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The article shares the results of a survey Accenture undertook to measure AI’s potential economic impact on 16 industries.

The goal was to understand the chief reasons for AI’s potential impact and to identify a set of strategies that companies in any industry can use to benefit from advances in AI technologies.

AI’s unique characteristics as a capital–labor hybrid, which confers the ability to augment human labor at scale and speed, self-learn and continuously improve over time—will require organizations to adopt new approaches and models in a variety of functional areas.

AI can augment labor productivity by taking on low value-added or supporting tasks and thus enable workers to focus on high value work. Bosch has adopted a “thinking factory” approach in one of its German automotive plants…to enable AI-powered machines to self-diagnose technical failures, order replacement parts autonomously and anticipate maintenance needs.

All industries studied stand to benefit from AI, though three -- information and communication, manufacturing and financial services sectors -- will likely realize the biggest gains. Businesses in every industry will need to consider AI as a potential change agent in their investment, innovation and human capital development strategies. To prepare their organizations for a successful future with AI, business leaders should adopt eight strategies.

The purpose of this study is to provide recommendations for policy framework on artificial intelligence (AI) in India.

Studies have been conducted through focus group discussion and the other sources such as different company websites using AI, Indian Government strategy reports on AI, literature studies, different policies implemented on AI in different locations and other relevant documents. After those studies, a charter of recommendation has been provided. This will help the authority to frame the AI policy for India.

This study highlights that “National Strategy for AI” for India needs improvement to provide comprehensive inputs for framing policy on AI. This study also implies that focus is to be given on security, privacy issues including issues of governance.

AI-related technology has immense potential toward the development of organizations. This study implies the necessity of framing a comprehensive policy on AI for India. If there is a comprehensive policy on AI for India, the Indian industries will derive many benefits.

This study provides inputs on how the industries of India can be benefitted with the help of AI and how R&D can develop the AI activities to harness maximum benefits from this innovative technology.

AI-related policy will have appreciable influence on the society in terms of human–device interactions and communications. The policy framework on AI for India is expected to project far-reaching effects toward deriving benefits to the society.

This paper has taken a holistic and unique attempt to provide inputs to the policymakers for framing a comprehensive and meaningful policy on AI for India.

The purpose of this paper is to examine to what extent national intangible capital (NIC) explains GDP growth and to assess its impact on GDP formation in different countries. The paper brings a new perspective to explaining hidden economic drivers.

The paper introduces a new theoretically and computationally justified method, so-called ELSS model that is based on expansion and augmentation of the Cobb-Douglas production function with a wide range of NIC indicators. The method is applied by using the database that contains NIC indices for 48 countries covering the period from 2001 to 2011.

The results show that intangible capital accounts for 45 per cent of world GDP. The figure for the USA is 70.3 per cent and for the European Union 51.6 per cent. The Nordic countries stand out with a higher figure at 64.7 per cent, with NIC contributing to 72.5 per cent of GDP in Sweden, 69.7 per cent in Finland and 67.6 per cent in Denmark.

The expanded Cobb-Douglas production function is sensitive to valuations of capital inputs and sensitive to estimates of production shares for various augmenting and expanding inputs. Therefore further work is needed to develop and test methodologies for the assessment of all of these.

ELSS production function helps to give a realistic picture of the value and impact of NIC and accordingly gives evidence for accurate investment decisions for the future.

The method will help policy makers figure out what steps are needed to reduce the cross-country NIC differences.

The authors have uncovered the value of NIC beyond monetary inputs, and at the same time taken account of country specifics. The ELSS formula is comprehensive yet not too complicated to replicate. The approach significantly contributes to the development of the current research tradition into intangibles.

The famous Uzawa (1961) balanced growth theorem has exercised a tyranny of sorts over macroeconomics for decades. It is the prime reason why researchers use Cobb–Douglas production functions and abstract from considering movements in factor shares. Others have had to recourse to complex explanations for long-run labor augmentation in technical progress. In this chapter, we discuss the issues arising from this problem and propose a way of achieving balanced growth with a short-run production function where the elasticity of factor substitution is less than one, and capital augmenting technology shocks can be permanent. We do so by allowing firms to choose the relative reliance on capital in the production technology and introducing a suitable modification of the production function. We also provide some model simulations in the context of a simple deterministic neoclassical growth model.

