AI-Augmented Executive Dashboards: Integrating Generative Artificial Intelligence and Data Visualization for Project Decision-Making in Saudi Arabia

References

[1] S. O. Abioye, L. O. Oyedele, L. Akanbi, A. Ajayi, J. M. Davila Delgado, M. Bilal, O. O. Akinade, and A. Ahmed, “Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges,” Journal of Building Engineering, vol. 44, Art. no. 103299, 2021, doi: 10.1016/j.jobe.2021.103299. Crossref

[2] T. H. Alaskar, K. Mezghani, and A. K. Alsadi, “Examining the adoption of Big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia,” Journal of Decision Systems, vol. 30, nos. 2–3, pp. 300–320, 2021, doi: 10.1080/12460125.2020.1859714. Taylor & Francis

[3] A. Alshahrani, D. Dennehy, and M. Mäntymäki, “An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia,” Government Information Quarterly, vol. 39, no. 4, Art. no. 101617, 2022, doi: 10.1016/j.giq.2021.101617. ScienceDirect

[4] M. Alojail, J. Alshehri, and S. B. Khan, “Critical success factors and challenges in adopting digital transformation in the Saudi Ministry of Education,” Sustainability, vol. 15, no. 21, Art. no. 15492, 2023, doi: 10.3390/su152115492. Crossref

[5] W. Alsumari and O. Alrwais, “Artificial intelligence project management methodologies: Insights from field experts in Saudi Arabia,” International Journal of Informatics, Information System and Computer Engineering, vol. 6, no. 2, pp. 246–258, 2025, doi: 10.34010/injiiscom.v6i2.16127. INJIISCOM

[6] G. Bansal, T. Wu, J. Zhou, R. Fok, B. Nushi, E. Kamar, M. T. Ribeiro, and D. S. Weld, “Does the whole exceed its parts? The effect of AI explanations on complementary team performance,” in Proc. 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–16, doi: 10.1145/3411764.3445717. ACM

[7] S. Bento, L. Pereira, R. Gonçalves, Á. Dias, and R. Lopes da Costa, “Artificial intelligence in project management: Systematic literature review,” International Journal of Technology Intelligence and Planning, vol. 13, no. 2, pp. 143–163, 2022, doi: 10.1504/IJTIP.2022.126841. Crossref

[8] E. Brynjolfsson, D. Li, and L. R. Raymond, “Generative AI at work,” The Quarterly Journal of Economics, vol. 140, no. 2, pp. 889–942, 2025, doi: 10.1093/qje/qjae044. Crossref

[9] Z. Buçinca, M. B. Malaya, and K. Z. Gajos, “To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making,” Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CSCW1, Art. no. 188, pp. 1–21, 2021, doi: 10.1145/3449287. ACM

[10] C. Burnay, M. Lega, and S. Bouraga, “Business intelligence and cognitive loads: Proposition of a dashboard adoption model,” Data & Knowledge Engineering, vol. 152, Art. no. 102310, 2024, doi: 10.1016/j.datak.2024.102310. Crossref

[11] Y. K. Dwivedi et al., “‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management, vol. 71, Art. no. 102642, 2023, doi: 10.1016/j.ijinfomgt.2023.102642. Crossref

[12] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Business & Information Systems Engineering, vol. 66, pp. 111–126, 2024, doi: 10.1007/s12599-023-00834-7. Springer

[13] A. Fügener, J. Grahl, A. Gupta, and W. Ketter, “Cognitive challenges in human-artificial intelligence collaboration: Investigating the path toward productive delegation,” Information Systems Research, vol. 33, no. 2, pp. 678–696, 2022, doi: 10.1287/isre.2021.1079. Crossref

[14] E. Glikson and A. W. Woolley, “Human trust in artificial intelligence: Review of empirical research,” Academy of Management Annals, vol. 14, no. 2, pp. 627–660, 2020, doi: 10.5465/annals.2018.0057. Crossref

[15] W. Han and H.-J. Schulz, “Providing visual analytics guidance through decision support,” Information Visualization, vol. 22, no. 2, pp. 140–165, 2023, doi: 10.1177/14738716221147289. Crossref

[16] A. Handler, K. R. Larsen, and R. Hackathorn, “Large language models present new questions for decision support,” International Journal of Information Management, vol. 79, Art. no. 102811, 2024, doi: 10.1016/j.ijinfomgt.2024.102811. Crossref

[17] S. Hjelle, P. Mikalef, N. Altwaijry, and V. Parida, “Organizational decision making and analytics: An experimental study on dashboard visualizations,” Information & Management, vol. 61, no. 6, Art. no. 104011, 2024, doi: 10.1016/j.im.2024.104011. ScienceDirect

[18] J. Jöhnk, M. Weißert, and K. Wyrtki, “Ready or not, AI comes: An interview study of organizational AI readiness factors,” Business & Information Systems Engineering, vol. 63, pp. 5–20, 2021, doi: 10.1007/s12599-020-00676-7. Springer

