Resource-based view.

The Resource-Based View (RBV) framework highlights the importance of combining strategic resources and capabilities to generate long-term competitive advantages, emphasizing internal capabilities in promoting AI adoption and enhancing SMEs’ performance [26]. By leveraging internal resources such as entrepreneurial talents, human resources, technical competence, innovative capabilities, and social networks, SMEs can effectively embrace AI technologies to boost their competitiveness in the digital age [27].

[28] suggest that SMEs can cultivate success and resilience by effectively navigating and maximizing these internal resources to support innovation and development. This aligns with [15] findings, which emphasize that long-term competitive advantage is based on distinctive, valuable, and difficult-to-replicate resources and capabilities. For SMEs, often constrained by limited financial and human resources, the RBV framework provides a path to success by focusing on unique capabilities.

Research on small manufacturing firms in Portugal underscores the significance of internal information systems, technical capabilities, and management attitudes towards technology adoption [15]. SMEs that develop their expertise, knowledge, and strategic management perspectives are better positioned to succeed in AI integration, consistent with the RBV principle that emphasizes distinctive skills.

The RBV framework assists SMEs in identifying and developing key capabilities for AI integration. By focusing on areas such as AI competence, data analytics skills, and technological infrastructure, SMEs can enhance their AI readiness and effectiveness. Additionally, the RBV framework helps SMEs utilize their unique resources to establish a competitive edge and deliver value through AI adoption.

PESTEL analysis

We applied the PESTEL framework to analyze the external macroenvironmental factors affecting SME operations and performance, focusing on Political, Economic, Social, Technological, Environmental, and Legal aspects [29]. Political factors, such as changes in trade policy or taxation, impact market access and operating costs. Economic variables, including inflation and local economic growth, influence SMEs’ financial health, market demand, and pricing strategies. Social factors, like evolving consumer preferences and demographics, compel SMEs to adjust marketing strategies and products to meet changing customer needs. Technological advancements, such as affordable cloud computing and digital marketing tools, can enhance SME productivity and market reach. Environmental considerations drive SMEs to adopt sustainable practices, such as waste reduction and energy efficiency, to meet customer expectations and legal requirements. Legal compliance with labor laws, health and safety regulations, and intellectual property rights is crucial for smooth SME operations.

By applying PESTEL to AI integration, SMEs can anticipate and mitigate risks while identifying opportunities for innovation and growth. Understanding technology trends through PESTEL enables SMEs to enhance operations, improve customer experiences, and maintain competitiveness. Additionally, staying informed about regulatory developments helps SMEs ensure compliance and avoid legal issues related to AI integration [25].

In summary, integrating RBV and PESTEL provides SMEs with a comprehensive framework to address internal capabilities and external pressures in AI integration. By leveraging internal resources and strategic planning while considering external factors like regulations, economic trends, and technological advances, SMEs can navigate AI adoption’s complexities, positioning themselves for sustainable growth and competitive advantage in the digital era.

Change management and acceptance.

SMEs face resistance from employees and clients due to a lack of understanding of AI, its complexity, fear of technology and job displacement, and leadership mindset. [31] noted that AI adoption’s complexity sets it apart from other digital technologies, emphasizing the need for customization aligned with an organization’s AI readiness and goals. Participants reported that change management and acceptance issues hinder AI implementation, with [36] highlighting insufficient awareness and understanding as a major challenge, leading to decreased employee engagement with AI. P3, the only female participant, specifically mentioned these challenges.

“It is true that most worldwide companies have already adopted IR 4.0, AI, and so on, but in Malaysia, we are still behind, one of the reasons being a lack of awareness.”

AI implementation poses significant challenges for workers, who must adapt to new operational models and cope with the psychological impact of machines taking over their roles. P6, an AI technology service provider, recounted an incident where blue-collar workers, fearing job replacement by robots, resisted by attempting to destroy a cleaning machine in protest.

