Artificial intelligence is no longer confined to large technology firms or multinational corporations. Across sectors and regions, small and medium-sized enterprises are increasingly experimenting with data-driven automation, machine learning tools, and intelligent decision systems. Yet despite this rapid uptake, confusion persists around how and why smaller firms adopt artificial intelligence, what enables success, and where the real barriers lie.
A major new study led by Julia Schwaeke and colleagues offers one of the most comprehensive answers to date. Published in the Journal of Small Business Management, the article titled “The new normal: The status quo of AI adoption in SMEs” systematically analyses more than two decades of academic research to clarify how artificial intelligence is being implemented across the SME landscape. The research was conducted primarily at HHL Leipzig Graduate School of Management, with contributions from Swansea University, the Free University of Bozen Bolzano, the University of Johannesburg, and Woxsen University.
Rather than focusing on individual case studies or specific technologies, the authors adopt a broader perspective. Their work synthesises 106 peer-reviewed studies to identify patterns, enablers, and blind spots in how SMEs engage with artificial intelligence. The result is a structured, theory-grounded map of AI adoption that is both relevant to practitioners and researchers.
Why artificial intelligence now matters for SMEs
The timing of this research is critical. Artificial intelligence technologies have reached a level of technical maturity and affordability that places them within reach of small firms. Cloud computing, software-as-a-service platforms, and pre-trained machine learning models have significantly reduced the cost and complexity of deployment. As a result, SMEs are no longer asking whether they can access AI, but whether they can afford not to.
The study highlights a growing consensus within the literature that failure to adopt artificial intelligence can place SMEs at a competitive disadvantage. Productivity losses, slower decision-making, weaker customer insights, and declining market relevance are increasingly linked to digital inertia. At the same time, overly complex or poorly aligned AI implementations can drain resources and undermine trust within organisations.
What distinguishes SMEs from larger enterprises is not a lack of ambition, but structural constraints. Limited financial capital, smaller workforces, and thinner margins mean that technology decisions carry greater risk. This makes understanding the conditions under which AI adoption succeeds especially important.
A framework for understanding AI adoption
To make sense of a fragmented research field, the researcehrs employ the Technology-Organisation-Environment (TOE) model, commonly referred to as the TOE framework. This model has long been used to study how organisations adopt innovations, but its application to artificial intelligence in SMEs has been inconsistent.
The TOE framework proposes that adoption decisions are shaped by three interacting dimensions. The technological context refers to existing systems, data, and infrastructure. The organisational context includes leadership, culture, skills, and resources. The environmental context encompasses external pressures, including competition, regulation, and collaboration networks.
By categorising findings from 106 studies into this framework, the authors identify eight recurring clusters that define the current state of AI adoption in SMEs. These clusters reveal not only what drives adoption, but also where research and practice remain underdeveloped.
Technology readiness and the challenge of compatibility
One of the strongest themes emerging from the review is the importance of compatibility. For SMEs, artificial intelligence rarely replaces existing systems overnight. Instead, it must integrate with legacy software, established workflows, and long-standing business processes.
The research shows that AI adoption is most successful when it aligns with existing IT architectures and strategic objectives. In manufacturing SMEs, for example, long equipment lifecycles make seamless integration essential. In service-oriented firms, compatibility extends beyond hardware and software to include work practices and decision routines.
Closely linked to compatibility is infrastructure readiness. Artificial intelligence systems depend on reliable data, stable networks, and scalable computing resources. SMEs with weak IT foundations often perceive AI as excessively complex or costly, even when low-cost tools are available. The study emphasises that AI readiness is not purely technical, but strategic. Firms that treat artificial intelligence as a business capability rather than an IT experiment are better positioned to realise value.
Human capital as a decisive factor
While technology matters, the study consistently finds that people matter more. The organisational dimension of AI adoption is dominated by issues of knowledge, skills, and attitudes.
Employee expertise and digital literacy are critical enablers. SMEs with higher levels of AI-related knowledge are more likely to perceive artificial intelligence as useful, trustworthy, and performance-enhancing. This perception directly influences willingness to adopt and experiment with new systems.
The research also highlights the role of learning cultures. SMEs that invest in continuous skill development and encourage knowledge sharing are better equipped to absorb technological change. Importantly, willingness to change often outweighs technical proficiency. Employees who believe that AI will support rather than replace their work are more likely to engage constructively with new tools.
Leadership emerges as a central influence. Senior management commitment can compensate for limited resources by accelerating decision-making, aligning priorities, and legitimising experimentation. In smaller firms, flatter hierarchies enable leaders to play a direct role in AI initiatives, thereby reducing communication gaps and fostering greater agility.
Reference
Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., and Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297 to 1331. https://doi.org/10.1080/00472778.2024.2379999