[19] H. Kaur, H. Nori, S. Jenkins, R. Caruana, H. Wallach, and J. W. Vaughan, “Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning,” in Proc. 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–14, doi: 10.1145/3313831.3376219. ACM

[20] A. Mohammad and B. Chirchir, “Challenges of integrating artificial intelligence in software project planning: A systematic literature review,” Digital, vol. 4, no. 3, pp. 555–571, 2024, doi: 10.3390/digital4030028. Crossref

[21] M. E. Nenni, F. De Felice, C. De Luca, and A. Forcina, “How artificial intelligence will transform project management in the age of digitization: A systematic literature review,” Management Review Quarterly, vol. 75, pp. 1669–1716, 2025, doi: 10.1007/s11301-024-00418-z. Springer

[22] S. Noy and W. Zhang, “Experimental evidence on the productivity effects of generative artificial intelligence,” Science, vol. 381, no. 6654, pp. 187–192, 2023, doi: 10.1126/science.adh2586. Crossref

[23] Y. Pan and L. Zhang, “Roles of artificial intelligence in construction engineering and management: A critical review and future trends,” Automation in Construction, vol. 122, Art. no. 103517, 2021, doi: 10.1016/j.autcon.2020.103517. Crossref

[24] P. Puranam, “Human-AI collaborative decision-making as an organization design problem,” Journal of Organization Design, vol. 10, pp. 75–80, 2021, doi: 10.1007/s41469-021-00095-2. Springer

[25] S. Raisch and S. Krakowski, “Artificial intelligence and management: The automation-augmentation paradox,” Academy of Management Review, vol. 46, no. 1, pp. 192–210, 2021, doi: 10.5465/amr.2018.0072. Crossref

[26] D. Shin, “The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI,” International Journal of Human-Computer Studies, vol. 146, Art. no. 102551, 2021, doi: 10.1016/j.ijhcs.2020.102551. Crossref

[27] B. Shneiderman, “Human-centered artificial intelligence: Reliable, safe & trustworthy,” International Journal of Human-Computer Interaction, vol. 36, no. 6, pp. 495–504, 2020, doi: 10.1080/10447318.2020.1741118. Taylor & Francis

[28] Taboada, A. Daneshpajouh, N. Toledo, and T. de Vass, “Artificial intelligence enabled project management: A systematic literature review,” Applied Sciences, vol. 13, no. 8, Art. no. 5014, 2023, doi: 10.3390/app13085014. Crossref

[29] P. C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J. Q. Dong, N. Fabian, and M. Haenlein, “Digital transformation: A multidisciplinary reflection and research agenda,” Journal of Business Research, vol. 122, pp. 889–901, 2021, doi: 10.1016/j.jbusres.2019.09.022. Crossref

[30] Y. Ye, J. Hao, Y. Hou, Z. Wang, S. Xiao, Y. Luo, and W. Zeng, “Generative AI for visualization: State of the art and future directions,” Visual Informatics, vol. 8, no. 2, pp. 43–66, 2024, doi: 10.1016/j.visinf.2024.04.003. ScienceDirect

@article{1723455,
      author = {Muhammad Khurram Toor},
      title = {AI-Augmented Executive Dashboards: Integrating Generative Artificial Intelligence and Data Visualization for Project Decision-Making in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2924-2937},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723455.pdf},
      abstract = {Executive project dashboards are no longer expected to do merely than display historical key performance indicators. In the case of complex portfolios, managers need to interpret weak signals, connect both structured and unstructured evidence, evaluate various courses of action, and have to justify their decisions all while under time pressure. This study examines how generative artificial intelligence can be combined with data visualisation in order to create AI-augmented executive dashboards for project decision-making in Saudi Arabia. A structured integrative review was conducted in order to gather thirty peer-reviewed studies published between 2020 and 2025 in the fields of project management, decision support, visual analytics, human-AI collaboration, generative AI, and Saudi digital transformation. The results indicate that the most value is not derived from making autonomous decisions, but rather from having a multi-layered interface in which validated project data, predictive models, visual representations, and a conversational generative element support executive judgement. The quality of the dashboard is assessed according to the currency of the information, its completeness, the suitability of the visual design, the degree of cognitive load, semantic consistency, and the clear communication of uncertainty. Generative AI can improve dashboards by allowing natural-language queries, giving narrative explanations, developing scenarios, carrying out cross-source synthesis, and providing interactive visualisations; nevertheless, these features involve risks such as hallucination, automation bias, opaque reasoning, and the diffusion of accountability. The level of adoption in Saudi Arabia is also affected by organisational readiness, data governance, the amount of attention given by leadership, the digital infrastructure, and the existing project delivery practices. The review proposes a human-governed framework which divides the stages of evidence, analytics, generation, presentation, and decision-making, while at the same time ensuring that traceability is maintained for each generated statement back to the source data. This framework provides a design approach for executive project intelligence in Saudi organisations and establishes a research agenda focused on decision quality, calibrated trust, multilingual interaction, portfolio-scale validation, and governance-by-design.},
      month = {September},
  }