AI stimulates consumer demand, enhances product quality, improves productivity and transforms enterprises in traditional industries.
AI has contributed to global GDP growth and is increasingly making an impact on the global economy. Based on a 2015 study found that robots added about 0.4% to annual GDP growth based on research on 17 countries from 1993 to 2007, accounting for one-tenth of the overall GDP growth during the time. McKinsey Global Institute (MGI) conducted a research covering 20 countries and 30 industries, predicting AI could potentially deliver the additional economic output of USD 13 trillion by 2030, pushing GDP by 1.2% each year. Similarly, a 2019 PwC research report estimates AI will drive the global economy up by USD 15.7 trillion, up 14% by 2030. Particularly, it indicates China is likely to gain the most from AI development, boosting up to 26% of GDP in 2030.
Based on EqualOcean analysis, in addition to direct production increase in the tech sector, AI contributes to the economic development from both the supply side and the demand side, by improving consumer satisfaction, productivity across industries, as well as efficiency and efficacy within enterprises.
Productivity gain, supply and demand
To know fundamental economic frameworks helps to understand the logic behind AI monetization. The foremost economic principle is that of rational decision making and the driving forces behind economics models are cost and price, production and consumption, supply and demand.
Cost and Price
AI is about lowering the cost, including pecuniary and non-pecuniary costs. Specifically, AI decreases the cost of prediction, which is an important input in decision making. The book Prediction Machines: The Simple Economics of Artificial Intelligence explains that AI makes predict faster, cheaper, and better. The drop in cost will cause 1) a growing use of prediction in traditional tasks, 2) an emerging use of prediction in newly discovered scenarios thanks to AI, and 3) a change in the value of the substitutes and complements of AI. The authors indicate that data, human judgment and actions are the complements of AI, which positively correlated with the demand for AI, while human prediction is the substitute of AI. As AI being used more often, data, human judgment and actions became popular and more valuable.
As a result, it lowers input costs and business expenses.
Production and productivity
The biggest economic value provided by AI is likely from productivity improvement, allowing output to increase faster than the input. In labor-intensive jobs, AI increases the efficiency and efficacy of routine corporate tasks by human labors. Furthermore, automation allows exponential productivity enhancement by removing labor input entirely.
According to the 2018 Artificial Intelligence Index report, certain Chinese industrial companies have automated away 40% of the human labor force during the past three years, despite the undisclosed degree of AI on the robots. PWC report also indicates that labor productivity improvements are expected to account for over 55% of all GDP gains by AI over the period 2017-2030.
Supply and demand
AI blurred the boundary between suppliers and demanders. For example, smart fitting rooms analyzes the popularities and size of each style, which helps manufacturers to meet new and changing customer requirements. As it develops, consumers are becoming producers in terms of incorporating elements to the products and services.
From the demand side: AI features could analyze a substantial amount of data and makes the user experience personalized. Different from customization, personalization is achieved through artificial intelligence serving people who do not know what exactly suits their needs given information explosion.
This feature lowers users’ interaction costs and leads to better results. The amount of time saved and the degree of quality improved essentially increase consumer utility, which is the determining factor of consumer demand.
According to a 2017 BCG article, personalization will push the revenue up about USD 800 billion over the next five years to the 15% of companies that employ it effectively in the three verticals, retail, health care, and financial services.”
This bottom-up force fundamentally changes the supply side of the traditional industries. AI development enables opportunities for niche markets. Inferred from the massive data generated every day, suppliers are able to capture and analyze individuals' behavior patterns painlessly. At the same time, AI assists suppliers in achieving the product/market fit. Because of the power of infinity, there are unlimited business opportunities to be discovered in the long tail.
Thanks to data and AI analytics skills, it’s getting easier for suppliers to identify new niche markets with an improved level of precision. Once identifying niche markets, suppliers are facing less competition due to high entry barriers. Since consumer demand is specific, niche suppliers do not have to spend massively in marketing.
AI transforms enterprises
AI and existing industries are not competing at the same level. On the contrary, AI provided tools and insights to the real economy.
A mature industry always faces a decline phase after going through the struggling emerging phase and accelerated growth stage. According to the industry life cycle model, an industry has stages of embryonic, growth, shakeout, mature, eventually decline and facing death just as the life of a human being. This 'natural law' puts pressure on the companies in a middle-age industry who is encountering a flattening curve, with performance indicators and financial numbers not improving.
Those enterprises in the mature industry who do not keep pace with the times would finally be driven out due to technological development, societal change and globalization. Though technology shock is a dominant factor in the death of an industry, it is, in turn, a technology with tremendous power to save companies from dying with the declined industry.
In the early stage of enterprise intelligent transformation, AI is better used in areas such as voice and image identification, which is difficult to help enterprises to enhance their actual operational capabilities.
While currently, the cost of technology is declining rapidly, rich data are generated and collected, internet impacts not only corporate but extends along the whole value chain. These three factors have contributed to the performance of AI in decision making. Value is generated from the core intelligence part, the analytical skills and the decision making competencies, rather than the perception abilities such as voice recognition and image recognition.
Market guide the direction of AI
For any disruptive technology, it evolves from a single product to an industry value chain and eventually becomes a social utility. During the process, market supply and demand guide the way and lead it to the final version. The case is illustrated by electricity, oil, coals, cars, computers, the internet, etc.
Not to mention, for AI, there is an economy of scale: more data brings disproportionate rewards. The market not only guides the direction but also providing endogenous improvement of the algorithms.
AI is set to be the next generation of common tools for the whole society. Therefore where it generates money is where it is most needed and where application scenarios are concentrated. In some industries, the first one to monetize a product, even just the beta version, has a greater chance to win, while the other industries witness 'free' as the best strategy. Therefore, timing and strategies of monetization are crucial.