Augmented Decision Systems: Integrating Multi-Criteria Modeling and Machine Learning for Organizational Agility
Keywords:
Human-AI Collaboration, Decision-Making, AHP-TOPSIS, Machine Learning, Trust in AI, Agent-Based Simulation, Strategic ManagementAbstract
In increasingly volatile and complex business environments, strategic decision-making requires the integration of advanced analytical tools and human expertise. This study investigates the effectiveness of human-AI collaboration models using a mixed-method approach comprising managerial surveys (N=187), hybrid decision modeling (AHP-TOPSIS with Gradient Boosted Trees), and 24-hour agent-based simulations. The methodology enables a comprehensive analysis of decision speed, accuracy, interpretability, and trust dynamics in real-time environments. Results show that AI-enhanced systems significantly improve decision performance, reducing late decisions by 84%, suboptimal outcomes by 70%, and increasing resilience by 56%. Managers reported high confidence in AI’s ability to reduce cognitive load and clarify trade-offs, with 91% supporting collaborative AI-human decision-making. Strategy selection using the hybrid model prioritized automation upgrades, supported by a high predicted ROI (17.8%) and model precision (RMSE = 0.084). The discussion integrates insights from Hybrid-Augmented Intelligence and human–AI work design models, emphasizing the role of trust, transparency, and stakeholder engagement in successful AI adoption. Findings underscore the need to balance algorithmic efficiency with interpretability to foster organizational readiness and acceptance. This research contributes a validated, scalable framework for AI-human collaborative decision-making, offering practical tools for strategic alignment and theoretical grounding for further exploration.
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