Barriers To Adoption Of Ai In SME’s

INTRODUCTION

While Artificial Intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), its adoption is significantly constrained by a combination of structural and contextual barriers. Chief among these are financial limitations, lack of technical expertise, regulatory uncertainty, organizational resistance, and limited access to SME-specific AI tools.

Financial constraints and lack of technical expertise are the most cited hurdles. SMEs often operate with limited financial resources, which restrict their ability to invest in AI technologies. This includes the high upfront costs of infrastructure upgrades, software licensing, and acquiring skilled personnel. Joseph (2023) identifies these financial and technical barriers as key inhibitors that prevent SMEs from scaling AI effectively. Le Dinh et al. (2025) emphasize that these budgetary constraints are particularly acute when SMEs are compared to larger enterprises, which can more readily absorb technology-related expenditures. Additionally, Mohib et al. (2025) note that limited access to software tools and in-house expertise hinders SMEs’ capacity to independently develop, deploy, and sustain AI-driven solutions, making them overly reliant on external vendors or consultants.

Beyond resource limitations, data privacy concerns and regulatory ambiguity present major challenges. The regulatory environment surrounding AI is complex and varies across regions. Adams et al. (2025) highlight significant disparities in global AI regulatory frameworks, noting the tension between ensuring innovation and protecting personal data. Heston (2025) underscores that SMEs face distinct challenges in complying with data privacy regulations such as the EU’s GDPR and U.S. sector-specific laws, which often lack clarity regarding AI applications that utilize user data for automation and personalization. Joswig & Kurz (2025) reinforce this point by exploring the difficulty SMEs face in aligning with ambiguous data governance policies, thereby increasing their perceived legal and reputational risks. In such an environment, SMEs, unlike larger firms with dedicated compliance teams, are often left to navigate regulatory uncertainty without adequate support.

A further organizational barrier stems from resistance to change and fears of automation-induced job loss. As Le Dinh et al. (2025) point out, successful AI adoption requires cultivating a data-driven culture, yet many SMEs face internal resistance due to employee concerns about job security. Mohd and Umar (2025) identify internal challenges such as fear of technological displacement and reluctance to adapt to change as critical inhibitors to AI adoption, especially in rapidly evolving technological environments. These fears are often exacerbated by the misconception that AI’s primary function is to replace human labor, thereby ensuring organizational inertia. Chhatre & Singh (2024) argue that effectively navigating AI adoption requires a clear understanding of job evolution, cultural transformation, and change management, enabling SMEs to reposition AI as a collaborative tool rather than a threat.

The reliability of AI vendors and the lack of SME-specific AI tools compound these issues. Einav et al. (2024) stress that SMEs need to strategically orchestrate AI resources into governance frameworks, but many vendors offer generic, enterprise-focused solutions that fail to account for SME-specific constraints such as fragmented IT ecosystems and limited scalability. Moilanen & Laatikainen (2023) note that integrating AI with legacy IT systems often demands significant time and resources, further straining SMEs. Le Dinh et al. (2025) confirm that many AI providers overlook the need for affordable, strategic, and deployable solutions that align with the unique realities of smaller businesses, effectively excluding SMEs from the benefits of AI.

To address these barriers, SMEs have turned to several strategic interventions. Public-private partnerships and government-supported initiatives play a vital role in easing financial and infrastructure-related constraints. Nwagbala et al. (2025) advocate for such partnerships, highlighting that governments can facilitate innovation ecosystems by linking SMEs with large corporations and research institutions, thereby granting them access to advanced technologies and expertise. Policy-driven incentives, including grants, tax credits, and subsidized training programs, can also lower entry barriers, especially when tailored to local contexts. Schwaeke et al. (2024) emphasize that regulatory frameworks, cultural norms, and economic conditions influence the effectiveness of AI adoption strategies, reinforcing the need for regionally adaptive interventions.

In addition, open-source AI platforms offer SMEs a cost-effective and customizable alternative to proprietary tools. However, successful adoption requires strong training and governance structures to manage risks and optimize impact. Rahman et al. (2025) argue that usability, structured training, and governance mechanisms are essential to overcoming adoption bottlenecks, particularly in resource-constrained SME environments. A human-in-the-loop approach, as advocated by Le Dinh et al. (2025), is also essential. Involving employees in AI workflows helps SMEs ease job displacement concerns and cultivate internal champions for adoption, while strategic upskilling, open-source access, and public-private partnerships collectively form a practical roadmap for inclusive, scalable AI integration amid multifaceted challenges.