Businesses are rapidly automating workplaces with new technologies (e.g., driverless cargo trucks, artificially intelligent mortgage approvals, machine-learning-based paralegals, algorithmic managers). Such technological advancement raises a host of questions for business and society. As Thomas Donaldson recently remarked, “It’s instance of a problem that more sophisticated engineering cannot solve, and that requires a more sophisticated approach to values” (Ufberg, 2017). In this chapter, we explore the value questions as follows: What is the purpose of business in the machine age? What model for business will best serve society in coming decades: profit maximization, stakeholder theory, or another conception entirely? Is it time for a new social contract between business and society? Do firms have a natural duty to offer employment? Are existing concepts of responsibility/liability adequate for an age in which companies use autonomous robots as scapegoats? How can we protect our humanity and dignity in an algorithm-based society? Do we need to teach ethics to robots?

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

The purpose of this paper is to propose and analyze a model to obtain a total factor productivity of an industry through quantitative empirical analysis in order to determine the joint contribution of the production and technology function, and the change and technical progress. The case of the Peruvian large shipbuilding industry between the years 1969 and 1990 was considered for the analysis of the proposed model. The large shipbuilding in Peru finished in 1992 and has restarted in 2014. The importance of the study lies in the fact that the analysis is focused on an industry which is resurfacing, and in this regards, the study of the first production period will yield more and accurate information to make decisions regarding its future development.

One way of considering the several effects of technical progress, in line with Sato (1970) such as growth and bias, is to specify a production function maintaining the linear homogeneity property, such as: Y(t)=F [A(t)K(t), B(t)L(t)], where Y(t) is the aggregate product over a period of time (t); K(t) is the capital; L(t) is the labor; and A(t) and B(t) are the efficiencies or augmentations of K(t) and L(t), respectively. Based on the regression analysis data, the value of σ can be estimated to a residual growth rate (Kennedy and Thirlwall, 1972) that allows assessing the technical knowledge that is not attributable to the factors’ efficiency grains: TCTR = T ˙ / T − ( α ( A ˙ / A ) + β ( B ˙ / B ) ) . This last expression measures the residual technological growth rate (TCTR, by its Spanish acronym).

The results of the analysis of the large shipbuilding at SIMA-Callao during the given period (22 years of operation, between 1969 and 1990) show that the necessary installed capacity and the technological knowledge was available in order to develop a complex industrial process in the South Pacific region, thus, contributing to the sector’s growth in the country. The evolution of the shipbuilding activities coincides with the GDP expansion and decline periods in Peru. According to the results, the total factor productivity increased during 1969-1976, 1979-1982, and 1986-1987 periods and it has been confirmed that the contribution of the efficiencies of the production factors were inversely related to the economies of scale and output growth.

The analysis is based on the activities carried out throughout 22 years of operations in SIMA-Callao shipyards (1969-1990). The data regarding the product, labor, imported materials costs, local material costs, direct expenses, wages, and man-day costs was obtained from several sources within the shipyard. Direct expenses correspond to classification, inspections, administrative expenses (dock, quality control, equipment rental, etc.), drawings, technical data, insurance, and materials freight. Additionally, the sources of information are project construction contracts, annual expenses reports, and man-day cost quarterly reports of the shipbuilding area. The man-day cost includes salary, social benefits, and the company’s functional cost.

There are different ways to obtain productivity index. In this case, the authors used the stated model. In addition, based on this experience, this can be applied to other industries.

We examine the two-level nested constant elasticity of substitution production function where both capital and labor are disaggregated in two classes. We propose a normalized system estimation method to retrieve estimates of the inter- and intra-class elasticities of substitution and factor-augmenting technical progress coefficients. The system is estimated for US data for the 1963–2006 period. Our findings reveal that skilled and unskilled labor classes are gross substitutes, capital structures and equipment are gross complements, and aggregate capital and aggregate labor are gross complements with an elasticity of substitution close to 0.5. We discuss the implications of our findings and methodology for the analysis of the causes of the increase in the skill premium and, by implication, inequality in a growing economy.

A framework is presented for the strategic use of technology choices as potential solutions for human resources problems arising from demographic trends and labour market conditions. Rather than having technology choice decisions driven by largely financial or engineering concerns, the framework starts with the availability of human resources as its major consideration in choosing technologies which will eliminate jobs which organisations may not be able to fill, and augment or capitalise on skills the workforce does possess. Combining human resource planning and technology planning can thus help organisations achieve a strategic advantage.