Artificial Intelligence and Big Data Analytics in Cameroon

Introduction. A comprehensive analysis of the development of information and communication technologies in the context of educational practice will contribute to the development of effective strategies for the modernisation of educational systems in the context of global technological change. Modern education can no longer function effectively without the integration of information and communication technologies into the educational process. Information and communication technologies open up new opportunities for personalisation of the educational process, development of independent work skills, creativity, critical thinking, and communication. At the same time, their implementation is accompanied by certain challenges: the need to modernise educational programmes, train teaching staff, provide technical infrastructure, and overcome digital inequality. The study of the historical stages of development of information and communication technologies in education allows us to understand the patterns of implementation of these technologies and identify effective strategies for their use today. Purpose. The purpose of the article is to study the stages of development of information and communication technologies in the context of their introduction into educational practice, to analyse the historical background of integration of information and communication technologies into education, and to identify current trends and prospects for their effective use in the educational process. Methods. The study used theoretical methods of scientific and pedagogical research analysis (dissection of historical and pedagogical material into its component parts (facts, ideas, concepts, events) allows to reveal the essence of certain phenomena, trends in the pedagogical past); synthesis (combining individual facts and provisions into a holistic view of pedagogical phenomena, used after analysis to form a generalised picture); historical and logical method (study of pedagogical phenomena in the chronological sequence and logic of their development, makes it possible to establish the patterns of development of pedagogical ideas); comparative and historical method (comparison of pedagogical phenomena, ideas, education systems in different historical periods or in different countries, allowing to identify common, different and unique); induction and deduction (induction – from individual facts to general conclusions, deduction – from general principles to explanation of individual historical and pedagogical facts); abstraction and generalisation (separation of essential features of pedagogical phenomena, construction of generalisations, concepts, typologies); prognostic method (use of historical and pedagogical experience to predict possible directions of education development). Results. The study has shown that information and communication technologies are the driving force behind the transformation of education, and their integration into the educational process requires not only technical support but also methodological training of teachers, updating of educational programmes, and rethinking of traditional educational models. The study identifies eight stages of the development of information and communication technologies; the periodisation of the development of information and communication technologies reflects the gradual transition from early forms of information storage and transmission to modern information and communication technologies. The further development of information and communication technologies in education will help to increase its accessibility, efficiency, and inclusiveness. Mass computerisation, the Internet, mobile technologies, cloud services, and artificial intelligence. The development of information and communication technologies is a multidimensional process that combines new opportunities with large-scale challenges. Successful digital transformation depends on the ability of society to effectively manage these challenges, ensuring inclusive, secure, ethical, and environmentally responsible development of information and communication technologies. Only by integrating innovation with the principles of sustainable development can a harmonious digital future be shaped. Originality. The article examines the evolution of information and communication technologies in the context of education, analyzes the key stages of their development, as well as current trends in the integration of information and communication technologies into educational practice. Particular attention is paid to the impact of digitalisation on educational processes and the formation of innovative pedagogical approaches. The main challenges and prospects for further implementation of information and communication technologies in education are outlined. In particular, attention is paid to the problems of digital inequality, cybersecurity, ethical aspects of the use of information and communication technologies, and the role of innovation in ensuring sustainable technological progress in the context of education. Conclusion. Information and communication technologies play a key role in the transformation of education, providing new opportunities for accessibility, flexibility, and personalisation of the educational process. The historical development of information and communication technologies shows their growing importance in pedagogical practice. Modern education is actively introducing distance learning formats, digital platforms, artificial intelligence, and other innovations. At the same time, there are challenges, such as digital inequality, the need for digital literacy of teachers, and cybersecurity issues. Effective integration of information and communication technologies into education requires a systematic approach, investment, and methodological support.

In a world of swift digital transformations, the global market is seeing notable structural shifts. This applies not only to novel technologies, but also to how brands engage with their consumers. Artificial intelligence (AI) and big data analytics are at the heart of this change. With these instruments, companies can analyze consumer behavior more profoundly and precisely, considering the cultural and behavioral traits of diverse audiences worldwide. This allows them to hone their marketing strategies and cultivate a strong emotional bond between the consumer and the brand. The article aims to identify and examine the opportunities that artificial intelligence and big data technologies offer to boost the efficacy of brand communication within the international market. The article reviews theoretical approaches to constructing brand communication in the global market, considering digital transformation, specifically concentrating on the concept and function of brand communication. The key models of brand communication are assessed. The potential of employing artificial intelligence to develop and manage brand communications is examined. The role of big data in making communication decisions, encompassing audience segmentation, analysis of consumer behavior, and content adjustment to cultural specificities is identified. Particular attention is given to the integration of AI and Big Data into a brand’s global communication plan. The pros and cons associated with incorporating AI and Big Data into brand communication strategies, including ethical and legal aspects, are outlined. The article suggests constructing brand communication centered on artificial intelligence and big data, and offers suggestions for brands on the effective application of digital technologies in communicating with consumers on a worldwide scale. Via artificial intelligence and data analytics, brands can ensure a consistent customer experience throughout all stages of interaction. This starts with social media and expands all the way to physical points of sale, which in turn helps to boost customer loyalty and satisfaction. Analytics of behavioral, demographic, and contextual data allows for precise segmentation, recognizing consumer needs, and predicting market trends. This produces the conditions for brands to more effectively tailor content to the cultural and regional attributes of their audience.

