Welcome to the Clearinghouse Project Library, where we highlight seminal and impactful articles focused on AI and its intersection with law, work, and society. Explore our searchable database of legal scholarly articles related to AI.
Featured Topics
- AI and Administrative Work
- AI and Criminal Justice
- AI and Education
- AI and Employment
- AI and Financial systems
- AI and Health
- AI, Immigration, and Human Rights
- AI Regulation and Strategies
- AI and Surveillance
- International/Comparative Regulation
- Al and War
- Al Race Law
- AI and Business
- AI and Creative Work
- Al and ESG
- AI and Medicine
- AI and Police Work
- AI and Managerial Work
- AI and White Collar Work
- AI and Blue Collar Work
We also introduce books, documentary films, and other media that have been created to address legal issues stemming from the use of automated decision-making. We hope that this clearinghouse will serve as a useful resource for a wide array of stakeholders including: legal scholars, practitioners, media, and students of AI and the Law at every level.
Explore Our Collection
Use the search function to discover articles, books, documentary films, and other media related to AI and the Future of Work, exploring the legal challenges and implications in various sectors.
1.
Sayyadi, Mostafa
How to improve data quality to empower business decision-making process and business strategy agility in the AI age Journal Article
In: Business Information Review, vol. 41, no. 3, pp. 124–129, 2024, ISSN: 0266-3821, (Publisher: SAGE Publications Ltd).
Abstract | Links | BibTeX | Tags: AI and Business
@article{sayyadi_how_2024,
title = {How to improve data quality to empower business decision-making process and business strategy agility in the AI age},
author = {Mostafa Sayyadi},
url = {https://doi.org/10.1177/02663821241264705},
doi = {10.1177/02663821241264705},
issn = {0266-3821},
year = {2024},
date = {2024-09-01},
urldate = {2024-11-22},
journal = {Business Information Review},
volume = {41},
number = {3},
pages = {124–129},
abstract = {Machine learning (ML) and predictive analytics (PA) can provide invaluable assistance for forecasting trends and informing decision-making using data. Considering the business strategy agility as a quick response to market dynamics, this article presents solutions to enhance data quality and management, model interpretation ability, and availability using Explainable AI, with cloud computing and distributed systems that can address scaling problems. When companies utilize these technologies wisely, they can gain an edge, achieve sustainable growth, and make informed decisions. We show how ML and PA can enhance decision-making and business strategy. We also remind that there are antecedents to data quality management: Data culture and Leadership, preparing the company to benefit from the information business strategy agility.},
note = {Publisher: SAGE Publications Ltd},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) and predictive analytics (PA) can provide invaluable assistance for forecasting trends and informing decision-making using data. Considering the business strategy agility as a quick response to market dynamics, this article presents solutions to enhance data quality and management, model interpretation ability, and availability using Explainable AI, with cloud computing and distributed systems that can address scaling problems. When companies utilize these technologies wisely, they can gain an edge, achieve sustainable growth, and make informed decisions. We show how ML and PA can enhance decision-making and business strategy. We also remind that there are antecedents to data quality management: Data culture and Leadership, preparing the company to benefit from the information business strategy agility.
2.
Ali, Muhammad; Khan, Tariq Iqbal; Khattak, Mohammad Nisar; Şener, İrge
Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 3, pp. 100352, 2024, ISSN: 2199-8531.
