How AI is transforming small and medium enterprises: Challenges and opportunities

The study projects that by 2030, AI adoption in SME manufacturing will experience significant growth, driven by several key advancements. One of the most transformative innovations will be AI-powered digital twins - virtual replicas of production lines that allow manufacturers to simulate, monitor, and optimize processes before real-world implementation. It will help SMEs reduce inefficiencies, minimize risks, and improve decision-making without disrupting actual operations.

Small and Medium Enterprises (SMEs) form the backbone of many economies, yet they face limited resources and technological constraints when integrating AI into their production processes. A new study, "A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises," published in AI 2025, takes a deep dive into how AI is shaping SME manufacturing, identifying key trends, adoption challenges, and future opportunities.

Industry 4.0 has driven the rapid digitization of manufacturing, and AI is at the heart of this transformation. The study highlights how AI-powered automation, predictive analytics, and robotics are helping SMEs:

• Enhance production efficiency with intelligent automation
• Optimize resource management by predicting demand and reducing waste
• Improve product customization with AI-driven flexible manufacturing
• Enhance decision-making through big data and real-time analytics

While these benefits are undeniable, the study highlights a widening gap between large corporations and SMEs in AI adoption. Why are small businesses struggling to integrate AI into their operations?

AI adoption in SMEs: What’s holding businesses back?

Despite its immense potential, AI adoption in SMEs remains slower compared to large enterprises. The study identifies four key challenges preventing widespread AI implementation in small businesses.

• High Costs of Implementation: Investing in AI infrastructure, cloud computing, and skilled personnel is expensive for SMEs.
• Limited Technical Expertise: Many SME owners and employees lack the necessary AI and data analytics skills.
• Uncertainty About ROI: SMEs often hesitate to adopt AI without clear proof of its long-term financial benefits.
• Integration Challenges: AI must be seamlessly integrated with existing production systems without disrupting operations.

The study emphasizes that while AI improves efficiency, product quality, and decision-making, SMEs must first overcome these barriers to fully harness the potential of AI.

How SMEs can leverage AI for competitive advantage

To bridge the AI adoption gap, the study outlines key strategies SMEs should adopt to make AI implementation successful. One of the most effective ways SMEs can integrate AI into their operations is by leveraging cloud-based AI solutions. Traditionally, implementing AI required costly infrastructure, making it inaccessible for smaller businesses. However, with cloud computing, SMEs can now access AI-powered tools without significant upfront investments and gain a competitive edge in production, supply chain management, and customer service without requiring extensive in-house expertise.

A key challenge many SMEs face is the lack of skilled employees who can effectively implement and manage AI systems. AI adoption must be accompanied by workforce upskilling to ensure that employees understand how to use these technologies effectively. Many SMEs struggle because they lack in-house data scientists or AI specialists, which is why training employees in automation, machine learning, and data analytics is crucial. 

Another area where AI can provide significant value to SMEs is predictive maintenance. Traditional maintenance strategies are either reactive (fixing machines after they break) or preventive (servicing machines based on a schedule, even if they don’t need repairs). Both approaches can be costly and inefficient. AI-powered predictive maintenance solves this issue by monitoring machinery in real-time, identifying small issues before they escalate into costly breakdowns.

For SMEs that are new to AI, the best approach is to start small with low-cost, high-impact applications before scaling up. This gradual approach ensures that SMEs see tangible benefits early on, increasing confidence in AI adoption while minimizing financial risk.

Future of AI in SME manufacturing: What’s next?

The study projects that by 2030, AI adoption in SME manufacturing will experience significant growth, driven by several key advancements. One of the most transformative innovations will be AI-powered digital twins - virtual replicas of production lines that allow manufacturers to simulate, monitor, and optimize processes before real-world implementation. It will help SMEs reduce inefficiencies, minimize risks, and improve decision-making without disrupting actual operations.

Another major development is Edge AI for real-time processing, which will enable AI systems to operate directly on devices rather than relying on cloud computing. This shift will make real-time data analysis and decision-making faster and more efficient, reducing dependency on external servers and improving response times in production environments.

Sustainability will also be a driving force behind AI adoption in SMEs. AI-driven sustainable manufacturing will play a crucial role in reducing carbon footprints by optimizing resource consumption, minimizing production waste, and enhancing energy efficiency. With growing environmental regulations and consumer demand for greener products, SMEs integrating AI for sustainability will have a competitive edge in global markets. At the same time, collaboration between SMEs and AI startups will lead to the rise of low-cost, plug-and-play AI solutions specifically designed for small businesses. These AI tools will be more accessible, eliminating the need for extensive technical expertise and large budgets, making it easier for SMEs to experiment with AI-driven automation.

Governments and private sector organizations are also expected to increase funding and policy support for AI integration in SMEs. AI adoption incentives, grants, and AI-as-a-service models will lower financial barriers, making AI more attainable for smaller businesses.