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Digitalisation, ICTs, artificial intelligence and sustainability (with some “smart growth and smart specialisation” ideas) are the issues on the table of most national decision-makers. Besides, these issues are somehow connected to business and entrepreneurship: without such closer connections it is almost impossible for any state to achieve a perspective socio-economic development.
European integration’s idea of smart growth, coped with smart specialisation in the member states, has been balancing recently in the national governance and political economy between digitalisation and sustainability; the latter also includes developmental effect on climate-related measures. However, a common denominator is still advanced digital technologies (i.e. artificial intelligence, AI) with their progressive effect on entrepreneurship.
Smart growth and SMEs in national strategies
First of all, modern national growth patterns are based on sustainability and circular economy: these challenges are not so easy to introduce into the age-old and traditional “collective thinking” in national governance: “introduction” needs both transformations in decision-making’s mindset and re-orientation in national priorities. Hence, “smart growth” concept can be of assistance.
Coronavirus heralded during last two years a “perfect storm” of challenges for national and regional political economies: in this regard, the European “strategic governance” has been already concentrating on combining global challenges, the EU political priorities and the member states’ growth patterns. Both the EU institutions and the member states are making re-assessments of their traditional approaches to governance in order to adequately react to “post-covid” aftermath as well as modern challenges and future chocks.
The EU institutions – in cooperation with the member states – have elaborated resilient political-economy’s structures adequate to perspective and sustainable growth. The national elites are fully aware of the “assignment’s” difficulties for governance system, existing public institutions as well the whole nations’ wellbeing: the tasks are really extraordinary and complex.
The pandemic’s crisis accelerated European policy priorities towards modern challenges, which include, among other directions, three most important: i.e. new industrial strategies, the “SME strategy for a sustainable and digital” transition, and the “European Green Deal”. These priorities have made the national governance re-oriented towards new and perspective guidelines in decision making in view of the following European priorities: a) more enterprises with cross-border operations, b) active start-ups, young entrepreneurs and SMEs, and c) efficient corporate strategies with attention to accounting, VAT procedures, corporate survival, etc.
Thus, for example transformations in the European ‘green deal” imply that the following aspects in the SMEs and corporate strategies shall be revised: corporate contingency planning and ethics, sustainability issues and optimal supply-chain structures, as well as cross-border mobility, etc.
People’s quality of life and well-being are increasingly considered as key determinants in politics and socio-economic development as the most fundamental factors in perspective growth; hence, measuring progress only using GDP-indicators means ignoring both the modern societies’ complexities and transformations in political economy’s role. In search for the multifaceted approaches to long-term recovery and creating resilient societies, modern governance has to take into account all growth factors, i.e. not only economic, but social, cultural, environmental, etc. which are in reality driving progress and peoples’ well-being.
Actually, “smart growth” is about a national “smart specialisation strategy, 3S”: hence, national recovery-resilience plans, RRPs are including common EU policy guidelines in accordance with the following main pillars: – green transition, – digital transformation, – smart, sustainable and inclusive growth, – social and territorial cohesion, with health and socio-economic “institutional resilience”, and – the next generation’s policies in education and skills. As is seen, smart growth concept has become an the RRPs integral part, which makes 3S an important part of states’ perspective development by incorporating economic cohesion, jobs and re-skilling, productivity, competitiveness, research and innovation with a positive effect on SMEs.
Artificial intelligence, AI
European artificial intelligence policy and legislation is in its early stage of development; suffice it to mention the first AI’s draft revealed in 2018). However, some AIs legislation does exist among digital instruments for businesses and entrepreneurship: AI’s provisions for corporate entities (although mostly national in “algorithmic governance”) could have some global considerations, e.g. AI’s high-risks in production, in services and transparency. However, it is evident that the AIs will have truly strong effect for big and small companies in the future. A wide range of AIs are already used in such sectors as aviation, automotive vehicles, boats, elevators, medical devices and industrial machinery, to name just a few.
The EU proposed recently new rules for AI to harness its full potential with the benefits of the European Digital Decade. These rules include the world’s first legal framework and the coordinated plan on AI: together, they outline a European approach to AI, ensure that developing AIs will guarantees safety and fundamental rights, while encouraging investment and innovation.
However, the EU’s AI legislation aspires to establish the first in the world AI’s comprehensive regulatory scheme: its impact would go far above the EU’s borders: some European decision- makers believe that such legislation would set a “worldwide standard” in global AI governance. However, the idea of setting a comprehensive new international standard for AI could only be effective when some vital AI components would be mutually agreed on by the global markets, and not only by the European ones.
Reference to the draft (2021) in: https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence
Thus, data management and processing, as well as internet and cloud application received recently the greatest amount of private AI investments: i.e. in 2021 it was 2.6 times the investment in 2020, followed by “medical and healthcare” and “fintech.” In 2021, the US has led the world in both total private investment in AI and the number of newly funded AI companies, three and two times higher, respectively, than China, the next country on the ranking.
Globally, the AI requirements are having the following provisions: 1) AI systems in regulated products will be significantly affected around the world, demonstrating the EU’s effect, although this will be highly mediated by existing markets, international standards bodies, and foreign governments. 2) High-risk AI systems for human services will be highly influenced if they are built into online or otherwise internationally interconnected platforms, but many AI systems that are more localized or individualized will not be significantly affected. 3) Transparency requirements for AI that interacts with humans, such as chat-bots and emotion detection systems, will lead to global disclosure on most websites and apps.
Some experts focuses on three core components of the EU’s AI legislation: high-risk AI in products, high-risk AI in human services, and AI transparency requirements; there are as well attempts to estimate their likely global impact, the efforts that require forecasting changes in corporate behavior in response to the AI.
