The implementation of artificial intelligence in organizations by functional areas: A review and conceptual model

The implementation of Artificial Intelligence (AI) in organizations in Colombia has been increasing in recent years, and it is expected to continue growing in the near future. Companies in Colombia have begun to use AI in different areas, such as data analysis, customer service, process automation, and decision-making. Among the strategies applied for the implementation of AI in organizations is the adoption of cloud technologies, the use of machine learning algorithms, collaboration with technology providers and the formation of specialized teams. However, the implementation of AI in organizations has also raised concerns regarding the impact on the employability of people. Although AI is expected to improve efficiency and productivity, some tasks and jobs are also expected to be damaged by automation. It is necessary that companies in Colombia adopt policies and strategies that allow a fair transition towards the implementation of AI, to minimize the negative impact on employability and to take advantage of the benefits that this technology can offer.

PurposeThis systematic literature review analyzes the academic literature to understand SC risk and resilience across different organizational sizes and industries. The academic literature has well discussed the causes of supply chain (SC) risk events, the impact of SC disruptions, and associated plans for SC resilience. However, the literature remains fragmented on the role of two fundamental elements in achieving SC resilience: the firm's size and the firm's industry as firms' contingent factors. Therefore, it is important to investigate and highlight SC resilience differences by size and industry type to establish more resilient firms.Design/methodology/approachBuilding upon the contingent resource-based view of the firm, the authors posit that organizational factors such as size and industry sector have important roles in developing organizational resilience capabilities. This systematic literature review and analysis is based on the structural and systematic analysis of high-ranked peer-reviewed journal papers from January 2000 to June 2021 collected through three global scientific databases (i.e. ProQuest, ScienceDirect, and Google Scholar) using relevant keywords.FindingsThis systematic literature review of 230 high-quality articles shows that SC risk events can be categorized into demand, supply, organizational, operational, environmental, and network/control risk events. This study suggests that the SC resilience plans developed by startups, small and mdium-sized enterprises (SMEs), and large organizations are not necessarily the same as those of large enterprises. While collaboration and networking and risk management are the most crucial resilience capabilities for all firms, applying lean and quality management principles and utilizing information technology are more crucial for SMEs. For large firms, knowledge management and contingency planning are more important.Originality/valueThis study provides a comprehensive review of the literature on SC resilience plans across different organizational sizes and industries, offering new insights into the nature and dynamics of startups', SMEs', and large enterprises' SC resilience in different industries. The study highlights the need for further investigation of SC risk and resilience for startups, SMEs, and different industries on a more detailed level using empirical data. This study’s findings have important implications for researchers and practitioners and guide the development of effective SC resilience strategies for different types of firms.

Artificial Intelligence (AI) implementation incorporates challenges that are unique to the context of AI, such as dealing with probabilistic outputs. To address these challenges, recent research suggests that organizations should develop specific capabilities for AI implementation. Currently, we lack a thorough understanding of how certain capabilities facilitate AI implementation. It remains unclear how they help organizations to cope with AI’s unique characteristics. To address this research gap, we employ a qualitative research approach and conduct 25 explorative interviews with experts on AI implementation. We derive four organizational capabilities for AI implementation: AI Project Planning and Co-Development help to cope with the inscrutability in AI, which complicates the planning of AI projects and communication between different stakeholders. Data Management and AI Model Lifecycle Management help to cope with the data dependency in AI, which challenges organizations to provide the proper data foundation and continuously adjust AI systems as the data evolves. We contribute to our understanding of the sociotechnical implications of AI’s characteristics and further develop the concept of organizational capabilities as an important success factor for AI implementation. For practice, we provide actionable recommendations to develop organizational capabilities for AI implementation.

