Platforms and Artificial Intelligence: The Next Generation of Competences


Platforms and Artificial Intelligence: The Next Generation of Competences
Authors: Ahmed Bounfour
Year: 2022
Publisher: Springer
Language: English
ISBN 13: 9783030901912
ISBN 10: 3030901912
Categories: Computers, Artificial Intelligence (AI)
Pages: 320 / 319

Availability: 5000 in stock

SKU: 9783030901912 Categories: ,

Platforms and Artificial Intelligence: The Next Generation of Competences Ahmed Bounfour
Artificial intelligence (AI) and platforms are closely related. Most of the investment
in AI, especially critical technologies, is provided by large platforms. More generally, platforms are the core players in organizational design and the digital transformation of our economies and societies. There is, therefore, a need to provide an
overall picture of these organizations’ behaviour and performance in markets, and
their control and use of resources. Taking a multidisciplinary approach, this book
aims to provide a better understanding of the role of platforms and digital—not only
today—but, more importantly, in the mid to long term. There is a specific focus on
how platforms organize for investment in AI, and how AI will impact the next
generation of competences. It, therefore, develops a two-sided approach. On the one
hand, the aim is to understand how platforms organize for investment in intangibles
and AI, and, on the other hand, it provides a framework that charts how AI will
transform jobs and competences in the future.
The book is structured into five parts that address the following key topics: (1) the
organizational design and strategy of platforms; (2) the role of digital data in
production systems, and the impact on human capital (employment); (3) AI and
intellectual property rights; (4) AI and the next generation of competences; and
(5) ethics and responsible society. Its added value can be summarized by four main
– Understanding how platforms organize to take advantage of digital’s potential for
innovation and the control of critical resources.
– Understanding how platforms invest in intangibles and AI.
– Charting how IA will transform jobs.
– Indicating how firms and organizations should prepare for the next generation of
In Part 1, Platforms, Platformisation and Foundations of their Business Models,
the aim is to provide a framework for the analysis of platform performance and
behaviour, including with regard to investment in AI.The first chapter, Digital Platform Modelling: Delineating the Foundations of
their Business Models, by Bounfour et al. reviews the current literature and provides
an overview of how platforms organize. Over the past 5 years, platforms have
emerged as a key organizational concept, notably due to the ubiquity of digital.
This chapter aims to: (1) analyze the key economic challenges relating to the
platform phenomenon; and (2) review current analytical approaches; before (3) proposing an empirical approach to how platforms work in practice. It concludes with
an exploration of business and policy implications for innovation.
The second chapter, Growth of Internet Digital Platforms in China: Stages,
Trends and Research Opportunities, by Guo et al. provides an interesting perspective on the development of digital platforms in China over the past two decades. The
authors analyze the four stages of the development of digital platforms, and integrate
ongoing changes into their organizational design from an institutional perspective.
The third chapter, Platforms, AI and the Spillover Effect, by Bounfour et al.
addresses the issue of platform spillover effects. The authors construct indicators of
knowledge creation, knowledge stock and knowledge spillovers, drawing upon
patent data, a panel of 27 platforms and 207 institutional applicants that have
collaborated with a platform at least once. Applicants are categorized by type of
institution (platforms, large firms, SMEs and universities), and the effect of different
types of spillovers on the three types of knowledge is studied via negative binomial
regressions. Overall, the results confirm the importance of knowledge diffusion
between firms, but identify key differences between categories. On the one hand,
all types of spillovers (stemming from each category) are found to affect overall
knowledge creation. On the other hand, not every category benefits from these
spillovers. Large firms benefit more, and from a greater number of spillovers.
Although platforms are the main creators of knowledge in the AI sector, they do
not benefit from spillovers. This is because they produce more knowledge internally
than they capture from other categories. What they obtain from others must be set
against leakage to other companies that take advantage of the platform’s innovative
technologies. These findings illustrate that the strategy adopted by platforms focuses
more on indigenous knowledge than open innovation.
Part 2 examines the AI policy agenda, and is divided into three chapters.
The fourth chapter, Artificial Intelligence: A Review of the Economic Context and
Policy Agenda, by Paunov and Guellec looks at the transformative nature of AI,
which leads national governments to define ad hoc programmes and allocate
resources. The authors review some leading policy agendas, in particular, 12 national
initiatives. A broad definition of AI is adopted, which is seen as “a set of technologies that produce information and knowledge from data processing”. This definition encompasses machine learning, along with various statistical data analysis
techniques. The chapter defines the conditions for the development of AI and policy
related to the digital economy (infrastructure, competences and access to data).
The fifth chapter, Patents and the Fourth Industrial Revolution. The Global
Technology Trends Enabling the Data Economy, by Ménière, builds on the recent
European Patent Office study of the technology trends that underpin the Fourth