This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.

Coronavirus Diseases (COVID-19) is an infectious disease caused by a newly discovered coronavirus that becomes world pandemic with 200 countries recorded affected, and nearly 1 million people died. Starting from Wuhan in December 2019, within three months, the spread across global with high reproduction rates (R Rates). There is evidence in one case, it spread to more than 100 people and creates his pandemic cluster. As the pandemic contributes to a large volume of data, Artificial Intelligence (AI) and Big Data Analytics (BDA) play a huge role in understanding the pan-demic to help necessary action can be deployed. Researchers and developers are increasingly using artificial intelligence, machine learning, and natural language processing to track and contain coronavirus and gain a more comprehensive understanding of the disease. So far, due to new diseases, there is a limited study to cover how AI and BDA will help in fighting COVID-19. Therefore, we provide a comprehensive analysis of the existing and potential of using AI and BDA to manage the COVID-19 outbreak based on COVID-19 Outbreak Life Cycle phases; detection, spread, management, recovery. We also presented the challenges needed to be overcome for BI in BDA in the fighting. To conclude, these findings show the necessity of AI and BDA as a critical tool to understand COVID-19 and there a lot of ongoing intensive works have been carried out to cope with COVID-19.KeywordsCOVID-9Artificial intelligenceBig Data AnalyticsMachine learning

Advancing total quality management in higher education through AI and big data analytics

This study examines the impact of artificial intelligence (AI) and big data analytics (BDA) on financial reporting quality (FRQ) in Nigeria, with particular emphasis on the mediating role of transparency. The increasing adoption of digital technologies in accounting has transformed how financial information is generated, processed, and disclosed. However, empirical evidence on how these technologies influence reporting quality in emerging economies remains limited. Drawing on Information Asymmetry Theory and the Technology–Organization–Environment (TOE) framework, this study investigates whether AI adoption and big data analytics capability enhance financial reporting quality directly and indirectly through transparency. A quantitative research design was employed using survey data collected from 312 accounting and finance professionals across public and private sector organizations in Nigeria. Structural Equation Modeling (PLS-SEM) was used to test the hypotheses. The results indicate that artificial intelligence and big data analytics significantly improve financial reporting quality, while transparency plays a partial mediating role. The findings contribute to the literature on digital transformation in accounting and provide practical insights for regulators and organizations in emerging economies.

PurposeIn the context of the digital economy, information technology (IT) investment has become a necessary way for enterprises to transform digitally. However, why and how IT investment can enhance digital transformation is lacking in the literature. Based on the resource-based view (RBV), this study explored the impact mechanism of IT infrastructure on the digital transformation of enterprises from the perspective of the digital transformation strategy. Further, this study examined the moderating role of top management on the relationships between IT infrastructure and digital transformation strategy and between digital transformation strategy and enterprise's digital transformation.Design/methodology/approachThrough a questionnaire survey of Chinese enterprises, 180 sample data were collected, and the partial least squares-structural equation modeling (PLS-SEM) method was used to test the hypothesis.FindingsDigital transformation strategy fully mediates the relationship between IT infrastructure and enterprise digital transformation. Furthermore, top management has a significant positive moderating effect on the relationship between IT infrastructure and digital transformation strategy, as well as the relationship between digital transformation strategy and digital transformation.Originality/valueThis study explores the moderating role of top management in the relationship between IT and enterprise performance, as well as the mediating role of digital transformation strategy in the relationship between IT infrastructure investment and digital transformation performance. As a result, the study adds significantly to the body of knowledge on IT business value, digital transformation and strategic management. The authors' findings can help update managers' perceptions of IT value and provide theoretical guidance on deriving digital transformation performance from IT infrastructure investments.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