CASE STUDIES

UK-Based SME Transformation Case Study: Derby City Council and West Berkshire Council

Derby City Council and Derby Homes have set a benchmark in public sector AI adoption by integrating conversational AI into their customer service operations through a strategic partnership with ICS.AI. Confronted with a £14 million funding gap, Derby implemented AI-powered assistants, Darcie and Ali, across web and phone channels, streamlining service delivery in over 1,100 council services. Within the first year, these AI assistants handled 500,000 phone calls and 57,000 website interactions, achieving a 46% call deflection rate, double the projected target and realizing £200,000 in cost savings within three months, with a full-scale transformation projected to generate £12.25 million annually. Their phased AI strategy, including human-in-the-loop oversight and continuous model refinement, has since expanded into critical service areas like Adult Social Care and Debt Recovery, setting a replicable model for SMEs seeking scalable, AI-driven operational efficiency (ICS.AI, 2022). Similarly, West Berkshire Council, serving 160,000 residents, partnered with Logicdialog to implement a conversational AI assistant aimed at alleviating high volumes of repetitive citizen queries, particularly in areas like waste management and parking permits. Through conducting an extensive data analysis to identify high-volume, low-complexity inquiries, the council deployed a digital assistant that now autonomously handles over 11,000 monthly enquiries, deflecting 54% of low-value interactions and freeing human agents to focus on complex, high-priority cases. The project achieved an 80% citizen satisfaction rate, significantly above the national average of 68%, while delivering substantial operational cost savings. Notably, the solution’s scalability, improved by staff training for autonomous feature deployment, enables ongoing expansion into additional service lines (Shepherd, 2025).

Both Derby and West Berkshire perfectly described how AI-enabled service models can drive efficiency, cost savings, and enhanced citizen engagement, even within resource-constrained public sector environments. Their structured approaches, from diagnostic assessment to AI adoption and iterative scaling, offer valuable insights for SMEs navigating digital transformation under similar operational and financial constraints. These cases underscore the importance of tailored AI solutions, workforce upskilling, and human oversight in achieving sustainable AI integration.

U.S.–Based SME Transformation Case: Kansas City 311 AI Pilot

Kansas City, Missouri (KCMO), in collaboration with Bloomberg Philanthropies’ City Data Alliance, is pioneering the integration of AI to transform its 311 service system, a vital non-emergency hotline and app that enables residents to report issues such as potholes, snow removal, and other municipal services. Faced with challenges of slow response times and disparities in service delivery across neighborhoods, KCMO is deploying AI to automate the categorization and routing of service requests, enhancing operational efficiency while ensuring equitable access to public services. AI-powered triage will expedite the handling of resident inquiries, enabling faster, more accurate responses and extending multi-language support to underserved communities, following successful models implemented in San Jose, California. With Bloomberg’s provision of technical tools, staff training, and human-in-the-loop governance structures, Kansas City aims to responsibly deploy AI while safeguarding against algorithmic bias and misinformation. The initiative’s ultimate objective is to scale AI integration across all public-facing departments, including water, parks, public works, and planning. Mayor Quinton Lucas emphasizes that the transformation is not just technological but deeply rooted in addressing systemic inequities in city service delivery, ensuring that “every Kansas Citian has a working fire hydrant in their neighborhood.” The City Data Alliance’s structured approach to AI adoption, blending policy alignment, capacity building, and phased implementation, positions KCMO as a leading example of how AI can drive meaningful improvements in urban service management, paralleling strategies SMEs can adopt to navigate digital transformation with limited resources.

Comparison & Alignment with Framework

The AI-driven transformations observed in Derby City Council, West Berkshire Council (UK), and Kansas City, Missouri (U.S.) offer practical illustrations of how public sector entities, operating under SME-like constraints, successfully leverage AI adoption through structured, phased approaches that mirror this article’s proposed AI Implementation Framework for SMEs. Despite variations in geographic, regulatory, and operational contexts, these cases consistently emphasize the importance of grounding AI adoption in clear assessments of digital readiness, strategic resource allocation, and continuous improvement cycles.

In Derby’s case, the Council’s diagnostic assessment of service demands, especially the over-reliance on phone interactions (60% of resident queries), informed a phased AI adoption strategy where initial deployments targeted high-volume, low-complexity touchpoints via AI-powered chatbots and phone-based assistants. This aligns with the Assessment and Adoption Strategy stages of the framework, wherein SMEs assess business needs and carefully choose between in-house development and external vendor partnerships. The integration of ICS.AI’s SMART platform reflects the Integration phase, which demonstrates seamless alignment of AI solutions with existing infrastructure, including a human-in-the-loop design to manage complex queries and ensure service inclusivity.

Similarly, West Berkshire Council’s deployment highlights a data-driven approach where service areas like waste collection and parking permits were identified as prime candidates for automation after thorough data analysis. Their strategy of empowering internal teams to build and iterate on AI capabilities through no-code platforms exemplifies the Training & Workforce Upskilling pillar of the framework, ensuring that AI integration is sustainable and adaptable. Both UK cases illustrate the strategic Monitoring & Continuous Improvement component, leveraging real-time feedback loops to refine AI models, expand use cases, and drive measurable outcomes like increased citizen satisfaction and significant cost savings.

