Closing the SME AI Adoption Gap

AI is dominating headlines, but the adoption gap between SMEs and larger companies is only widening. According to a recent report from the OECD, 40% of firms with 250 or more employees have adopted AI, as opposed to just 11.9% of firms with less than 50. 

There are a number of reasons behind this ongoing, widening gap: scale and scope of business, technology maturity and automation, cost and budget barriers, missing knowledge, and internal resistance to change. 

But this widening adoption gap also translates into a concerning competitive disadvantage. When adopted properly, AI can provide significant advantages to resilience and agility. Here’s why manufacturing SMEs need to close the adoption gap, and how to do so.

The Economic Case for Balanced Adoption

Cost-saving and productivity rank among the top reasons for AI adoption in most organizations. However, the emphasis on cost-cutting and profit maximization leads to workforce reductions, which, as an analysis from Harvard Business Review points out, is based on AI’s potential, not its actual performance. Attached to that is a hugely concerning connected risk: the ensuing widespread hit to economic health. 

This is where SMEs come in. They often absorb labor displacement as displaced workers move from larger companies to smaller ones, playing a vital role in sustaining demand and economic stability around the world. In fact, two-thirds of jobs worldwide are provided by SMEs, contributing more than 50% of GDP. 

Moreover, SMEs’ participation in AI transformation is necessary to avoid skewed competition and imbalance in the industrial landscape. Suppliers and vendors have a key role in downstream supply chain activities. With more balanced AI adoption across the ecosystem, innovation and productivity, balanced by human-in-the-loop processes, extend across the supply chain.

Pursue Both Horizontal and Vertical Integration

Vertical integration is a vital consideration for AI adoption, particularly in a manufacturing setting that calls for customized solutions for varying operational needs. However, it is also important to acknowledge the horizontal aspects of AI adoption. This enhances adaptability and agility—crucial traits for SMEs and large corporations alike that are tackling ongoing supply chain challenges and accelerated demand in tighter timeframes. 

Horizontal considerations include AI adoption for more generic outcomes, like decision-making and improving productivity. These ‘horizontal’ tasks are more versatile and universal in their nature, simply augmenting human-led processes instead of replacing them.

Another consideration is not to limit AI adoption to purely administrative purposes, but rather to strategically adopt technology as part of strengthening competitive advantage. This is where long-term strategic thinking—not immediate innovation and an emphasis on hype—comes into play. 

In manufacturing and supply chains, this usually pans out in the form of AI-powered automated inventory management, health and safety and environment (HSE) oversight, ensuring consistently high production quality, operations management, and machine and equipment maintenance.

For instance, automotive parts suppliers leverage AI systems to optimize machinery maintenance based on the ongoing state of the supply chain. On the other hand, in steel processing, in-house AI is used across internal production systems as opposed to external customer relationship management or enterprise resource planning. This is because operation is the source of differentiation and competitive advantage.

Building Adaptable, Confident Know-How

At the heart of successful AI strategies are the people who will be overseeing the tools. All organizations, including SMEs, must embed internal processes that protect core operations and workflows. Too often, there’s a slippery slope to seeing AI as a ‘magic bullet.’ The overreliance on these tools can lead to issues resulting from bias and hallucinations, particularly when the data foundations are not in place to ensure these tools function reliably.  

Internal workflows must be built on repetition and precision, with a priority framework that moves across security, safety, quality, and productivity. This is a must to lessen concerns and resistance and to increase motivation and incentives. 

Importantly, critical operational know-how should remain proprietary. That means adopting general AI tools for non-core functions, but building in-house systems for high-value processes to maintain competitive advantage. Generative tools make knowledge transfer easier, as long as teams are well-versed in using these. Success also depends on embedding these tools within the company’s unique workflow to meet its particular wider operational and business needs. Systems should be orchestrated and interoperable; otherwise, backlogs and errors become a common guarantee. 

Any AI systems that are integrated into workflows and processes must be accompanied by training. Training programs should be structured so teams are constantly strengthening AI skills, expertise, and confidence in overseeing these tools, encompassing relevant skills such as data literacy and an understanding of ethical and legal considerations, such as GDPR and protection of intellectual properties.

Finally, cross-industry collaboration for knowledge sharing can help establish best practices. Knowledge sharing between larger corporations, which tend to have more experience with AI tools for automated processes, can empower SME AI adoption. 

Closing the AI gap is about driving competitiveness, resilience, and agility in an increasingly volatile landscape. SMEs that take a strategic, people-centered approach to AI can strengthen resilience while preserving their unique operational advantages. SMEs are an important driver of an AI-forward future where efficiency and speed are maximized.

Article by Shinichiro (SHIN) Nakamura, President of one to ONE Holdings