Authored by Sofia Cacchione, MPP candidate
Researchers at the John J. Heldrich Center for Workforce Development, with funding from the New Jersey State Policy Lab, are currently engaged in a project to examine how New Jersey’s public artificial intelligence (AI) initiatives can better align with the evolving needs of small businesses in the state. This blog post provides an overview of the current landscape of literature around small businesses and AI, highlighting overarching themes, potential policy interventions, and knowledge gaps in need of further exploration.
Echoing the findings discussed in the project’s previous blog post, there remains a lack of a standardized definition of small businesses within the literature, with the majority of studies providing no definition at all (Oldemeyer et al., 2024). The lack of standardized definitions complicates efforts to analyze small businesses experiences and may prevent certain research findings from being appropriately applied to local initiatives.
Integration of AI tools within small businesses offers an array of benefits. Improvements in operational efficiency (Kramarenko, 2025) and customer engagement (Govori & Sejdija, 2023) emerged as the most frequently observed benefits, followed by enhancements in human resource management (Schwaeke et al., 2025), marketing (Bahaw et al., 2025), product innovation (Grashof & Kopka, 2023), decision making (Kramarenko, 2025), and data analytics (Govori & Sejdija, 2023). It is important to note that the majority of academic literature has been conducted outside of the U.S. However, U.S. based analyses have seen the majority of small businesses report positive changes from AI adoption such as increased sales and satisfaction among employees and customers, as well as positive perceptions regarding the future of AI and their business (Garapati, 2024; Chandler et al., 2025; U.S. Chamber of Commerce, 2025). Certain literature also highlights the important distinction between AI usage as a general-purpose technology or as a driver of innovation, with the latter typically being less feasible among small businesses due to a lack of financial capital (Kopka & Fornahl, 2024; Grashof & Kopka, 2023). As such, future policy initiatives should support business usage of AI not only for general operational purposes, but for research and development as well.
Despite the potential benefits to AI adoption, many barriers to adoption remain among small businesses. Lack of adequate financial resources (Govori & Sejdija, 2023) and technological skill (Oldemeyer et al., 2024) were the most frequently observed barriers, followed by poor IT infrastructure (Ayinaddis, 2025), perceived ease of use (Bahaw et al., 2025), perceived investment risks (Schwaeke et al., 2025), lack of guidance (Crockett et al., 2023), and lack of awareness of AI benefits (Ingalagi et al., 2021). Another substantial barrier faced by small businesses is the difficulty of accessing quality data necessary for AI integration (Huynh & Nguyen, 2026). Other significant factors that influence AI adoption include management and company culture (Huynh & Nguyen, 2026), external competition (Schwaeke et al., 2025), and level of government supports (Ingalagi et al., 2021). Studies also highlighted concerns among small businesses regarding data privacy and security (Crocket et al., 2023; Govori & Sejdija, 2023; Kramarenko, 2025; Ayinaddis, 2025).
A particular focus of this project is targeted toward women and minority-owned businesses’ experiences with AI. Though there remains a substantial lack of research around this sub-population, existing research suggests that women and minority-owned firms who integrate AI into their business practices can see improvements like those experienced within the larger small business population. For firm owners from historically underserved populations, AI adoption can be particularly useful in overcoming systemic barriers that have derived from human interactions, such as their exclusion from accessing financial and social capital (King, 2025; Singh, 2025). However, women and minority small business owners in the U.S. report lower levels of comfort with AI tools, and male owners continue to outpace female owners in AI investment (Chandler et al., 2025; Garapati, 2024). Furthermore, researchers highlight the importance of protecting against racial and gender-based biases currently embedded within AI algorithms (Crockett et al., 2023; Kramarenko, 2025; Anwar et al., 2025; King, 2025; Chandler et al., 2025; Singh, 2025; Anabtwi et al., 2024). These findings suggest a need for targeted investment, training initiatives, and inclusive regulatory frameworks to protect against the exacerbation of existing inequalities throughout technological innovation.
It is important to add that much of the existing literature conflates women and minority-owned small businesses with entrepreneurship and start-ups, presenting a need for more refined research to address some of these nuances.
