Artificial Intelligence (AI) could trigger a new production revolution, radically transforming business practices and conditions. This chapter aims to provide an understanding of what AI is, its potential impact on SME activities, and barriers to adoption. The first section examines the rise of data-driven AI systems. The second section looks at the implications of these technological changes on SME practices and business environment. It looks at how AI can drive greater efficiency in the SME sector, across different industries and along SMEs’ internal value chain, as well as how AI can improve SME business conditions. The third section discusses how AI diffuses differently within the SME sector, and elaborates on the barriers and challenges smaller businesses face when they consider AI adoption. Overall, this work intends to stress some areas where policy intervention could be considered.
- Recent progress in the field of Artificial Intelligence (AI) is largely due to the wide adoption of data-driven statistical methods and breakthroughs in machine learning, supported by greater data availability, increased computing power and growing algorithmic efficiency.
- AI systems are built on sensors (to capture data), an operational logic (to analyse data and infer decisions), and actuators (to intervene in the physical or virtual world). They are trained with data, and machine learning algorithms can adjust constantly while processing information, with little human supervision.
- The self-improving nature of AI poses challenges, with a risk that AI systems could be prejudicial to the real world (i.e.?an over reliance on biased or poisoned data, and a lack of explainability of algorithms). In addition, the scope for replicating and scaling up AI solutions remains limited because their features are little transferable from one environment to another.
- The main business applications of AI relate to automation, image/face recognition, natural language processing, data analytics and predictive capacity.
- New AI systems make possible to automate non-routine tasks. Automation could help SMEs increase productivity, e.g. by refocusing activities on higher value-added functions, or by reducing costs. Such systems could also help small businesses overcome administrative bottlenecks and increase responsiveness.
- AI allows a significant drop in prediction price and facilitates decision?making. SMEs can execute predictive analytics to lower their exposure to risks, automate business forecasts with real-time data, or increase efficiency in asset management. Enhanced prediction capability also allows for greater market segmentation and opens new opportunities for SMEs to innovate.
- AI can be applied to most sectors, including services and low-tech sectors, as well as to all business functions, from pre-production to post-production. Marketing and sales, supply chain management and production are functions where AI could have great impact. Retail trade, transport and logistics services, or automotive and assembly manufacturing are sectors where AI could contribute to creating significant value.
- AI can substantially affect SME business environment, by enhancing the efficiency of public administration, courts and tax authorities, reducing red tape, securing digital infrastructure, improving SMEs’ access to finance, easing skills management and job matching, or reducing the costs of experimentation and innovation. At the same time, algorithms increase the risk of tacit collusion on product and labour markets, and of (likely large) firms sustaining profits and prices above a fair competitive level, at the detriment of smaller businesses.
- Evidence suggests different degrees of AI diffusion across countries, sectors and firm sizes. This is not without consequence on the capacity of governments to reduce inequalities and achieve greater inclusiveness. There are concerns that most of the AI benefits could be reaped by first adopters, while laggards have low or no benefits at all.
- Businesses in most countries show low level of data analytics adoption with leading countries tend to head the ranking in all sectors, while lagging countries tend to lag in all of them. New AI practices are diffusing across all sectors, with services adapting faster than manufacturing or construction. In information and communication services, there is already an early majority of enterprises that are performing data analytics.
- There is also evidence of an SME gap in using data analytics and/or implementing AI solutions. SMEs face several barriers to adoption: a lack of data culture; a lack of awareness about what AI could bring; a need for retraining managers and workers; high sunk costs for internalising AI, plus a need for engaging complementary investments; few evidence and little visibility on the returns on investment; and reputational and legal risks.
- SMEs can source external AI expertise and solutions from knowledge markets that typically compensate for a lack of internal capacity. Cloud computing-based Software as a Services (SaaS) and Machine learning as a Service (MLaaS) offer advantages such as the scalability of AI solutions and costs, no prerequisite of technical knowledge (for Saas), digital security features directly embedded in the software.
- However, SaaS and MLaaS raise additional challenges related to data ownership and portability, and lock-ins effects. Moreover, since both are cloud-based, SMEs need an access to a minimum speed and quality internet connection. Although digital network infrastructure has gained in reach, speed and sophistication, smaller firms remain less connected.
- Data is the key. Governments have a role to play in supporting SMEs in building a culture of data and improving digital risk management practices.
- The human factor is critical. Raising awareness among SME managers and workers on AI benefits, and building the conditions of a trustworthy transition are required. National and local governments should also co-ordinate actions for reskilling SME managers and workers, and ensure a participatory approach in redesigning work processes and training AI models.
- The issue of financing should be addressed, first by building more evidence on the return on investment of AI business applications, in order to inform not only SME managers and business owners, but also investors and financial institutions, and by identifying mechanisms for bridging the financing gap until AI can deliver its full promises.
- Regulators and policy makers should ensure the well-functioning of knowledge markets that provide cloud solutions embedding AI technologies, as well as the transfer of knowledge that could enable SMEs to scale up their capacity before being eventually able to develop their own AI solutions.
- Adopting a differentiated industrial approach on AI transition(s), through sectoral studies and business use cases, could help inform relevant stakeholders and account for the low transferability of AI knowledge across environments.