Artificial Intelligence-Driven Hybrid Models for Enhancing Strategic Management Decisions and Corporate Competitiveness

The Analysis of Strategic Management Decisions and Corporate Competitiveness Based on Artificial Intelligence

Abstract

Strategic decision-making in corporations faces challenges due to complex data and fast market changes. Traditional methods often rely on managerial experience, which can limit accuracy and responsiveness. This article presents a hybrid optimization model that combines Transformer models with reinforcement learning (RL) algorithms to improve strategic decisions and boost corporate competitiveness.

The model development involves data collection, preprocessing, algorithm selection, training, and validation. Testing with real-world data shows the hybrid approach converges within 150 iterations and outperforms traditional methods, achieving prediction accuracies of 92% for market share, 91% for profit growth, and 89% for customer satisfaction. Applying this model helps companies strengthen market position, brand influence, and innovation capabilities.

This approach enhances both the scientific basis and precision of decision-making while increasing market responsiveness, offering a practical decision-support tool for maintaining competitive advantage in complex environments.

Introduction

Strategic management decisions are vital for enterprises aiming to allocate resources effectively and maintain competitiveness in dynamic markets. Traditional decision-making often depends on subjective managerial judgment and limited analytical tools, which struggle with large-scale, high-frequency data and dynamic market shifts.

Artificial intelligence (AI) brings powerful capabilities for data mining, pattern recognition, and continuous learning, making it suitable for supporting strategic decisions. AI helps companies predict market trends, analyze competitors, and optimize strategies in real time, improving accuracy and speed.

Conventional decision methods face three main challenges: limited processing of complex, high-dimensional data; static models that can’t adapt quickly; and cognitive biases affecting human judgment. AI addresses these by processing diverse data types dynamically and reducing bias through data-driven learning.

Combining machine learning, deep learning, and RL enables enterprises to create adaptable and forward-looking strategic frameworks. AI systems can simulate multiple scenarios to guide decisions under uncertainty and optimize strategies continuously.

Research Background and Existing Studies

Recent studies have applied AI to strategic management with promising results. Transformer models, known for their self-attention mechanism, handle complex data sequences effectively and improve prediction accuracy. Reinforcement learning, especially Deep Q-Network (DQN), optimizes dynamic decision-making by learning from rewards and penalties in simulated environments.

Bayesian optimization has been shown to enhance hyperparameter tuning of AI models, improving prediction stability and performance. However, existing research often focuses on single algorithms, short-term forecasting, or lacks integration with strategic management theory.

This work addresses these gaps by combining Transformer models and RL into a hybrid framework that integrates strategic management principles and evaluates long-term corporate competitiveness.

Research Model

Theoretical Foundation

The model builds on core strategic management theories including Porter’s Competitive Advantage Theory, Resource-Based View (RBV), and Dynamic Capabilities Theory. These emphasize achieving competitive advantage through cost leadership, differentiation, and resource optimization while adapting capabilities to changing environments.

Research Design and Variables

• Data Collection and Preprocessing: Gathering comprehensive data sets covering market trends, internal operations, and competitor information.
• Algorithm Selection and Model Construction: Employing Transformer models for feature extraction and RL algorithms for dynamic decision optimization.
• Model Training and Validation: Ensuring generalization through careful training and testing.
• Decision Support and Output: Generating real-time strategic recommendations based on new data inputs.
• Continuous Optimization: Iterative model updates to maintain adaptability.

Algorithm Model Construction

The Transformer model’s self-attention mechanism captures long-term dependencies in sequence data through encoder-decoder layers. Reinforcement learning, particularly Q-learning with experience replay, supports dynamic decision-making by learning policies that maximize long-term rewards.

Experience replay buffers store past interactions to reduce sample correlation, improving training stability. The hybrid approach uses Transformer outputs as state representations for the RL agent, enabling continuous strategy refinement.

Algorithm Optimization

Optimization techniques include:

• Adaptive learning rate adjustment with the Adam optimizer to improve convergence speed and stability.
• Bayesian Optimization for efficient hyperparameter tuning of the Transformer model.
• Multi-objective optimization using NSGA-II to balance different strategic goals in RL policy learning.
• Ensemble learning to combine predictions from Transformer and RL components, enhancing overall performance.

Experimental Design and Performance Evaluation

Three public datasets representing market data, corporate operations, and competitor activity were used to train and validate the model. Key evaluation metrics included training time, convergence speed, and predictive accuracy.

The model was applied to real-world enterprise data from 2023 and demonstrated significant improvements in market share, profit growth, and customer satisfaction. Additionally, the company showed strengthened market position, brand influence, and innovation capabilities over time.

Discussion

The hybrid model outperforms single-algorithm approaches by leveraging complementary strengths. It effectively handles multivariate data and complex patterns with greater flexibility, resulting in faster training and more accurate predictions.

Conclusion

This study presents a hybrid AI model that integrates deep learning, reinforcement learning, and Bayesian optimization to enhance strategic decision-making. The model consistently improves accuracy and real-time performance, providing a reliable tool for executives seeking better strategic insights.

Despite its success, limitations remain in cross-industry generalizability and handling noisy or incomplete data. Future work should expand application scenarios, optimize computational efficiency, and incorporate external factors like policy changes and market trends to further improve decision support.

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