Innovation under pressure: How AI adoption builds resilience in enterprises

AI’s advanced analytics and machine learning capabilities enable firms to perceive changes in the external environment swiftly and make real-time decisions. By automating tasks and reallocating resources efficiently, AI empowers firms to sustain core innovation functions during periods of crisis or financial instability. Additionally, AI-integrated platforms dismantle organizational silos, allowing knowledge to flow seamlessly across teams and partners, which is critical for collaborative innovation and system adaptability.

In an era marked by economic turbulence, technological disruption, and geopolitical uncertainty, artificial intelligence (AI) is proving to be more than just a productivity tool - it is emerging as a critical enabler of organizational resilience. A new empirical study titled “The Impact Mechanism of AI Technology on Enterprise Innovation Resilience”, published in the journal Sustainability (2025, 17, 5169), presents a comprehensive investigation into how AI adoption strengthens the ability of firms to sustain and adapt their innovation activities amid external shocks.

Drawing on panel data from Chinese A-share listed companies between 2013 and 2023, researchers Xun Zhang and Yamei Wei construct a robust theoretical and statistical model grounded in dynamic capability theory. The findings affirm that AI enhances innovation resilience through capability transformation and is most effective under resource constraints and competitive pressure.

How does AI strengthen innovation resilience?

The study defines innovation resilience as a firm's capacity to maintain, restore, and evolve innovation processes in response to disruption - a shift from traditional metrics such as innovation output or R&D spending. AI supports this capacity through three primary mechanisms: enhanced information processing, optimized resource allocation, and accelerated knowledge integration.

AI’s advanced analytics and machine learning capabilities enable firms to perceive changes in the external environment swiftly and make real-time decisions. By automating tasks and reallocating resources efficiently, AI empowers firms to sustain core innovation functions during periods of crisis or financial instability. Additionally, AI-integrated platforms dismantle organizational silos, allowing knowledge to flow seamlessly across teams and partners, which is critical for collaborative innovation and system adaptability.

These mechanisms collectively enable firms to avoid innovation paralysis during disruptions like the COVID-19 pandemic, regulatory shocks, or supply chain breakdowns. AI thus becomes a stabilizing force, ensuring continuity and transformation within innovation systems.

What role do dynamic capabilities play in the AI-resilience link?

While AI facilitates operational agility, its influence on innovation resilience is not automatic. According to the study, AI enhances innovation resilience by strengthening firms’ dynamic capabilities, specifically absorptive, innovative, and adaptive capacities. These capabilities function as the internal conduits that translate technological adoption into systemic innovation recovery and growth.

Empirical models used in the study reveal a significant mediating effect: firms that develop stronger dynamic capabilities in tandem with AI adoption experience more robust innovation resilience. AI boosts absorptive capacity by enabling better scanning and integration of external knowledge (e.g., via big data analytics), strengthens innovation capacity by accelerating R&D processes and flexible manufacturing, and enhances adaptive capacity by supporting scenario simulations and AI-powered decision support systems.

This dynamic capability pathway is especially critical for firms operating in volatile institutional environments, such as China’s rapidly transitioning digital economy. The research aligns with foundational theory by Teece (1997), validating that sustained innovation requires more than tools - it requires the evolution of organizational competencies and learning structures.

Under what conditions is AI most effective?

One of the study’s most novel insights lies in its examination of financial constraints as a moderating factor. Contrary to conventional wisdom, AI adoption has a stronger positive effect on innovation resilience when firms face tighter financial constraints. Limited resources compel firms to be more selective and strategic in deploying AI, concentrating investments on high-impact applications directly linked to core innovation objectives. This focused deployment results in greater marginal returns and a tighter coupling between AI and resilience outcomes.

The study also finds significant heterogeneity in the impact of AI based on ownership structure, firm size, and industry competition. Non-state-owned enterprises (NSOEs), large firms, and companies in highly competitive sectors benefit most from AI-enhanced innovation resilience. NSOEs, driven by market forces and devoid of bureaucratic cushioning, are more motivated to treat AI as a strategic tool rather than a symbolic adoption. Larger firms, with their superior resource endowments and mature systems, are better positioned to integrate AI into core innovation functions. Meanwhile, intense competition compels firms to utilize AI for rapid learning and adjustment, thereby maximizing resilience benefits.

These findings suggest that both internal organizational characteristics and external market pressures significantly shape how effectively AI can foster innovation resilience.

Policymakers are urged to invest in digital infrastructure and promote inclusive access to AI technologies, especially for small and financially constrained firms. Firms, in turn, must align governance structures and innovation strategies to fully harness AI’s resilience-enhancing potential.