Nowadays, the revolution of technology plays a major role in organizations' success, Artificial intelligence emerged widely in most of business sectors, and became a crucial part in some businesses. Artificial intelligence (AI), complex algorithms, machine learning and data analytics greatly influence the human live and societies nowadays, even more than before. The benefits of AI applications are wide ranged and it finds it place in various domains and the possibilities are even far-reaching. The AI application is proven in form of unmanned vehicles, medical diagnosis, transport management, air traffic management, environmental sustainability and many more. Thanks to the latest progress in computer hardware, some AI advancements have already gone beyond the human experts’ capacities. The purpose of this research paper is to demonstrate the power of Artificial intelligence in organizations' operations, highlight the current benefits of the AI technology and also gave an insight on the possible challenges and risks associated with this technology. The research is based on previous researches, articles, and specialists’ opinions.KeywordsArtificial intelligenceMachine learningRobots

The rapid development of artificial intelligence (AI) systems has raised concerns about their ethical, legal, and social risks. Despite notable progress in the development of responsible AI frameworks, methods, and tools, research shows that many organizations struggle to effectively implement responsible AI. I review prior research on responsible AI to explain the insufficient implementation of responsible AI in organizations. Drawing on neo-institutional theory, I find that policy-practice decoupling (i.e., organizational responsible AI policies are adopted but not implemented in practice) and means-end decoupling (i.e., organizational responsible AI policies are implemented in practice but do not achieve their intended goals) can explain the ineffective implementation of responsible AI, with AI practitioners playing a key role as institutional entrepreneurs or custodians in driving or inhibiting the implementation of responsible AI. I contribute to the literature on responsible AI by exploring the institutional pressures that drive or inhibit its implementation, synthesizing the challenges to its implementation, and providing an overview of the roles and strategies AI practitioners use to deal with the implementation of responsible AI. I propose several avenues for future research and discuss implications for research and practice.

Purpose: This research attempts to propose a conceptual model of whether supply chain resilience and organizational recovery capability constitute direct antecedents to supply chain performance. The study is based on the dynamic capability theory. Design/methodology/approach: The literature-based review is drawn up to link supply chain performance, supply chain resilience and organizational recovery capability to create a conceptual framework. Findings: This conceptual paper suggests that supply chain resilience has a positive direct and indirect impact on supply chain performance. It proposes that as the level of supply chain resilience increases, so does the level of organizational recovery capability, leading to improved supply chain performance. Additionally, the paper suggests that organizational recovery capability plays a mediating role in the relationship between supply chain resilience and supply chain performance. Research limitations/implications: The research on the organizational recovery capability and determining variables towards the supply chain performance of small and medium-sized manufacturing enterprises is still limited despite ample evidence demonstrating the performance of these businesses. Practical implications: This concept can provide practitioners with insights into the advantages of organizational recovery capability and supply chain resilience for supply chain performance, and it may serve as a basis for further empirical research. Originality/value: The study underlines that organizational strategies should be designed with organizational recovery capability as supply chain resilience alone is inadequate for organizations to attain competitive advantage.

This article reviews the advances, benefits and contributions of artificial intelligence to business organizations, without ignoring the need for skilled human labor and talent that companies have always required and which is essential for the economic development of a company. The implementation of legal frameworks and regulations that will in turn address issues such as organizational privacy, cybersecurity and liability are important aspects to be addressed in this article. Thus, this paper focuses on the compilation of scientific articles, books and other texts in which important topics were evidenced such as (i). - Innovation and invention, (ii). - Big Data, (iii). -Implementation of artificial intelligence in organizations, and (iv). -legality and responsibility of decisions, likewise, it examines the legal challenges of AI and highlights that smart contracts are a technological innovation that allows secure, fast and low-risk transactions using blockchain technology, this is why the literature review aims to explore how technology, artificial intelligence and communication tools can facilitate the work of the population, decreasing the time spent on some activities

