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Open research questions in Big Data and Business Intelligence

303 unresolved questions extracted from the limitations and future-work sections of 1,209 Big Data and Business Intelligence papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • the study only analyzed American enterprises, - the study only examined the period from 2014 to 2022, - the sample was limited to 41 firms

    Artificial Intelligence Integration in Sustainable Business Practices: A Text Mining Analysis of USA Firms · 2024 · DOI

  • exploring AI's role in sustainability in other regions, - examining the impact of AI on environmental and social challenges, - investigating the cost-benefit analysis of AI adoption in sustainable practices

    Artificial Intelligence Integration in Sustainable Business Practices: A Text Mining Analysis of USA Firms · 2024 · DOI

  • There is a need to explore the role of AI, IoT, and AIoT in realizing sustainable development and achieving SDGs. The study aims to fill this gap by providing a review and analyzing the evolution of AI, IoT, and AIoT in the context of SDGs.

    The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art · 2024 · DOI

  • Purpose Although Chinese manufacturing firms are increasing investments in artificial intelligence (AI), evidence remains limited on how the intensity of AI use creates sustainable competitiveness.

    AI adoption intensity and sustainable competitiveness in Chinese manufacturing firms: the roles of information visibility and operational resilience · 2026 · DOI

  • The findings reveal the critical role of emerging technologies in driving the next industrial revolution, suggesting that future research should focus on integrating these technologies with socio-economic objectives to fully realize the potential of Industry 5.

    Industry 5.0 as seen through its academic literature: an investigation using co-word analysis · 2025 · DOI

  • Despite this, its implementation in public universities-which are considered key drivers of sustainable development-remains underexplored.

    Determinants of business intelligence adoption in the public Jordanian Universities · 2025 · DOI

  • Nevertheless, limited research has theoretically outlined and empirically established the frameworks and constructs through which BDAC impacts the performance of small and medium enterprises (SMEs).

    The financial and market impact of big data analytics and big data talent analytics capability: a knowledge management perspective · 2025 · DOI

  • Integration of AITT with other, traditional or modern, decision-making methods broadens the problem comprehension, even when expert human input is scarce or expensive, as future researchers can verify.

    Artificial Intelligence Think Tank: a modern problem-solving framework · 2025 · DOI

  • The study examines the results, discusses the main findings, presents open issues, and suggests new research directions.

    The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art · 2024 · DOI

  • However, the extent to which AI has the potential to take over key tasks and the decision-making process at the individual, organisational, or societal level remains to be seen.

    Exploring the multifaceted impacts of artificial intelligence on public organizations, business, and society · 2024 · DOI

  • The study identifies a gap in the application of strategic planning as an influencer of cloud BI outcomes. The research aims to address the lack of understanding of the impact of strategic planning on cloud business intelligence in the banking sector.

    The Role of Strategic Planning in Enhancing Cloud-Based Business Intelligence to Achieve Organizational Goals in Jordanian Commercial Banks · 2026 · DOI

  • Future research should explore the specific types of business intelligence capabilities and technologies (e.g., advanced analytics, AI, real-time dashboards) that are most effective in moderating the SCA-SFP relationship in resource-constrained emerging economies.

    The Moderating Role of Business Intelligence in the Relationship between Supply Chain Alliances and Sustainable Firm Performance within Emerging Economic Environment · 2026 · DOI

  • The cross-sectional design limits the ability to establish causal relationships and understand the temporal dynamics of how BI moderates the SCA-SFP linkage in emerging markets.

    The Moderating Role of Business Intelligence in the Relationship between Supply Chain Alliances and Sustainable Firm Performance within Emerging Economic Environment · 2026 · DOI

  • The study identifies a gap in the existing literature on the impact of AI on strategic management and decision-making effectiveness

    Artificial intelligence-enabled strategic management: A review of its impact on decision-making effectiveness and organizational performance · 2026 · DOI

  • Future research can explore the role of AI in business model transformation. Future research can examine the impact of digital transformation on organizational performance and competitive advantage.

    Mapping Research on Business Model Transformation Strategies as a Response to Digital Disruption to Achieve Competitive Advantage · 2026 · DOI

  • Existing studies on digital disruption, business model transformation, and competitive advantage remain fragmented. There is a need for a comprehensive understanding of research trends in this field.

    Mapping Research on Business Model Transformation Strategies as a Response to Digital Disruption to Achieve Competitive Advantage · 2026 · DOI

  • Inconsistent interpretation of business metrics across teams. Limited flexibility and slow decision-making in traditional Business Intelligence systems.