References:
Anabtawi, M., AlDaaja, Y., Alhur, M., Alzboun, N., Ghaboush1, R.A., Adwan, R., Alshurideh, M. (2024). Empowering women entrepreneurs: Navigating the adoption of generative AI tools through innovation diffusion theory. Evolutionary Studies in Imaginative Culture, 8(3), 498-514. https://www.researchgate.net/publication/385104103_Empowering_Women_Entrepreneurs_Navigating_the_Adoption_of_Generative_AI_Tools_Through_Innovation_Diffusion_Theory.
Ayinaddis, S.G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis. Journal of Innovation and Knowledge, 10(3). https://doi.org/10.1016/j.jik.2025.100682.
Bahaw, P., Forgenie, D., Sadiq, G., Sookhai, S. (2025). Generative AI for business sustainability: Examining usability, usefulness, and triple bottom line impacts in small and medium enterprises. Sustainable Futures, 10(100815). https://doi-org.proxy.libraries.rutgers.edu/10.1016/j.sftr.2025.100815.
Chandler, M., Wial, H., Bookman, D.O. (2025). AI in business: How small business owners are learning, using, and navigating challenges with AI tools. Initiative for a Competitive Inner City. https://icic.org/research/small-business-entrepreneurship/ai-in-business/.
Crockett, K., Colyer, E., Gerber, L., & Latham, A. (2023). Building trustworthy AI solutions: A case for practical solutions for small businesses. IEEE Transactions on Artificial Intelligence, 4(4), 778–791. https://doi.org/10.1109/TAI.2021.3137091.
Garapati, S. (2024, March 18). Poll shows small businesses are interested in and benefit from AI. Bipartisan Policy Center. https://bipartisanpolicy.org/article/poll-shows-small-businesses-are-interested-in-and-benefit-from-ai/.
Govori, A., Sejdija, Q. (2023). Future prospects and challenges of integrating artificial intelligence within the business practices of small and medium enterprises. Journal of Governance and Regulation, 12(2), 176–183. https://doi.org/10.22495/jgrv12i2art16.
Grashof, N., Kopka, A. (2023). Artificial intelligence and radical innovation: an opportunity for all companies? Small Business Economics, 61(2), 771-797. https://doi.org/10.1007/s11187-022-00698-3.
Huynh Cao Tuan, & Nguyen Thanh Tung. (2026). Determinants of AI adoption and how the adoption affects the business performance of small and medium-scale enterprises. Ianna Journal of Interdisciplinary Studies, 8(1), 25-37. https://doi.org/10.5281/zenodo.17689249.
Ingalagi, S.S. (2021). Artificial Intelligence (AI) adaptation: Analysis of determinants among small to medium-sized enterprises (SMEs). IOP Conference Series. Materials Science and Engineering, 1049(1). https://doi.org/10.1088/1757-899X/1049/1/012017.
King, R. (2025). How artificial intelligence can help minority-owned businesses succeed in competitive sectors. American International Journal of Business Management, 8(1), 167-171.
Kopka, A., & Fornahl, D. (2024). Artificial intelligence and firm growth — catch-up processes of SMEs through integrating AI into their knowledge bases. Small Business Economics, 62(1), 63–85. https://doi.org/10.1007/s11187-023-00754-6.
Kramarenko, A. (2025). Artificial intelligence for small and medium businesses: Perspectives and challenges. Journal of Engineering Management and Competitiveness, 15(1), 43-56. https://doi.org/10.5937/JEMC2501043K.
Oldemeyer, L., Jede, A., Teuteberg, F. (2024). Investigation of artificial intelligence in SMEs: A systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 75, 1185-1227. https://doi.org/10.1007/s11301-024-00405-4.
Schwaeke, J., Peters, A., Kanbach, D.K., Kraus, S., Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297-1331. https://doi.org/10.1080/00472778.2024.2379999.
Singh, R.P. (2025). Artificial intelligence: Implications and impacts on black entrepreneurial ecosystems. Administrative Sciences, 15(10), 402. https://doi.org/10.3390/admsci15100402.
U.S. Chamber of Commerce. (2025). Empowering small business: The impact of Technology on U.S. Small Business, fourth edition. https://www.uschamber.com/technology/empowering-small-business-the-impact-of-technology-on-u-s-small-business.
Author
Sofia Cacchione is a graduate student enrolled in the Master of Public Policy program at the Bloustein School of Planning and Public Policy and works as a graduate assistant with the Heldrich Center for Workforce Development.