Abstract | Links | BibTeX | Tags: AI and Business
@article{ali_synergizing_2024,
title = {Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises},
author = {Muhammad Ali and Tariq Iqbal Khan and Mohammad Nisar Khattak and İrge Şener},
url = {https://www.sciencedirect.com/science/article/pii/S219985312400146X},
doi = {10.1016/j.joitmc.2024.100352},
issn = {2199-8531},
year = {2024},
date = {2024-09-01},
urldate = {2024-11-22},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {10},
number = {3},
pages = {100352},
abstract = {The study was conducted on 125 US based high-tech firms from software engineering, hardware production, biotechnology, and telecommunications. Senior-level executives, including CEOs, board members, and CTOs, provided insights through structured questionnaires. Key findings indicate that AI adoption significantly enhances organizational capabilities in terms of employees’ innovation, creativity, and experimentation. Moreover, AI adaptation positively impacts decision making thus yielding more accurate and timely valuable decisions. These findings contribute to both theoretical understanding and managerial practice by guiding strategic investments in AI technologies, fostering innovation, and advocating for ethical AI deployment practices. Future study should examine longitudinal impacts across industries and regions to optimize benefits and minimize risks in digital transformation efforts. It should also integrate qualitative methods for deeper insights and appropriate AI governance systems.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
The study was conducted on 125 US based high-tech firms from software engineering, hardware production, biotechnology, and telecommunications. Senior-level executives, including CEOs, board members, and CTOs, provided insights through structured questionnaires. Key findings indicate that AI adoption significantly enhances organizational capabilities in terms of employees’ innovation, creativity, and experimentation. Moreover, AI adaptation positively impacts decision making thus yielding more accurate and timely valuable decisions. These findings contribute to both theoretical understanding and managerial practice by guiding strategic investments in AI technologies, fostering innovation, and advocating for ethical AI deployment practices. Future study should examine longitudinal impacts across industries and regions to optimize benefits and minimize risks in digital transformation efforts. It should also integrate qualitative methods for deeper insights and appropriate AI governance systems.
3.
Black, Stuart; Samson, Daniel; Ellis, Alon
Moving beyond ‘proof points’: Factors underpinning AI-enabled business model transformation Journal Article
In: International Journal of Information Management, vol. 77, pp. 102796, 2024, ISSN: 0268-4012.
Abstract | Links | BibTeX | Tags: AI and Business
@article{black_moving_2024,
title = {Moving beyond ‘proof points’: Factors underpinning AI-enabled business model transformation},
author = {Stuart Black and Daniel Samson and Alon Ellis},
url = {https://www.sciencedirect.com/science/article/pii/S0268401224000446},
doi = {10.1016/j.ijinfomgt.2024.102796},
issn = {0268-4012},
year = {2024},
date = {2024-08-01},
urldate = {2024-11-22},
journal = {International Journal of Information Management},
volume = {77},
pages = {102796},
abstract = {Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.
4.
Yaiprasert, Chairote; Hidayanto, Achmad Nizar
AI-powered ensemble machine learning to optimize cost strategies in logistics business Journal Article
In: International Journal of Information Management Data Insights, vol. 4, no. 1, pp. 100209, 2024, ISSN: 2667-0968.
Abstract | Links | BibTeX | Tags: AI and Business
@article{yaiprasert_ai-powered_2024,
title = {AI-powered ensemble machine learning to optimize cost strategies in logistics business},
author = {Chairote Yaiprasert and Achmad Nizar Hidayanto},
url = {https://www.sciencedirect.com/science/article/pii/S2667096823000551},
doi = {10.1016/j.jjimei.2023.100209},
issn = {2667-0968},
year = {2024},
date = {2024-04-01},
urldate = {2024-11-22},
journal = {International Journal of Information Management Data Insights},
volume = {4},
number = {1},
pages = {100209},
abstract = {This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.
5.
John, Meenu Mary; Olsson, Helena Holmström; Bosch, Jan
Towards an AI-driven business development framework: A multi-case study Journal Article
In: Journal of Software: Evolution and Process, vol. 35, no. 6, pp. e2432, 2023, ISSN: 2047-7481, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/smr.2432).
Abstract | Links | BibTeX | Tags: AI and Business
@article{john_towards_2023,
title = {Towards an AI-driven business development framework: A multi-case study},
author = {Meenu Mary John and Helena Holmström Olsson and Jan Bosch},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/smr.2432},
doi = {10.1002/smr.2432},
issn = {2047-7481},
year = {2023},
date = {2023-01-01},
urldate = {2024-11-22},
journal = {Journal of Software: Evolution and Process},
volume = {35},
number = {6},
pages = {e2432},
abstract = {Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/smr.2432},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.
