Designing Cloud-Native Intelligent Systems for Workforce Analytics and Enterprise Decision-Making

Authors

  • Anusha Atluri Lead Solution/Technical Architect at Acosta, USA. Author

DOI:

https://doi.org/10.63282/3050-9416.ICAIDSCT26-113

Keywords:

Workforce Analytics, Cloud-Native Systems, Intelligent Decision Support, Enterprise Analytics, Ai-Driven Insights, Mlops, Microservices Architecture

Abstract

Businesses that want to develop, get more done, make their employees healthier, and use new technology require labor analytics. Businesses may do more than simply fill out standard HR paperwork. They may also need to know about workers who are supposed to turn up and those who are already scheduled to work. Cloud computing, artificial intelligence, and the collection of more data have all become better, which is why these developments occurred. You may use the information from these tools to help you make decisions regarding hiring, performance evaluations, team building, and getting staff interested, among other things. A lot of firms still utilize outdated, rigid, and data-driven solutions to run their HR and make decisions. A lot of legacy labor analytics tools use models and data sources that don't perform well together and can't be used together. So, it could be challenging to acquire information in real time and cooperate with individuals from various areas. If a firm needs more complex analytics, needs to incorporate data from several sources, or has to modify how it does things, these options may not work for them. People that work in open, hybrid, or international environments will have an easier time understanding strange HR practices. This article speaks about how smart system designs and cloud-based features affect the way digital organizations employ labor analytics. You may combine global data sources, AI-powered decision support systems, and analytical tools into one framework. This is a really new and advanced way to create a system. This allows companies develop workforce intelligence systems that can manage more intricate situations while still obeying the regulations and keeping everyone safe. The main findings reveal that the proposed strategy makes it simpler to receive information, makes people think, and helps individuals make choices in varied work environments. The book may help groups plan and figure out how to bring everything together in a manner that works.

References

1. Bukhari, Tahir Tayor, et al. "Cloud-native business intelligence transformation: Migrating legacy systems to modern analytics stacks for scalable decision-making." International Journal of Scientific Research in Humanities and Social Sciences 1.2 (2024): 744-762.

2. Bejerano-Blázquez, Isabel, and Miguel Familiar-Cabero. "On the Application of Artificial Intelligence and Cloud-Native Computing to Clinical Research Information Systems: A Systematic Literature Review." Information 16.8 (2025): 684.

3. Thabo, Nkosi, and Khumalo Nomvula. "Cloud-Native HCM: Redefining Workforce Management for a Distributed Workforce." International Journal of Trend in Scientific Research and Development 2.4 (2018): 3112-3124.

4. Ugwueze, Vincent Uchenna. "Cloud native application development: Best practices and challenges." International Journal of Research Publication and Reviews 5.12 (2024): 2399-2412.

5. Tabbassum, Ayisha, et al. "Developing Cloud-Native Autonomous Systems for Real-Time Edge Analytics." 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS). IEEE, 2024.

6. Smith, Taylor. "DESIGNING CLOUD-NATIVE HR DATA LAKES: A BLUEPRINT FOR SCALABLE WORKFORCE ANALYTICS." (2025).

7. Nadeem, Kinza, and Saleem Aslam. "Cloud-native DevOps strategies: Redefining enterprise architecture with artificial intelligence." (2024).

8. Katasani, Durga Prasad. "Real-Time Analytics: Integrating Cloud-Native Data Processing and Warehousing Platforms." Journal of Computer Science and Technology Studies 7.9 (2025): 516-524.

9. Panda, Swarup. Scalable Artificial Intelligence Systems: Cloud-Native, Edge-AI, MLOps, and Governance for Real-World Deployment. Deep Science Publishing, 2025.

10. Sikha, Vijay Kartik. "Cloud-Native Application Development for AI-Conducive Architectures."

11. Lakkarasu, Phanish. Building Cloud-Native AI and MLOps Platforms for Scalable, Secure, and Mission-Critical Intelligence Systems. AQUA PUBLICATIONS.

12. Tathed, Roshan Atulkumar. "AI-Native Enterprise Application Design for Cross-Industry Engagement and Growth." (2025).

13. Oshoba, Theophilus Onyekachukwu, Kabir Sholagberu Ahmed, and Olushola Damilare Odejobi. "Proactive Threat Intelligence and Detection Model Using Cloud-Native Security Tools."

14. SANGHI, SOURABH, and DR AJAY KUMAR CHAURASIA. Enterprise DevOps Architecture: From Legacy Systems to Cloud-Native Platforms 2025. YASHITA PRAKASHAN PRIVATE LIMITED.

15. Abbas, Ghulam, and Henrietta Nicola. "Optimizing Enterprise Architecture with Cloud-Native AI Solutions: A DevOps and DataOps Perspective." (2018).

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Published

2026-02-17

How to Cite

1.
Atluri A. Designing Cloud-Native Intelligent Systems for Workforce Analytics and Enterprise Decision-Making. IJAIBDCMS [Internet]. 2026 Feb. 17 [cited 2026 Feb. 17];:111-7. Available from: https://ijaibdcms.org/index.php/ijaibdcms/article/view/402