Kansas City’s AI-driven 311 service overhaul reflects a comparable adherence to the framework’s principles, also in a U.S. context. Their participation in Bloomberg Philanthropies’ City Data Alliance provided structured guidance for digital readiness assessment and a phased adoption strategy that prioritizes AI deployment in service routing and multilingual support. The embedment of human oversight into AI workflows to prevent bias and ensure accountability, KCMO strengthens the framework’s emphasis on human-in-the-loop governance. Also, the city’s focus on scaling AI across public-facing departments mirrors the Integration and Monitoring stages, ensuring AI solutions evolve in tandem with operational needs and community expectations.

Examining these three cases presented, the importance of ecosystem support, whether through vendor partnerships, public-private collaborations, or capacity-building initiatives, is observed. This aligns with the framework’s recognition that SMEs, much like public sector entities, require external enablers to overcome barriers related to budget constraints, fragmented tech ecosystems, and skills shortages. Furthermore, their commitment to leveraging AI as more than a cost-cutting automation tool but as an enabler of enhanced service quality, inclusivity, and strategic agility illustrates a mature, holistic approach to digital transformation, aligning with the approach of this article framework, which aims to enable the same in SMEs.

POLICY IMPLICATIONS AND STRATEGIC RECOMMENDATIONS

To successfully transform SMEs with the integration of AI, there is an urgent need for AI-friendly policies that lower the barriers to entry for small businesses. Financial constraints and lack of technical expertise are one of the major barriers (Joseph, 2023; Le Dinh et al., 2025; Mohib et al., 2025), creating the need for policy interventions that provide targeted grants, subsidized AI training programs, and cloud service credits to enable SMEs to access critical infrastructure without disproportionate financial strain. Programs similar to Bloomberg Philanthropies’ City Data Alliance, which provided technical resources and governance frameworks to Kansas City and other U.S. cities, demonstrate how structured, ecosystem-level support can accelerate AI adoption while ensuring responsible deployment aligned with public interest.

The role of digital innovation hubs, accelerators, and public-private partnerships is equally important for the integration. As highlighted by Nwagbala et al. (2025), ensuring collaboration between SMEs, large corporations, research institutions, and government agencies is essential for democratizing access to AI expertise, platforms, and best practices. Ecosystem support mechanisms, such as those seen in Derby City Council’s partnership with ICS.AI and West Berkshire’s collaboration with Logicdialog, are typical examples of how shared resources and domain-specific AI models can help SMEs overcome vendor-related challenges and technological fragmentation. This is also highlighted by Einav et al. (2024).

Policy frameworks must also prioritize data governance, ethics, and regulatory clarity to ensure trust and facilitate AI adoption among SMEs. The existing ambiguity in AI-related regulations, particularly concerning data privacy and compliance standards (Adams et al., 2025; Joswig & Kurz, 2025), poses significant risks and operational uncertainties for small businesses lacking in-house legal expertise. Harmonizing AI policies to balance innovation with ethical safeguards is crucial to ensure SMEs can deploy AI technologies responsibly without the fear of regulatory non-compliance or reputational damage.

Lastly, AI adoption in SMEs must be positioned as a strategic lever for economic revitalization and job creation. Contrary to fears of job displacement, AI has the potential to augment human capabilities, driving productivity gains, new business models, and workforce upskilling (Le Dinh et al., 2025; Mohd & Umar, 2025; Chhatre & Singh, 2024). Therefore, local and federal governments must craft policies that encourage AI-driven SME innovation, such as a technological upgrade and a national competitiveness agenda aimed at encouraging inclusive economic growth, reducing digital divides, and strengthening global supply chains.

CONCLUSION

This article has demonstrated that Artificial Intelligence (AI)  has gone beyond an abstract or exclusive frontier for large enterprises, but also a practical, scalable lever for digital transformation in small and medium-sized enterprises (SMEs). From streamlining customer service through chatbots and enhancing decision-making with predictive analytics to automating core business processes, AI is fundamentally reshaping how SMEs compete, operate, and engage with customers. The case studies examined emphasize that with the right frameworks, when anchored in digital readiness assessments, human-in-the-loop governance, and continuous feedback loops, AI-driven transformation is achievable and can yield replicable efficiencies and service enhancements across diverse economic and regulatory contexts.

However, the journey towards widespread AI adoption in SMEs remains hindered by structural barriers such as financial constraints, regulatory ambiguity, and skills shortages. Addressing these challenges will require coordinated efforts between policymakers, innovation ecosystems, and SMEs themselves to ensure AI-friendly environments that promote equitable access to technology, ensure ethical data governance, and prioritize workforce inclusion.

Looking forward, future research should prioritize longitudinal studies that track AI adoption trajectories in SMEs over extended periods, offering deeper insights into the scalability and sustainability of AI-driven business models. Sector-specific implementation frameworks are also needed to account for the unique operational dynamics of industries such as healthcare, manufacturing, and retail. Additionally, an important yet underexplored path lies in examining AI’s potential for driving social impact within SMEs, from enhancing service delivery in underserved communities to enabling SMEs to participate meaningfully in the digital economy. AI’s transformative potential for SMEs is unequivocal, but unlocking it at scale demands a holistic approach that transcends technology, which anchors on AI adoption in strategic policy, ecosystem collaboration, and a human-centric vision for inclusive innovation.