BackgroundThe ability to perform complex tasks has seen artificial intelligence (AI) used to support radiology in clinical settings, including lung cancer detection and diagnosis. Evidence suggests that AI can contribute to accurate diagnosis, reduce errors, and improve efficiency. The National Health Service England (NHSE)–funded Artificial Intelligence Diagnostic Fund (AIDF) is currently supporting 12 National Health Service (NHS) networks to implement AI for chest diagnostic imaging. There is, however, limited evidence on real-world AI implementation and use, including staff, patient, and caregiver experience, and costs and cost-effectiveness. A National Institute for Health and Care Research Rapid Service Evaluation Team Phase 1 evaluation provided insights into the early implementation of these tools and developed a framework for monitoring and evaluation of AI tools for chest diagnostic imaging in practice.ObjectiveThis mixed methods evaluation of AI tools for chest diagnostic imaging aims to address previous research gaps by exploring the implementation of AI tools for chest diagnostic imaging, the impact and costs of implementing these service models, and the experiences of patients, caregivers, and staff.MethodsThis study will be a mixed method evaluation of implementation, experiences, impact, and costs of AI for chest diagnostic imaging in NHS services in England, with the evaluation informed by the Major System Change Framework. Trust-level case studies (3 in-depth and up to 9 light-touch) will be performed, including staff member, patient, and caregiver; NHSE AIDF team interviews; meeting observations; and analysis of key relevant documentation. Qualitative data will be analyzed using Rapid Assessment Procedures and inductive thematic analysis, supplemented by in-depth deductive thematic analysis. Data from case study sites and other relevant sources will be used to assess outcomes at the other sites and for comparators. A pragmatic economic model of the chest diagnostic imaging pathway will be developed to estimate key costs and resource use associated with AI tool deployment. Together with input from national stakeholders and staff workshops, the study findings will then be finalized for reporting.ResultsAs of September 2025, trust-level research and development approvals with participating sites are complete, and data collection has commenced. Results are expected to be reported by the end of February 2026.ConclusionsThe study will provide new insights into the facilitators and barriers to the adoption of AI technology in health care and the perceptions of both the general public and health care staff on its use. It will also inform best practices in approaches for service performance evaluation, for the implementation of AI into existing care pathways, and for the development of models to best support evidence-based decision-making. It will thus establish a framework upon which the greatest benefits of the use of AI in health care can be realized.International Registered Report Identifier (IRRID)DERR1-10.2196/81421

Economic growth, employment creation and resilience of businesses and industry in the 4th Industrial Revolution which is intertwined with climate change realities depends much on the implementation of digital technologies and Artificial Intelligence (AI). The agricultural sector is no exception to these developmental realities. That is to say, the sector is equally compelled to implement AI in almost all the stages of agricultural production. From the cultivation of crops to transportation of the products to the target markets or the public. These will include having farmers and Extension Services implementing AI for crop yield detection, soil nutrients and moisture contents, climatic conditions predictions, milking and harvesting as well as weeds, pests and diseases identification and management. This paper explored the opportunities and challenges of AI in the implementation of Agricultural Advisory Services (AASs) for Sustainable Agriculture. These were achieved through extensive literature review which comprised of a conceptual framework for the implementation of AI in the AASs. The findings show the leveraging benefit of AI in the production costs among farmers, increase in farm productivity, and ease of access of AASs which was always almost a mission to achieve, especially, in the developing countries. Therefore, it is recommended that the relationship between youth participation in the agricultural sector and the implementation of AI and Digital Technology in the sector be explored, with the impact of the implementation of AI in the sector and the contribution of the sector towards developing countries’ Gross Domestic Products (GDPs). The ethical implications of AI in AASs and the Agricultural Sector as a whole must be explored to unveil issues that may hamper the future acceptance of these digital skills and innovations.

Background/Purpose: The agriculture sector is the backbone of every nation which contributes to the global economy. The implementation of technology in agriculture has brought revolutionary development in its outcome. Due to this, a drastic improvement in the global economy from the agricultural sector is expected. Moreover, the implementation of artificial intelligence (AI) improves the productivity of farmers giving solutions to various challenges faced by the farmers. The various AI tools that are developed for the agriculture sector include precision farming, predictive analytics, automated machinery, smart irrigation systems, crop and soil monitoring, supply chain optimization, weather forecasting, and livestock management. Adopting AI in agriculture faces several challenges despite its long-term benefits. The high upfront costs to be invested in implementing AI technology make it difficult for small-scale and developing farmers to invest in AI. Implementing the above technology needs technical skills, fast internet connectivity, and costlier equipment. Due to the lack of the above-mentioned requirements, the AI technologies that are meant for agriculture do not reach the farmers. This results in the wastage of resources for AI without the outcome. Considering the above issues an appropriate simplified model is proposed that facilitates the adaptation of the AI technology by small and medium-scale farmers in their agriculture to improve the performance. Objective: The objective of this paper is to review the various journals related to the implementation of AI in Agriculture and to study the various issues related to its implementation. It also aims at identifying the research gap which will help to develop a model suitable for the end like small-scale and medium-scale farmers. Design/Methodology/Approach: A systematic literature review was conducted by gathering and examining relevant literature from international and national journals, conferences, databases, and other resources accessed via Google Scholar and various search engines. Findings/Result: The agriculture sector, crucial to every nation's economy, has seen revolutionary advancements through technology, especially AI. AI tools like precision farming, predictive analytics, and smart irrigation promise to enhance productivity and address various agricultural challenges. However, high implementation costs, resistance to new technologies, and lack of necessary infrastructure hinder widespread adoption among small-scale and developing farmers. To overcome these obstacles, a model is proposed to effectively support farmers in adopting AI technologies to boost agricultural performance. Originality/Value: The implementation of AI and ML tools in agriculture from diverse sources is done. This area needs study due to recent challenges faced by small and medium-scale farmers in the implementation of AI and ML tools in agriculture. The information acquired will help to create a new model by improving the outcomes of the existing scenario. Paper Type: Literature Review.

