The Role of Digital Skills Gaps in Shaping AI Value Yield Among Small and Medium-Sized Enterprises

James Whitfield

Department of Information Systems and Digital Innovation, Ashworth Business School, University of Westbridge, Manchester, United Kingdom.

Abstract

Artificial intelligence (AI) is diffusing quickly through the business population, yet small and medium-sized enterprises (SMEs) capture a disproportionately small share of its value. This paper examines how digital skills gaps shape what we term AI value yield: the economic value a firm realises from AI relative to the resources it commits. Using a secondary-data design, the study triangulates official enterprise statistics from Eurostat (2023–2025), two OECD evidence bases on SME AI adoption and generative AI use (2025–2026), peer-reviewed field experiments on generative AI productivity, and recent survey evidence from Nigeria. Three findings emerge. First, the size gap in AI use is wide and widening in absolute terms: in 2025, 17.0% of small EU enterprises used AI against 55.0% of large enterprises, and the large-firm share grew by roughly 14 percentage points in one year against 6 points for small firms. Second, internal digital capacity is even more unequal than AI use itself: large enterprises were about 5.6 times as likely as small ones to employ ICT specialists and about 4.2 times as likely to provide ICT training. Third, skills are the binding constraint on conversion from intent to use: among EU enterprises that considered AI but did not adopt it, 70.3% cited a lack of relevant expertise, ahead of legal and data-protection concerns, while 50% of SMEs in the OECD generative AI survey reported that employees lacked the skills to use the tools effectively. Building on absorptive capacity and complementarity theory, the paper proposes a three-stage model in which skills gaps reduce value yield at the adoption, deployment-depth and value-conversion stages. It also identifies a countervailing effect: generative AI raises the output of less-skilled workers most, so it can partly substitute for the very skills SMEs lack, provided a minimum threshold of digital literacy is in place. The paper concludes with managerial and policy implications, including skills-first adoption sequencing and threshold-oriented training for SMEs in emerging economies such as Nigeria.

Keywords: Artificial intelligence; Digital skills gap; Small and medium-sized enterprises; AI value yield; Absorptive capacity; Generative AI; Digital divide; Nigeria

References

1. AI World (2026) The real obstacle to AI in European businesses is not the technology. https://aiworld.eu/story/the-real-obstacle-to-ai-in-european-businesses-is-not-the-technology
2. Bresnahan, T.F., Brynjolfsson, E. and Hitt, L.M. (2002) ‘Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence’, The Quarterly Journal of Economics, 117(1), pp. 339–376. https://doi.org/10.1162/003355302753399526
3. Brynjolfsson, E., Li, D. and Raymond, L. (2025) ‘Generative AI at work’, The Quarterly Journal of Economics, 140(2), pp. 889–942. https://www.nber.org/papers/w31161
4. Brynjolfsson, E., Rock, D. and Syverson, C. (2021) ‘The productivity J-curve: How intangibles complement general purpose technologies’, American Economic Journal: Macroeconomics, 13(1), pp. 333–372. https://doi.org/10.1257/mac.20180386
5. Cohen, W.M. and Levinthal, D.A. (1990) ‘Absorptive capacity: A new perspective on learning and innovation’, Administrative Science Quarterly, 35(1), pp. 128–152. https://doi.org/10.2307/2393553
6. Dataphyte (2026) The limits of Nigeria’s AI growth. https://www.dataphyte.com/topic/technology/the-limits-of-nigerias-ai-growth
7. Dell’Acqua, F., McFowland III, E., Mollick, E., et al. (2023) Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper No. 24-013. Harvard Business School. https://www.hbs.edu/faculty/Pages/item.aspx?num=64700
8. EDC and eTranzact (2025) AI readiness of Nigerian SMEs. Enterprise Development Centre, Pan-Atlantic University. https://www.brandtimes.com.ng/edc-and-etranzact-launch-report-to-boost-ai-adoption-among-nigerian-smes/
9. Eurostat (2025a) Use of artificial intelligence in enterprises. Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php/Use_of_artificial_intelligence_in_enterprises
10. Eurostat (2025b) ICT specialists – statistics on hard-to-fill vacancies in enterprises. Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php/ICT_specialists_-_statistics_on_hard-to-fill_vacancies_in_enterprises
11. Eurostat (2026) Digitalisation in Europe – 2026 edition. https://ec.europa.eu/eurostat/web/interactive-publications/digitalisation-2026
12. GIZ Digital Transformation Center Nigeria (2024) Characterisation and digital maturity assessment of MSMEs in Nigeria. https://dtcnigeria.com/wp-content/uploads/2025/03/Final-Updated-Characterisation-and-Digital-Maturity-Assessment-of-MSMEs-in-Nigeria_Illustrated-Report.pdf
13. Mohammed, A.L., Kutar, M. and Albakri, M. (2025) ‘Bridging the AI divide: Barriers and challenges to AI adoption for Nigerian SMEs’. UK Academy for Information Systems Conference Proceedings 2025 (Paper 28). https://aisel.aisnet.org/ukais2025/28
14. Noy, S. and Zhang, W. (2023) ‘Experimental evidence on the productivity effects of generative artificial intelligence’ Science, 381(6654), pp. 187–192. https://doi.org/10.1126/science.adh2586
15. OECD (2025a) AI adoption by small and medium-sized enterprises: OECD discussion paper for the G7. Organisation for Economic Co-operation and Development Publishing. https://www.oecd.org/en/publications/ai-adoption-by-small-and-medium-sized-enterprises_426399c1-en.html
16. OECD (2025b). Generative AI and the SME workforce: New survey evidence. Organisation for Economic Co-operation and Development Publishing. https://www.oecd.org/en/publications/generative-ai-and-the-sme-workforce_2d08b99d-en.html
17. OECD (2026) AI use by individuals surges across the OECD as adoption by firms continues to expand. Organisation for Economic Co-operation and Development. https://www.oecd.org/en/about/news/announcements/2026/01/ai-use-by-individuals-surges-across-the-oecd-as-adoption-by-firms-continues-to-expand.html
18. Shehu, M. (2025) How Nigerian MSMEs can deploy AI without big budgets. Techeconomy. https://techeconomy.ng/how-nigerian-msmes-can-deploy-ai-without-big-budgets
19. Tornatzky, L.G. and Fleischer, M. (1990) The processes of technological innovation. Lexington Books. https://link.springer.com/article/10.1007/BF02371446
20. van Deursen, A.J.A.M. and van Dijk, J.A.G.M. (2014) ‘The digital divide shifts to differences in usage’, New Media & Society, 16(3), pp. 507–526. https://doi.org/10.1177/1461444813487959
21. Yevsiukova, V. (2026) ‘Constraint-native artificial intelligence optimization for small and medium-sized enterprises: The Value-Yield Optimization (VYO) framework’, SSRN. https://doi.org/10.2139/ssrn.7198979
22. Zahra, S.A. and George, G. (2002) ‘Absorptive capacity: A review, reconceptualization, and extension’, Academy of Management Review, 27(2), pp. 185–203. https://doi.org/10.5465/amr.2002.6587995