    AI Driven BI: How Large Language Models Transform Enterprise Analytics · 2026 · DOI

  • Traditional Business Intelligence systems struggle to keep pace with dynamic analytical questions. There is a need for more adaptive and intuitive analytical experiences.

    AI Driven BI: How Large Language Models Transform Enterprise Analytics · 2026 · DOI

  • Technical instruction disconnected from business contexts. Superficial AI integration. Practical training misaligned with industry needs.

    A Dual-Driven Teaching Reform for the Business Intelligence Course: Integrating Scenario-Based Learning and Industry-Education Collaboration · 2026 · DOI

  • There is a shortage of qualified talent in the field of AI. Recruiting and retaining skilled professionals is both difficult and expensive. Existing employees may lack the necessary technical knowledge and require extensive training.

    The Role of Artificial Intelligence in Business Decision-Making · 2026 · DOI

  • There is a lack of skilled professionals in the field of AI. Organizations face several challenges while implementing AI technologies, including high implementation costs and lack of technical expertise.

    The Role of Artificial Intelligence in Business Decision-Making · 2026 · DOI

  • The gap between the competencies required for the Chief AI Officer role and the credentials selected for. The lack of a clear consensus on what the CAIO role requires.

    The CAIO Competency Problem: Why Enterprise AI Fails and What the Right Leadership Profile Actually Requires · 2026 · DOI

  • This paper has several limitations that constrain the confidence with which its conclusions should be held. The most significant limitation is the absence of controlled empirical evidence for the central causal claim. The paper establishes a correlation: failure rates are high; failure modes are predominantly non-technical; current CAIO hiring prioritises technical credentials. From this correlation it infers that the hiring mismatch is a contributor to the failure rate. This inference is plausible and consistent with the evidence but is not definitively established. A properly designed study would follow a cohort of CAIO appointments over time, measure both credential profile and adoption outcomes, and control for organisational confounders such as sector, company size, AI investment level, and board sophistication. No such study currently exists in the published literature to the author's knowledge. This is itself a research gap worth naming explicitly. Page 22 · Not peer-reviewed · © 2026 MustafarAI · CC BY 4.0 Mohd Fadzil · The CAIO Competency Problem · WP-2026-07 · Preprint, April 2026 Second, the paper's argument applies most cleanly to large, non-technology-native enterprises — banks, healthcare providers, government agencies, manufacturing groups — where the commercial and organisational complexity of AI adoption is high relative to the internal technical capability. For technology-native organisations, AI-focused startups, or research-intensive industries, the relative weight of technical versus commercial competency is different, and the hiring logic that currently dominates may be more appropriate. The paper does not claim universal application. Third, the analysis of CAIO hiring draws primarily on FTSE 100 and US enterprise data, which reflects the available published research. APAC hiring patterns are less well documented, and the argument made in Section 9 about APAC-specific dynamics relies more heavily on the author's direct observation than on published research. More systematic documentation of CAIO hiring practices in Southeast Asia, East Asia, and South Asia would strengthen or complicate the APAC-specific claims. Fourth, and most importantly, the author's background — as an independent AI inventor and commercial practitioner who is explicitly positioning for the kind of CAIO role this paper argues for — creates an obvious conflict of interest that has been disclosed but cannot be fully corrected for. The argument should be assessed on its evidence and logic. Readers should nonetheless weigh the conflict. Future research that would most usefully extend or challenge the argument includes: longitudinal studies of CAIO appointment outcomes stratified by credential background; comparative analysis of AI adoption outcomes in organisations that have applied competency- led versus credential-led CAIO hiring; qualitative case studies of AI adoption successes and failures that document the role of CAIO competency profile in each outcome; and systematic content analysis of CAIO job postings over time to track whether the stated requirements are shifting toward the competency profile the evidence supports.

    The CAIO Competency Problem: Why Enterprise AI Fails and What the Right Leadership Profile Actually Requires · 2026 · DOI

  • The need for longitudinal studies to track companies before and after they adopt AI. The lack of cross-country comparative analyses that take into account institutional factors.

    The Impact of Artificial Intelligence on Strategic Decision-Making in Global Business Management · 2026 · DOI

  • The study documents that companies with formal AI ethics policies achieve 25% higher stakeholder trust, but lacks granular data on which specific governance frameworks (e.g., algorithmic audit procedures, bias detection protocols, transparency standards) most effectively improve ethical compliance ratings from 55/100 to 72/100.

    The Impact of Artificial Intelligence on Strategic Decision-Making in Global Business Management · 2026 · DOI

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303 open questions have been extracted from the limitations and future-work passages of 1,209 Big Data and Business Intelligence papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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