The rapid growth of multidimensional datasets in scientific, industrial, and social contexts has amplified the demand for adaptive artificial intelligence (AI) strategies capable of evaluating complex, high-dimensional information. Conventional machine learning approaches often struggle to account for intricate interdependencies among features, leading to suboptimal predictive performance and limited interpretability. This research proposes an adaptive AI strategy grounded in relationship-centric models, which leverage hierarchical and graph-based representations to analyze multidimensional datasets effectively. The strategy integrates dynamic relationship modeling, feature interconnectivity assessment, and adaptive learning mechanisms to enhance predictive precision and scalability.

The theoretical foundation draws upon resource-based theory, emphasizing the strategic value of information as a competitive resource (Barney, 2001; Wade & Hulland, 2004), and contemporary advances in graph-based deep learning for tabular data (Mirza et al., 2025). By employing relationship-centric modeling, the approach captures latent dependencies across features, thereby enabling nuanced predictions that account for context-specific interactions. Hierarchical abstraction further facilitates multilevel representation of datasets, improving computational efficiency and interpretability while mitigating risks of overfitting.

Empirical evaluation employs diverse multidimensional datasets simulating real-world applications, including precision medicine, performance analytics, and intelligent robotic systems (DeGroat et al., 2024; Dafni Rose et al., 2019; M. T et al., 2024). Results indicate superior performance of the proposed AI strategy relative to conventional tabular and networked models, demonstrating increased predictive accuracy, robustness to noisy data, and adaptive responsiveness to evolving feature interrelations.

Implications extend to multiple domains where multidimensional datasets are prevalent, including healthcare, education, and cyber-physical systems. Limitations include computational complexity in large-scale deployments and dependency on optimal feature selection, suggesting avenues for future work in hybrid model integration and real-time adaptive mechanisms. This study advances the understanding of relationship-centric AI strategies, offering a theoretically grounded and practically robust framework for multidimensional dataset evaluation.