacts, complementarities between AI and human capital, diffusion constraints in low-resource settings, and the interplay between AI adoption and market structure.

Notes for researchers and policymakers: interpret the study’s positive findings about AI as conditional — benefits depend on access, skills, and institutional arrangements. Future causal and representative work is needed to quantify net welfare and distributional outcomes at scale.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional survey of AI adopters without an exogenous source of variation, random assignment, or clear strategies to address selection/endogeneity; reported associations may reflect reverse causality, omitted variables, and common-method bias. Methods Rigormedium — The study uses standard quantitative tools (descriptive stats, reliability tests, regressions, structural equation modelling) and a reasonable sample size (n=350), but key methodological details are missing (sampling frame, measurement validity, control strategy for confounders, robustness checks), and the design cannot support causal claims. SampleCross-sectional survey of 350 entrepreneurs and managers from small and medium-sized enterprises who report having adopted AI in their operations; details on country/context, industry mix, sampling method, and response rate are not specified. Themesinnovation adoption human_ai_collab labor_markets GeneralizabilityRestricted to SMEs that have already adopted AI (selection bias) — excludes non-adopters and larger firms, Unclear geographic and sectoral coverage — may not generalize across countries or industries, Self-reported measures and cross-sectional design limit external validity for causal inference, Moderate sample size (n=350) limits ability to analyze heterogeneous effects across subgroups, Possible non-response or convenience sampling undermines representativeness

Claims (8)

Claim	Direction	Confidence	Outcome	Details
AI adoption significantly enhanced opportunity recognition by enabling entrepreneurs to identify emerging market trends, assess risks, and make informed strategic decisions. Innovation Output	positive	medium	opportunity recognition (ability to identify market trends, assess risks, make strategic decisions)	n=350 significant positive effect of AI adoption on opportunity recognition 0.09
AI facilitated labour substitution by automating repetitive tasks, allowing human resources to focus on creative and analytical roles. Automation Exposure	positive	medium	labour substitution / automation of routine tasks and reallocation of human roles to creative/analytical work	n=350 descriptive/link between AI adoption and labour substitution/role reallocation 0.09
AI-driven innovation processes accelerated product development, improved operational efficiency, and supported experimentation, thereby strengthening entrepreneurial performance. Innovation Output	positive	medium	product development speed, operational efficiency, experimentation capability, entrepreneurial performance	n=350 positive associations between AI adoption and product development speed, operational efficiency, experimentation, entrepreneurial performance 0.09
Despite positive outcomes, challenges such as workforce displacement, ethical concerns, and limited access to AI technologies were identified as barriers to full adoption. Adoption Rate	negative	medium	barriers to AI adoption (perceived workforce displacement, ethical concerns, limited access to AI tech)	n=350 respondents reported barriers: workforce displacement concerns, ethical issues, limited access 0.09
The study used a quantitative research design and collected data from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations. Other	null_result	high	not applicable (methodological detail)	n=350 quantitative design; sample size = 350 0.15
Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes. Other	null_result	high	not applicable (methodological detail)	0.15
AI functions as a strategic enabler that reshapes entrepreneurial practices, labour dynamics, and innovation strategies. Innovation Output	positive	medium	overall entrepreneurial practices, labour dynamics, and innovation strategy orientation	n=350 0.09
The findings provide valuable insights for entrepreneurs, policymakers, and academic institutions to implement adaptive strategies for sustainable and inclusive entrepreneurial growth in the era of artificial intelligence. Governance And Regulation	positive	medium	policy and practice guidance for sustainable and inclusive entrepreneurial growth (implication, not an empirical dependent variable)	n=350 0.09