BackgroundArtificial Intelligence (AI) has significantly reshaped Primary Health Care (PHC), offering various possibilities and complexities across all functional dimensions. The objective is to review and synthesize available evidence on the opportunities, challenges, and requirements of AI implementation in PHC based on the Primary Care Evaluation Tool (PCET).MethodsWe conducted a systematic review, following the Cochrane Collaboration method, to identify the latest evidence regarding AI implementation in PHC. A comprehensive search across eight databases- PubMed, Web of Science, Scopus, Science Direct, Embase, CINAHL, IEEE, and Cochrane was conducted using MeSH terms alongside the SPIDER framework to pinpoint quantitative and qualitative literature published from 2000 to 2024. Two reviewers independently applied inclusion and exclusion criteria, guided by the SPIDER framework, to review full texts and extract data. We synthesized extracted data from the study characteristics, opportunities, challenges, and requirements, employing thematic-framework analysis, according to the PCET model. The quality of the studies was evaluated using the JBI critical appraisal tools.ResultsIn this review, we included a total of 109 articles, most of which were conducted in North America (n = 49, 44%), followed by Europe (n = 36, 33%). The included studies employed a diverse range of study designs. Using the PCET model, we categorized AI-related opportunities, challenges, and requirements across four key dimensions. The greatest opportunities for AI integration in PHC were centered on enhancing comprehensive service delivery, particularly by improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction. However, the most challenges emerged within the stewardship and resource generation functions, with key concerns related to data security and privacy, technical performance issues, and limitations in data accessibility. Ensuring successful AI integration requires a robust stewardship function, strategic investments in resource generation, and a collaborative approach that fosters co-development, scientific advancements, and continuous evaluation.ConclusionsSuccessful AI integration in PHC requires a coordinated, multidimensional approach, with stewardship, resource generation, and financing playing key roles in enabling service delivery. Addressing existing knowledge gaps, examining interactions among these dimensions, and fostering a collaborative approach in developing AI solutions among stakeholders are essential steps toward achieving an equitable and efficient AI-driven PHC system.Protocol.Registered in Open Science Framework (OSF) (https://doi.org/10.17605/OSF.IO/HG2DV).

Background: The prospect of Artificial Intelligence (AI) implementation in the healthcare field will revolutionize clinical decision-making and nursing practice. Nevertheless, senior nurses tend to have some reservations toward AI-based Clinical Decision Support Systems (AI-CDSS), especially on the issues of professional autonomy, workload, and even ethics. Purpose: it was to understand the issues and possibilities of introducing AI-based devices to experienced nursing personnel, their attitudes, obstacles, and possible ways of sustainable implementation. Methodology: A narrative review of PubMed, Scopus, CINAHL, and Web of Science (2018–2025) was conducted; eligible full texts were screened and thematically synthesized from 18 included studies. Results: The key obstacles to AI implementation were found to be more cultural and psychological than technical for decision makers. Nurses raised worries regarding the loss of expertise, risk to autonomy, and automation bias. A training co-pilot- a specialist mediating between advanced technology and clinical reality- was needed in the process of effective integration. Strategic imperatives like explainable AI, ethical sensitization, workload alignment, and institutional transparency became essential success factors. Conclusion: The paper highlights how effective AI implementation needs more than just technical implementation; it would need cultural adjustment, institutional openness, and training on specific skills designed to fit experienced personnel. To provide safe and sustainable integration of AI into nursing, it is necessary to create nurse informaticists as co-pilots and introduce ethical, transparent, and explainable practices.