“When we first started in 2014, mobile robots were very rare in the manufacturing industry, so we had people who would actually sabotage the robots. Workers would come and damage the robots. They felt like they were suddenly competing with a robot that does not get tired and can run 24/7, which we understand… Recently, one time, I believe one of our teams was deploying it at hospital X. When one cleaner arrived, she turned the robot cleaner off and fled. We understand and can feel the worry and concern because this is a job that she used to do. Suddenly, a robot is doing it much quicker and more efficiently.”

This finding is in line with [37] study on radiologists, which highlighted the intricacy of AI and concerns about job loss as obstacles to AI implementation. Therefore, organisations need to implement more sensitive and effective change management practices in the workplace when embracing AI technology. [38] highlighted that the success of AI technology depends on workers’ acceptance. The results imply that SMEs should prioritise aligning AI projects with the technology environment to promote easier integration and acceptability among workers [39].

Limited resources and infrastructure.

SMEs often face significant challenges in AI integration due to limited financial and human resources and inadequate technology infrastructure [40,41]. [38] note that financial constraints and outdated infrastructure are major barriers, as AI requires substantial upfront investments, ongoing maintenance, and upgrades. [42] emphasizes the need for advanced technological infrastructure—high-speed internet, advanced computing power, and adequate data storage—to support AI, which many SMEs lack, leading to integration and performance issues. P1, a head manager, highlighted the crucial role that infrastructure plays in either supporting or hindering AI integration within an organization.

“The challenges are where the infrastructure and environment are not ready. Sometimes, not because workers are reluctant to adapt, but because they are not exposed to the infrastructure... When the organisation is located on the outskirts of town or in a rural place, it is challenging to get access to the high and better coverage of infrastructure.”

In a competitive environment, high-performance AI tools can significantly enhance business performance. However, implementing AI requires substantial resources, posing challenges for SMEs with limited financial capacity [43]. [44] found that SMEs often face financial constraints, limiting investment in technology, which affects employment, productivity, and wages. Both P5 and P2 highlighted that these financial constraints are a major concern for many Malaysian companies due to the high capital required to sustain and advance AI technologies. P2 specifically noted the difficulties in maintaining and upgrading AI systems.

“I think one of the challenges that all Malaysian companies have right now is the cost of adopting these types of technologies.”

Workforce skills gap.

Many SMEs lack employees with AI expertise, leading to a reliance on costly external consultants or the challenge of attracting limited AI talent. Despite financial constraints, SMEs should invest in high-value assets to attract or retain AI professionals, as they often struggle to compete with larger firms due to budget limitations [45,46]. P1 noted that the shortage of AI professionals makes it difficult for SMEs to attract talent, with costs frequently exceeding their budgets.

“… my challenge is to hire a colleague who has expertise in data, but they are limited, and his fee is very high. Very expensive. So, we ourselves cannot afford it.”

P5, the CEO of SMEs, who is also a doctor, pointed out that although local institutions are producing more graduates in AI-related subjects, there is a skill mismatch with the organisation’s requirements to work with current AI technology. The rapid advancement of technology leads to constantly evolving skill requirements for AI integration, making it difficult for talents to keep up to date [45]. Consequently, some highly educated graduates are compelled to accept blue-collar jobs to secure employment.

“A lot of young talent our university provides is very good, but they don’t have the required skills. It is very sad when you hear this. AI software engineer graduated from top universities but works as a food panda driver. It is not right. Something is wrong.”

Market and competitive pressures.

Integrating AI provides a competitive edge by improving operational efficiencies and product offerings, while those slow to adopt risk falling behind early adopters [47]. The competitive environment drives companies to strategically invest in AI and hire top talent, balancing short-term needs with long-term goals [48]. P6 acknowledged that competition from larger firms makes it difficult for SMEs to attract and retain top talent, hindering their growth and limiting their ability to fully leverage AI.

“It is a challenge to find experts in AI. I mean, to the extent that you hear of companies and major players poaching or stealing workers from each other. Because of the limited supply, it is quite an emerging market.”

Another challenge faced by SMEs in terms of market pressures is the adoption of advanced technologies, particularly in the context of AI integration [49]. In comparison, larger organisations are more likely to implement cutting-edge technology due to their greater access to resources and larger opportunities, which gives them an advantage in competing with smaller firms, including SMEs. P2 stated that in the digital era, SMEs strive for either the top talent or the most cutting-edge technology when compared to their competitors.