6.
Gudigantala, Naveen; Madhavaram, Sreedhar; Bicen, Pelin
An AI decision-making framework for business value maximization Journal Article
In: AI Magazine, vol. 44, no. 1, pp. 67–84, 2023, ISSN: 2371-9621, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12076).
Abstract | Links | BibTeX | Tags: AI and Business
@article{gudigantala_ai_2023,
title = {An AI decision-making framework for business value maximization},
author = {Naveen Gudigantala and Sreedhar Madhavaram and Pelin Bicen},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aaai.12076},
doi = {10.1002/aaai.12076},
issn = {2371-9621},
year = {2023},
date = {2023-01-01},
urldate = {2024-11-22},
journal = {AI Magazine},
volume = {44},
number = {1},
pages = {67–84},
abstract = {This article addresses a key question of why businesses are failing to maximize business value from their artificial intelligence (AI) investments and proposes a strategic decision-making framework for AI decision-making to address this problem. We suggest that a firm's business strategy must drive AI-driven business outcomes and measurements, which in turn should drive the AI implementation decisions. Very often, we find that businesses fail to successfully cast business problems into AI problems. To bridge this gap, we propose that firms use a performance management system such as objectives and key results (OKRs) to ensure that the business and AI goals & objectives are well defined, tightly aligned, and made transparent across the company, and the AI efforts are approached in an integrated manner by the different parts of a firm. We use McDonald's use of AI initiatives as a business use case to demonstrate support for our AI decision-making framework. We argue that using the business strategy as a primary driver will enable firms to solve the right problems using AI, turning it to be a source of technology innovation and competitive advantage.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12076},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
This article addresses a key question of why businesses are failing to maximize business value from their artificial intelligence (AI) investments and proposes a strategic decision-making framework for AI decision-making to address this problem. We suggest that a firm's business strategy must drive AI-driven business outcomes and measurements, which in turn should drive the AI implementation decisions. Very often, we find that businesses fail to successfully cast business problems into AI problems. To bridge this gap, we propose that firms use a performance management system such as objectives and key results (OKRs) to ensure that the business and AI goals & objectives are well defined, tightly aligned, and made transparent across the company, and the AI efforts are approached in an integrated manner by the different parts of a firm. We use McDonald's use of AI initiatives as a business use case to demonstrate support for our AI decision-making framework. We argue that using the business strategy as a primary driver will enable firms to solve the right problems using AI, turning it to be a source of technology innovation and competitive advantage.
7.
Mathur, Balafakir Subramanian
Businesses Innovation & AI: How AI and Analytics Can Encourage Innovation Journal Article
In: Business & IT, vol. XIII, no. 2, pp. 39–46, 2023, ISSN: 25707434.
Abstract | Links | BibTeX | Tags: AI and Business
@article{mathur_businesses_2023,
title = {Businesses Innovation & AI: How AI and Analytics Can Encourage Innovation},
author = {Balafakir Subramanian Mathur},
url = {http://bit.fsv.cvut.cz/doi/bit.2023.02.04.html},
doi = {10.14311/bit.2023.02.04},
issn = {25707434},
year = {2023},
date = {2023-01-01},
urldate = {2024-11-22},
journal = {Business & IT},
volume = {XIII},
number = {2},
pages = {39–46},
abstract = {Artificial intelligence is about imbuing machines with a kind of intelligence that is largely attributed to humans. Extant literature, in addition to the encounters of ours as providers, indicates that while AI may not be entirely set to take over revolutionary areas within the improvement procedure, it indicates promise as a significant guidance to advancement superiors. In this particular content, we broadly relate to the derivation of PC enabled, data-driven insights, models, and visualizations within the development pastime as innovation analytics. AI may play a crucial role in the innovation exercise by switching many aspects of innovation analytics. We existing four case studies which are distinct of AI in motion according to the prior job of ours in the market. We highlight the benefits and limitations of utilizing AI in growth, and conclude with strategic implications and additional resources for development managers.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence is about imbuing machines with a kind of intelligence that is largely attributed to humans. Extant literature, in addition to the encounters of ours as providers, indicates that while AI may not be entirely set to take over revolutionary areas within the improvement procedure, it indicates promise as a significant guidance to advancement superiors. In this particular content, we broadly relate to the derivation of PC enabled, data-driven insights, models, and visualizations within the development pastime as innovation analytics. AI may play a crucial role in the innovation exercise by switching many aspects of innovation analytics. We existing four case studies which are distinct of AI in motion according to the prior job of ours in the market. We highlight the benefits and limitations of utilizing AI in growth, and conclude with strategic implications and additional resources for development managers.