Aim . The presented study aims to determine the impact of artificial intelligence as a modern breakthrough technology on productivity, to explore how the implementation of artificial intelligence technology will affect the preservation of jobs in different industries, what opportunities it will create for business in terms of increasing productivity along the entire value chain, and how this will affect GDP growth and key economic indicators in various countries. Tasks . The authors identify priority directions for the development and implementation of artificial intelligence in various economic sectors; analyze econometric results obtained during previous studies; substantiate the advantages and opportunities of artificial intelligence to facilitate its implementation in the business processes of organizations. Methods . This study uses the methods of analysis, systematization, and correlation analysis. Results . Various definitions of artificial intelligence, levels of its functionality, and fields of application are analyzed. The ways and prospects of using artificial intelligence in different countries are examined regressively by industry and geographical region, with an emphasis on the ways of using artificial intelligence systems (wired/special and adaptive) and automation technologies in the implementation of artificial intelligence. The potential effects of artificial intelligence at each stage of the company's value chain are described. Examples from different industrial sectors are provided. Based on the correlation analysis, the relationship between the implementation of artificial intelligence and productivity growth is presented. Conclusions . Implementation of artificial intelligence has a global economic impact on key economic indicators such as employment and GDP, which is especially important in the current crisis situation. The effect of artificial intelligence should be enough to maintain the rate of economic growth in the long term. The direct impact of artificial intelligence on GDP is due to increased income and employment in firms and industries engaged in the development or production of artificial intelligence technologies. Secondary (indirect) effects will come from other sectors that use certain artificial intelligence technologies to increase the efficiency of their processes and solutions and improve the accessibility of information. Regions implementing an artificial intelligence technology of higher quality can expect its impact on labor productivity to be even more significant.

The paper analyzes the implementation of artificial intelligence (AI) in project-oriented organizations through a comparative study of Serbia and the region. It focuses on the impact of AI on improving efficiency, innovation, and decision-making quality, as well as the role of knowledge management and continuous learning in the digital transformation process. Using theoretical frameworks, empirical data, and case studies, the paper identifies key advantages and challenges of AI adoption in Serbia, which lags behind more developed regional countries like Slovenia and Croatia in terms of digitalization and integration of advanced technologies. Concrete examples of successful AI applications in various sectors (energy, construction, creative industries, finance) are presented, along with obstacles such as a lack of skilled personnel, limited resources, and organizational inertia. Based on the comparative analysis, the paper provides recommendations for enhancing the digital transformation of project-oriented organizations in Serbia, confirming the hypothesis that the integration of AI and knowledge management significantly contributes to increased efficiency, innovation, and competitiveness.

Actuality. Nowadays, the development of artificial intelligence affects the activities of all human life spheres, and the restaurant business as well. It can be explained by the fact that restaurant business enterprises are very sensitive to changes in the environment, and they need to constantly improve their activities for competitive functioning. It is artificial intelligence that will allow to work effectively in the market of restaurant services, influencing the demand and increasing the income. Therefore, the topic of application and implementation of artificial intelligence in restaurant business enterprises is relevant. The aim of the article is to substantiate the essence of the “artificial intelligence” concept, identify its advantages and disadvantages, as well as the possibility of its application in restaurant business establishments. Research methods: abstract-logical (when systematising information sources on application and implementation of artificial intelligence), argumentative (when setting a study problem and formulating scientific novelty), descriptive (when interpreting terms), analysis and logical generalisation (when considering advantages and disadvantages of AI application), conclusion (when describing AI application in different processes of enterprise activity), generalisation (when making research conclusions). Results. A few basic concepts of “artificial intelligence” are described in this article, as well as their interpretation is presented by the author. The article describes. The testimonial of different types of artificial intelligence is given. The advantages and disadvantages of using AI in the restaurant business are highlighted. The importance of using artificial intelligence technologies for restaurant business enterprises is proven. Conclusions and discussion. It is offered to apply AI in the following processes of restaurant business enterprises: order formation, customer service, personnel selection, maintenance and equipment of the establishment, purchase of raw materials and products, planning the activities of the enterprise structural partitions, marketing and advertising. The main advantages of using artificial intelligence in the restaurant business are such: reducing production waste, optimising the production process, improving the quality of customer service, increasing the income. The scientific novelty of the study consists in the analysis of using the artificial intelligence system in different activity processes of service sector enterprises, which will contribute to the efficiency of restaurant business enterprises.