The week's pick
Reseach Article
Human-AI Collaborative Decision Support Systems: An Empirical Investigation in Enterprise Operations
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 102 |
| Year of Publication: 2026 |
| Authors: Gopalakrishnan Marimuthu |
10.5120/ijca7f9a6f14c8bd
|
Gopalakrishnan Marimuthu . Human-AI Collaborative Decision Support Systems: An Empirical Investigation in Enterprise Operations. International Journal of Computer Applications. 187, 102 ( May 2026), 15-21. DOI=10.5120/ijca7f9a6f14c8bd
Abstract
The integration of Human-AI collaborative systems into enterprise decision-making processes represents a significant advancement in operational management. This paper investigates the performance characteristics of such hybrid systems within logistics and supply chain contexts, where the combination of human cognitive flexibility and computational precision offers measurable advantages over traditional approaches. A structured experimental study was conducted using 428 operational scenarios derived from validated industrial benchmarks, with 47 participants from diverse professional backgrounds engaging with a custombuilt collaborative decision support platform. The analysis employed Python-based machine learning frameworks alongside statistical evaluation methods to assess decision accuracy, processing latency, and resource utilization efficiency. Results demonstrate that collaborative human-AI configurations achieved 94.2% decision accuracy compared to 78.3% for human-only and 82.7% for AI-only approaches (F(2, 92) = 47.3, p < 0.001, partial η2 = 0.51). Processing time decreased by 47% across experimental trials as participants developed familiarity with the system interface. The collaborative condition also achieved superior resource utilization efficiency (91.6 vs. 79.8 for AI-only and 72.4 for human-only). The findings carry practical implications for manufacturing, healthcare logistics, financial services, and retail supply chain management, where real-time decision support can substantially reduce operational costs and improve service delivery.
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