Yapay zekâ ve büyük veri analitiği, 21. yüzyılın bilgi odaklı dünyasında yalnızca teknoloji alanında değil aynı zamanda eğitim, sağlık, kamu yönetimi ve bilgi hizmetleri gibi birçok farklı alanda da dönüştürücü etkiler yaratmaktadır. Burak Savaş Sarıçoban tarafından kaleme alınan ‘Kütüphanelerin Geleceği: Yapay Zekâ ve Büyük Verinin Kütüphanelerde Kullanımı’ başlıklı kitap, bu teknolojik dönüşümün kütüphaneler bağlamındaki yansımalarını hem kuramsal hem de uygulamalı düzeyde ele alan bir çalışmadır. Yazarın yüksek lisans tezine dayanan bu eser, yapay zekâ ve büyük veri analitiğinin kütüphaneler üzerindeki etkilerini inceleyerek, dijitalleşme sürecinde yapay zekânın en etkili teknolojilerden biri olduğu savını temel almaktadır. Kitapta, kütüphanelerin bu dönüşüm sürecinde nasıl bir yol izleyebileceği literatür taraması, saha araştırması ve uluslararası örneklerle desteklenerek ayrıntılı bir biçimde tartışılmakta ve alana özgün bir katkı sunmaktadır.

The advancements in technology over the past quarter-century have yielded significant transformations in supply chain management, alongside several other sectors. The utilization of Artificial Intelligence (AI) and Big Data Analytics (BDA) has the capacity to greatly enhance the resilience of supply networks and enable more efficient management of resources within the supply chain. The advancements in AI and BDA have brought about significant transformations in the operational practices related to supply chain resilience, making them the forefront of these innovations. It is vital to elucidate the correlation between AI, BDA and supply chain resilience in order to comprehend the manner in which these transformations manifest themselves within this domain. The integration of AI and BDA technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. The objective of this study is to explore the benefits of AI and BDA in boosting supply chain resilience and visibility many. The researchers employed the Kitchenham guidelines to conduct a systematic literature review. The investigation is carried out utilizing online scholarly database systems, yielding a total of 207 initial publications, which are further narrowed down to 30 published studies spanning the period from 2013 to 2023. The research paper could be of interest to pertinent players in the business sector due to its provision of a comprehensive examination of the potential benefits associated with AI and BDA in the context of supply chain resilience and visibility.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

In today’s rapidly evolving business landscape, digital transformation has become a strategic imperative for growth enterprises seeking to enhance operational efficiency, improve customer experiences, and drive innovation. However, the successful execution of digital transformation initiatives hinges on the alignment between Information Technology (IT) and business objectives. This alignment ensures that technological investments directly support organizational goals and contribute to sustainable growth. Despite the significant attention given to digital transformation, there remains a gap in understanding the best practices and frameworks for achieving optimal IT-business alignment in growing enterprises. This explores recent advances in digital transformation strategies, with a focus on the role of IT-business alignment in driving business success. It examines the emerging trends in aligning IT initiatives with business goals, including agile methodologies, cloud adoption, data-driven decision-making, and the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. By reviewing case studies of growth enterprises, this highlights the critical factors that contribute to successful IT-business alignment, such as leadership commitment, cross-functional collaboration, and continuous adaptation to market changes. Additionally, this delves into the challenges faced by enterprises in achieving seamless integration between IT and business strategies, including resource constraints, organizational silos, and the complexity of managing legacy systems alongside new digital tools. The findings underscore the importance of a dynamic approach to alignment that evolves with technological advancements and business priorities. Ultimately, this research provides valuable insights for growth enterprises looking to refine their digital transformation strategies. It offers actionable recommendations on fostering stronger IT-business alignment, emphasizing its pivotal role in ensuring the long-term success of digital initiatives and driving competitive advantage in an increasingly digital world.

The rapid advancements in Artificial Intelligence (AI) and Big Data Analytics have significantly transformed financial markets, enabling more precise, data-driven, and strategic decision-making. This study explores the profound impact of AI-driven analytics in financial decision-making, focusing on how machine learning algorithms, predictive analytics, and automation enhance market efficiency, risk assessment, and investment strategies. The research highlights how AI-powered Big Data Analytics processes vast amounts of structured and unstructured financial data to identify patterns, trends, and anomalies that influence market movements. It further examines how financial institutions leverage AI for fraud detection, sentiment analysis, and algorithmic trading, leading to more informed and efficient investment decisions. Through a survey-based approach, this study collects data from financial professionals, analysts, and investors to evaluate their perspectives on AI’s role in improving data accuracy, reducing uncertainty, and optimizing financial strategies. The findings provide valuable insights into how AI-driven analytics contributes to minimizing risks, enhancing forecasting accuracy, and supporting regulatory compliance in financial markets. The study concludes that the integration of AI in Big Data Analytics is revolutionizing financial decision-making by improving efficiency, reducing human biases, and enabling real-time strategic actions. However, challenges such as data privacy concerns, ethical considerations, and the need for advanced AI governance frameworks remain crucial areas for future exploration. Keywords—Artificial Intelligence, Big Data Analytics, Financial Markets, Strategic Decision-Making, Algorithmic Trading, Risk Assessment, Predictive Analytics.

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton ∗ km. In the end, the three of them were selected as company Z's transportation suppliers.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.