Authors: Nick Drydakis
This study examines whether AI Capital, defined as AI-related knowledge, skills and capabilities, is associated with business innovation among SMEs in England.
https://doi.org/10.1016/j.jsinno.2026.200570
Previous research.
While prior research primarily explored AI adoption through technological indicators such as patents and external market drivers (Al Dhaheri et al., 2024; Grashof and Kopka, 2023; Segarra-Blasco et al., 2025), this study shifted the focus towards a human-centred approach, recognising that a business’s ability to leverage AI effectively depends not only on access to AI technologies but also on the internal knowledge, skills and capabilities required to utilise them strategically.
Theory in this research:
- GPT (General Purpose Technology) is used to explain that AI is a general purpose technology that able to drive innovation across multiple industries through the decision making, automating the process and enable business model. The theory is used to study the economic growth, which imply that the technological change is the major driver of economic growth. GPT is identified with a technology that continuously evolving over time and is classified with their utilities. There are 6 characteristics of GPTs. 1. GPTs play role as an enabling technologies, which open more opportunities. 2. GPTs enable new downstream inventions and innovations that were previously technically impossible without them. These innovations can then lead to further inventions and improvements in the original GPT. GPTs are pervasive, meaning that mature GPTs can have multiple or widespread uses across different industries. GPTs typically have few or no close substitutes for many of their applications.
Many technologies have the potential to evolve into a GPT. Some are easily identified early on as having this potential. For example, it was clear early on that both nano technology and bio-technology had the potential to become GPTs. On interaction, a technology that does not interact with other technologies cannot become a GPT
- Dynamic Capabilities theory is also used to explain how AI supports business innovation. DCT shows how a firm can adapt with the environment and use AI for identifying market trends, predict consumer behaviour.
Another theory that this research use is:
- Innovation Diffusion Theory (IDT) explains that the company which use technology has obtain the first mover advantage and the shape the industry trends.
- Based on human capital theory, AI is used as a critical assets and has influence in productivity and innovation. Not like the conventional information technology, AI is not measured as a standard digital tools.
This study employed a structured survey methodology to collect longitudinal data from SMEs operating in England. SMEs were
identified using commercial business databases and organisational listings that provide sectoral classification and contact details for
registered businesses.
There are 5 domains that conceptualised the business innovation, product and service innovation, process innovation, technology adoption, market and customer engagement, and organisational culture and strategy.
AI capital consists of three components: AI knowledge, AI skills, AI capabilities
Methodology :
This study employed a structured survey methodology to collect longitudinal data from SMEs operating in England. The survey was distributed electronically to key personnel within these organisations, primarily via email-based e-surveys that directed participants to a dedicated online survey platform.
The questionnaire covered business characteristics, such as revenue growth rate, number of employees, and years of operation, alongside technical questions on AI and innovation. Given the nature of the study, certain sections were designed for finance personnel and general managers or directors, while others were intended for information and technology personnel.
The opening letter invited recipients to forward the survey to the relevant departments or individuals, ensuring that responses were provided by those responsible for potential AI-related decision-making, including senior management, IT specialists, innovation managers, and R&D personnel. The survey structure was designed to capture insights from individuals directly involved in AI adoption and implementation.
The research is done in twelve sectors:
- manufacturing
- construction
- wholesale and retail trade
- financial and insurance services
- information and communication
- transportation and storage
- real estate
- professional scientific and technical services
- administrative and support services
- education
- health and social work services
- and leisure, hospitality and tourism.
Research design: Longitudinal research design. Using two survey waves. 1. data collection in January 2024 and 2. data collection in January – February 2025.
To understand what the AI capital in the business innovation, 83 items is developed. The 83 items are reviewed in 4 steps by expert in business experts, IT and technology strategists and business executives.
Findings
- AI-related knowledge, skills, and capabilities within SMEs were found to be associated with business innovation
- Relationship between AI Capital and business innovation is not sensitive to specific econometric modelling choices but is instead robust across a range of specifications.
- AI knowledge enables businesses to identify cutting-edge technologies
- AI Capital influences multiple dimensions of innovation, including product and service development, process optimisation, technology adoption, market and customer engagement, and organisational culture and strategy.
- businesses with stronger financial performance exhibited higher
- AI Capital and innovation scores, suggesting that financial stability enables businesses to invest in digital transformation and advanced technological capabilities.
- The findings indicate that larger businesses demonstrated a stronger association between AI Capital and innovation.
- workforce capacity is a key factor in AI adoption, as skilled personnel can facilitate the implementation and effective utilisation of AI technologies
- established businesses, i.e., those that have been in operation for a longer period, demonstrated a stronger association between AI Capital and innovation. This suggests that accumulated experience, institutional knowledge, and strategic foresight contribute to AI adoption
Research contribution.
Furthermore, the study aligned with and contributed to the resource-based view of business (Barney, 1991) by demonstrating that AI Capital constitutes a valuable resource that enhances SMEs’ innovation potential.
Suggestions.
- A lack of AI expertise represents a significant challenge for SMEs, as many businesses face limited technical knowledge and shortages of skilled personnel. Without adequate AI Capital, SMEs may struggle to fully exploit AI’s potential or mismanage implementation, creating inefficiencies or operational risks. To address this, governments could prioritise AI education by integrating AI and
digital literacy into national curricula, vocational training, and lifelong learning programmes.
- Governments could develop clear regulatory guidelines promoting responsible AI use while ensuring compliance with data protection legislation, such as the UK General Data. Protection Regulation. Establishing clear AI governance frameworks may help businesses navigate regulatory requirements and mitigate risks associated with AI deployment.
Limitations
The focus on SMEs in England limits the generalisability of findings to
other regions and business contexts. While the UK presents a strong case for examining AI adoption in SMEs, differences in business environments, regulatory frameworks, and digital infrastructures across countries could affect AI-driven innovation
Another limitation is the lack of qualitative insights into how SMEs implement AI strategies in practice. While the study quantifies the association between AI Capital and business innovation, it does not explore how businesses develop AI-driven solutions, overcome implementation barriers, or integrate AI into decision-making processes and operational workflows
