Artificial Intelligence as One of the Development Strategies for Business Organizations “Toyota Model”

2021 ◽  
pp. 3-21
Author(s):  
Lila Ayad ◽  
Mouloudi Abdelghani ◽  
Ahmed Halali ◽  
Bishr Muhamed Muwafak
Keyword(s):  
Artificial Intelligence ◽  
Development Strategies ◽  
Business Organizations

TRENDS IN THE DEVELOPMENT OF RISK MANAGEMENT

2020 ◽  
Vol 18 (Suppl.1) ◽  
pp. 417-421
Author(s):  
P. Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Industry 4.0 ◽  
Business Organizations ◽  
Main Subject ◽  
Scientific Publications

Awareness of the need for effective risk management is gaining ground, both among business organizations and in scientific publications in the field. The prospects for development in risk management must be tailored to the growing and transformative needs of Industry 4.0. The paper focuses on different directions, priorities of the Federation of European Riskmanagement Associations (Ferma). The main subject of research is placed on risk management through artificial intelligence. Both the risks of its introduction and the benefits of it have been identified.

scholarly journals In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

2019 ◽  
Vol 33 (3) ◽  
pp. 523-539 ◽  
Author(s):  
Andrea Ferrario ◽  
Michele Loi ◽  
Eleonora Viganò
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ethical Dilemmas ◽  
Academic Research ◽  
Business Organizations ◽  
Layer Model ◽  
Incremental Model ◽  
Program Of Study ◽  
Proposed Model

Abstract Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

scholarly journals Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation

2021 ◽  
Author(s):  
Andreas Fügener ◽  
Jörn Grahl ◽  
Alok Gupta ◽  
Wolfgang Ketter
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Work ◽  
Work Environments ◽  
The Other ◽  
Business Organizations ◽  
Human Capabilities ◽  
Work Arrangements ◽  
Other Hand ◽  
The World ◽  
The Future

A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

scholarly journals The Phenomenon of Competitive Human Resources and How It Impacts Business Growth

2021 ◽  
Author(s):  
Arvin Winatha
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Human Resources ◽  
Industrial Revolution ◽  
Business Organizations ◽  
Business Growth ◽  
Global Era ◽  
It Impacts ◽  
The World ◽  
Fourth Wave

Increasingly tight business competition map of industry has been the main focus for everyone in the world, especially in the industry we call it as the Industry era 4.0 . The awareness of this competiton has made many business organizations in the world, including Indonesia busy preparing themselves, particularly those related to the development of human resources, to be ready to compete in this global era. The Fourth wave of industrial revolution is marked by the use of information technology, artificial intelligence, and automatic engines or vehicles that have been going on since years before.

scholarly journals On the Commoditization of Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Abdullah A. Abonamah ◽  
Muhammad Usman Tariq ◽  
Samar Shilbayeh
Keyword(s):  
Artificial Intelligence ◽  
Perceived Value ◽  
Value Added ◽  
Business Organizations ◽  
Seminal Paper ◽  
Organizational Framework ◽  
The Impact ◽  
Tremendous Impact

As artificial intelligence's potential and pervasiveness continue to increase, its strategic importance, effects, and management must be closely examined. Societies, governments, and business organizations need to view artificial intelligence (AI) technologies and their usage from an entirely different perspective. AI is poised to have a tremendous impact on every aspect of our lives. Therefore, it must have a broader view that transcends AI's technical capabilities and perceived value, including areas of AI's impact and influence. Nicholas G. Carr's seminal paper “IT Does not Matter (Carr, 2003) explained how IT's potential and ubiquity have increased, but IT's strategic importance has declined with time. AI is poised to meet the same fate as IT. In fact, the commoditization of AI has already begun. This paper presents the arguments to demonstrate that AI is moving rapidly in this direction. It also proposes an artificial intelligence-based organizational framework to gain value-added elements for lowering the impact of AI commoditization.

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

scholarly journals Comparative Study on Global Development Strategies of Artificial Intelligence in Healthcare

2019 ◽  
Vol 21 (6) ◽  
pp. 106
Author(s):  
Yingying Li ◽  
Jiannan Zhang ◽  
Yanju Gu ◽  
Yelin Zhu ◽  
Qianfeng He ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Development Strategies ◽  
Global Development

Machine Learning and its role in Diverse Business Systems

2021 ◽  
pp. 213-217
Author(s):  
Neha Bhateja ◽  
Nishu Sethi ◽  
Shivangi Kaushal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Methods ◽  
Business Value ◽  
Massive Data ◽  
Human Intervention ◽  
Business Organizations ◽  
Business Systems ◽  
Rapid Pace

Machine learning as a branch of Artificial Intelligence is growing at a very rapid pace. It has shown significant benefits across a number of different industry verticals in helping them improve their productivity and making them less reliant on humans. The success and the growth of any industry depends on the manageability of massive data, using the data for predictions and deriving business value, automating the processes without the need of human intervention, provide satisfactory services to their clients and the security of client's information. Machine learning is a method that provides a way to transform the processes that leads to growth by using the statistical methods. The focus of this paper is to provide an overview of machine learning and highlight the various areas where machine learning is implemented by the business organizations and industries.

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