8.
Katsamakas, Evangelos; Pavlov, Oleg
AI and Business Model Innovation: Leveraging the AI feedback loop Journal Article
In: Journal of Business Models, pp. 22–30 Sider, 2020, (Artwork Size: 22-30 Sider Publisher: Journal of Business Models).
Abstract | Links | BibTeX | Tags: AI and Business
@article{katsamakas_ai_2020,
title = {AI and Business Model Innovation: Leveraging the AI feedback loop},
author = {Evangelos Katsamakas and Oleg Pavlov},
url = {https://barracudaex.aub.aau.dk/index.php/JOBM/article/view/3532},
doi = {10.5278/OJS.JBM.V8I2.3532},
year = {2020},
date = {2020-07-01},
urldate = {2024-11-22},
journal = {Journal of Business Models},
pages = {22–30 Sider},
abstract = {Purpose: The article analyzes the effects of Artificial Intelligence (AI) on Business Model Innovation (BMI), focusing on the platform business model.
Design/Methodology/Approach: Proposes a CLD (Causal Loop Diagram) model and analyzes the model to discuss insights about the structure and performance of the business model.
Findings: Shows that AI enables key strategic feedback loops that constitute the core structure of the business model.
Practical Implications: Managers and entrepreneurs who seek to leverage AI should invest in the AI feedback loops. An AI strategy for BMI should seek to create, strengthen, and speed-up AI feedback loops in the business model.
Originality/Value: Analyzes the effects of AI on BMI while accounting for dynamic complexity as a business model property to be understood and leveraged. Contributes to our understanding of the business value and impact of AI.},
note = {Artwork Size: 22-30 Sider
Publisher: Journal of Business Models},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Purpose: The article analyzes the effects of Artificial Intelligence (AI) on Business Model Innovation (BMI), focusing on the platform business model.
Design/Methodology/Approach: Proposes a CLD (Causal Loop Diagram) model and analyzes the model to discuss insights about the structure and performance of the business model.
Findings: Shows that AI enables key strategic feedback loops that constitute the core structure of the business model.
Practical Implications: Managers and entrepreneurs who seek to leverage AI should invest in the AI feedback loops. An AI strategy for BMI should seek to create, strengthen, and speed-up AI feedback loops in the business model.
Originality/Value: Analyzes the effects of AI on BMI while accounting for dynamic complexity as a business model property to be understood and leveraged. Contributes to our understanding of the business value and impact of AI.
Design/Methodology/Approach: Proposes a CLD (Causal Loop Diagram) model and analyzes the model to discuss insights about the structure and performance of the business model.
Findings: Shows that AI enables key strategic feedback loops that constitute the core structure of the business model.
Practical Implications: Managers and entrepreneurs who seek to leverage AI should invest in the AI feedback loops. An AI strategy for BMI should seek to create, strengthen, and speed-up AI feedback loops in the business model.
Originality/Value: Analyzes the effects of AI on BMI while accounting for dynamic complexity as a business model property to be understood and leveraged. Contributes to our understanding of the business value and impact of AI.
9.
Wamba-Taguimdje, Serge-Lopez; Wamba, Samuel Fosso; Kamdjoug, Jean Robert Kala; Wanko, Chris Emmanuel Tchatchouang
Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects Journal Article
In: Business Process Management Journal, vol. 26, no. 7, pp. 1893–1924, 2020, ISSN: 1463-7154, (Publisher: Emerald Publishing Limited).
