Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

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.

The impact of artificial intelligence and machine learning on the global economy and its implication for the hospitality sector in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhanshu Bhushan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Global Economy ◽  
Regulatory System ◽  
Content Type ◽  
Original Contribution ◽  
Hospitality Sector ◽  
Global Regulatory System ◽  
The Impact ◽  
Practical Implications

Purpose The purpose of this paper is to explore and evaluate the existing and future impact of artificial intelligence (AI) and machine learning on the global economy. It includes viewing the inclusion of AI in different sectors, its impact on industries, the trends of the forerunning companies that are capitalizing on AI and the idea of crystalizing exponential growth while maintaining a balance between the understanding of humans and the subsequent possibilities of AI. Design/methodology/approach This paper is based on secondary research, reviewing literature based on different industries and perspectives. Findings The global potential of AI is exponential; the development of AI should be effective. Globally, we see contrasting views, defining the consequences of AI. Hence, the balance between humans and AI, protocols and a global regulatory system needs to be established to prevent catastrophic results soon. Practical implications The benefits of AI are enormous. The rising incorporation of AI must take into consideration the basic safety fundamentals for a better future. Social implications This paper will enable readers to understand the importance of AI in the global economy, its current involvement in major industries and the subsequent need for balance in technology. Originality/value This conceptual review is by its nature and original contribution and, specifically, an interpretation for India.

Information and reformation in KM systems: big data and strategic decision-making

2017 ◽  
Vol 21 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Ali Intezari ◽  
Simone Gressel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Content Type ◽  
For Profit ◽  
Not For Profit ◽  
Advanced Analytics ◽  
Practical Implications

Purpose The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems. Design/methodology/approach To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics. Practical implications The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value This is the first typology of data-based decision-making considering advanced analytics.

To which extent do projects explore the opportunity space?

2014 ◽  
Vol 7 (3) ◽  
pp. 473-492 ◽  
Author(s):  
Knut Samset ◽  
Bjorn Andersen ◽  
Kjell Austeng
Keyword(s):  
Theoretical Models ◽  
Concept Development ◽  
Decision Processes ◽  
Decision Makers ◽  
Final Choice ◽  
Content Type ◽  
Execution Phase ◽  
Objective Reasoning ◽  
Practical Implications ◽  
Selection Of

Purpose – The purpose of this paper is to explore a selection of projects to understand how conceptual appraisals and choice of concepts are handled, and to which extent the conceptual opportunity space is exploited. Design/methodology/approach – The study is essentially case based, and rooted in a number of in-depth studies of single-project cases. Its study combines information from document studies with interview data, and culminates in normative recommendations. Findings – The study found that the projects do indeed not exploit the opportunity space to a very large extent. The lessons from the present study is that the final choice is determined more by decision makers than the analysts, and will often be the result of policy and preferences more than objective reasoning. Which again suggests that the efforts as analysts will often be in vain. Research limitations/implications – These findings could influence theoretical models outlining project establishment and decision processes. Practical implications – The study has identified many shortcomings in public sector processes that could be utilized to alter such processes. Originality/value – The study is original in that it focusses on the concept development phase of projects, rather than the traditional execution phase, and has studied decision processes.

The dual drivetrain model of digital transformation: role of industrial big-data-based affordance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Wei Wang ◽  
Zuopeng (Justin) Zhang
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Content Type ◽  
Industrial Big Data ◽  
Technology Affordance ◽  
Effective Use ◽  
Practical Implications

PurposeTo better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”Design/methodology/approachThis study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.FindingsDrawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.Originality/valueThis study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

Exploring travelers’ decision-making styles

2018 ◽  
Vol 30 (1) ◽  
pp. 618-636 ◽  
Author(s):  
Hilmi A. Atadil ◽  
Ercan Sirakaya-Turk ◽  
Fang Meng ◽  
Alain Decrop
Keyword(s):  
Decision Making ◽  
Strong Relationship ◽  
Decision Makers ◽  
Destination Image ◽  
Cluster Approach ◽  
Content Type ◽  
Tourism Studies ◽  
Decision Making Styles ◽  
Attitudinal Characteristics ◽  
Practical Implications

Purpose The purpose of this study is to profile market segments using travelers’ decision-making styles (DMS) as segmentation bases and to identify similarities and differences between traveler segments regarding a series of psychographic and attitudinal characteristics. Design/methodology/approach Data are gathered from a sample of 426 travelers in Dubai and Shanghai via self-reported surveys. Analyses included factor, k-means cluster, discriminant and MANOVA. Findings Study findings reveal significant differences among the rational, adaptive and daydreamer decision-makers’ segments in their behavioral and attitudinal characteristics with respect to tourism involvement and destination images. Practical implications Findings provide important practical implications for generating effective marketing and positioning strategies based on the identified attitudinal characteristics of the traveler segments for destination marketing organizations. Originality/value A stream of recent tourism studies shows a strong relationship between tourism involvement and destination images, yet very little research has tackled the issue of how these critical variables can be affected by individuals’ decision-making styles. This study explores and tests the relationships among DMS, tourism involvement and destination image using a factor-cluster approach.

Machine reengineering: robots and people working smarter together

2017 ◽  
Vol 45 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Prashant Shukla ◽  
H. James Wilson ◽  
Allan Alter ◽  
David Lavieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Methodology ◽  
It Professionals ◽  
Content Type ◽  
Public Agency ◽  
Intelligent Machines ◽  
Intelligent Machine ◽  
Working Together ◽  
Practical Implications

Purpose The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering. Design/methodology/approach The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning. Findings Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities. Practical implications Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working. Originality/value A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

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