McKinsey, in its report on the impact of AI on the world economy, confirmed that by 2030 AI is expected to gradually add 16% (about US$13 trillion) to the global economic output.
AI will annually contribute to efficiency development of around 1.2% between 2020 and 2030, says a McKinsey report based on the simulation models of the impact of AI at the national, sector, organizational, and worker levels.
The report is focused on AI adoption of five general classifications of AI technologies: NLP, RPA, and advanced ML, among other things. It was based on a survey conducted from around 3,000 firms and economic information from various organizations, including the World Bank, the United Nations, and the World Economic Forum.
Prominent companies and government organizations are prioritizing AI and ML as rising effectiveness and productivity are allowing exponential growth of the worldwide economy. Most nations have barely started to contemplate their AI future, even though the largest economies in the world have launched their AI initiatives in 2017 and 2018.
Various components influence the AI-driven efficiency of nations, including innovation, labor automation, and new competition. Miniaturized scale factors, like, the pace of adoption of AI, and full-scale factors, including the global connectedness or labor-market structure of a nation, also contribute to the impact.
As it accelerates, the pace of AI growth will by no means be linear. The commitment to growth needs to be several times higher by 2030. An S-curve pattern of adoption of AI is actually a modest beginning. This is because the expenses and investment in deploying these advancements are enormous. There is, nevertheless, a need for acceleration driven by increasing competition and innovation-led improvement in corresponding capabilities.
After a certain level of AI adoption, the globally optimized value chain will offer a way for digital technology adoption to become cheaper, faster, and easier. It will be far easier for more integration across products and services to happen thereafter. Clearly, this will influence the growth of independent global platforms for goods and services exchange.
Today, the most significant near-term challenge for ML to become a part of the transformation journey is just sharp innovation. The world currently needs to focus on technologies like AI, to increase creativity, flexibility, and drive strong problem-solving skills.
AI is perceived as the fundamental driver of future development, innovation, productivity, competitiveness, and job creation for the 21st century. It is now the responsibility of business leaders and policy-makers to take measurable actions to address the challenges, support data scientists, researcher’s business analysts for complete AI inclusion in the ecosystem.