Artificial Intelligence and Digital Transformation: Analyzing Future Trends

Simoes, Rachel Vital; Parreiras, Marcus; Silva, Ana Clara Correa Da; Barbosa, Carlos Eduardo; Lima, Yuri Oliveira de; Souza, Jano Moreira de

doi:10.1109/smc53654.2022.9945429

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…The findings from this study underscore the trend towards more autonomous, user-friendly, and AI-powered BI tools, enabling businesses to harness the power of data analytics more efficiently and effectively. Simões et al (2022) conduct a systematic review to analyze future trends in AI and digital transformation. Their methodology, based on Future-Oriented Technology Analysis (FTA) techniques, identifies key events and feasible timelines related to digital transformation across various industry fields.…”

Section: Thematic Analysis Results In Ai-driven Business Intelligencementioning

confidence: 99%

Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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This paper delves into the transformative role of Artificial Intelligence (AI) and Data Analytics in the realm of Business Intelligence (BI), marking a significant shift in the landscape of business decision-making and strategic planning. The study's purpose was to comprehensively explore the evolution of BI, underscored by the integration of AI and advanced data analytics, and to project the future trajectory of these technologies within the business context. Adopting a systematic literature review as its methodology, the study meticulously analyzed a wide array of scholarly articles and industry reports. This approach facilitated a deep understanding of the historical development of BI, the current synergy between AI, Data Analytics, and BI, and the emerging trends shaping their future. The inclusion and exclusion criteria for sources were rigorously applied to ensure the relevance and quality of the information gathered. The findings of the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes. Conclusively, the study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations. It recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries. The study's classical and engaging tone aims to captivate and inform a diverse audience, from academic researchers to industry practitioners. Keywords: Artificial Intelligence, Business Intelligence, Data Analytics, Predictive Analytics.

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Section: Thematic Analysis Results In Ai-driven Business Intelligencementioning

confidence: 99%

Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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“…We built the NERMAP software, a collaborative Foresight Support System [22] based on the NERMAP framework. The software supports on-site and remote (through the Internet) research of Futures Studies and Foresight fields to generate timelines of future events, as shown in [1][2][3]. The NERMAP model, architecture, and system are discussed in the following sections.…”

Section: Nermapmentioning

confidence: 99%

“…The increased processing speed allows for analysis that would otherwise be infeasible to perform manually. This analysis provided by NERMAP and further refined by humans can be a complete analysis for a Roadmapping process during Futures Studies, as shown in [1][2][3][4]. We can also use it as input for other stages of future studies…”

Section: Introductionmentioning

confidence: 99%

Toward computer-supported semi-automated timelines of future events

et al. 2023

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During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs.

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“…The main advantage of using such a tool was the reduced time needed for document analysis. NERMAP has been used in applications such as the technology roadmapping of future Industry 4.0 trends [24,25] and smart and sustainable cities [26]. NERMAP offers a cost-effective solution, an attractive choice for organizations seeking thorough analyses of extensive document volumes that would be impractical to carry out manually.…”

mentioning

confidence: 99%

Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era

El-Warrak,

Nunes,

Luna

et al. 2023

Healthcare

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The COVID-19 pandemic, a transformative event in modern society, has disrupted routine, work, behavior, and human relationships. Organizations, amidst the chaos, have innovatively adapted to the evolving situation. However, many countries were unprepared for the magnitude of the challenge, revealing the fragility of health responses due to inadequate leadership, insufficient resources, and poor information system integration. Structural changes in health systems are imperative, particularly in leadership, governance, human resources, financing, information systems, technology, and health service provision. This research utilizes the Technological Roadmapping method to analyze the health sector, focusing on public health, drawing on articles from SCOPUS and PubMed databases, and creating a roadmap extending to 2050. The research presents three long-term scenarios based on the literature-derived roadmap and explores various alternatives, including integrated care, telemedicine, Big Data utilization, nanotechnology, and Big Tech’s AI services. The results underscore the anticipation of post-pandemic public health with high expectations, emphasizing the importance of integrating health history access, encouraging self-care, and leveraging technology for streamlined treatment. Practical implications include insights for decision makers and stakeholders to inform strategic planning and adapt to evolving industry demands, recognizing the significance of preventive services and the humanizing potential of technology.

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Artificial Intelligence and Digital Transformation: Analyzing Future Trends

Cited by 11 publications

References 20 publications

Business Intelligence Transformation Through Ai and Data Analytics

Business Intelligence Transformation Through Ai and Data Analytics

Toward computer-supported semi-automated timelines of future events

Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era

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