Industrial Revolution (4IR). Based on an analysis of patent data, the chapter provides some very interesting insights into which countries, companies and regional clusters
are leading the 4IR. It also provides policy and business guidelines on how to invest
in these critical technologies.
The sixth chapter, by Shives et al. is titled Comparing the Methodology for the
Development and Project Management of Artificial Intelligence Systems, and
addresses a topic that is particularly relevant to public organizations: which methodologies should be used to organize for AI acquisition. The experience of the
United States Department of Defense (DoD) is taken as an illustration. One of the
many issues project managers must consider is that many AI systems are developed
by start-up companies, while most government suppliers are large firms that are used
to doing business with the DoD community. The authors compare three methodologies and aim to identify the context each methodology is most suited to, in order to
improve the acquisition lifecycle for AI systems.
Part 3 takes a look forward, and considers what might be the next generation of
competences, taking into account the nature of AI.
In chapter, Artificial intelligence: Productivity Growth and the Transformation of
Capitalism, Ernst considers different dimensions of the transformative nature of
AI. The author discusses the issue of the non-acceleration of productivity despite
high investment in AI. The chapter also underlines rising inequality, due to both a
shift towards AI technologies and slow adoption. Finally, a wide range of policy
issues are evaluated, ranging from social protection to competition policy. These
instruments are considered as necessary to address the impacts of inequality and the
restructuring of the workforce.
The eighth chapter, by Sabouret, is titled What AI Can Do and What it Cannot
Do. The author underlines that AI is not the solution to everything. This pedagogical
chapter outlines the limitations of AI, and the importance of human knowledge in
overcoming these limitations. It analyses AI programmes and determines the relationship to human competences.
In chapter, AI, Platforms and the Next Generation of Competences, Bounfour
provides a framework to chart the profiles of the next generation of competences,
taking into account different institutional spaces: nations, global platforms, firms
(excluding global platforms) and territories (regions, cities, local communities). The
author proposes a three-step sequence: issues ! risks/outcomes ! required competences/capabilities. While it is clear that different competences are needed for each
of these layers, the chapter underlines complementarities among capabilities (cognitive/analytical, behavioural and technological).
Part 4 addresses the topic of AI, Productivity and the Digital Divide, and is
organized into three chapters.
In chapter, Are we Pretender of Digitalization?–Towards a New Management
Using Telework and Digital Transformation, Hara et al. takes a look at how Japanese
firms dealt with digital during the COVID-19 crisis. This chapter is based on the
Emergency Organization Survey, conducted in April 2020 and examines general
changes in work organizations, with a particular focus on the extent to which
teleworking has been adopted by Japanese firms.
Real-Time Management: When AI goes Fast and Flow is the subject of chapter,
by Rydén and El Sawy. The authors analyze how real-time management of AI has
become a key enabling function for coping with rapid market change and growing
demands from stakeholders. They present the key concept of Fast & Flow, which is
defined with respect to two ideas: the monetary dimension of time; and control of
time, considered in terms of presence. Three scenarios are defined, one of which
discusses the humanistic deployment of AI in firms and societies.
“Artificial Intelligence and the Digital Divide – from an innovation perspective”,
is the subject of chapter by Kitsara. The chapter analyses the current AI divide based
on scientific and patent publications related to key AI indicators. It explores how
different profiles and geographies have access to the needed resources. Furthermore,
the chapter existing policies targeting skills and competences.
Part 5 examines AI and Ethics, and is divided into two chapters.
The thirteenth chapter, by Murata, is titled Post-Truth Society: Organizational
Social Responsibility in an AI-driven Society. The author discusses the advent of
what is referred to as a “post-truth” society, which, with the generalization of AI
systems, will be characterized by the fact that “truth about individuals, groups,
organizations, communities, societies, nations, things, events and the world become
meaningless or worthless; individuals are treated as black boxes to be manipulated
and exploited by malicious AI-based system operators”. Moreover, accountability
will be weakened, due to the unpredictability and uncontrollability of AI-based
systems. To mitigate these fundamental societal risks, leading AI developers must
proactively address the ethical and societal issues resulting from the widespread
implementation of these systems.
The fourteenth chapter, Co-constructing Shared Values and Ethical Practices for
the Next Generation: Lessons Learned from a Curriculum on Information Ethics,
Baudel analyses the content and lessons learned from an online course on scientific
integrity, and research and information ethics taken by over 2000 PhD and engineering students at the Université Paris-Saclay (France) and now available online as
a MOOC. The course addresses the issue of putting ethics into practice, notably with
respect to questions such as intellectual property rights, freedom of expression or
Computers – Artificial Intelligence (AI)
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Platforms and Artificial Intelligence: The Next Generation of Competences

Availability: 5000 in stock