Reference to: Engler A. The EU AI Act Will Have Global Impact, but a Limited Brussels Effect. – Brookings. June 8, 2022. In: https://www.brookings.edu/research/the-eu-ai-act-will-have-global-impact-but-a-limited-brussels-effect/
AI’s corporate effect: de-facto and de-jure
The AI’s spread in the EU has two related forms, “de facto” and “de jure,” which can be distinguished using the case of the EU’s data privacy rules, specifically the General Data Protection Regulation (GDPR) and the e-Privacy Directive. Rather than developing separate AI processes, many of the world’s websites adopted the EU’s requirements asking users for consent to process personal data and use cookies. This reflects the de facto EU-AI effect, in which companies universally follow the EU’s rules in order to standardize a product or service, making their business processes simpler. This is often followed by the de jure EU effect, in which formal legislation is passed in other countries that align with the EU law, in part because multinational companies follow adopted rules to avoid conflicts with the recently standardized processes.
The analysis of the AI’s effect on businesses and the private-sector’s services needs to see a “distinction between AI used in platforms and AI used in other software”, as noted by A. Engler (the reference above); in particular in the AI’s international dimension. The author correctly acknowledged that the more AI algorithms are “incorporated into geographically dispersed platforms”, the more AI’s requirements would affect international trade, rather than just the EU markets.
For example, LinkedIn (as well as its parent company, Microsoft) are expected, argued A. Engler, “strongly resist additional legislative requirements on AI (in goods and services) from other countries”, which would turn these goods/services less likely to be in terms with the AIs: a clear manifestation of the EU’s legislative effect, both de facto and de-jure.
Many commercial apps are already designed specifically for different countries due to different languages or other legal requirements. In this case, the apps within European markets will show some disclosures; though those operating elsewhere may not feel any pressure by this change.
Related AIs international development
The EU’s approach requires that companies would place AI systems into regulated products sold in the EU; they would need to implement a risk management process, conform to higher data standards, more thoroughly document the systems, systematically record its actions, provide information to users about its function, and enable human oversight and ongoing monitoring.
Some of these requirements have already been implemented by sectoral AI regulators, but most are just starting; as a result the AI systems within regulated products will need to be documented, assessed, and monitored on their own, rather than just evaluating the broader function of the product. This sets a new and higher floor for considering AI systems in products.
Another example is Educational Technology software, which may connect a network of students, but will generally be deployed independently in different localities.
The growing use of artificial intelligence (AI) in everyday life, across industries, and around the world generates numerous questions about how AI is shaping the economy and education—and, conversely, how the economy and education are adapting to AI. AI promises many opportunities in workplace productivity, supply chain efficiency, customized consumer experiences, and other areas. Private investment in AI globally in 2021 totaled around $93.5 billion: more than double the total private investment in 2020. However, the number of newly funded AI companies continues to drop, from 1051 companies in 2019 and 762 companies in 2020, to 746 companies in 2021. In 2020, there were 4 funding rounds for corporate AI “injections” worth $500 million or more; in 2021, there were 15.
Source: https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Chapter-4.pdf
European private investment in AI increased dramatically from 2020 to 2021: from $2 billion to about $6.5 billion; this investment was eclipsed by the rate of growth of both the US and China; the EU remains far below its rivals with $53 and $17 billion, respectively. However, the EU has gained in the total worldwide share of AI investment in the same period (from around 4.5% to just over 7%), which is notable since this occurred during public debate about AI; although it is hard to predict what would have happened without the proposed EU legislation.
Reference to: https://aiindex.stanford.edu/vibrancy/. On “economy and education” in Artificial Intelligence Index Repoprt-2022: https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Chapter-4.pdf
Global regional comparison in AI funding in 2021 shows that the US is the global leader in overall private investment at approximately $52.9 billion, which is over three times the next country China ( $17.2 billion). On the third place is the UK with $4.65 billion, followed by Israel -$2.4 billion and Germany -$1.98 billion. The same ranking applies when combining total private investment during 2013-2021: US investment totaled $149 billion and Chinese investment totaled $61.9 billion, followed by the UK ($10.8 billion), India ($10.77 billion), and Israel ($6.1 billion). Notably, the US private investment in AI during 2013-21 was more than double the total in China, which itself was about six times the total investment from the United Kingdom in the same period. Broken out by geographic area, the US, China, and the European Union all grew their investments during 2020-21, with the US leading China and the European Union by 3.1 and 8.2 times the investment amount, respectively.
Source: Preview, Artificial Intelligence Index Report 2022, Chapter 4, p. 16
Regional Comparison by Newly Funded AI Companies for 2021 has shown that the gaps between global regions have been significant: thus, the UN is a leader with 299 companies, followed by China with 119, the UK with 49, and Israel with 28. However, the number of newly funded AI companies has declined in both the US and China since 2018-19; despite the downward trend, the US still leads in the number of newly funded AI companies, with 299 firms in 2021, followed by China (119) and the EU (96). Ibid, chapter 4, p.18.
AI’s adoption in corporate activity: global level
AI activity in the corporate sector, made by McKinsey in the “State of AI in 2021” report on the basis of a global online survey of 1,843 participants in early 2021. Thus, India was a leader with 65% AI’s adoption, followed by “Developed Asia-Pacific” (64%), “Developing markets”, including China with 57% and North America with 55%. The average adoption rate across all global regions was 56%, up 6 percent from 2020. Notably, “Developing markets (incl. China)” registered a 21% increase from 2020, as well as India with eight percent increase; the EU showed an increase from 47% in 2020 to 51% in 2021.
Greatest AI adoption was in product and/or service development for sectors in high technology and telecommunications (45%), followed by service operations for financial services (40%), service operations for high tech/telecommunications (34%), and “risk function” for financial services (32%).