Dynamic Capabilities for AI-Enabled Exploration: Antecedents, Mechanisms, and Innovation Outcomes

Abstract: While the operational benefits of AI are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the black box of AI value creation by integrating the Technology-Organization-Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term AI-Enabled Exploration. Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity.

• The paper operationalizes a sequential model linking organizational readiness and sensing capabilities to AI-enabled exploration and innovation outcomes.
• It employs PLS-SEM with robust psychometric validation, confirming organizational and technological antecedents as key drivers.
• The findings emphasize that successful AI investments require dedicated sensing mechanisms to translate capabilities into strategic innovation.

Dynamic Capabilities for AI-Enabled Exploration: Mechanisms, Empirical Evidence, and Strategic Implications

Theoretical Integration: Dynamic Capabilities, TOE Framework, and Beyond

This paper operationalizes the intersection of the Dynamic Capabilities View (DCV) and the Technology–Organization–Environment (TOE) framework in the context of AI-enabled strategic exploration. DCV, following Teece et al., frames innovation as a sequential process of sensing (opportunity recognition), seizing (resource mobilization), and transforming (strategic renewal), while TOE delineates the technological, organizational, and environmental antecedents that condition capability development. The research specifically reconceptualizes "AI-Enabled Exploration" as a micro-foundational, seizing-oriented dynamic capability, distinguishing it from both general digital innovation and broader constructs such as organizational ambidexterity or traditional exploratory innovation.

AI-Enabled Exploration is positioned as an organizational capability to deploy advanced AI—especially generative and simulative models—for prototyping new business models, simulating market entry, and generating creative artifacts beyond incremental efficiency improvements. Crucially, the paper decouples this from traditional R&D by emphasizing the specificity of AI as a medium for experimentation at scale and speed unattainable by conventional means.

Empirical Model and Methodology

Leveraging survey data from 245 senior and middle managers across multiple sectors in Saudi Arabia—a prototypical "leapfrog" economy characterized by resource abundance, state-led digital transformation, and uniform institutional support—the study deploys Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a chain of mediation and moderation hypotheses.

Construct validity is rigorously established for all measures, including the novel AI-Enabled Exploration construct (Cronbach’s Alpha 0.936, AVE 0.840), and discriminant validity is confirmed using Fornell-Larcker and HTMT checks. Common method bias appears negligible following Harman’s test, collinearity diagnostics, and common latent factor assessment.

Key Numerical Findings

• Organizational Readiness (OR) and Technology Compatibility (TC) are strong, statistically significant predictors of Sensing Capability (β=0.376 and β=0.261, respectively; p<0.001).
• Government Support exhibits no significant effect on Sensing Capability, contrary to conventional institutional theory expectations (β=0.098, p>0.05).
• Sensing Capability exerts a large effect on AI-Enabled Exploration (β=0.530, p<0.001, f^2=0.412), and AI-Enabled Exploration strongly drives Innovation Performance (β=0.604, p<0.001, f^2=0.576).
• The indirect, serial mediation Organizational Readiness → Sensing Capability → AI-Enabled Exploration → Innovation Performance is significant (β=0.120, p<0.001), while the direct effect is negligible, indicating complete mediation.
• Competitive Pressure introduces a robust moderation: the OR→AIE relationship is intensified under high competition (interaction β=0.154, t=2.87, p<0.01).
• The model explains 42.1% of the variance in AI-Enabled Exploration and 36.5% in Innovation Performance, substantially outperforming rival direct-effects models.

These results specify that simple possession of AI resources does not translate to innovation performance; rather, capability-building in sensing and active AI-enabled exploration mediates this effect. Government support functions as a baseline condition with reduced explanatory variance after reaching ecosystem-wide saturation.

Implications and Theoretical Advances

Analytically, the research advances several nuanced contributions:

1. Granular Mechanistic Elucidation: By operationalizing the serial mediation from readiness and compatibility through sensing and AI-enabled exploration, the model challenges deterministic resource-based narratives. The capacity to leverage AI for strategic exploration is not an immediate consequence of AI adoption but depends on the sequential accumulation of dynamic capabilities.
2. Boundary Conditions for External Drivers: The finding that government support loses explanatory power once institutional adoption saturates revises assumptions about the continuous effectiveness of external incentives in capability-building, emphasizing a stage-contingent role aligned with institutional theory.
3. Contingency-Driven Activation of Slack Resources: Under high competitive pressure, resource slack is activated into exploratory action, bypassing the slower, capability-building path—articulating a parallel, urgency-driven mechanism and reconciling DCV with contingency theory.
4. AI as Enabler of Ambidexterity (Partial): While AI-Enabled Exploration only addresses the exploration half of the ambidexterity equation, it demonstrates that digital assets, when coupled with dynamic capabilities, can catalyze a firm's ability to recombine knowledge and enter new domains, even in resource-rich, inertia-prone contexts.

Practical and Policy Implications

For practitioners, the results underscore the futility of "technology-push" AI investments without commensurate investments in organizational sensing infrastructure and processes. Firms are advised to develop dedicated sensing units and cross-functional experimentation platforms rather than relying solely on capex-intensive approaches.

Policymakers are cautioned that financial incentives and regulatory facilitation, once widely available, cease to be differentiators—shifting the policy emphasis toward fostering competition and investing in organizational and human capital development to drive depth and breadth of capability adoption.

Directions for Future Research

The cross-sectional, perception-based dataset precludes direct analysis of long-horizon organizational learning and failure modes in AI-driven exploration. Longitudinal and event-based studies—especially around the efficiency vs. over-exploration boundary—are necessary for quantifying lagged effects and negative externalities. Comparative studies in other state-led ecosystems will further validate the stage-contingent institutional hypotheses presented.

Conclusion

This research articulates an empirically grounded, theoretically integrated process model connecting AI resource deployment to innovation outcomes via dynamic capabilities in sensing and AI-enabled exploration. The model is robust to controls for firm size and industry, impervious to endogeneity, and supported by strong psychometric evidence for its novel constructs. The findings challenge deterministic narratives of digital transformation in resource-rich economies and refine theories of institutional influence, contingency, and microfoundational capability-building. The active orchestration of AI-driven sensing and exploration emerges as the principal mechanism for strategic renewal, with organizational and competitive context determining the degree of realized value, rather than the simple presence of AI assets. This position is of immediate analytic and practical relevance for both emerging markets and mature economies undergoing digitally mediated structural change.

Reference: "Dynamic Capabilities for AI-Enabled Exploration: Antecedents, Mechanisms, and Innovation Outcomes" (2607.02645)