SME Competition in the Age of AI-Shift

In an age where the fast technological changes overhaul the operations of businesses, AI has indeed become very important on the frontier of new technologies, innovation, and efficiency. A transition to AI-driven business models (BMs) for small and medium-sized enterprises (SMEs) is not less a strategic advantage than a necessity to keep pace. In a more recent report, Thormundsson (2023) argues the sheer scale of this transformation, with the AI market set to grow from its current valuation of just under $100bn to an amazing $2tn by 2030.

Corporates are realizing the need for adoption of AI with a lot more urgency. According to McKinsey & Company, AI adoption rates are over double that in 2017, with the percentage of companies integrating AI into their operations standing at 35% as of May 2022. And from what we can see, this is bound to be felt—this is a further shift, quite radical in how business uses technology. Now early adopters are beginning to reap the benefits of improved productivity and likely leadership over their competitors with the help of AI.

However, the way for moving towards such AI-driven models is very hard for SMEs. What comes through is a stark reality from the 2022 Gartner survey: 54% of the AI initiatives manage to get beyond a successful pilot into full production. In this light, the statistic is representative of the broader difficulties that companies face in moving AI from experimental applications to practical, scalable solutions.

The major challenges are an AI-skills gap, financial bottleneck, and lack of the right tools: 34% of businesses reported to be struggling with the AI expertise gap, and nearly a quarter were held back by the high cost and operational complexities associated with AI projects. The potential benefit of AI is huge, despite such hurdles. That's because AI can drive the birth of new organizations, business models, and services in addition to developing leading-edge products and services. Theoretical insights posit that business models are an important link between technology and economic value, offering a blueprint for how firms create, deliver, and capture value. Against this background, knowing and implementing an effective AI-driven BM becomes indispensable for SMEs who are seeking to harness AI for their long-term success.

The gulf in AI adoption between corporate giants and SMEs is astounding.

Indeed, large enterprises are 60% more likely to have a well-defined AI strategy implemented across their organization, compared to the 40% of small businesses that have a comprehensive one. This underlines a important gap—since large corporations do usually lead in scaling AI initiatives, sometimes leave SMEs lagging or even struggle to navigate such complexities while thinking of integrating AI technologies. Bridging this gap, therefore, requires understanding by the SMEs in two dimensions: an understanding of the practical know-how of AI integration and understanding the theoretical basis for successful business model transformation. The pathway to AI-driven BMs goes much deeper than technology adoption and includes a profound understanding of how AI can be commercialized.

In conclusion, the evolution towards AI-driven business models is a most exciting and, at the same time, challenging journey for SMEs. Therefore, flexibility for SMEs to be adaptive and innovative will be of utmost importance in the face of an AI market that will continue its expansion at a very fast rate. AI that SMEs embrace will open new opportunity doors to growth and competitiveness that AI will not only support business success in the future but be the driver for them, in order not to be left redundant in a future where AI will be the driver for businesses. This transition, however complex, presents the path to redefine how value is created and delivered in our ever-more digital world.