Abstract | Links | BibTeX | Tags: AI and Business
@article{wamba-taguimdje_influence_2020,
title = {Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects},
author = {Serge-Lopez Wamba-Taguimdje and Samuel Fosso Wamba and Jean Robert Kala Kamdjoug and Chris Emmanuel Tchatchouang Wanko},
url = {https://www.emerald.com/insight/content/doi/10.1108/bpmj-10-2019-0411/full/html},
doi = {10.1108/BPMJ-10-2019-0411},
issn = {1463-7154},
year = {2020},
date = {2020-05-01},
urldate = {2024-11-22},
journal = {Business Process Management Journal},
volume = {26},
number = {7},
pages = {1893–1924},
abstract = {The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.,This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.,AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.,AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.,This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.},
note = {Publisher: Emerald Publishing Limited},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.,This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.,AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.,AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.,This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.
10.
Namaki, M. S. S. El
How Companies are Applying AI to the Business Strategy Formulation Journal Article
In: Scholedge International Journal of Business Policy & Governance ISSN 2394-3351, vol. 5, no. 8, pp. 77–82, 2019, ISSN: 2394-3351.
Abstract | Links | BibTeX | Tags: AI and Business
@article{namaki_how_2019,
title = {How Companies are Applying AI to the Business Strategy Formulation},
author = {M. S. S. El Namaki},
url = {https://thescholedge.org/index.php/sijbpg/article/view/503},
doi = {10.19085/journal.sijbpg050801},
issn = {2394-3351},
year = {2019},
date = {2019-01-01},
urldate = {2024-11-22},
journal = {Scholedge International Journal of Business Policy & Governance ISSN 2394-3351},
volume = {5},
number = {8},
pages = {77–82},
abstract = {Computing equipment capable of what one may term partial and quasi-intelligent behaviour, commonly referred to as Artificial Intelligence (AI), is assuming a key role in business. The probability is high that this Artificial Intelligence (AI) will lead to a fundamental change in the process of business strategy formulation as much as the very contents of this strategic behaviour. Product and market strategies and the resultant competitive behaviour will, more likely than not, be the outcome of those artificial intelligence processes and reiterations. A start is made and one can observe substantial progress in this direction. Who has done it and is there a conceptual framework behind this strategic behaviour? This will be the focus of this article The article starts with a brief definition of artificial intelligence and a basic framework of the concept. Seven case studies follow supporting the hypothesis that AI is penetrating the business strategy arena and leading to a fundamental change in the concept as much as the application. Those cases were drawn from different industries, and countries. A conceptual framework is accordingly derived and positioning of those case companies within this conceptual framework is done. The article is based on contemporary frameworks of AI and the cases are drawn from contemporary analysis of strategic behaviour. The conceptual model could provide an instrument for business AI application.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
Computing equipment capable of what one may term partial and quasi-intelligent behaviour, commonly referred to as Artificial Intelligence (AI), is assuming a key role in business. The probability is high that this Artificial Intelligence (AI) will lead to a fundamental change in the process of business strategy formulation as much as the very contents of this strategic behaviour. Product and market strategies and the resultant competitive behaviour will, more likely than not, be the outcome of those artificial intelligence processes and reiterations. A start is made and one can observe substantial progress in this direction. Who has done it and is there a conceptual framework behind this strategic behaviour? This will be the focus of this article The article starts with a brief definition of artificial intelligence and a basic framework of the concept. Seven case studies follow supporting the hypothesis that AI is penetrating the business strategy arena and leading to a fundamental change in the concept as much as the application. Those cases were drawn from different industries, and countries. A conceptual framework is accordingly derived and positioning of those case companies within this conceptual framework is done. The article is based on contemporary frameworks of AI and the cases are drawn from contemporary analysis of strategic behaviour. The conceptual model could provide an instrument for business AI application.