“Limitation means that the organisation needs professional power. In terms of business, it will cost more for the organisation to compete with... to get the best talent and technology.”

Regulatory and legal considerations.

The study identifies regulatory compliance, lack of established policy, and insufficient government support as key challenges in integrating AI into SMEs. The evolving regulatory and legal framework for AI increases complexity, making it essential for companies to navigate these requirements to avoid penalties. Data privacy and protection are major concerns, as AI systems require substantial data, necessitating compliance with laws like Malaysia’s Personal Data Protection Act (PDPA). This involves obtaining proper consent, implementing robust security measures, and ensuring transparency in AI data use [50]. [51] emphasize that while adhering to these evolving standards is challenging, it is crucial for sustaining operations. P5 suggests that developing consistent policies can help manage legal norms over time. However, P5 also noted that frequent policy changes due to political concerns hinder organizational progress, as SMEs lack stable guidance for advancement. The need for a consistent and clear regulatory environment is critical for supporting SMEs in effectively integrating AI while maintaining compliance.

“In Malaysia, all the movements start from top to bottom, not bottom to top. So, government policy is important. The stability of the government is very important. The policy cannot change if the government runs the country or if the healthcare policy in AI is like this. And then, when it comes to government B (run the country), because I experienced it from 2017, 2018 and to 2019. It is a totally different policy. Because the different policies have different adoption angles and different funding programs, the policy needs to be in line with the AI.”

[52], in their study, advised for appropriate policies, regulations, ethical advice, and a legal framework to prevent the misuse of AI in the organisation. At the same time, the comprehensive policy can regulate AI within ethical standards and in the context of human rights’ protection [25]. Similarly, P6 underlined that embracing AI means that businesses, no matter how small or large, must prioritise ethical issues or face more disadvantages than benefits. He then provided examples of what happens when we ignore ethical issues.

“For example, I am sure you have heard recently that there are these AI image generators that you would give something to, and then it would generate an image. And it caused controversy recently when there was an art competition, and someone used an AI image generator and won first place in the art competition. So, these things will create some concerns, and we are seeing some of that play out. So yeah, of course there are ethical issues; the good thing is that the artist who won first place came up and said, I did not draw this; I used an AI-generating tool, and his reason was that he wants to raise awareness of this issue.”

Resource acquisition challenges.

The final theme identified in this study is resource acquisition challenges, focusing on insufficient funding from banks or government agencies and the lack of infrastructure to support AI integration. SMEs often struggle with limited financial, human, and technological resources, unlike larger companies [53]. This resource shortage impairs their ability to compete effectively and adapt to changing economic conditions [54]. Without sufficient capital, SMEs cannot invest in modern technology and infrastructure upgrades, placing them at a competitive disadvantage in terms of efficiency and output. P4, representing a SME providing technology to farmers, highlighted the difficulty in securing financing from banks or government organizations due to their small size, often making loans inaccessible. As a result, they rely on government subsidies or grants to support their business, but are frequently met with bureaucratic hurdles or insufficient funding. This forced the company to use whatever resources it had to survive the AI integration process, underscoring the significant challenges SMEs face in acquiring the necessary resources to advance and sustain AI adoption.

“The biggest challenge is funding. Because we are new, getting help from a bank or government agency is difficult and takes years. Sometimes it takes 16 months or more (before being able to get funds). So, if we want to run the operation, I need to find funding for myself, which is the most difficult thing for me because we need to use it immediately.”

Another group of participants, P1, P2, P3, P4, and P5, emphasised that, besides budget constraints, inadequate infrastructure, such as old equipment or insufficient logistics networks, can hinder operations and restrict expansion potential. P4 believed that the infrastructure should be provided by local authorities or government agencies because this is something that the government can do to facilitate small firms progress in the digital era.

“Because IKS (refers to SMEs) have limited resources and we do not have or afford to invest in providing better infrastructure, we are often left behind... Infrastructure is something that the government or local authority should provide in order to support our expenditures.”